esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Effective Bio-Cover Materials for Mitigating Methane Emission at a Tropical Landfill
## 3 Results and Discussion
### Methane Oxidation Capacities of Bio-Cover Materials  
The presence of biological methane oxidation in bio-cover materials can also be confirmed through the determination of their methanotrophic activity in batch experiments. The moisture contents in SL, C, and SW materials examined in batch experiments were 23%, 58.5%, and 32%, respectively, which were in situ moisture contents of the bio-cover materials during the sampling time.  
Table 4 presents the methanotrophic activities observed in the bio-cover materials. It appears that C material had the highest _MOR_ among the types of materials examined. The observed _MOR_ of C material was found highest (2.03-2.49 mol/g.dry solids/h) and they were relatively consistent along the depth. For SL material, the most active zone for _MOR_ was found at the top part (0-0.20 m) at 1.3 mol CH\({}_{4}\)/g dry soil/h, while SW material had the lowest _MOR_ at 0.12-0.2 mol CH\({}_{4}\)/g dry solids/h, being highest at the bottom of the material layer. The MOR, to some extent, is related to the observed MRRs  
Figure 3: Relationship between methane loading and removal rates in bio-cover materials.  
in the bio-cover. Higher MORs were detected in SL and C materials than those found in SW material. Nevertheless, their _MOR_ was found to be highest at the top of the material bed where oxygen is highly available. On the other hand, the MRRs in those materials were found to be highest at the bottom of the material bed where higher methane contents in gas were available. As the _MOR_ were examined under high methane and oxygen condition to determine the maximum methane removal potential of the bio-cover material, the discrepancies between MORs obtained from batch experiments and MRRs observed in the field operation of bio-cover would be due to their differences in methane and oxygen conditions. The _MOR_ data could also help to confirm the presence of microbial activities for methane removals in bio-cover materials. Based on the MOR/CPR ratio observed during batch experiments, higher values were found in C material (0.81-0.87) which suggested that methane oxidation is mainly responsible for methane removal as they are close to the stoichiometric ratio (1.0) required for the reaction. Meanwhile, the lower ratios observed in the other materials suggest the production of carbon dioxide from other reactions, e.g., oxidation of organic substances. According to previous research, methane in landfill gas could be completely consumed within 20 h in compost and 100 h in sandy loam cover material where methane oxidation was active [14]. For SW material, lower bulk density, as well as high porosity of media, could result in lower MORs but higher oxygen availability would be expected in the deeper zone of the material bed [21].
# Performance-Based Reliability Estimates for Highway Bridges Considering Previous Inspection Data
## 3 Performance-Based Resistance Deterioration Model  
### Standard Performance Degradation Model  
The standard performance degradation models proposed for domestic bridges in Korea by Korea Infrastructure Safety and Technology Corporation (KISTEC) [16] are used to estimate the degradation of the target bridge members over time. In this study, it is assumed that the condition of a bridge member is closely related to the performance of the bridge. The standard performance degradation models for bridge members are adopted to develop the performance degradation model of the bridge. Equations (2) and (3) by KISTEC [16] present the standard performance degradation models of the RC slab and PSC-I girder, respectively.  
\[y_{slab}=-0.00027502x^{3}+0.00957326x^{2}-0.19328448x+5 \tag{2}\]  
\[y_{PSC}=-0.00017159x^{3}+0.00700464x^{2}-0.16058476x+5 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Vehicle** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Mode** \\ \end{tabular} & \begin{tabular}{c} **Distribution** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Coefficients** \\ \end{tabular} & \begin{tabular}{c} **Min.** \\ **(Tons)** \\ \end{tabular} & \
\begin{tabular}{c} **Max.** \\ **(Tons)** \\ \end{tabular} &
\begin{tabular}{c} **Correction** \\ **Coefficients** \\ \end{tabular} \\ \hline P & 1 & L-N & 0.398 & 0.317 & 0.7 & 5.0 & 1.0000 \\ \hline B & 1 & Normal & 4.089 & 1.020 & 1.4 & 17.1 & 0.0980 \\  & 2 & Normal & 11.552 & 1.542 & 4.0 & 24.0 & 0.9020 \\ \hline T & 1 & L-N & 1.338 & 0.620 & 1.25 & 24.1 & 0.7330 \\  & 2 & L-N & 2.721 & 0.221 & 1.25 & 40.0 & 0.2945 \\ \hline TT & 1 & L-N & 2.467 & 0.178 & 7.3 & 41.3 & 0.2190 \\  & 2 & L-N & 3.253 & 0.203 & 7.3 & 62 & 0.7818 \\ \hline ST & 1 & Normal & 18.541 & 3.000 & 11.3 & 63.4 & 0.2600 \\  & 2 & L-N & 3.420 & 0.225 & 59.7 & 90 & 0.7421 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Probabilistic characteristics of the vehicle weight model [14; 15].  
where, \(y_{slab}\) represents the condition of the RC slab, \(y_{PSC}\) represents the condition level of the PSC-I girder, and \(x\) is the service life of the structure (year).  
