esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Bacterial Viability  
Two different assays were used for checking the bacterial viability, i.e., MTT assay and live and dead staining. MTT assay was performed to determine the change in bacterial viability upon treatment with the test compound [29]. BHIS broth containing 5, 10, 15 and 20 \(\mu\)g/mL of MD-1, MF-1, clove oil and CHX was used to grow _S. mutans_ for 4-6 h at 37 \({}^{\circ}\)C. Aliquots of 40 \(\mu\)L from each culture was then added in triplicates to the wells of the 96-well plates. To these wells, 10 \(\mu\)L of the MTT (Sigma, Ronkonkoma, NY, USA) solution prepared in PBS (5 mg/mL, pH 7.0) was added and the plates were incubated for 4 h at 37 \({}^{\circ}\)C. An aliquot of 50 \(\mu\)L of lysis buffer containing 20% SDS in 50% DMF (pH 4.7) was added to each well and the plates were incubated overnight at 37 \({}^{\circ}\)C. After incubation, pipettingwas done to thoroughly mix the cells in each well and the absorbance at 570 nm was determined using a microtitre plate reader (Multikskan Ascent, Labsystems, Finland).  
Live and dead staining was carried out by growing _S. mutans_ with 5 \(\upmu\)g/mL of MD-1, MF-1, clove oil and CHX at 37 \({}^{\circ}\)C for 8 h on a shaker. Following incubation, cells were centrifuged at 900\(\times\)_g_ for 10 min and were washed with PBS buffer. Cells were suspended again in sterile PBS (pH 7.5) and the BacLight(tm) Bacterial Viability Kit was used for performing Live and dead staining (LIVE/DEAD(r), Molecular Probes, Eugene, OR, USA) according to the manufacturer's protocol. After staining cells were visualized under a Nikon Eclipse 80i fluorescence microscope (Nikon Co., Tokyo, Japan). Cells appearing green and red in colour were recorded as total and dead cells, respectively. Ten fields were counted for every treatment and the population of live cells was calculated by subtracting the population of dead cells from the population of total cells in the same field.
# Supercapacitor-Based Energy Storage in Elevators to Improve Energy Efficiency of Buildings
## 5 Simulation Results
### Consecutive Regenerative Trips  
Simulation tests were performed for two consecutive regenerative trips with maximum regenerative energy, since the SC bank is dimensioned to save the energy for this case. The initial voltage of the SC bank was assumed to be 155 V, which is slightly higher than the lower limit of the operating voltage of the SC bank. The simulation was performed as a reference cycle where the empty elevator travels from the ground floor to the top floor and from the top floor to the ground floor with the nominal load. Figure 13a shows the speed setpoint and load profile during two consecutive regenerative trips. A positive speed indicates an upward movement of the elevator car, while a negative sign represents a downward movement.  
Figure 12: Simulation results for the reference cycle. (**a**) DC bus voltage. (**b**) SC bank power and braking resistor power. (**c**) Voltage and current of the SC bank.  
The input power of the elevator system with and without EESS is shown in Figure 13b. During two consecutive regenerative trips, the elevator operates in regenerative mode and the power from the grid is almost the same for the elevator system with and without EESS. However, since the EESS has a standby power of 24 W, the consumed power with EESS is slightly higher than the power without EESS. In addition, a power disturbance is observed at 35 s because the elevator load has changed and the mechanical brake is not simulated.  
The DC bus voltage is shown in Figure 14a. During the first regenerative trip, the DC bus voltage increases to 720 V, which means that the SC bank is not able to store all the regenerative energy, and there is a power dissipation at the braking resistor. Since the braking resistor is controlled by a relay with hysteresis, there is a voltage fluctuation on the DC bus. During the second regenerative trip, the DC bus voltage is about 650 V, and all the regenerative energy is stored in the SC bank.  
The power of the SC bank and the power of the braking resistor are shown in Figure 14b. During the first regenerative trip, there is power dissipation at the braking resistor. During the second trip, there is no power dissipation at the braking resistor, and the power of the SC bank is constant and equal to the regenerative power of the elevator.  
The current and voltage of the SC bank are shown in Figure 14c. During the first regenerative trip, the current of the SC bank reaches its maximum value of 27 A. This means that the charging power of the SC bank is limited by the maximum charging current of the DC/DC converter.  
Figure 13: Simulation results for two consecutive regenerative trips. (**a**) Elevator speed setpoint and mass of the load. (**b**) Input power.
