esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Channel Perception of UAV Data Link Network Based on Data Fusion
## 3 Node Weight Allocation Strategy Based on Data Fusion
### Falsity of Network Node Data  
Falsity is a measure of evidence conflict and is commonly used in evidence theory to assess the degree of unreliability of certain evidence within an evaluation system. In this paper, it was employed to measure the level of non-acceptance of the characteristic parameters of network node devices. Based on the theory of evidence, the global conflict [28] of characteristic parameters from node devices is expressed as:  
\[k_{0}=\sum_{\gamma_{j=1}^{n}A_{ij}=\varnothing}\left\{\prod\
olimits_{i=1}^{ n}m_{i}\big{(}A_{ij}\big{)}\right\} \tag{12}\]  
where \(A_{ij}\) is the focal element of \(m_{i}\) and \(m_{i}(A_{ij})\) represents the basic probability assignment for the \(j\)-th focal element within the \(i\)-th source of characteristic parameters. Furthermore, \(\cap_{j=1}^{n}A_{ij}=\varnothing\) represents a focal element with a non-empty intersection.  
After removing the \(j\)-th characteristic parameter from the node device, the local conflict among the remaining characteristic parameters is expressed as:  
\[k_{j}=\sum_{\gamma_{j=1,j\
eq i}^{n}A_{ij}=\varnothing}\left\{\prod\
olimits_ {i=1,i\
eq j}^{n}m_{i}\big{(}A_{ij}\big{)}\right\} \tag{13}\]  
Taking into account both the global and local conflicts, the falsity of the \(j\)-th characteristic parameter of the node device can be expressed as:  
\[F\big{(}m_{j}\big{)}=\frac{k_{0}-k_{j}}{1-k_{j}} \tag{14}\]  
Thus, the falsity vector for characteristic parameters of network node devices can be expressed as:  
\[F(m)=[F(m_{1}),F(m_{2}),\ldots,F(m_{n})] \tag{15}\]
# Impacts of Climate Change on Blue Carbon Stocks and Fluxes in Mangrove Forests
## 2 Mangrove Blue Carbon: An Update
### Carbon Burial Rates  
Rates of carbon sequestration in mangrove forests average 183.6 g C\({}_{\text{ORG}}\) m\({}^{-2}\) a\({}^{-1}\) (1 SE = 14.7 g C\({}_{\text{ORG}}\) m\({}^{-2}\) a\({}^{-1}\)) with a median of 114.8 g C\({}_{\text{ORG}}\) m\({}^{-2}\) a\({}^{-1}\) and a very wide range from 0.001 to 1722.2 g C\({}_{\text{ORG}}\) m\({}^{-2}\) a\({}^{-1}\) (data in Alongi [8] updated with additional refs. [9; 28; 32; 33; 34; 35; 36; 37; 38; 39; 40]). More than half of all measurements were \(<\)100 g C\({}_{\text{ORG}}\) m\({}^{-2}\) a\({}^{-1}\) with the highest rates observed in degraded or heavily impacted, disturbed forests and catchments such as in China, South and Southeast Asia, and Africa. Multiplying the median by both low and high estimates of mangrove area [29; 30], carbon burial in the world's mangrove forests equates to 9.6-15.8 Tg C\({}_{\text{ORG}}\) a\({}^{-1}\) which is several times greater than for the tropical coastal ocean (3.9 Tg C\({}_{\text{ORG}}\) a\({}^{-1}\)), slightly greater than for salt marshes (11.7 Tg C\({}_{\text{ORG}}\) a\({}^{-1}\)) but less than in seagrass meadows (35.3 Tg C\({}_{\text{ORG}}\) a\({}^{-1}\)) [8]. The wide variability in C\({}_{\text{ORG}}\) burial rates reflects large differences in forests of different ages, types, and locations; rates are more likely to be a function of multiple interrelated drivers such as geomorphology, tidal inundation frequency, forest age, species composition, storms, tidal prism, soil grain size, and oceanic, riverine, and anthropogenic inputs, as sequestration rates exhibited no significant relationship with latitude [41]. Further, in many tropical river deltas, sediment deposition and subsequent C\({}_{\text{ORG}}\) accumulation and burial in mangroves are often highly dynamic with episodes of sediment starvation due to damming and episodes of massive bedload transport and deposition that smother mangroves, as found in the Mekong delta [32; 42].