Figure 2 shows the standard performance degradation models for the bridge members. Levels A-E are substituted with 5-1, respectively. Tables 4 and 5 summarize the evaluation criteria for the condition of the RC slab and PSC-I girder, respectively. Level A refers to the best condition, and level E refers to the lowest condition.  
Although it is valid to estimate the degradation trend of bridge members using Equations (2) and (3), these equations are believed to have limitations while considering a variety of site environments. It is not reasonable to analyze the degradation tendencies of bridge members with different site environment variables using this standard performance degradation model in the same manner. Therefore, the standard performance degradation model must be calibrated, considering the inspection results of the condition of the bridge.
# Semantically Controlled Adaptive Equalisation in Reduced Dimensionality Parameter Space +
## 5 Results
### Parameter Weighting  
In order to evaluate the effectiveness of the signal specific weights, we measure the reconstruction accuracy of each system after the weights have been applied (see Table 5). Overall, the systems exhibit a general improvement in the reconstruction accuracy of the gain and Q parameters. All the systems have improved accuracy measurements, with the highest performing pair being PCA with SVR, achieving an error of 0.059. Similarly, the sAE with the same architecture, with hidden layer sizes \([9,2]\), is able to achieve a reconstruction accuracy of 0.06--a further improvement from the 0.0748 error observed with unweighted parameters. For the weighted parameters we found that a three-layer denoising autoencoder was able to outperform our two-layer autoencoder, improving the reconstruction accuracy by 0.02.  
Figure 5: _Trustworthiness_ and _continuity_ plots across the different dimensionality reduction techniques for number of neighbors (\(1:250\)). (**a**) Trustworthiness; (**b**) Continuity; (**c**) Trustworthiness (Weighted Parameters); (**d**) Continuity (Weighted Parameters).  
Finally, the parameter weighting stage improves the _trustworthiness_ of the low-dimensional mapping when using PCA, pPCA, kPCA, DM, and sAE, whilst FA and LDA exhibited significantly lower scores, as presented in Table 6. On the other hand, the _continuity_ of the systems had very little change, with pPCA, kPCA, DM, FA, and sAE showing very minor reductions, LDA showing significant reduction, and PCA showing an improvement. In this case, sAE with parameter weighting still outperforms the other techniques in terms of _trustworthiness_ for a lower number of neighbours, as in Figure 5c, and the performance in terms of continuity sees the sAE performing better than FA (Figure 5d).
## 6 Conclusions  
The training of single-user data has strong personalized features, but when a user has little or no historical record, the user will not be able to make a good prediction. In general, the integration of multi-user data can often achieve higher accuracy. However, the integration of multi-user data generally requires centralized management, which is not conducive to the privacy protection of users. Combining federated learning and AHNEAP methods, in this paper we propose FL-AHNEAP, a mobile App start-up prediction method under privacy protection. It integrates multi-user data to train a representation learning model for the attributed heterogeneous network, and the terminal separately trains the link prediction model based on the neural network to achieve personalized predictions for users. Experimental analysis shows that, in the LiveLab dataset, the accuracy of the FL-AHNEAP method is slightly lower than that of the AHNEAP method based on single-user data because the model aggregation destroys the structure of the user's heterogeneous network. However, through the analysis of cold start experiments for new users, it is shown that the FL-AHNEAP method can alleviate the cold start problem of new users to a certain extent. Under the assumption that users have previous Apps, the prediction accuracy can reach 71%.  
