esa-sceva/satcom-chunk-collection huggingface dataset:
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Organization  
This text is organized into sections. The proposed methodology relies on the membership mappings for data representation learning. Therefore, Section 2 has been dedicated to the review of membership mappings. An application of membership mappings to solve an inverse modeling problem by developing a variational membership-mapping Bayesian model is considered in Section 3. Section 4 presents the most important result of this study on the variational approximation of information leakage and development of a computational algorithm for calculating information leakage. The measures for privacy leakage, interpretability, and transferability are formally introduced in Section 5. Section 5 further provides an algorithm to study the privacy, interpretability, and transferability aspects in a unified manner. The application of proposed measures to study the trade-offs is also demonstrated through the experiments made on the widely used MNIST and \"Office+Caltech256\" datasets in Section 6. Section 6 further considers a biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis. Finally, the concluding remarks are provided in Section 7.
## 3 Results and Discussion
### GPC Determination  
In studying the macromolecular properties of lignin, gel permeation chromatography (GPC) is an important method. The average molecular weight (Mw), number average molecular weight (Mn), and polydispersity (PDI) of MWL were analysed by GPC. It was believed that Mw highlights the significance of the highest molecular weight fraction in determining the average molecular weight, whereas Mn emphasizes the influence of the largest number of fractions on the average molecular weight. Competitive reactions involving depolymerization and condensation commonly occur in lignin during pretreatment [45]. Following ozone pretreatment, both the Mw and PDI of MWL exhibited an increase, while Mn exhibited a decrease (Table 6). This can be attributed to the initial depolymerization  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Lignin**} & \multicolumn{3}{c}{**Lignin Samples**} \\ \cline{2-4}  & **MWL** & **OL60** & **OL120** \\ \hline Hydrophobicity (L/g) & 0.951 & 0.889 & 0.615 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hydrophobicity of lignin samples before and after ozone pretreatment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Lignin**} & \multicolumn{3}{c}{**Lignin Samples**} \\ \cline{2-4}  & **MWL** & **OL60** & **OL120** \\ \hline zeta (mv) & \(-10.3\) & \(-11.8\) & \(-15.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Zeta potential of lignin samples before and after ozone pretreatment.  
reaction of lignin during ozone pretreatment, resulting in the production of small molecular phenolic compounds and consequently augmenting the population of lignin with low molecular weight. However, certain small molecular phenolic substances undergo condensation reactions with lignin macromolecular chains, thereby further elevating the molecular weight of certain lignin with higher molecular weight. Consequently, this results in a marginal increase in the Mw of lignin, which aligns with the findings obtained from the HSQC-NMR analysis. Moreover, an increase in polydispersity may be beneficial in reducing the adsorption of cellulase onto lignin [46].
## 3 Routing Maintenance and Reconstruction Algorithm
### Routing Reconstruction Algorithm  
As shown in Figure 4, we assume that the central node 13 in logical layer 2 has not received any information sent by node 11, after a data processing delay \(t_{proc}\), confirming that node 11 is a bad node. Node 13 then initiates the following process to reconstruct the local routing:  
\begin{table}
\begin{tabular}{c c c} \hline
**Source node** & **Destination node** & **Relay nodes** \\ \hline
1 & 5 & 7, 20 \\
1 & 9 & 37, 24 \\
1 & 22 & 11, 15 \\ \hline \end{tabular}
\end{table}
Table 1: Example of routing.  
Figure 5: Tree topology model.  
Figure 6: Results of ACRA.  
1 Central node 13 sends a reconstruction broadcast to the nodes in its same subnet.
2 Nodes that intercept the instructions for routing reconstruction forward the instructions in turns and add their own logical ID into the data packet.
3 Node 11 records the physical strength of the reconstruction broadcast signals; calculates and selects the node with max \(\beta\), the composite index of the signal strength, as a relay node (node 12 in this paper); and sends a response to node 12. Node 11 establishes a communication route to central node 13 with node 12 as relay.