## 3 Previous Wind Tunnel Studies on NVLBs
### The Flow Studies
#### 3.1.2 The Effect of Building Orientation and Shape  
[PERSON] et al. [51] studied the impact of the orientation of swine finisher buildings on the air velocity and turbulence characteristics around these buildings. The buildings were scaled down to 1:300 and used in the following configurations: with one building oriented parallel and perpendicular to airflow and with four buildings oriented parallel, perpendicular, and at a 30\({}^{\circ}\) angle to airflow. Their study showed that perpendicularly oriented buildings create a large (equal to 10 times the height of the mean building) zone of reduced streamwise velocity and increased turbulence intensity in the wake of the buildings. The authors suggested that the entrainment and transport of pollutants from manure storage structures may be reduced when these structures are in this zone. However, the authors correctly admitted that the increased turbulence in the wake zone may counter the reduced pollutant emission, and hence, further studies are needed to clarify this effect.  
An example of validation of CFD simulations of the flow around different-shaped livestock buildings by data obtained from a wind tunnel experiment can be found in [PERSON] et al. [54]. As they used a direct numerical approach for their simulation (where the Navier-Stokes equations were solved directly, without any parametrisation), the validation process had a reciprocal purpose. Indeed, the wind tunnel tests were in good agreement with the numerical simulations. However, a low building Re was modelled (\(Re_{B}=H\)U/\(v=1284\), where \(H\) is the building height and \(U\) is the inlet velocity in the wind tunnel or the computational domain). More importantly, different roof geometries and obstacles affected both the instantaneous and time-mean-averaged downstream flow parameters. On the contrary, these parameters did not significantly affect the flow impacting the building.  
[PERSON] et al. [58] studied the airflow characteristics downwind of a naturally ventilated pig barn. The changing parameter was the slope (\(5^{\circ},15^{\circ}\), and \(25^{\circ}\)) of a roofed outdoor exercise yard. Detailed airflow characteristics downwind of the building were measured by a 2D LDA method. Their results showed that the roof slope has a significant impact on the wake region--and, hence, on the pollutant dispersion--behind the barn, where the flow recirculation and higher turbulence intensity occur. The authors suggested applying other treatment technologies to trap the high-concentrated gaseous pollutants (e.g., odours or ammonia) in this region. Such treatments might contribute to the mitigation of both the pollutant emissions and the burden of the surrounding environment. The authors also pointed out the use of mean air velocity and air turbulence data to validate future CFD studies.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 1 Introduction
A method is proposed to construct a global coordinate map even in the absence of GNSS signals. 3. A multi-submap management module is proposed, in which multiple submaps are used to represent relatively larger environments. The submaps are classified based on the vehicle's position, ensuring efficient memory allocation for each submap. 4. Real-time map storage and retrieval functions are implemented, ensuring that the generated global coordinate maps are reusable and support subsequent mapping tasks. The proposed method is extensively tested using a self-constructed experimental platform in real-world scenarios. The resulting grid map achieves sufficient accuracy for navigation tasks and enables real-time mapping in outdoor environments. The rest of the paper is structured as follows. Section 2 reviews existing research on visual mapping methods. Section 3 provides an overview of the system. Section 4 offers a detailed description of the proposed method, including the incremental ground fitting algorithm and the multi-submap fusion mapping approach. Section 5 outlines the experimental methods and processes, followed by a presentation of the results and their analysis. Section 6 concludes the paper and discusses future work.
