esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dye Adsorption Experiments  
An appropriate mass of raw and chitosan-treated jute fabrics was immersed in 100 mL of Reactive Orange 16 (RO 16) (Figure 1) aqueous solution and constantly shaken in the mentioned water bath. The optimization of RO 16 adsorption was done in two steps. First step included optimization of the initial solution pH: \(c_{0}\) = 25 mg/L, \(t\) = 21 h, \(T\) = 25 \({}^{\circ}\)C, adsorbent mass of 0.50 g, pH = 2-7. The second step included optimization of adsorbent mass: \(c_{0}\) = 25 mg/L, \(t\) = 21 h, \(T\) = 25 \({}^{\circ}\)C, pH = 2 for fabrics J, J0.5% CS, and J1.0% CS or pH = 4 for fabric J2.0% CS, an adsorbent mass of 0.25, 0.50, 0.75, or 1.00 g.  
Dye removal (\(R\)) by studied fabrics was calculated by using Equation (1):  
\[R\ (\%)=\frac{A_{o}-A_{t}}{A_{o}}\cdot 100 \tag{1}\]  
where: \(A_{o}\) and \(A_{t}\) are the absorbances of the initial dye solution and the dye solution after a certain time, respectively.  
The fabric adsorption potential, i.e., the mass of dye adsorbed per gram of adsorbent (\(q\), mg/g), was calculated using Equation (2):  
\[q\;(\text{mg/g})=\frac{c_{0}-c_{t}}{m}\cdot V \tag{2}\]  
where: \(c_{0}\) and \(c_{t}\) (mg/L) are the initial dye concentrations in the solution and in the solution after a certain time, respectively, \(m\) (g) is the adsorbent mass, \(V\) (L) is the solution volume.  
The RO 16 concentration in the aqueous solution was determined based on the UV-Vis Shimadzu 1700 spectrophotometer (Shimadzu, Kyoto, Japan) absorbance spectra at \(\lambda_{\text{max}}=493\) nm. The presented results are the mean values of three measurements per sample whereby the coefficients of variation were below 2.04%.  
After finishing the optimization, isotherm, thermodynamic, and kinetic experiments were done under the conditions shown in Figure 2. The obtained data were further modeled using well-known models.  
Figure 1: Structure of RO 16.  
Figure 2: Schematic representation of adsorption experiments and modeling of the obtained data.
## 3 Simulations and Results
### Model Sensitivity Analysis  
The proposed model is evaluated above for the case of COVID-19, as indicated by Table 1. These values would change for different pathogens and therefore the obtained results as well. In order to evaluate the sensitivity of the proposed model to the parameter values, we tested two properties of the model: the number of pathogen particles needed to infect an individual and the mask efficiency. We chose these two parameters as one  
Figure 5: The influence of mask-wearing and air-ventilation PIPs on the number of infected individuals over a period of 90 min (an h and a half). The results are shown as the average value of all the rooms from the same type.  
is associated with the infection rate (commonly denoted by \(\beta\) in extended \(SIR\) models) and therefore a property of the pathogen, and the latter indicates the efficiency of a PIP to the pandemic spread which also changes during the course of the pandemic due to technological developments. We assumed 12% of the population wears masks, following measurements from the beginning of the COVID-19 pandemic (March 2020) in the US [82]. Figure 6 presents the result of this analysis, where the average portion of exposed individuals out of the population after 90 min for \(n=200\) realisations (10 realisations for each room in Table 2) is used as the evaluation metric. In order to analyze the results shown in Figure 6 analytically, we utilized the _SciMED_ symbolic regression tool [86], obtaining:  
\[E(t=90)\approx 5.31\cdot 10^{-1}-2.72\cdot 10^{-9}(\phi_{\delta}-ln(\phi_{ \delta}))-4.75\cdot 10^{-2}\kappa-7.57\cdot 10^{-4}\kappa^{2}, \tag{4}\]  
with a coefficient of determination \(R^{2}=0.628\).
