esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
## 9 Simulation Results  
Figure 21 shows the BER performance of the OCDMA system versus the number of active users based on the proposed HPC. The graph compares HPC with OOC, MPC, EMPC, and WMPC (please refer to legend). The result illustrates that when \(n=2\), \(L=24\), and \(W=4\) the HPC outperforms the other codes and the OCDMA can accommodate 80 users at BER equal to \(10^{-10}\). This means lower MAI as a result of good code correlation characteristics. If the code length and code weight are increased, the system can accommodate a higher number of active users at slightly higher BER due to the incremental increase in the MAI. Despite this increase, the proposed code is still better than the other codes as shown in Figure 22.  
Considering the optimum value of BER and the value of system multiplicity \(M\), Figure 23 presents the system users-throughput product against the number of active users \(N\) in case of the proposed HPC in comparison with the existing MPC. The results show that at any value of \(N\), the system throughput product decreases as the system multiplicity increases. In addition, the results show that when the HPC is used the users-throughput product slightly increases as the number of active users \(N\) increases. This due to the dependence of \(N\) and the code length \(L\) on the used prime numbers \(P_{1}\) & \(P_{2}\). On the other hand, when the MPC is used the users-throughput product remains the same when the number of active users increase, which is due to the minimum available number of codes. Finally, the system throughput product in the case of HPC outperforms the throughput  
Figure 21: BER of OCDMA systems versus the number of active users N (\(n=2\), \(L=24\) and \(W=4\)).  
product in the case of MPC due to the difference in the chip rate illustrated in the previous results in Figure 22 and the higher number of codes available.  
Figure 24 shows EVM% versus the OSNR. This illustrates that at any value of EVM, the proposed HPC code outperforms the other codes. Moreover, at any value of OSNR, the error magnitude is lowest when the proposed code is recommended. For example, at 10 dB OSNR, the values of EVM% are 5%, 8%, and 15% for HPC, MPC, and OOC, respectively. This is because the good correlation properties achieve the minimum MAI and better BER.  
Figure 23: User throughput product versus the number of active users N.  
Figure 22: BER of OCDMA systems versus the number of active users N (\(n=3\), \(L=36\) and \(W=6\)).
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.1 Mutual Authentication (MA)  
We can justify the domination that \(\mathcal{A}\) has over a legitimate login response, and reply authentication response is significant. Thus, by checking the validity of the sent communications, \(SN_{n}\) and \(S_{s}\) may authenticate one another using \(TA\). Consequently, mutual authentication may be accomplished using the suggested approach.
## 4 Discussion
The analysis conducted with an exclusive focus on the grassland environment provides important indications of both the extent of these phenological changes and their distribution within the European continent, as well as the influence of increasing temperatures. The phenological estimates obtained from MODIS satellite data over the time period 2001-2021 represent key information for understanding the evolution of grassland phenology that has occurred in recent years and the trend towards which it is heading, providing policy makers and stakeholders with useful indications for the identification of possible adaptation and mitigation strategies. Despite the uncertainties, the methodology presented in this paper can represent a first step in a European-wide assessment of grassland phenology, opening up the possibility of investigating phenological trends over large and numerous grassland areas of the continent. Concerning future perspectives, a specific end-of-season study could be important to reduce uncertainties in EOS detection from satellites and refine the methodology. In addition, the analysis performed can be extended by focusing on the differences that may occur in grasslands characterised by the presence of different dominant species and groups. The analysis of the factors driving the phenological changes can also be extended to water conditions (i.e., precipitation and snowmelt date) in the case of high-quality data over an extended period of time.
## 3 Results
### Development of the Empirical Equation  
The empirical equation for the failure pressure prediction of circumferentially aligned interacting corrosion defects subjected to internal pressure and axial compressive stress is based on the representation of the developed ANN in matrix form. The developed equations have been organised into four steps to arrive at the failure pressure prediction of the corroded pipe with circumferentially aligned defects. The steps involved are summarised as follows.  