## 7 Distribution in Elevation of the Damping Force
\begin{array}{l}F_{y,DB,L1}^{(j)}=(r_{F,L1}^{(j)}.r_{y,F}^{*})/\left( n_{DB,L1}\cdot cos\ \phi_{s,L1}\right)\\ F_{y,DB,L2}^{(j)}=(r_{F,L2}^{(j)}.r_{y,F}^{*})/\left(n_{DB,L2}\cdot cos\ \phi_{s,L2}\right)\end{array} \right. \tag{21}\]  
NLTH analyses are carried out at each \(j\)th iteration (see Section 8). The maximum inter-storey lateral displacements (\(d_{max,L1}\) and \(d_{max,L2}\)) are calculated and compared with the established performance targets (\(d_{PP,L1}\) and \(d_{PP,L2}\)). In this regard, the damper distribution obtained at the first iteration (\(j=1\)) should already guarantee the achievement of the performance target at roof level, i.e., \((d_{max,L1}+d_{max,L2})\leq d_{PP}=27.0 mm\). However, adjustments (i.e., iterations) could be necessary when the maximum inter-storey drifts (i.e., \(d_{max,L1}\) and \(d_{max,L2}\)) are too different from the relevant performance targets (i.e., \(d_{PP,L1}\) and \(d_{PP,L2}\)). For this purpose, the ratios \(r_{k}^{(j)}\) and \(r_{k}^{(j)}\) are iteratively adjusted as follows:  
\[\left\{ \begin{array}{l}r_{k,L1}^{(j+1)}=r_{k,L1}^{(j)}.(d_{max,L1}/d_{PP,L1}) \\ r_{k,L2}^{(j+1)}=r_{k,L2}^{(j)}.(d_{max,L2}/d_{PP,L2})\end{array} \right. \tag{22}\]  
\[\left\{ \begin{array}{l}r_{F,L1}^{(j+1)}=r_{F,L1}^{(j)}.(d_{max,L1}/d_{PP,L1}) \\ r_{F,L2}^{(j+1)}=r_{F,L2}^{(j)}.(d_{max,L2}/d_{PP,L2})\end{array} \right. \tag{23}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Level** & **Dir. X** & **Dir. Y** \\ \hline \(n_{DB}\) & L1 & 4 & 2 \\ (-) & L2 & 4 & n.a. \\ \hline \(\phi_{s}\) & L1 & 46.3 & 41.7 \\ (\({}^{\circ}\)) & L2 & 29.7 & n.a. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of DB units and relevant inclination angle at the two levels of the longitudinal (X) frame. The iterations are interrupted when these two convergence criterions are simultaneously fulfilled: \(|(d_{max,L1}-d_{PP,L1})/d_{PP,L1}|\leq\epsilon\) and \(|(d_{max,L2}-d_{PP,L2})/d_{PP,L2}|\leq\epsilon\) (with \(\epsilon\) being the convergence threshold; hereinafter, \(\epsilon=0.15\)). In the considered case study building, two iterations were sufficient. The iterative dampers' design parameters are reported in Table 5, while the final layout is shown in Figure 12.  
**Y Direction**: unlike before, in the transversal direction, thanks to the absence of any significant discontinuity in the masonry infills, it is reasonable to arrange the damping force on a single level (i.e., subscript L1 from foundations to roof). Therefore, only two dampers (\(n_{DB}=2\)) are installed along very stiff connection braces (with inclination angle \(\phi_{s}=41.7^{\circ}\)) at both end-sides of the building (see Figure 12a,c). Each device unit features the following design parameters: (1) yielding force \(F_{y,DB,L1}=\left(\Gamma\cdot F_{y,DB}^{*}\right)/(n_{DB}\cdot\cos\ \phi_{s})=335.5 kN\); (2) elastic stiffness \(k_{el,DB,L1}=k_{el,DB}/\left(n_{DB}\cdot cos^{2}\ \phi_{s}\right)=129.8 kN/mm\).
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 3 Distributed Trustable Engine Architecture  
### The Role of the DTE  
The AI/ML-aided DTE can take the form of a core network function that is compliant with NWDAF specifications and supports data collection from diverse sources within the 6G infrastructure. Another aspect handled by the DTE is data management before the actual training, by employing appropriate policies for anomaly detection in the sense of tampered data and data anonymization. DTE provides a programming interface to serve ML models and predictions to other 6G core modules, supporting, in this manner, distributed trustable AI-assisted cybersecurity tools. In the context of the smart networks and services joint undertaking (SNS JU) holistic, omnipresent, resilient services (HORSE) for future 6G wireless and computing ecosystems project, the DTE communicates with two core modules: the detector and mitigation engine (DEME) and the intent-based interface (IBI) [31].  
First, it receives input from the DEME, developing algorithms focusing on network parameters, protocol headers and relevant data for threat detection and mitigation. Then, DTE trains and stores ML models in order to define and impose the optimum set of security and privacy policies to safeguard the network against a wide range of attacks. When the ML model is trained, and according to the identified type of attack and the prediction accuracy level, appropriate mitigation measures and methodologies from well-established knowledge bases, such as 3 GPP TR 33.92 and the MITRE ATT&CK, are exploited. In the next step, and in accordance with the intent-based networking paradigm, the DTE builds mitigation intents [3] and sends them to the IBI via REST API. IBI is then responsible for applying the proposed intents of the DTE.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 8 Conclusions  
In this paper, LoRaWAN v1.1 Security framework was examined and its vulnerabilities were analyzed. An enhanced key management framework was proposed with improvement in several aspects. A key generation scheme based on Rabbit PRNG is proposed to replace the recommended AES algorithm for its better computing efficiency. This new scheme has been shown to have comparable randomness and uniqueness as the AES in LoRaWAN v1.1. A full key management scheme including key update, key backup, key recovery, and key revocation has been proposed to complete the existing key framework in the v1.1. The proposed key management scheme enhances the overall security of LoRaWAN and it can be included in its future release and considered in other LPWAN security frameworks. Future research can focus on developing a more advanced lightweight key generation algorithm and improving the backward compatibility scheme of the proposed version.  