## 2 Results
### Sensitivity and Selectivity  
Overall, 48 volatiles from nine classes of compounds, alcohols, aldehydes, alkanes, carboxylic acids, ketones, lactones, sulphur compounds, nitrogen compounds, and terpen were identified in the sheep milk by the four different extraction techniques (Table 1 and Figure 1). The SAFE method detected 20 compounds and the total apparent concentration was 627 ug/kg. The SPME method also detected 20 compounds, while SBSE and HSSE detected 45 compounds and 37 compounds, respectively. The headspace techniques, SPME and HSSE were the least sensitive, with 173 ug/kg and 271 ug/kg of volatiles. The SBSE method was the most sensitive and detected an apparent concentration of 657 ug/kg, mostly due to greater detection of lactones. Among the compounds identified were eight lactones including \(\delta\)-hexalactone, \(\delta\)-declactone, \(\delta\)-decalactone, \(\gamma\)-decalactone, \(\delta\)-dodecalactone, \(\gamma\)-dodecalactone, \(\delta\)-tetradcalactone, and \(\delta\)-hexadecalactone (Figure 2). [PERSON] et al. had previously reported \(\delta\)-decalactone and \(\delta\)-dodecalactone in sheep milk, but to the best knowledge of the authors, this is the first time the other six lactones have been identified in ovine milk.  
Figure 1: The percentage distribution of volatile compounds in each compound class detected in sheep milk by Solvent Assisted Flavour Evaporation (SAFE), Solid Phase Microextraction (SPME), Headspace Sportive Extraction (HSSE) and Stir Bar Sportive Extraction (SBSE).  
Compounds with moderate polarity and volatility, such as heptanal, were detected similarly by all four techniques (Table 1). Alkanes were also detected on a similar scale across all four methods. SPME and SAFE were better than the single phased (PDMS) stir bar techniques for extracting small polar analytes such as dimethyl sulphone (Table 1 and Figure 3). SPME was the only technique that detected dimethyl sulphide and pentanal. Carboxylic acids were detected by all four methods but the sensitivity of SPME was noticeably lower than the other three techniques. Ketones were only detected at very low concentrations and only by the PDMS stir bar methods, HSSE and SBSE. Terpenes were detected by all the methods except for SPME. The headspace methods, SPME and HSSE, also detected higher levels of alcohols, especially the relatively polar 1-pentanol, but were only able to detect trace levels of lactones. Conversely, SAFE and SBSE barely detected 1-pentanol but extracted high concentrations of lactones. SAFE was best for the simultaneous extraction of small polar and larger non-polar molecules (e.g., dimethyl sulphone and \(\delta\)-dodeclactone). SBSE was the most sensitive technique for the lactones.  
Figure 2: Apparent concentrations of lactones extracted by Solvent Assisted Flavour Evaporation (SAFE), Solid Phase Microextraction (SPME), Headspace Sportive Extraction (HSSE) and Stir Bar Sportive Extraction (SBSE). Error bars represent the standard deviations.
###### Abstract  
A surface acoustic wave-based NO\({}_{2}\) sensor and its interface electronics, utilizing monolayered two-dimensional sensing materials, were developed for internal pollution monitoring in spacecraft. The sensor system consists of a two-port SAW delay line with monolayered graphene/MoS\({}_{2}\) flakes in the cavity region between two interdigital transducers, along with the interface electronics. A microheater was integrated adjacent to the sensor to maintain a stable temperature field on the sensor surface, thereby enhancing sensitivity, response/recovery times, and selectivity. The monolayered graphene/MoS\({}_{2}\) sensing material, with its high surface-to-volume ratio, excellent mobility, and moderate bonding force with target molecules, enables the rapid response and recovery times of less than 2.5 and 8 s, respectively--among the fastest reported in SAW gas sensor technology. The developed sensor combines the conductivity changes, the mass loading effect, and a synergistic effect that promotes carrier separation caused by a built-in potential barrier between the two monolayers, providing exceptionally high sensitivity of 578 Hz/ppm. Additionally, the sensor's interface electronics were engineered to mitigate the effects of external factors, such as temperature and humidity, ensuring a stable and reliable performance under varying harsh conditions.  