# A Sustainability Analysis Based on the LCA-Energy-Carbon Emission Approach in the Building System
## 4 Results and Discussion
### LCA-Carbon Emission Analysis
#### 4.2.3 The Carbon Emission in the Building Operation Stage  
Because the operational phase takes into account a 20-year period, the amount of carbon emissions is huge, amounting to 1.14 \(\times\) 10\({}^{7}\) tons. In total, heat carbon emission has 9.62 \(\times\) 10\({}^{6}\) tCO\({}_{
# Cooperative Jamming with AF Relay in Power Monitoring and Communication Systems for Mining
###### Abstract  
In underground mines, physical layer security (PLS) technology is a promising method for the effective and secure communication to monitor the mining process. Therefore, in this paper, we investigate the PLS of an amplify-and-forward relay-aided system in power monitoring and communication systems for mining, with the consideration of multiple eavesdroppers. Explicitly, we propose a PLS scheme of cooperative jamming and precoding for a full-duplex system considering imperfect channel state information. To maximize the secrecy rate of the communications, an effective block coordinate descent algorithm is used to design the precoding and jamming matrix at both the source and the relay. Furthermore, the effectiveness and convergence of the proposed scheme with high channel state information uncertainty have been proven.  
physical layer security; multiple eavesdroppers; full-duplex; underground mine; amplify-and-forward relay +
Footnote †: journal: Electronics Letters
## 4 Results and Discussion
### Regression Models  
In this part of the paper, the resulted mathematical models relative to each method are given. Reduced models after eliminating statistically insignificant factors with respect to ANOVA are shown in the following equations (Equations (21)-(32)). Additionally, we plotted experimental responses versus the predicted ones in order to visualize the agreement between measured values of the responses and the ones calculated from the mathematical models (see Figure 7).  
From the ANOVA tables, we note that Taguchi models have higher adjusted R-squared values: 0.982, 0.981, 0.858, and 0.788 for throughput, packet loss ratio, average end to end delay and routing overhead, respectively. The Plackett-Burman method comes in the second rank with adjusted R-squared values between 0.791 and 0.849, whereas, the full factorial method shows quite inferior values (0.633, 0.671, 0.634, 0.681). This states that the Taguchi method has better predictive qualities than the Plackett-Burman and the full factorial methods.  
Two-level full factorial models  
\[\hat{\gamma}_{1}(TH)=28.4764-0.0384x_{1}-5.0745x_{3}-6.2686x_{4} \tag{21}\]  
\[\hat{\gamma}_{2}(PLR)=43.0880+0.0767x_{1}+10.1445x_{3}+12.5325x_{4} \tag{22}\]  
\[\hat{\gamma}_{3}(EDED)=2.2868+0.3015x_{1}+355.4039x_{2}-22.6936x_{3} \tag{23}\]  
\[\hat{\gamma}_{4}(RO)=-591.0738+7.4172x_{1}+1086.5367x_{2}+402.6649x_{4} \tag{24}\]  
Plackett-Burman models  
\[\hat{\gamma}_{1}(TH)=31.1578-0.0499x_{1}-6.8054x_{3}-6.0242x_{4} \tag{25}\]  
\[\hat{\gamma}_{2}(PLR)=37.7360+0.0997x_{1}+13.6011x_{3}+12.0410x_{4} \tag{26}\]  
\[\hat{\gamma}_{3}(EDED)=26.9985+0.1888x_{1}+280.6431x_{2}-42.2375x_{3} \tag{27}\]  
\[\hat{\gamma}_{4}(RO)=-296.1545+8.3173x_{1} \tag{28}\]  
The Taguchi models  
\[\hat{\gamma}_{1}(TH)=31.7435-0.0666x_{1}-6.6461x_{3}-5.3924x_{4} \tag{29}\]  
\[\hat{\gamma}_{2}(PLR)=26.4479+0.1331x_{1}+20.5388x_{2}+13.2862x_{3}+10.7835x_{4} \tag{30}\]  
\[\hat{\gamma}_{3}(EDED)=33.8116+0.3171x_{1}+233.2547x_{2}-34.6554x_{3} \tag{31}\]  
Figure 7: Measured versus predicted (**a**) throughput, (**b**) packet loss ratio, (**c**) average end-to-end delay, (**d**) routing overhead from FFM, PBM and TM.  
\[\hat{y}_{4}(RO)=-7.6884+5.9135x_{1}+2381.3656x_{2} \tag{32}\]  
Figure 7 presented below outlines the scatterplots of the predicted values versus the measured values for each response obtained from the three DOE methods. It is observed that the plots of the Taguchi method show stronger fitting between the measured and predicted values, followed by the Plackett-Burman method. However, the models revealed by the two-level full factorial method show moderate adequation.