**Author Contributions:** Conceptualization, X.C.; methodology, X.C., L.W.; software, X.C.; validation, X.C.; formal analysis, X.C.; investigation, X.C.; writing--original draft preparation, X.C.; writing editing, X.C.; review, X.C., M.L., L.W. ; visualization, X.C.; supervision, M.L., L.W.; Please turn to the CRediT taxonomy for the term explanation. Authorship must be limited to those who have contributed substantially to the work reported. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Acknowledgments:** The author, [PERSON], of this research, is supported by an Australian Government Research Training Program (RTP) Fees Offset Scholarship. [PERSON] would like to especially thank Dr. [PERSON] for his supports and helps toward this paper during his time at RMIT University previously. Also, the authors would like to thank the reviewers and editor for their valuable comments and suggestions on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest regarding the publication of this paper.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 4 NDR-Net Assisted Channel Estimation
### NDR-Net  
The framework of the proposed NDR-Net based channel estimator is presented in Figure 3. The NDR-Net is composed of noise level estimation subnet \(CNN_{N}\) and denoising subnet \(CNN_{D}\). The \(CNN_{N}\) module is composed of the NLE, and the \(CNN_{D}\) module includes DnCNN and Residual learning. The receiver first makes a rough LS estimation of the channel to obtain \(\hat{H}_{LS}\), then inputs \(\hat{H}_{LS}\) as the noisy channel matrix to the NLE network to obtain the noise level \(\hat{\sigma}\) set. It needs to be noted here that \(\hat{\sigma}_{s}\) represents the estimated noise level that is the SNR value, \(\hat{\sigma}_{ch}\) represents the Root Mean Squared (RMS) delay spread corresponding to the estimated channel model, and \(\hat{\sigma}_{v}\) represents the estimated receiver movement speed. Then inputs \(\hat{\sigma}\) set and \(\hat{H}_{LS}\) together to the DnCNN cascade network to obtain \(\hat{H}_{E}\), finally use residual learning to obtain \(\hat{H}_{D}\). It can be expressed as:  
\[\hat{H}=\hat{H}_{D}=f\Big{(}\Theta;\hat{H}_{LS}\Big{)}=f_{D}\Big{(}f_{N}\Big{(} \Theta_{N};\hat{H}_{LS}\Big{)};\Theta_{D}\Big{)} \tag{10}\]  
where \(f_{N}\) and \(f_{D}\) represent the functions of \(CNN_{N}\) and \(CNN_{D}\) models respectively, \(\Theta_{N}\) and \(\Theta_{D}\) represent the hyperparameters of \(CNN_{N}\) and \(CNN_{D}\) models respectively. \(f\) and \(\Theta\) denote the function and the set of hyperparameters of the NDR-Net model, respectively. The loss function of NDR-Net is expressed as the Mean Square Error (MSE) between the estimated channel response value and the actual channel response value:  
\[L=\underset{\theta\in\Theta}{\arg\min}\mathbb{E}\Big{\{}\|\hat{H}-H\|^{2} \Big{\}} \tag{11}\]  
Figure 2: The network structure of the DnCNN.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 3 Proposed Scheme
### Object-Based Classification  
Following segmentation, the object classification stage classifies each object into different land covers based on each object's spectral and geometrical features. In reality, most land cover is the composition of several similar LiDAR points identified through human interpretation, but identifying the properties of a single LiDAR point is more difficult. The process of object-based classification is more similar to the human interpretation and has been widely used to improve the classification accuracy for remote sensing data [33]. For object-based classification in this study, we used a supervised support vector machine (SVM) designed to handle the complex features by maximizing the margins of hyperplanes using support vectors, and the SVM then classified different features based on training data in the hyperplane. The kernel of SVM in this study was the radial basis function (RBF) [34; 35]. To examine the capability of multi-wavelength features, this study used different features in segmentation and classification. Note that the same training and check areas were used in all combinations.  