surface acoustic wave; NO\({}_{2}\) sensor; 2D sensing material; graphene/MoS\({}_{2}\) heterostructure; interface electronics; spacecraft Article
# Scheduling for Media Function Virtualization
## 5 Evaluation
### End-to-End Delay  
End-to-end delay is an important metric for broadcast media services, especially for live production scenarios. Here, we report the end-to-end (E2E) delay in a VMF-FG, i.e., the maximum delay among all possible paths between the VMF-FG's sources and sinks for both the BFS and DFS traversal of the proposed heuristic. In Figure 6a, we compare the average E2E delay for different cycle time (\(T_{c}\)) values. It can be observed that the E2E delay increases with \(T_{c}\). This results from the fact that the delay along the physical paths corresponding to the virtual links increases with \(T_{c}\) as the queuing delay in switching nodes increases.  
Figure 5: Illustration of the VMF-FG used for the evaluation.  
It is interesting to note that the E2E delay as well the VMF queuing delay for DFS is more than with BFS. As scheduling occurs on a path-by-path basis, the scheduling on one path is impacted by the previously scheduled path, thus increasing the E2E delay on the longest path. On the other hand, for BFS, all paths have relatively similar delays, thus reducing the E2E delay along the longest path.  
We also report the E2E delay variation with \(M\). The decomposition of a VMF-FG leads to VMF decomposition, which results in the reduction of VMF processing delay. This results in the decreasing E2E VM-FG delay with \(M\).  
The are various components of E2E delay. The contribution due to video frame queuing is an important metric because it highlights the efficiency of VMF-FG scheduling. Here, we define the average VMF queuing delay as the mean of VMF queuing delay in all VMFs of the VMF-FG. The smaller the value of the average VMF queuing delay is, the better is the VMF-FG scheduling. The average queuing delay slightly decreases with \(T_{c}\) as shown in Figure 6b. However, VMF-FG decomposition results in increasing the average VMF queuing delay due to higher consolidation of VMFs per node.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value or Range** & **Units** \\ \hline Topology & Fat-tree (\(\kappa=8\)) & - \\ Formats & HD@30 fps, HD@60 fps, FHD@30 fps & - \\ CPU cores/node & 20 & - \\ Link bandwidth & 10 & Gbps \\ Cycle time (\(T_{c}\)) & \{100, 200, 300, 400\} & \(\mu\)s \\ VMF processing delay & 5 & ms \\ Node (switch) process- & 40 & \(\mu\)s \\ ing delay (\(d_{n_{u},n_{s}}\)) & & \\ Links delay (\(d_{n}\)) & 80 & \(\mu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Default values/range of various parameters involved in the evaluation.  
Figure 6: (**a**) The average E2E delay and (**b**) the average queuing delay versus cycle time (\(T_{c}\)).
## 3 Results and Discussion
### Discrimination and Prediction  
As stated above, the first principal component described above constitutes a valid variable to discriminate samples. The discriminant model correctly classified all but one of the samples. Thus, the sensitivity of the model was about 96% and the specificity was 100%. In order to test the robustness of such a model, a leave-one-out cross-validation calculation was conducted. The procedure consisted of the elimination of one sample at a time and then redoing all of the calculations with the remaining 55 (i.e., reading all the bulk chromatographic files from scratch, defining the grid and dichotomizing it, constructing the SSIR model, performing the PCA calculation and deriving the linear discriminant function). The discriminant function was then applied to the removed sample in order to predict whether it was treated or non-treated. All of the 56 samples passed their own individual cross-validation tests, and for all but two of them the prediction was correct (96% performance). Additionally, discriminant functions involving 2 to 5 of the first principal components were also considered in all the leave-one-out procedures. When the model involved the two first principal components, only one classification error was found. The models involving 3 to 5 principal components correctly classified all of the samples.