However, the FL-AHNEAP method under privacy protection only uses FederatedAveraging, a simple federated learning optimization algorithm, and even if the idea of federated learning is used, the user's privacy is not absolutely secure. There are many aspects worthy of discussion in the context of mobile App start-up prediction based on federated learning, such as data security in the communication process and other algorithms for model aggregation. The FL-AHNEAP method destroys the network structure when performing model aggregation. In the future, it can be considered how to realize not only the aggregation of parameters, but also the merger of the network under the federated learning mechanism. Although the idea of federated learning realizes the privacy protection of user data to a certain extent, it cannot guarantee absolute data security. Studies have shown that user-related information can be extracted from model parameters, and FL-AHNEAP can  
Figure 10: Effect of historical data on FL-AHNEAP.  
be optimized from the perspective of federated optimization, such as model aggregation algorithm, homomorphic encryption, and differential privacy, to further ensure privacy and security. Moreover, model training is resource intensive, and for the terminal, model training consumes a large proportion of resources. Due to the importance of attracting terminal holders to participate in federated learning training, the study of user incentive mechanisms is a possible future research direction.  
Conceptualization, Z.D.; Project administration, S.L.; Resources, X.L.; Supervision, L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable, the study does not report any data.  
The authors declare no conflict of interest.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 2 Materials and Methods
### Loss Function  
At the output level, the smooth \(\mathbf{L}_{1}\) loss is employed to train the proposed DVKT framework. The smooth \(\mathbf{L}_{1}\) loss guarantees proximity between the reconstructed haze-free image and the ground truth, demonstrating superior performance over \(\mathbf{L}_{2}\) loss and the traditional \(\mathbf{L}_{1}\) loss in numerous low-level visual tasks. Let \(\mathbf{I}\) denote the input haze-degraded image, \(\mathbf{O}_{\mathbf{t}}\) denote the reconstructed haze-free image obtained by the teacher network, \(\mathbf{O}_{\mathbf{s}}\) denote the reconstructed haze-free image obtained by the student network, and \(\mathbf{I}_{\mathrm{gt}}\) denote the ground truth clear image. The formula for the smooth \(\mathbf{L}_{1}\) loss can be articulated as follows:  
\[\mathbf{L}_{\mathrm{SL1}}=\frac{1}{K}\!\!\sum_{k=1}^{K}\sum_{l=1}^{3}\!\! \mathrm{smooth}_{L1}\!\left(\mathbf{O}_{\mathbf{s}}^{1}(k)-\mathbf{I}_{ \mathrm{gt}}^{1}(k)\right)+\frac{1}{K}\!\sum_{k=1}^{K}\sum_{l=1}^{3}\!\! \mathrm{smooth}_{L1}\!\left(\mathbf{O}_{\mathbf{s}}^{1}(k)-\mathbf{O}_{ \mathbf{t}}^{1}(k)\right), \tag{8}\]  
where  
\[\mathrm{smooth}_{L1}(r)=\left\{\begin{aligned} 0.5r^{2},& \quad if\ |r|<1\\ |r|-0.5,&\quad\mathrm{otherwise}\end{aligned}\right. \tag{9}\]  
where \(K\) is the total number of pixels within the image, and \(l\) denotes the \(l\)-th color channel.  
Ultimately, the optimization of parameters within the 1/n-SCSNet is achieved through the minimization of the overall loss function:  
\[\mathbf{L}_{\mathrm{total}}=\lambda_{1}\cdot\mathbf{L}_{\mathrm{intra}}+ \lambda_{2}\cdot\mathbf{L}_{\mathrm{inter}}+\lambda_{3}\cdot\mathbf{L}_{ \mathrm{SL1}}, \tag{10}\]  
where \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) represent the respective weights assigned to the various loss components.
## 5 Discussion
This can be delivered through the E-Rank metric that enables the effective integration of performance metrics with resource utilization, offering a comprehensive evaluation of the overall software performance of LWSCs. According to the E-Rank, Salsa demonstrates an optimal performance in terms of throughput, memory usage, and energy consumption, followed by Sosemanuk and then Grain-128a. These LWSCs effectively optimize resource consumption and uphold high-performance standards, making them favorable for resource-intensive IoT applications and, in the context of this paper, live video feed encryption. Grain-v1, Mickey-v1, and Trivium showed the lowest E-Rank, and thus, while using those ciphers in the resource-intensive live video feed encryption, these LWSCs demonstrated processing bottlenecks, scalability issues, and security risks due to prolonged encryption spans. The speed-optimized version of Grain-128a demonstrated a higher E-Rank compared to Grain-v1, Mickey, and Trivium. Therefore, it displayed intermediate favorability for encrypting resource-intensive live video feeds on resource-constrained ARM11 processors. The efficacy and suitability of ciphers are observed through the FPSenc metric. This analysis reveals that Salsa, Sosemanuk, and the optimized version of Grain-128a are notably more suitable and effective for resource-intensive applications, as demonstrated by their encryption frame rates. This characteristic enables reduced latency during the transmission of continuous encrypted data streams from resource-constrained devices to other devices within a network.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 2 Numerical Simulation  
### Mathematical Model  
The following assumptions are made in order to simplify the calculation:  