4 Node 12 sends information to node 13, and node 13 updates the routing table.
5 Node 13 sends the information layer by layer up to the BS of the corresponding node to update the routing table. Then, the reconstruction process for routing a bad node is completed.  
When bad nodes appear in a network, it is not necessary for all nodes to reconstruct their routing. Only the nodes in the same subnet as the bad nodes are involved, which dramatically shortens the route reconstruction and maintenance time and improves the efficiency of the system.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Multi-Profile Parameter Set Approach  
To address scenarios in which the original three-parameter fuzzy system (\(CT\), \(NN\), and \(SS\)) may no longer suffice, we define multiple candidate parameter sets (i.e., \(\mathcal{P}_{1},\mathcal{P}_{2},\ldots,\mathcal{P}_{k}\)) that can incorporate additional parameters such as energy constraints, delay tolerance, or node distribution patterns. For each parameter set, the fuzzy membership functions differ not only by their ranges, but also by the specific subset of parameters included. Hence, one profile might prioritize (\(NN\)) and remaining energy, while another might emphasize (\(CT\)) and latency requirements.  
Formally, each profile \(\mathcal{P}_{j}\) encapsulates the membership definitions for the chosen parameters. Let us denote these parameters collectively as  
\[X\in\{\mathit{CT},\,NN,\,SS,\,E_{\text{res}},\,D_{\text{tol}},\,\ldots\}\,,\]  
depending on the design focus of \(\mathcal{P}_{j}\). We then have  
\[\mu_{\text{Label}}^{(j)}(X)=\Big{\{}\ldots\quad(\text{Profile}\,\,\mathcal{P}_{ j})\;, \tag{2}\]  
where \(\mu_{\text{Label}}^{(j)}\) could be, for instance, \"Low Energy,\" \"High Energy,\" or \"Strict Delay\" according to the newly introduced parameters. During network operation, the fuzzy engine selects _one_ profile at a time as the active membership definition based on the network's current macro-level conditions.
# NOVA Mobility Assistive System: Developed and Remotely Controlled with IOPT-Tools
## 4 NOVA Mobility Assistive System  
The proposed mobility assistive system (NOVA-MAS) and its development approach are presented in this section. The NOVA-MAS has a Service-Oriented Architecture (SOA) and it is composed of Wi-Fi beacons, named NOVA-WiFi-Beacons (NWBs) and Wi-Fi clients, named NOVA-WiFi-Clients (NWCs). NWBs are embedded in city infrastructures, whereas NWCs are embedded in vehicles or can be mobile applications.
## 4 Discussion
### Other Aliphatic Compounds  
\(n\)-Alkan-2-ones in the range C\({}_{10}\)-C\({}_{19}\) were observed in the melanins, mainly in the first thermal desorption shot (300 \({}^{\circ}\)C) and in the thermochemolysis at 250 and 400 \({}^{\circ}\)C. Methyl ketones can be produced by fungi by decarboxylation of \(\beta\)-keto acids [58], however, these ketones probably corresponded to thermal reaction products of fatty acids [59]. This was also supported by the occurrence of \(n\)-alkan-2-ones with a similar distribution that the lipids in the first shot of the non-hydrolyzable organic fraction of a soil [35].  
[PERSON] et al. [60] reported that, pyrolysis at high temperature increased the yield of aromatic compounds and furans, whereas the release of ketones was more characteristic of samples heated at lower temperatures.  
Heating (300 \({}^{\circ}\)C) of benzoic acid and fatty acids in the presence of iron salts yielded alkyl phenyl ketones, benzophenone, and benzaldehyde [60]. Thus, probably, the identification of alkyl phenyl ketones in the melanins can be explained by reaction of fatty acids in the presence of aromatic rings. The C\({}_{16}\) and C\({}_{18}\) alkyl phenyl ketones were evident in the second shot thermochemolysis at 400 \({}^{\circ}\)C of the acid hydrolyzed melanin.  