## 5 Simulation and Results
### Discussion  
This section outlined some insights into the experimental results and extract some findings to reveal the importance of the adopted method. The performance of the weighted  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **CA (\%)** & **MCR (\%)** & \begin{tabular}{c} **Sensitivity** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} & **NPV (\%)** & **FPR (\%)** & **FNR (\%)** &
\begin{tabular}{c} **F1-Score** \\ **(\%)** \\ \end{tabular} \\ \hline LM & 84.48 & 15.51 & 70.90 & 90.30 & 87.8 & 9.69 & 29.09 & 73.26 \\ BR & 89.26 & 10.73 & 78.93 & 93.67 & 91.24 & 6.32 & 21.06 & 81.47 \\ SCG & 93.50 & 6.49 & 85.11 & 97.21 & 93.65 & 2.78 & 14.88 & 88.93 \\ WFL & 98.85 & 1.15 & 98.13 & 99.13 & 99.27 & 2.2 & 1.8 & 94.21 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical performance of LM, BR, SCG, and WFL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Refs. No** & **Article** & **Algorithm** & **Classification Accuracy** \\ \hline
[11] & [PERSON] et al. (2021) & ML+Statistical Methods & 97.65\% \\
[17] & [PERSON] et al. (2018) & SVM & 95.24\% \\
[18] & [PERSON] et al. (2020) & SVM+GA & 98.03\% \\
[19] & [PERSON] et al. (2019) & ACO & 97.4\% \\
[20] & [PERSON] et al. (2021) & Entropy & 98.2\% \\
[21] & [PERSON] et al.(2021) & GAN & 94.38\% \\
[22] & [PERSON] et al. (2020) & Factorization Machine & 95.80\% \\
[23] & [PERSON]. (2022) & DFL & 97\% \\
[25] & [PERSON]. & MapReduce & 95.7\% \\
[26] & [PERSON] et al. & P\&F 98.06\% \\
[27] & [PERSON] et al. & Kalman NN & 97.49\% \\
[28] & [PERSON] et al. & ML-RNN & 98.27\% \\
[24] & [PERSON] et al. & RTS-DELM-CSIDS & 92.37\% \\
**Proposed** & **Low Rate DDoS Detection using Weighted Federated Learning in SDN Control Plane in IoT Network** & **WFL** & **98.85\%** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with the existing approaches.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **0 (Predicted Normal)** & **1 (Predicted Attack)** \\ \hline