Step 1: Calculation of the normalised effective length and depth of defect.  
\[(l/D)_{e}=\frac{l_{1}+(s_{1}+l_{2})}{D} \tag{7}\]  
\[(d/t)_{e}=\frac{\left(\frac{d_{1}l_{1}+d_{2}l_{2}}{h_{1,2}}\right)}{t} \tag{8}\]  
Step 2: Normalization of input parameters  
\[(\sigma_{UTS}^{*})_{n}=\frac{\sigma_{UTS}^{*}-612}{53}-1 \tag{9}\]  
\[(\sigma c/\sigma y)_{n}=\frac{\sigma c/\sigma y}{0.3}-1 \tag{10}\]  
\[(d/t)_{e_{n}}=2.5(d/t)_{e}-1 \tag{11}\]  
Figure 6: [PERSON]’s plot of the ANN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **MAE** & **MSE** & **R\({}^{2}\)** \\ \hline Training & 0.0567 & 0.0683 & 0.9996 \\ \hline Validation & 0.0486 & 0.0777 & 0.9997 \\ \hline Test & 0.0326 & 0.0893 & 0.9996 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Mean absolute error (MAE), MSE, and R\({}^{2}\) values of the model for training, validation, and test phase.  
\[\left(l/D\right)_{e_{n}}=0.678(l/D)_{e}-1 \tag{12}\]  
\[\left(s/\sqrt{D/t}\right)_{n}=\frac{2\left(s_{c}/\sqrt{D/t}\right)}{3}-1 \tag{13}\]  
Step 3: Calculation of the normalised output value.  
\[\left[\begin{array}{c}n_{1}\\ n_{2}\\ n_{3}\\ n_{4}\\ n_{5}\\ n_{6}\\ n_{7}\\ \end{array}\right]=\left[\begin{array}{cccccc}0.0090&-0.0609&-1.1841&0.6062&0. 5307\\ -0.1119&0.0045&-1.2803&1.8546&-0.4618\\ -0.2298&0.3656&-0.3188&0.0330&-0.0963\\ -0.2254&-0.3022&0.3233&-0.0408&0.0970\\ -0.1050&-0.0294&-0.3749&0.0271&-0.0846\\ 0.1235&-0.0031&1.3550&-1.8656&0.5484\\ 4.1728&10.9990&0.5839&-0.0468&0.1443\\ \end{array}\right]\left[\begin{array}{c}\sigma_{UTS}^{e}\\ s_{c}/\sqrt{Dt}\\ (l/D)_{e}\\ (d/l)_{e}\\ \sigma_{c}/\sigma_{y}\\ \end{array}\right]+\left[\begin{array}{c}-1.3419\\ 2.1418\\ 0.2197\\ -0.1921\\ -0.2161\\ 14.4094\\ \end{array}\right] \tag{14}\]  
\[o_{n}=\left[\begin{array}{c}a(n_{1})\\ a(n_{2})\\ a(n_{3})\\ a(n_{4})\\ a(n_{5})\\ a(n_{6})\\ a(n_{7})\\ \end{array}\right]\left[\begin{array}{cccccc}0.1477&-10.3176&17.5740&20.70 71&5.7860&-9.4221&-0.1675&]+\left[\begin{array}{c}-0.1378\\ -0.1378\\ \end{array}\right]\end{array}\right] \tag{15}\]  
\[a(n_{x})=\frac{2}{1+e^{-2(n_{x})}}-1 \tag{16}\]  
Step 4: Calculation of failure pressure.  
\[P_{f}=0.93(0.405o_{n}+0.595)P_{i} \tag{17}\]
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 1 Introduction  
From the network operator's point of view, the ever-increasing demand for high-speed data services translates into the need to continually upgrade the networks to increase the data transmission rate per optical fiber. An engineer who is responsible for the task of continual upgrading of dense wavelength division multiplexing (DWDM) networks [1] needs specific software tools that allow for estimation of the network's performance and optimization of the architecture with respect to operational and capital costs. Thus, the specific task considered in this contribution related to DWDM network optimization was formulated as an integer programming (IP) problem and solved by the available general purpose solvers [2]. However, the IP optimization results show that even if the IP approach is applicable, it is inefficient for larger networks since the design task is, in general, NP-complete [3]. The numerical efficiency limitations are particularly acute in the context of routing and wavelength assignment (RWA) [4; 5; 6] and routing and spectrum allocation (RSA) [7; 8; 9; 10] problems,which are at the heart of DWDM network optimization. Moreover, the constraints and cost functions are in a general case nonlinear and thus may further complicate the situation.  