The presence of 2-alkanones among the pyrolysis products points to their formation by a mechanism involving decarboxylation and dehydration [59]. The hypothesis that \(n\)-alkanones occur by thermal cleavage of ether bonds in the pyrolysis of kerogens and alagenans (biopolymers of microalga cell walls) [41; 61] likely cannot explain their presence in the pyrolyzate of the extracted melanins due to the fact that these highly aliphatic biopolymers has not been reported in fungi. However, the presence of other type of ether bonds in the melanins cannot be discarded.  
Alkylamides are pyrolysis products formed by reaction between the ammonia evolved from the pyrolysis of proteins and the fatty acids. Alkylnitriles are formed via dehydration of alkylamides [62; 63]. Nitriles and amides have been identified in different pyrolyzates of the melanins and their presence indicated that acid hydrolysis did not remove all melanin nitrogen and the same can be applied to the fatty acids. In this way, the most abundant fatty acids (hexadecanoic and octadecanoic acids) yield the corresponding alkylamides and alkylnitriles. In addition, alkylamides, alkylnitriles, and fatty acids were abundantly recovered in the pyrolyzates of the microalga _Aurantiochytrium_ sp., whose major components were carbohydrates, proteins, and lipids [64].  
Considering the data discussed, everything points to the fact that likely many of the alkanes, alkenes, ketones, alkanals, \(\gamma\)-lactones, amides, and nitriles could be secondary pyrolysis products from the abundant fatty acids present in the extracted melanins.
## 5 Cell Experiments
### Cell Proliferation  
A549 cells were divided into four groups: Group A, Group B, Group C, and Group D. Cells in Group A were stimulated using a magnetic field, those in Group B were stimulated using a magnetic field cooperated with MNPs, those in Group C were the control group, and those in Group D were cultured with MNPs without the magnetic field. Our previous study showed that a magnetic field can suppress the growth of A549 cells [20,21]. To study the non-thermal effects of MNPs with a magnetic field, the cells were counted by an automated cell counter (TC10, BIO-RAD, Hercules, CA, USA) every 24 h and captured every 12 h. As shown in Figure 9, after 72 h, the ratio of cell proliferation in Group A has been suppressed by the magnetic field. Also, the ratio of cell proliferation in Group B is 14.7% lower than that in Group A. The group with only MNPs has no significant effect on cell proliferation. To ensure verifiable repeatability, the cell experiment was repeated three times.  
Figure 8: **(a)** The MNPs were endocytosed by cells and stained by lentivirus. **(b)** MNPs endocytosed by tumor cells. **(c)** Cell apoptosis driven by MNPs cooperated with a magnetic field.  
Figure 9: The cell counting of proliferation assay.
## 5 Experiment
### Performance Comparison with Setup 2  
We have selected the most representative GCN and GAT models as the targets for the adversarial attacks depicted in Figure 2. Additionally, we have chosen the Cora dataset characterized by discrete features and the PubMed dataset with continuous features for experimental comparisons. Since TDGIA only supports data with continuous features, the experiments on it were conducted solely on the PubMed dataset. In Setup 3, multiple fake nodes are directly connected to the target node. Consequently, during the classification process, the target model aggregates the features from the fake nodes into the representation of the target node, which makes target nodes with a higher number of fake nodes more likely to be misclassified. The experimental results indicate that all attack methods benefit from an increase in the number of fake nodes. With the escalation in fake node number, the proposed GCIA methods achieve higher misclassification rates, and consistently maintain a leading position. This experiment demonstrates that the proposed adversarial loss serves as an effective proxy for misclassification rates. When multiple fake nodes are injected into the graph, GCIA-1 can further enhance the attack performance, indicating that the GCIA method can fully leverage the unsupervised information extracted from the graph contrastive learning model to generate adversarial fake nodes.