1. The flow in the tube is three-dimensional steady flow and the medium is incompressible Newtonian fluid.
2. Gravity and buoyancy force are ignored.
3. There is no slipping on the wall.
4. The flow and heat transfer are assumed to be steady state processes [10].  
According to the above assumptions, the fluid in the tube should satisfy the following equations [11].  
The continuity conservation equation can be written as:  
\[\frac{\partial(\rho u_{x})}{\partial x}+\frac{\partial(\rho u_{y})}{\partial y }+\frac{\partial(\rho u_{z})}{\partial z}=0 \tag{1}\]  
The momentum conservation equation can be written as:  
\[\frac{\partial(\rho u_{x})}{\partial t}+\
abla\cdot\left(\rho u_{x}\vec{u} \right)=\frac{\partial\mathbf{p}}{\partial x}+\frac{\partial\tau_{xx}}{\partial x }+\frac{\partial\tau_{yx}}{\partial y}+\frac{\partial\tau_{xx}}{\partial z}+ \rho f_{x} \tag{2}\]\[\frac{\partial\big{(}\rho\mathbf{u}_{y}\big{)}}{\partial\mathbf{t}}+\
abla\cdot\Big{(} \mathbf{\rho u}_{y}\overset{\rightarrow}{u}\Big{)} =\frac{\partial\mathbf{p}}{\partial y}+\frac{\partial\mathbf{\tau}_{xy}}{ \partial x}+\frac{\partial\mathbf{\tau}_{yy}}{\partial y}+\frac{\partial\mathbf{\tau}_{ xy}}{\partial z}+\mathbf{\rho f}_{y} \tag{3}\] \[\frac{\partial(\rho\mathbf{u}_{z})}{\partial\mathbf{t}}+\
abla\cdot\Big{(} \mathbf{\rho u}_{z}\overset{\rightarrow}{u}\Big{)} =\frac{\partial\mathbf{p}}{\partial z}+\frac{\partial\mathbf{\tau}_{xz}}{ \partial x}+\frac{\partial\mathbf{\tau}_{yz}}{\partial y}+\frac{\partial\mathbf{\tau}_ {zz}}{\partial z}+\mathbf{\rho f}_{z} \tag{4}\]  
where \(\tau_{xx}\), \(\tau_{xy}\), and \(\tau_{xz}\) are viscous force component of three directions. \(f_{x}\),\(f_{y}\), and \(f_{z}\) are three components of force per unit mass.  
The problem of heat transfer between a fluid and a solid region needs to be calculated, and the model consists of two flow regions separated by a solid [12].  
The energy conservation equation can be written as:  
\[\frac{\partial}{\partial x_{i}}(\mu_{i}(\rho E+P))=\frac{\partial}{\partial x _{i}}\left(k_{eff}\frac{\partial T}{\partial x_{i}}-\sum_{j^{\prime}}h_{j^{ \prime}}f_{j^{\prime}}+\mu_{j}\big{(}\tau_{ij}\big{)}_{eff}\right)+S_{h} \tag{5}\]  
Energy conservation equation of solid:  
\[\frac{\partial}{\partial x_{i}}(\mu_{i}\rho E)=\frac{\partial}{\partial x_{i} }\left(k\frac{\partial T}{\partial x_{i}}\right)+S_{h} \tag{6}\]  
where \(\tau_{ij}\) is stress tensor, internal energy, \(E\) is internal energy, \(k_{eff}\) is effective thermal conductivity. \(J_{j^{\prime}}\) is component diffusion flux, \(S_{h}\) is heat source item.