Figure 4: Extracted features. (**a**) nDSM of 532 nm channel; (**b**) nDSM of 1064 nm channel; (**c**) nDSM of 1550 nm channel; (**d**) curvature of 532 nm channel; (**e**) curvature of 1064 nm channel; (**f**) curvature of 1550 nm channel; (**g**) \(\text{NDFI}_{\text{MIR-G}}\) image from LiDAR; (**h**) \(\text{NDFI}_{\text{NIR-G}}\) image from LiDAR; (**i**) \(\text{NDVI}_{\text{1}}\) image from WV-3; (**j**) \(\text{NDVI}_{\text{2}}\) image from WV-3.
## 2 Modified Hammerstein-Like (MHL) Model and Its Identification Method  
### Rate-Independent Hysteresis Model Based on [PERSON]'s Rules  
In the early 20 th century, German physicist [PERSON] summarized hysteresis phenomena and proposed several rules that later bore his name. [PERSON]'s rules are inherently satisfied in all the aforementioned differential equation-based and operator-based hysteresis models. In our previous studies [19; 20], a rate-independent model was developed based on [PERSON]'s rules. This model comprises two parts: The first part is an algorithm that converts [PERSON]'s rules, described in natural language, into a computer program to automatically implement the wiping-out mechanism [30; 31]. The proposed model naturally satisfies [PERSON]'s rules. The second part concerns the description method for hysteresis curves. Based on the similarities between internal hysteresis loops and the major hysteresis loops, we utilize the ascending and descending curves of the major hysteresis loop to describe the hysteresis curves, as shown in Figure 2. The major ascending curve \(f_{0}(x)\) and descending curve \(g_{0}(x)\) can be mathematically expressed as  
\[f_{0}(x) = p_{5}x^{5}+p_{4}x^{4}+p_{3}x^{3}+p_{2}x^{2}+p_{1}x \tag{1}\] \[g_{0}(x) = q_{5}x^{5}+q_{4}x^{4}+q_{3}x^{3}+q_{2}x^{2}+q_{1}x \tag{2}\]  
It is worth pointing out that the rate-independent model only requires identifying the parameters of the major hysteresis curves \(f_{0}(x)\) and \(g_{0}(x)\). The forms of Equations (1) and (2) are not limited to polynomials only, and they can also be represented by other forms of functions, such as piecewise linear functions.
## 4 Results and Discussion
### Comparison of Present Results  
#### 4.5.1 Proposed Results with Analytical Method  
Figure 6 shows a comparisons of the outcomes of the suggested simulation with those analytically obtained using BER Equation (12). The graph demonstrates that the BER values for the SNR range of 6-8 dB was essentially the same as the simulation findings, with very little variance between them. This means that our proposed simulation results were very close to the analytical results. The accuracy of simulated and proposed results was very close at the median range of SNR (from 6 to 8 dB) compared to highest and lowest values of SNR. The BER values of the simulation findings slightly differed from the analytical results for SNR ranges of 2-4 dB and 8-12 dB. In order to calculate the average BER for the simulation results given that the data were generated at random, the suggested MIMO diversity technique with wavelet transform was iterated a number of times.  
Table 5 lists the estimated average BER values, as well as the BER values derived from the BER Equation (12). The table demonstrates that there was only a 0.001 difference between the BER values for the SNR range of 6-8 dB. For the SNR ranges of both 2-6 dB and 8-12 dB, the analytical BER values were higher than the simulated BER values. When the two results were contrasted, however, the BER values revealed less discrepancy. As a result, there was good agreement between the proposed simulation findings and the analytical results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SNR** & **Analytical BER Value** & **Average BER (Simulation)** \\ \hline
2 & \(21.84\times 10^{-3}\) & \(10.69\times 10^{-3}\) \\
4 & \(11.04\times 10^{-3}\) & \(7.70\times 10^{-3}\) \\
6 & \(5.22\times 10^{-3}\) & \(4.97\times 10^{-3}\) \\
8 & \(2.42\times 10^{-3}\) & \(2.54\times 10^{-3}\) \\
10 & \(1.11\times 10^{-3}\) & \(0.79\times 10^{-3}\) \\
12 & \(0.50\times 10^{-3}\) & \(0.34\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation of BER values.  
Figure 6: Comparison of results.