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Attack**} & \multicolumn{4}{c}{**Reddit**} & \multicolumn{4}{c}{**PubMed**} \\ \cline{2-9}  & **GCN** & **SAGE** & **GAT** & **APPNP** & **GCN** & **SAGE** & **GAT** & **APPNP** \\ \hline Random-10 & 0.306 & 0.249 & 0.229 & 0.296 & 0.381 & 0.316 & 0.33 & 0.265 \\ \hline G-NIA [19] & 0.848 & 0.648 & 0.631 & 0.675 & **0.92** & 0.704 & 0.646 & 0.71 \\ \hline TDGIA [21] & 0.939 & 0.883 & 0.714 & 0.768 & 0.883 & **0.871** & 0.662 & **0.857** \\ \hline G\({}^{2}\)A2C [22] & 0.971 & 0.941 & 0.921 & 0.699 & 0.652 & 0.569 & 0.589 & 0.409 \\ \hline GCIA-1 (ours) & 0.991 & 0.97 & 0.706 & 0.983 & 0.688 & 0.581 & 0.652 & 0.534 \\ \hline GCIA-10 (ours) & **1** & **1** & **0.992** & **0.999** & 0.911 & 0.844 & **0.938** & 0.808 \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 2: Misclassification rates of various attack methods in Setup 2. The horizontal coordinate corresponds to the different attack methods with different fake node numbers, while the verticalcoordinate illustrates the associated misclassification rates. The blue, orange, and green bars denote the conditions in which the fake node number \(\Delta_{v}\) is 1, 2, or 3, respectively, with each fake node directly connected to the target node. (**a**) Result in GCN model with Cora dataset, (**b**) result in GCN model with PubMed dataset, (**c**) result in GAT model with Cora dataset, (**d**) result in GAT model with PubMed dataset.
## 2 Methodology  
### General Framework for Image Deconvolution  
Image deconvolution is a computational technique used to reconstruct the 'true' image \(I_{\text{true}}(x,y)\) that has been blurred by the instrument's PSF and the superimposed noise. The observed image \(I_{\text{obs}}(x,y)\) is expressed as follows:  
\[I_{\text{obs}}(x,y)\ =\ I_{\text{true}}(x,y)*PSF(x,y)\ +\ N(x,y), \tag{1}\]  
where \(*\) denotes convolution, and \(N(x,y)\) represents the noise at each image point \((x,y)\). The goal of deconvolution is to estimate \(I_{\text{true}}(x,y)\) by reversing the effects of the PSF while accounting for noise.  
Deconvolution is an ill-posed problem because the noise in the observed image can be amplified during the reduction process. To address this issue, regularization techniques, and additional constraints are applied. Common deconvolution approaches include the following:  
1. **Direct methods:** These include Fourier-based deconvolution and Wiener filtering, which are computationally efficient but very sensitive to the noise content.
2. **Iterative methods:** Methods such as the Richardson-Lucy algorithm refine the estimate during successive iterations and are very effective against Poisson noise.
3. **Bayesian methods:** These incorporate prior knowledge about the image, such as smoothness or sparsity, to guide the cleaning algorithm.
4. **Machine learning approaches:** Neural networks trained on simulated data can directly predict the 'true' image from the observed outcome [19]. The general update rule for iterative deconvolution is written as follows:  
\[I_{\text{true}}^{(k+1)}(x,y)\ =\ I_{\text{true}}^{(k)}(x,y)\ +\ \lambda\cdot \mathcal{R}(I_{\text{true}}^{(k)},I_{\text{obs}},PSF), \tag{2}\]  
where \(I_{\text{true}}^{(k)}(x,y)\) is the estimate after \(k\) iterations, \(\lambda\) is a step size, and \(\mathcal{R}(\ldots)\) is a residual term based on the convolution mismatch. Iterations continue for as long as the residual is minimized and an adopted convergence criterion is met.
# Evaluating the Effectiveness of Natural Carbon Sinks Through a Temperature-Dependent Model
## 2 Materials and Methods
### The Top-Down Carbon Sink Model from the Continuity Equation
#### 2.1.1 Finding the Appropriate Data Resolution  
There are two aspects that motivate the observation of monthly data. Primarily, the rise in concentration growth happened within a short time interval in 2023. Working with monthly data also opens the possibility to investigate time lags. In the simple yearly model of Equation (4), there is an implicit time lag of 1 year between a change in concentration and a change in concentration growth. Using monthly data can make it possible to actually estimate the optimal time lag.  