Since the invention of computationally efficient exact algorithms is rather unlikely, heuristic discrete optimization methods seem to be best suited for the optimization of DWDM networks with realistic size and high modularity of node resources while taking into account optical network impairments, such as attenuation or an optical signal to noise ratio (OSNR) [11; 12]. Therefore, the main aim of this paper is to study the properties of heuristic algorithms that are applicable to the optimization of DWDM networks, whereby particular attention is paid to nature-inspired algorithms, with problem-specific operators that were specifically adapted by the authors to the problem studied. The performances of all optimization algorithms were compared for selected test networks with differing complexity. All networks used for comparison were optimized, subject to traffic demand sets extracted from real traffic data [13].  
This paper is organized as follows. In Section 2 the problem is formulated and the network models are described. Due to space constraints we decided to keep the presentation of the problem to a minimum and refer to our previous article. In Section 3 general methodology is described. In Section 4 the proposed metaheuristics are described. Next, in Section 5 a testbed of four network structures is presented and results of series of numerical experiments testing the algorithms are provided and compared. Finally, Section 6 provides a summary of the research findings.
# Fast Sound Source Localization Based on SRP-PHAT Using Density Peaks Clustering
## 3 SRP-PHAT Algorithm  
SRP-PHAT calculates firstly the steered response power (SRP) value for all positions in the search space. Then the maximum SRP value can be used to estimate the sound source position. SRP-PHAT has characteristics of short-time analysis. A short signal, such as a frame of a sound signal, can also be used to compute the SRP value.  
The SRP value, which is the sum of the generalized cross-correlation phase transform (GCC-PHAT) function with the signals collected by all microphone pairs, can be expressed as follows:  
\[\hat{P}_{i}(q)=\sum_{m=1}^{M}\sum_{n=m+1}^{M}\hat{R}_{mn}^{(i)}[\tau_{m,n}(q)] \tag{5}\]  
\(\hat{P}_{i}(q)\) represents the SRP value at candidate location \(q\) calculated using the \(i\)-th frame of the signal, \(\hat{R}_{mn}^{(i)}[\tau_{m,n}(q)]\) is the GCC-PHAT function of the \(i\)-th frame of the signals collected by the \(m\)-th and \(n\)-th microphones, and its expression is:  
\[\hat{R}_{mn}^{(i)}(\tau)=\frac{1}{L}\sum_{k=0}^{L-1}\frac{X_{m,i}(k)X_{n,j}^{ *}(k)}{\left|X_{m,i}(k)X_{n,i}^{*}(k)\right|}e^{j\omega\tau} \tag{6}\]  
where, \(X_{m,i}(k)\) is the discrete Fourier transform (DFT) of \(x_{m,i}(l)\). \(x_{m,i}(l)\) is the \(i\)-th frame of the signal collected by the \(m\)-th microphone, and \"*\" means conjugate. \(L\) is the number of DFT points, \(\omega\) is the analog angular frequency. \(\tau\) is the abbreviation of \(\tau_{m,n}(q)\), which represents the TDOA from the imaginary sound source to the \(m\)-th and \(n\)-th microphones. \(r_{m}\) and \(r_{n}\) represent the rectangular coordinate vector of the \(m\)-th and the \(n\)-th microphones respectively. If \(c\) denotes the speed of sound in the air (about 340 m/s), the expression of \(\tau_{m,n}(q)\) is:  
\[\tau_{m,n}(q)=\frac{(\parallel q-r_{m}\parallel-\parallel q-r_{n}\parallel)}{ c} \tag{7}\]  
After calculating the SRP for each candidate locations, the candidate location with the largest SRP is designated as the location estimate, and can be expressed as follows:  
\[\hat{q}=\underset{q}{\operatorname{argmax}}\hat{P}_{i}(q). \tag{8}\]  
Due to the effect of noise and reverberation, the location estimate obtained by different frame of a sound signal usually varies. This can be dealt with by clustering, which will be addressed in Section 4.1.