# Anomaly Detection in Network Traffic via Cross-Domain Federated Graph Representation Learning
## 2 Background
### Graph Neural Networks  
Graphs are a type of non-Euclidean data characterized by complex topological structures and a richer capacity for information representation. In real-world applications, many forms of data inherently exhibit graph structures, such as social networks, knowledge graphs, and protein-protein interaction networks [6].  
GNNs are a class of neural network specifically designed to process graph-structured data. The fundamental building block of a GNN is the graph convolution layer, which iteratively updates node representations by aggregating features from target nodes and their neighbors. A general update formula is given below:  
\[H^{(l+1)}=\sigma(\hat{A}H^{(l)}W^{(l)}) \tag{1}\]  
where \(H^{(l)}\) denotes the node feature matrix at layer \(l\) and \(H^{(0)}=X\) represents the input node features. \(\hat{A}\) is the normalized adjacency matrix, typically computed as \(\hat{A}=D^{-1/2}AD^{-1/2}\), where \(A\) is the adjacency matrix of the original graph and \(D\) is the degree matrix. \(W^{(l)}\) is the trainable weight matrix at layer \(l\), and \(\sigma\) is a nonlinear activation function. Stacking multiple graph convolutional layers enables the model to capture deeper structural dependencies. The resulting node representations can be used for downstream tasks such as node classification, graph classification, and link prediction.  
The core objective of GNNs is graph representation learning, which aims to map nodes, edges, or subgraphs into low-dimensional vectors by aggregating local neighborhood information. This process ensures that the resulting vector space reflects both the topological structure of the graph and the attribute information of nodes [7]. In the context of computer networks, interactions between hosts can be naturally modeled as graphs, where IP addresses represent nodes and communication flows represent edges. GNNs are well suited to exploiting network traffic data encoded in this format, making their application to traffic anomaly detection a promising research direction.
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 4 Implementation of the Improved Ant Colony Optimization Algorithm  
The specific steps of the improved ant colony optimization algorithm are as follows:  
Step 1: Initialization: Construct a 2D static grid environment map and initialize parameters, including the number of ants \(M\), start node \(S\), target node \(E\), maximum iterations \(T\), pheromone evaporation coefficient \(\rho\), pheromone importance factor \(\alpha\), and heuristic importance factor \(\beta\).  
Step 2: Ant Path Selection: Place \(M\) ants at the start node \(S\). Initialize the tabu list and add \(S\) to it. Use Equation (5) to select the next node \(j\), add \(j\) to the tabu list, and update the path length. Repeat this process until the ants reach the target node or encounter a deadlock.  
Step 3: Pheromone Increment Update: After each iteration, discard ants that fail to reach the target node. For successful ants, update the pheromone increment using Equations (9)-(11).  
Step 4: Pheromone Concentration Update: Update the pheromone concentration using Equation (3).  
Step 5: Output the Pre-Trimming Path: Repeat Steps 2-4 until the maximum number of iterations is reached. Output the pre-trimmed path.  
Step 6: Triangular Trimming: Perform triangular trimming on the output path from Step 5. Remove redundant nodes and connect the remaining nodes to obtain the final optimal path.  
The flow of the improved ACO algorithm is shown in Figure 2.  
Our improvements to ACO include the state transfer strategy, the pheromone incremental update strategy, and path pruning. Since the state transfer strategy and the pheromone incremental update strategy just replace the original ones, and the time complexity of path pruning is at a linear level, the time complexity of ITACO is at the same level as that of ACO.
## 3 Adaptive Routing Optimization Algorithm
### Analysis of Node Relations
#### 3.2.2 Social Features  
The social features of nodes can be represented by physical features and logical features of a person (for example, membership in an organization, such as age, gender, blood group, and members of the organization. [PERSON] et al. in [29] present that the more the same social features of two nodes is, the more frequent their contact is. Therefore, we think the social features can also reflect a certain relationship between nodes.  
The 2006 Infocom datasets [34] provides 10 social features based on questionnaires, and they are nationality, studies, languages, affiliation, position, city, country, topics, and so on. The MIT reality mining datasets [35] provide, also, several social features, such as neighborhood, daily commute, hangouts, working hour, affiliation, and research group. In these social features, only a small part of them have a significant influence on the relationship between nodes, and they can be obtained by computing their Shannon entropy as follows:  
\[E(f_{i})=-\sum\limits_{j=1}^{k_{j}}p(x_{j})\log(x_{j}) \tag{4}\]  
where,\(f_{i}\) is the \(i\)th social features of nodes, \(x_{j}\) is a value of \(f_{i}\), and \(p(x_{j})\) is the probability of \(x_{j}\). Obviously, if the value of \(E(f_{j})\) is bigger, the social feature \(f_{i}\) is more important.  