On the other hand, there is much more noise at the monthly scale, with the undesirable consequence that any model with only a few parameters will only explain a small fraction of the total variance, making it difficult to evaluate the quality of a statistical model.  
In order to balance both criteria, the data of year \(i\) are decomposed into data of month \(k\) the following way. The monthly anthropogenic emissions add up during the year:  
\[E_{i}^{year}=\sum_{k=12i}^{12i+11}E_{k}^{month} \tag{8}\]  
Concentrations of 12 consecutive months are averaged and thus are automatically deseasonalized:  
\[C_{i}^{year}=\frac{1}{12}\sum_{k=12i}^{12i+11}C_{k}^{month} \tag{9}\]  
The time lag of \(1\) year transforms into 12 months  
\[C_{i-1}^{year}=\frac{1}{12}\sum_{k=12i}^{12i+11}C_{k-12}^{month} \tag{10}\]  
and therefore concentration growth is  
\[G_{i}^{year}=\frac{1}{12}\sum_{k=12i}^{12i+11}(C_{k}^{month}-C_{k-12}^{month}) \tag{11}\]  
With substitutions from Equations (8)-(11) and replacing the fixed concentration time lag 12 with \(lagC\), the ordinary least squares minimization becomes  
\[\min_{\alpha^{\prime},\alpha}\sum_{i}\left(\sum_{k=12i}^{12i+11}\left(E_{k}^{ month}-\frac{1}{12}(C_{k}^{month}-C_{k-12}^{month})\right)-\hat{S}_{i}(C) \right)^{2} \tag{12}\]  
with the sink model  
\[\hat{S}_{i}(C)=a^{\prime}\cdot\sum_{k=12i}^{12i+11}\frac{1}{12}C_{k-lagC}^{ month}-n \tag{13}\]
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 4 Case Study
### Countermeasures  
The risk assessment results indicate that equipment and blasting risks are the most critical factors affecting the construction safety of the Daliangshan No. 1 Tunnel. To mitigate these risks, targeted measures are proposed. For detonation network reliability, a series-parallel hybrid network with precise millisecond delay control should be adopted, with parameters regularly adjusted based on monitoring feedback. Ventilation efficiency can be enhanced by integrating transport and exhaust passages, implementing wet drilling combined with high-pressure sprayers and strategically placed mist purification water curtains (20-30 m from the face), while real-time monitoring of environmental parameters ensures dynamic system optimization. blasting operations require strict control of perimeter hole charges using interval charging techniques, with hole spacing and explosive consumption continuously corrected according to geological conditions and vibration data. Post-blast evaluations of rock fragmentation, contour accuracy, and overbreak/underbreakshould guide iterative parameter refinement. Additionally, employing highly trained personnel for blasting operations and improving surveyor expertise through enhanced training programs will ensure precise hole layout and contour control. These integrated measures, emphasizing adaptive management and continuous process improvement, are designed to effectively control construction risks while maintaining operational efficiency.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 4 Case Study
### Discussion  
Lack of infrastructure sharing is thought to be largely responsible for the high cost of running the telecommunication infrastructure, especially in developing countries. In this paper it is argued that with appropriate ICT tool support, telecommunication infrastructure has high potential to be shared, and colocation can potentially reduce capital and operational expenditure without undue impact on network coverage. In this case study, as reflected in Tables 5 and 6, this does indeed seem to be the case, and this work provides initial supportive evidence not just for the potential of infrastructure sharing but also an initial validation of the models and algorithm proposed in Section 4 of this paper. Further work, on potentially larger case studies, can explore such concepts in more depth and potentially add further weight to these arguments. The impact of uncontrolled mast erection will lead to proliferation of masts, littering and defacing of the environment. The presence of so many BTSs in a residential environment is causing concern. The case of Nigeria is problematic as the BTSs are powered with diesel fueled electricity generators, posing a threat to the environment. Collapsing masts have been reported in different urban cities in Nigeria, killing several people and injuring others and destroying properties; moreover, the fumes and noise from generators were also reported as problematic. Infrastructure sharing has become one of the most viable options for reducing the total number of BTSs/masts and addressing the associated environmental issues.