## 1 Introduction  
### Background and Objectives of the Study  
This study focuses on the prevention of grass fires (wildfires) and the response to such fires by utilizing drones. In South Korea, 62.6% of the total land is covered with forests, and confiferous forests, which are composed primarily of pine trees, account for 36.9%. Therefore, the region is highly susceptible to wildfires [1].  
Over the past decade, on average, 481 wildfires have occurred in the country, resulting in the loss of 1087 hectares (per year) of forested land [2]. Grass fires, caused primarily by negligent activities, have been identified as the main cause. In the last five years alone, 6538 grass fires occurred, resulting in 234 casualties, and 95% of these were attributed to negligence [3].  
Particularly alarming is the fact that 27% of the grass fires originated from activities such as the burning of trash or agricultural residues that spread into mountainous areas [2]. The impact was more severe when these fires occurred near residential areas and mountainous border zones. This study aims to investigate the use of drones for deploying swift responses and effective preventive measures in the early stages of grass fire outbreaks to ultimately minimize the resulting damage.  
Because it is practically impossible to completely prevent wildfires, a prompt and systematic response system is required to limit the damage caused by them. Early suppression of wildfires and the establishment of firebreaks are crucial response strategies for preventing the spread of wildfires; however, the lack of personnel and equipment hinders the construction of firebreaks for wildfire containment, particularly in large mountainous areas, which are prone to wildfires. Therefore, the use of drones in the initial stages of firebreak construction can help overcome these limitations and compensate for the lack of firefighting personnel [4]. In this study, we aim to study the effectiveness and impacts of using drones in the initial stages of firebreak construction.
## 2 Requirements of 5G-Based V2X Use Cases
### QoS Requirements
#### 2.1.3 Packet Drop  
In regular stream-like applications, the packet drop is a crucial performance indicator [28]. The exchange of medium- or large-sized encoded data or data protected by stream encryption methods is usually useless in the case that some of the packets are missing from the data stream. The packet drop might also increase the performance requirements against clients, like their TCP buffer size. Contrary to this, V2X facilitates compact data segments, which can travel in single packets. Since V2X applications are primarily interested in themost recent data, the packet drop parameter partially loses its importance and eventually becomes an access technology-specific trade-off between the packet drop, latency, and bandwidth. The adjusted parameter can be the message frequency. The evaluation criteria of [120] is eye-opening since it highlights well that the goal of V2X communication is not to limit the number of packet drops but to minimize the age of the information.
## 4 Training Process and Results
Compared with time-domain data, frequency-domain data can also provide learning features such as frequency response and frequency band distribution for the two-dimensional CNN model. Figure 16: Classification results of one-dimensional CNN model for simulated data for (**a**) the A-scan and (**b**) the spectrum of the A-scan. Figure 17: Classification results of two-dimensional CNN model for simulated data for (**a**) the A-scan and (**b**) the spectrum of the A-scan. In order to improve the generalization ability of the two-dimensional CNN model, simulated data and experimental data are combined for training and classification (see Figure 20). From the results, it can be seen that errors mainly occur in the feature recognition of voids, while the feature classification of cracks and non-dense solids is more accurate. Figure 21 provides a more intuitive display of the classification and statistical results of experimental and simulated data trained using one-dimensional and two-dimensional CNN models in this study. In the results of the one-dimensional CNN model, the training results of A-scan data are higher than those of spectrum data. In the training results of the two-dimensional CNN model, the overall recognition accuracy is significantly higher than that of the one-dimensional CNN model. At the same time, the results of A-scan spectrum data are higher than those of A-scan data, further verifying the learning ability of the two-dimensional CNN model for spatial and local features of complex data. Figure 19: Classification results of two-dimensional CNN model for experimental data for (**a**) the A-scan and (**b**) the spectrum of the A-scan. Figure 18: Classification results of one-dimensional CNN model for experimental data for (**a**) the A-scan and (**b**) the spectrum of the A-scan.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 4 Trends and Prospects of Carbon Emission Research on Biomass Combustion
### Interdisciplinary Cooperation and International Collaboration  
(1) Interdisciplinary collaboration: The study of carbon emissions from biomass burning is a complex undertaking that requires the expertise of numerous disciplines, including ecology, meteorology, remote sensing technology, and atmospheric chemistry. Additionally, it necessitates the integration of social and economic considerations. Future research will increasingly prioritize interdisciplinary collaboration, with the integration of knowledge and technology from diverse fields facilitating more comprehensive carbon emission monitoring and assessments. For example, the study conducted by [PERSON] and colleagues integrated atmospheric chemistry, social economics, and remote sensing technology to develop a comprehensive methodology for estimating emissions from biomass burning [45]. (2) International collaboration and data sharing: In the context of globalization, international collaboration in the study of biomass burning emissions is of particular importance. The establishment of a global observation network and data sharing platform enables researchers from disparate countries to collaborate on addressing the challenges of global climate change. As evidenced by the Sixth Assessment Report (AR6) of the IPCC and the research of [PERSON] et al., international collaboration and data sharing can markedly enhance the precision of global carbon emission monitoring, thereby facilitating countries' collective efforts to tackle climate change [46].