**0 (Actual Normal)** & 12,337 & 279 \\
**1 (Actual Attack)** & 235 & 32,146 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix of federated model.  
federated learning was evaluated using various parameters and shows a substantial performance when compared with the existing work done in the same field. The efficiency of the intrusion detection system depends upon the correct prediction of the attack and normal traffic. The adopted method has shown a remarkable overall classification accuracy of 98.85%. and very low misclassification rate of 1.15%. Since the adopted method is based on model-centric federated learning, the local client only requires to send the weights of the locally trained model, which are very low in quantity as compared to the data of the client, so Intuitively reduce the memory and processing requirements. The results of the adopted method to detect the LR-DDoS attack are promising and show the suitability of WFL for intrusion detection to the SDN-Based IoT networks.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 4 Analog-Digital Receiver Design
### Iterative Analog-Digital Equalizer
#### 4.1.1 Digital Feed-Forward Equalizer Design  
First, we compute the feed-forward digital part of the equalizer as a function of the analog matrix, \(\mathbf{W}_{a}^{(i)}\). According to (22), for a given analog equalizer matrix \(\mathbf{W}_{a}^{(i)}\), the optimum digital part of the equalizer for the \(k\)th subcarrier is the solution of the following convex optimization problem  
\[\begin{array}{c}\mathbf{W}_{d,k}^{(i)}[\mathbf{W}_{a}^{(i)}]=\\ \text{arg min}\sum\limits_{k=1}^{S}\overline{\text{MSE}}_{k}^{(i)}\\ \text{s.t.}\sum\limits_{k=1}^{S}\text{diag}(\mathbf{W}_{d,k}^{(i)}{\left( \mathbf{W}_{a}^{(i)}\right)}^{H}\mathbf{H}_{k})=S\mathbf{I}_{U},\end{array} \tag{23}\]  
whose solution is [29]  
\[\mathbf{W}_{d,k}^{(i)}[\mathbf{W}_{a}^{(i)}]=\mathbf{\Omega}_{d}\mathbf{H}_{k} ^{H}\mathbf{W}_{a}^{(i)}\!\left(\mathbf{R}_{d,k}^{(i-1)}\right)^{\!\!-1}\!\!, \tag{24}\]with  
\[\mathbf{\Omega}_{d}=\mathcal{S}\!\!\left(\sum\limits_{k=1}^{\mathcal{S}}\text{diag} \!\left(\mathbf{H}_{k}^{H}\mathbf{W}_{a}^{(i)}\!\left(\mathbf{R}_{d,k}^{(i-1)} \right)^{-1}\!\!\left(\mathbf{W}_{a}^{(i)}\right)^{H}\!\!\mathbf{H}_{k}\right) \right)^{-1}\!\!, \tag{25}\]  
\[\mathbf{R}_{d,k}^{(i-1)}=\left(\mathbf{W}_{a}^{(i)}\right)^{H}\!\!\overline{ \mathbf{R}}_{k}^{(i-1)}\mathbf{W}_{a}^{(i)}, \tag{26}\]  
where \(\mathbf{\Omega}_{d}\) is used to normalize the received power.
## 3 Analysis Process
### Data Analysis  
The data obtained from the screening process were analyzed using various analytical methods and evaluated in detail under the following headings:  
* **Geographical Distribution of Carbon Footprint Studies:** The intensity and contributions of research from different countries were examined.
* **Impact of ERP Systems and Industry 4.0 Technologies on Environmental Sustainability:** The contributions of these systems to energy efficiency and carbon management were analyzed.
* **Energy Efficiency Improvement Strategies and Implementation Challenges:** Energy-saving strategies and the challenges encountered in their implementation were discussed.  
This section details the various analytical methods used in the study: For example, LCA was utilized to evaluate the environmental impact of manufacturing processes, with a particular focus on reducing carbon emissions. LCA serves as a critical tool for analyzing the environmental impacts across all stages of production and achieving sustainability goals.  
**Data Envelopment Analysis (DEA)** was applied to identify energy inefficiencies in different production stages and optimize these processes. DEA examines the relationship between energy consumption and efficiency, contributing to more sustainable production practices.  
Additionally, **bibliometric analysis** was employed to identify research trends and gaps in the literature. The following results were obtained from this analysis:  
* **Gaps in the Literature:** Geographical distribution analysis indicates that most research on carbon footprint and Industry 4.0 has been concentrated in the US, China, and Europe. However, there is a significant lack of studies focusing on these topics in developing countries, highlighting a global perspective gap in carbon footprint management and sustainable production research.
* **Research Trends:** Between 2014 and 2024, research on carbon footprint and sustainable production topics increased by 30%. However, studies exploring the role of ERPsystems in these areas are still limited, indicating that the potential of ERP systems in the context of environmental sustainability has not been adequately explored.  
The methodology applied in this study provides a comprehensive overview of research on carbon footprint management and sustainable production processes. Key findings and analytical methods in the literature have been evaluated regarding the applicability of various strategic approaches to environmental sustainability. The study's findings shed light on the current state of energy efficiency, carbon management, and sustainable supply chains, as well as the directions for future research.