## 2 Results and Discussion
### m-CPBA-Mediated Epoxidation  
The Prizehaev reaction at room temperature gave quantitative conversions within 30 min for all substrates. No epoxides were detected on GC-MS nor NMR, as they rapidly reacted to product A and B (and a mixture of minor products). The reaction worked well with free phenolic groups as **1** had a quantitative conversion to products **1A** (isolated yield of 60% of four isomers) and **1B** (isolated yield of 12% of two isomers) within 30 min at room temperature. Substrate **2** with protected phenolics (as methoxyl groups) also gave a quantitative conversion within 20 min at room temperature to products **2A** (42% isolated yield as four isomers) and **2B** (46% isolated yield as four isomers). The Prizehaev reaction gave 96% conversion of **3** after 30 min at room temperature to a reaction mixture with a product distribution, according to NMR, of 71% **3A** (total isolated yield for the four isomers was 51%) and 24% **3B** (total isolated yield for two isomers was 19%). Furthermore, the isolated product **3A** could quantitatively be converted to **3B** by stirring in trifluoroacetic acid (TFA) at room temperature for 20 min. Thus, a final isolated yield of 70% of **3B** could be reached. The Prizehaev reaction of **4** using one equivalent _m_CPBA for 30 min resulted in an 83% conversion. Two products were observed, product **4B** and **4D**, (a 7-_m_CBA-coupled adduct, Figure 3) in a 4:6 ratio. Substrate **4** could not react by pathway A, and the nucleophilic attack by _m_CBA was competing with the slower pathway **B**. This type of reaction has been reported previously [26]. The byproduct **4D** could, however, be quantitatively hydrolyzed to **4B** by stirring in TFA at room temperature for 20 min.
# A Survey of the Applications of Text Mining for the Food Domain
## 5 Text Mining in Food Quality Control
### Analysis of Supply Chain Data  
Traceability is critical in the food supply chain for rapidly identifying the origins of quality issues [81]. Text mining technology offers a means to explore traceability data at each node, detecting vulnerabilities and risk control points across the supply chain. Researchers are increasingly integrating text analytics to enhance the informational value of traceability systems, contributing to ongoing improvements in quality management throughout food enterprises [82].  
A significant challenge in the food supply chain is food waste, estimated at about one-third of the total production [83]. The Internet of Things (IoT) and blockchain technology are emerging solutions, providing transparency, sustainability, and efficiency by collecting data at every stage of the supply chain [84; 85].  
Text mining tools empower food companies to identify crucial control nodes where product quality may be compromised. Extracting business intelligence from consumer opinions can optimize food production. For example, a case study using text mining on Twitter posts revealed key issues with beef products, offering insights for developing a consumer-centric beef supply chain [55]. Digital technologies are also enhancing the efficiency of the retail food supply chain, transforming the entire system to be more sustainable [82].  
Mining and analyzing traceability text data are essential for improving the quality control of the food supply chain. By analyzing traceability text data using information extraction, as well as text clustering techniques, food companies can gain a better understanding of the product's production process, the nodes in the supply chain, and related transaction and shipping information. The deep integration of text mining with emerging technologies like the IoT and blockchain will further drive the digital transformation of the food supply chain, ushering in an era of increased efficiency and transparency.
## 3 Results
### Correlation Between FDS on LST  
A linear regression model was used to quantify the influence of FDS on the LST variations (Figure 4). The correlation between \(\Delta\) FDS and \(\Delta\) LST showed a significant (R\({}^{2}\) = 0.38, \(p<0.01\)) negative relationship (Figure 4a). Additionally, at a given stage, \(\Delta\) FDS-\(\Delta\) LST showed a significant negative correlation (\(p<0.01\)); after 2001, \(\Delta\) LST was generally higher. Moreover, the explanatory power of \(\Delta\) FDS on \(\Delta\) LST was stronger during the \(\Delta\) FDS \(>\) 3 stage (R\({}^{2}\) = 0.09) compared to that during the \(\Delta\) FDS \(\leq\) 3 stage (R\({}^{2}\) = 0.07) (Figure 4b). Spatially, the correlation between \(\Delta\) FDS and \(\Delta\) LST was significant (\(p<0.05\)) across extensive regions, with the strongest relationship predominantly observed in Central and Eastern Europe (Figure 4c).  
Figure 3: Spatiotemporal patterns of \(\Delta\) Albedo and \(\Delta\) ET in Europe between 1986 and 2016. (**a**) \(\Delta\) Albedo time series. (**b**) Spatial patterns of \(\Delta_{\rm s}\) Albedo. (**c**) \(\Delta\) ET time series. (**d**) Spatial patterns of \(\Delta_{\rm s}\) ET. Orange and green dashed lines in (**a,c**) represent linear regression lines fitted to data for 1986–2000 and 2001–2016 periods, respectively.