###### Abstract  
In the distributed detection system with multiple sensors, there are two ways for local sensors to deliver their local decisions to the fusion center (FC): a one-bit hard decision and a multiple-bit soft decision. Compared with the soft decision, the hard decision has worse detection performance due to the loss of sensing information but has the main advantage of smaller communication costs. To get a tradeoff between communication costs and detection performance, we propose a soft-hard combination decision fusion scheme for the clustered distributed detection system with multiple sensors and non-ideal communication channels. A clustered distributed detection system is configured by a fuzzy logic system and a fuzzy c-means clustering algorithm. In clusters, each local sensor transmits its local multiple-bit soft decision to its corresponding cluster head (CH) under the non-ideal channel, in which a simple and efficient soft decision fusion method is used. Between clusters, the fusion center combines all cluster heads' one-bit hard decisions into a final global decision by using an optimal fusion rule. We show that the clustered distributed system with the proposed scheme has a good performance that is close to that of the centralized system, but it consumes much less energy than the centralized system at the same time. In addition, the system with the proposed scheme significantly outperforms the conventional distributed detection system that only uses a hard decision fusion. Using simulation results, we also show that the detection performance increases when more bits are delivered in the soft decision in the distributed detection system.  
distributed detection system; soft decision; hard decision; clustering; fuzzy logic system; fuzzy c-means algorithm +
Footnote †: journal: Sensors
# Immobilization of Levocyetririzine on Mesoporous Silica for Antiallergenic Gel Formulation
## 1 Introduction
Furthermore, there is a new method of increasing effectiveness, which is becoming more and more common in the cosmetics and pharmaceutical industry; it is the application of drug delivery systems (DDSs) using various organic (liposomes, polymer particles, dendrimers, various nanoparticles) and inorganic carriers (mesoporous silicon dioxide material; MSM) [17; 18; 19]. Inorganic silica carriers have many favorable properties, such as being non-toxic and biocompatible and having a large surface area and a large pore volume with a well-defined pore size. Additionally, their surface contains silanol groups that are easily modifiable. The bioavailability of the active ingredient increases in the case of amorphous drugs fixed in mesopores, as the crystallization of these active ingredients in the silica is significantly reduced [20; 21]. Figure 1: Levocetriize dihydrochloride (**A**) and dextrocetriize dihydrochloride (**B**). Silica MSM is primarily known as a carrier of less-soluble active ingredients, increasing their apparent solubility, since they are mainly in amorphous form. However, according to the literature, MSM has proven to be a special carrier that is capable of controlled and on-demand delivery of hydrophobic and hydrophilic drug molecules as well [22]. For example, the hydrophilic gemcitabine can also be well fixed on the functionalized MSM [23]. In this work, we aimed to develop a gel containing Levo that has a topical effect on the skin, which rapidly relieves allergic symptoms, reduces local irritation, and exhibits long-lasting effects due to the immobilization of drug molecules within the micropores of the MSM. To ensure good bioavailability of Levo, we investigated which form of the active ingredient is best suited for fixing on MSM to obtain DDSs.