To measure the relationship between nodes based on the social features, we used the number of their social features in common as follows:  
\[F_{ij}=n_{ij} \tag{5}\]  
Obviously, if the value of the metric \(F_{ij}\) is bigger, node \(v_{i}\) and \(v_{j}\) is closer.
## 3 Results and Discussion
### Effects of Thermal Load Duty Ratio on Satellite Health  
Figure 11 shows the effect of different heat load duty cycles on the dynamic temperature of the target damage component. In both cases, the thermal load amplitude and cycle number are the same, and the loading time of thermal load in one cycle is the same, which is 10 s. The duty ratio of pulsed thermal load directly affects the cooling time of a cycle, and finally leads to the difference of the total acting time of thermal load. The total acting time of pulse of case I is 600 s, while the total acting time of pulse of case III is 200 s. However, compared to case I, Case III causes the temperature of damaged components to rise more rapidly and eventually reach a higher temperature. This is because the pulse duty ratio is small, resulting in insufficient cooling of the damaged components and eventually continuous accumulation of temperature.  
Figure 12 shows the changes of satellite system health indicators when the duty cycle of thermal load is 1/2. The health degree of the satellite thermal control subsystem decreased exponentially to 0.97. In order to intuitively compare and analyze the impact of thermal load duty ratio on the health of satellite subsystem, this section shows the health impact of Case I and Case III on the thermal control subsystem in Figure 13. As shown in Figure 13a, when the duty ratio of thermal load is 1/2, the health degree of the thermal control subsystem decreased to 0.97, while when the duty ratio of thermal load is 1/6, and the health degree of the thermal control subsystem decreased to 0.95. Although the higher the thermal load duty ratio will lead to the higher temperature rise of the damaged components, the total loading time will be reduced. In general, components have the ability to respond to extreme environments in the short term, so short-term high temperatures do not necessarily lead to complete failure. As shown in Figure 13b, the higher the duty cycle of the thermal load causes the thermal control  
Figure 11: Dynamic temperature of damaged components under Case I and Case III.  
Figure 10: Health indicators of thermal control subsystem (TCS) (Compare Case I and Case II): (a) Health degree. (b) Failure factor.  
subsystem to fail earlier. In addition, it can also be found from the fit curve of health in Figure 13a that the slope of Case III is larger than that of Case I. This is because the heating time in a cycle is the same, the greater the thermal load duty ratio, the more drastic the temperature change of the damaged components.
## 4 WEAMR Overview
### Assumptions  
We assume a two-tiered heterogeneous network. At first level, we have typical sensor nodes with constraints of energy and low power transmission. All the nodes are randomly distributed in a two dimensional grid. At second level, sensor gateways are present. These gateway nodes act as a cluster head. Moreover, the gateway nodes control the traffic flow and manage sensor motes deployed in different geographical locations. Gateway nodes are not energy constrained devices and are connected with each other in a bus topology. In traditional networks the gateways are connected with long haulwireless links but for this protocol the gateways are connected with high speed reliable links such as Ethernet or point-to-point wireless. We now introduce the important components of a hotline-based reliable WSN topology. Different mechanisms, like dividing the network into clusters, gateway association/dissociation and node-node, node-gateway and gateway-gateway communication will be described in detail in the following sub-sections. In traditional WSNs routing the expected path of end-to-end communication would include multiple hops. Packet delivery is dependent on several intermediate relaying sensor nodes. These sensor nodes forward the packets until they reach their destination. WSNs have inherently high link error rates. With multi-hop routing, the cumulative probability of error increases exponentially with the increasing number of hops. Thus the chances of failure are likely to increase with every additional hop. Reliability is therefore a main concern for mission-critical WSN deployments. This topology addresses the reliability issue by using the concept of hotlines between gateways. Hotlines reduce the number of average hops between a source and destination and provide better reliability as compared to traditional WSN routing. Our network topology is static for both sensor and gateway nodes. Figure 2 shows the overall network architecture:  
We describe the topology and WEAMR in the following sections.