## 2 Case Studies
### Case A Study  
In case A, a three-dimensional truss structure composed of 22 tubular rods organized into seven groups is examined (Figure 1). Each group is defined by a specific cross-sectional area (\(Ai\), where \(1\leq i\leq 22\)), resulting in a parameterization that includes 14 design variables (7 outer diameters ranging from \(OD_{min}\) = 30 mm to \(OD_{max}\) = 200 mm and 7 thicknesses ranging from \(TK_{min}\) = 0.1 mm to \(TK_{max}\) = 14 mm). A configuration is considered feasible if it satisfies the prescribed limits on three state variables (\(SVi\), where \(1\leq i\leq 3\)):  
* \(SV1\): Minimum allowable cross-sectional area \(A_{min}\) = 64.5 mm\({}^{2}\);
* \(SV2\): Stress limits of the members (see Table 1);
* \(SV3\): Displacement limits of each node \(DL=\pm 50\) mm.  
In all case studies, the Objective Function Value (OFV), defined as the total weight of the structure, is minimized.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 3 Materials and Methods
### Calibration  
To determine the actual spectral range and spectral resolution of the optical system, we used a low-pressure mercury lamp as the light source for wavelength calibration. The mercury lamp has characteristic peaks at 546.075 nm, 573.964 nm, and 579.067 nm. Figure 3 shows the original interferogram and reconstructed spectrum of the mercury lamp.  
According to Equations (11) and (12), two characteristic spectral lines (546.075 nm and 579.067 nm) of the mercury lamp were used for the calibration. The results show that the actual Littrow wavelength of the system is 540.904 nm, the Littrow angle is 2.324\({}^{\circ}\), the system resolution is 4.945 cm\({}^{-1}\) (0.149 THz), the spectral range is 2531.84 cm\({}^{-1}\) (75.963 THz), and the FWHM of the 546.075 nm characteristic spectral line in the restored spectrum is 5.1 cm\({}^{-1}\) (0.153 THz), which is almost the same as the theoretical resolution.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Applicability of Damage Evolution Equation
#### 3.5.3. Consideration of Unloading, Water Softening and Time Effects  
This kind of calculation is applicable to the tunnel excavation under water-rich soft surrounding rock and considering the damage deterioration with time, and Equation (38) is used in combination with Equations (36) and (37). This method can not only calculate the final damage value after excavation, but also obtain the rock damage values at any moment during the softening period. The time history curve of damage variable considering dual softening and time effect is depicted with the other two conditions in Figure 10.  
\[D(\epsilon,t)=\begin{cases}\frac{D_{1}}{D_{12}\overline{t}_{1}}t&0\leq t<t_{1 }\\ -\left(1-\frac{D_{1}}{D_{12}}\right)\exp\left[-B(t-t_{1})\right]+1&t_{1}\leq t< t_{2}\\ 1&t_{2}\leq t\end{cases} \tag{38}\]  
where, \(D_{1}\), \(D_{2}\), and \(D_{12}\) are the damage variables under unloading softening, water-softening, and the two softening effects. \(t=0\) denotes the start of unloading softening, \(t=t_{1}\) corresponds to the end of unloading softening and the start of water softening, and \(t=t_{2}\) marks the end of water softening. In general, \(t_{2}-t_{1}\) can be taken as 90 days.  
Figure 9: Schematic diagram of the evolution of damage variables under dual softening: (**a**) Tensile damage variable, (**b**) shear damage variable.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### Parameter Space Evaluation
#### 5.1.2 Class Preservation  
The classification of 1-NN in the original dataset achieves an average of 91.21% for 100 iterations of the algorithm. None of the dimensionality reduction techniques are able to replicate this response, with pPCA achieving the highest score (87.92%), as seen in Table 2. On the other hand, the sAE achieved an accuracy of 84.01%, the lowest among the techniques being tested, 7.2% worse than the classification accuracy of the algorithm in the high-dimensional dataset. This result reveals that sAE is not as capable as other reduction techniques in preserving the classes on the low-dimensional space; however, as sAE is able to achieve better results than the other techniques for _trustworthiness_ for a lower number of neighbours, and its performance in 1-NN is not drastically worse (3.91%) than the best technique in pPCA, it can be considered a minor problem.