## 5 Complexity Analysis  
The complexity of the Approximate CROWDMATCH Algorithm is \(O(N\cdot M+M)\). Specifically, the complexity of the Approximate CROWDMATCH Algorithm's component where the crowdworkers select their preferred crowdsourcers is \(O(N\cdot M)\) and the complexity of the algorithm's part where the crowdsourcers select their preferred crowdworkers is \(O(M)\). The complexity of the Accurate CROWDMATCH Algorithm is \(O(ite)\), where \(ite\) denotes the number of iterations that the Accurate CROWDMATCH Algorithm needs to converge to the optimal matching \(\Lambda^{*}\).  
Specifically, the Approximate CROWDMATCH algorithm follows the matching theory and the Accurate CROWDMATCH algorithm follows the theory of coalition games. Specifically, focusing on the Approximate CROWDMATCH algorithm, given the calculation of the approximate utilities for the crowdworkers and the crowdsourcers, each crowdworker creates a sorted list of their preferred crowdsourcers based on their calculated approximate utility Equation (5). Initially, all the crowdworkers belong to the unmatched set \(\mathcal{N}^{*}\), and they send their invitations to the crowdsourcers. Then, the crowdsourcers, considering that they still have capacity in terms of accommodating recruited crowdworkers (line 10 in the Approximate CROWDMATCH algorithm), select their most preferred crowdworkers by calculating their utility function and following the preference relation defined in Equation (6). Then, the crowdworkers that were not selected by a crowdsourcer delete that crowdsourcer from their alternative crowdsourcer list to avoid resending another matching invitation to a crowdsourcer that has already rejected them. This process is continued iteratively until all crowdworkers are matched to the crowdsourcers or until the crowdsourcers have reached their capacity in terms of crowdworkers that they can accommodate. In order for the Approximate CROWDMATCH algorithm to be implemented, the crowdworkers need to know the provided rewards \(P_{m}\) by the crowdsourcers and the crowdsourcers require the information of the value of the utility of the crowdworkers that have sent a pair invitation to them. In a realistic implementation, this information can be exchanged in one packet transmitted in both directions that includes a very small amount of information, i.e., a single value. Focusing on the Accurate CROWDMATCH algorithm, the analysis is very similar in terms of the complexity and signaling that is needed, as the crowdworkers calculate their utility based on the announced rewards announced by the crowdsourcers, and then they follow the switching rules described in Definition 4 to converge to a stable matching. Based on the provided numerical results in Section 6, it is shown that, even for a large-scale setup, i.e., thousands of crowdworkers and tens of crowdsourcers, the Approximate and Accurate CROWDMATCH algorithms converge to a stable matching in a few seconds.
## 4 Inventory and Analysis
### Analysis of Rooftop Agricultural Production Potential in Shibei District  
Based on the completion of the above inventory, we calculated the annual production potential of rooftop agriculture and analyzed the supply and demand of urban vegetables. Considering the two influencing factors of roof area and building type comprehensively, we selected different vegetable cultivation methods, and combined with the annual output per unit area of vegetables under the corresponding cultivation methods, the annual production of rooftop vegetables was calculated, and then, the urban vegetable supply and demand analysis was carried out in combination with the annual vegetable consumption standard and the number of people in the Shibei District of Qingdao. Firstly, we calculated the annual yield per unit area under different vegetable cultivation methods.
###### Abstract  
Fourier-transform imaging spectroscopy (FTIS) faces inherent limitations in spectral resolution due to the maximum optical path difference (OPD) achievable by its interferometer. To overcome this constraint, we propose a novel spectral super-resolution technology integrating a Fabry-Perot interferometer (FPI) with FTIS, termed multi-component joint interferometric hyperspectral imaging (MJI-HI). This method leverages the FPI to periodically modulate the target spectrum, enabling FTIS to capture a modulated interferogram. By encoding high-frequency spectral interference information into low-frequency interference regions through FPI modulation, an advanced inversion algorithm is developed to reconstruct the encoded high-frequency components, thereby achieving spectral super-resolution. This study analyzes the impact of primary optical errors and tolerance thresholds in the FPI and FTIS on the interferograms and spectral fidelity of MJJ-HI, along with proposing algorithmic improvements. Notably, certain errors in the FTIS and FPI exhibit mutual interference. The theoretical framework for error analysis is validated and discussed through numerical simulations, providing critical theoretical support for subsequent instrument development and laying a foundation for advancing novel spectral super-resolution technologies.  
Keywords:optical error analysis; spectral super-resolution; Fabry-Perot interferometer; Fourier-transform imaging spectroscopy; robustness enhancement +
Footnote †: journal: Applied sciences