esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Future Directions  
While this study acknowledges the alteration of pseudotachylytes post-formation, additional limitations--such as sampling constraints and uncertainties in temperature estimation--warrant explicit discussion. Sampling is restricted to specific fault zones (e.g., Anning River fault zone), potentially limiting the generalizability of microstructural and geochemical conclusions to other tectonic settings with varying lithologies or stress regimes. Temperature estimates, primarily derived from quartz EBSD slip systems (250-350 \({}^{\circ}\)C for fractured rocks), rely on empirical relationships between crystallographic preferred orientations and thermobarometric frameworks. However, these correlations may not fully account for strain rate effects or fluid-induced recrystallization, which could obscure original deformation temperatures.  
Future research could mitigate these gaps through in situ thermometry techniques (e.g., Raman spectroscopy of carbonaceous phases or laser microprobe analysis of fluid inclusions), which provide direct constraints on peak temperatures during pseudotachylyte formation. Additionally, integrating multi-scale structural transects across diverse fault segments (e.g., from shallow brittle to deeper ductile domains) would clarify how temperature-depth relationships vary spatially and temporally within seismogenic zones. Such approaches would enhance the robustness of thermomechanical models linking microstructural observations to macroscale seismic processes, ensuring more reliable interpretations of fault zone evolution and earthquake dynamics.
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Thermal-Fluid Finite Volume Model of the TPMLG
#### 3.2.2 Boundary Conditions  
Boundary conditions as a group of particular solutions are required to be definite conditions of thermal simulation. As can be seen from Figure 4, in the case of natural air cooling, the side surfaces S1, S2, S3, and S4 of the air domain are set as velocity inlet boundaries, and the upper surface S5 is set as a pressure outlet boundary. The bottom surface S6 is set as the wall boundary considering its direct contract with the platform of the test rig, and the thermal boundary condition is set as the Dirichlet boundary condition considering the wall thickness of the base. In the case of forced air cooling, air comes from one side and leaves from the other side. Therefore, the left-side surface S1 of the air domain is set as the velocity inlet boundary, while the right-side surface S3 is set as the pressure outlet boundary. The remaining surfaces are set as wall boundaries.  
In the CFD simulation using FVM, convective heat transfer coefficients can be gained from the fluid and thermal coupling field analysis directly. The interface between fluid and solid is set as the coupled boundary. Compared with the limitation that traditional motor temperature simulation uses analytical or empirical-based correlations to acquire the equivalent convective heat transfer coefficients for convection boundaries, FVM calculation results are more accurate. In addition, the ambient temperature is 40 \({}^{\circ}\)C and a standard atmospheric pressure is applied.  
Figure 5: Mesh of the TPMLG physical model: (**a**) linear generator; (**b**) local axial section.
## 2 Results and Discussion
### Acid versus _Base Elution and Transesterification_  
Synthesis of FAMEs can be catalyzed by either base or acid. The base-catalyzed reaction is faster but can also generate soaps [4]. The sulfuric acid-catalyzed reaction, while slower, is also better at esterifying free fatty acids by dehydration [27]. Heat can speed up the acid-catalyzed reaction but there is a tradeoff between speed and the cost of heating. The previous results showed that 5% sulfuric acid in methanol could both elute algae from the resin and convert lipids to FAME. To test how well a base-catalyzed transesterification reagent would work, algae bound to Amberlite was eluted with either potassium hydroxide/methanol or sodium methoxide/methanol. We found that FAME yields from the sodium hydroxide/methanol reaction were undetectable and yields from the sodium methoxide/methanol reagent were 4% lower than using the sulfuric acid/methanol reagent. The reduced levels of FAME may be due to residual water, as shown by [PERSON] [20], or to binding and depletion of the hydroxide or methoxide ions from solution by the anion exchange resin. For acid-catalyzed transesterification, we found that ethanol could be substituted for methanol with essentially identical results (Supplementary Figure S7). Because of its low cost and wide availability, methanol is often used for biodiesel synthesis [4]. However, ethanol cost may decrease in the future as advances in bioethanol productions lead to increases in supply.
###### Abstract  
Intensive agriculture has led to the increasing application of pesticides, such as malathion, thus generating large volumes of untreated cropland wastewater (CropWW). In this work, a hybrid system constructed wetlands (CW) coupled in continuous with an optimized UV/H\({}_{2}\)O\({}_{2}\) pretreatment was evaluated for the efficient removal of malathion contained in CropWW. In the first stage, 90 min UV irradiation time (UV IR) and 65 mM hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) were identified as optimal operation parameters through a central composite design. The second stage consisted of CW planted with _Phragmites australis_ collected from the agricultural discharge area and operated as a piston flow reactor. Furthermore, CW hydraulic residence times (HRT) of 1, 2 and 3 days, including hydraulic coupling, were evaluated. The removal efficiencies obtained in the first stage (UV/H\({}_{2}\)O\({}_{2}\)) were \(94\pm 2.5\%\) of malathion and \(45\pm 2.5\%\) of total organic carbon (TOC). In stage two (CW) \(65\pm 9.6\%\) TOC removal was achieved during the first 17 days, from which around 24% was associated to the biosorption of malathion byproducts. Subsequently, and until the operation ends, CW removed about 80% of TOC for 2 and 3 days HRT, with no significant differences (\(p>0.2\)), which is higher than those reported in several studies involving only advanced oxidation processes (AOP) with UV IR times above 240 min and even for systems using catalysts. The results obtained indicate that the system UV/H\({}_{2}\)O\({}_{2}\)-CW is a technically suitable option for the treatment of CropWW with a high content of malathion mainly found in developing countries. Moreover, the hybrid system proposed also represent significant reduction in the size of the treatment plant.  
constructed wetlands; cropland wastewater; malathion; AOP optimization Article
## 3 Literature Review
### Tourism  
In the tourism industry, LLMs can be applied for diverse purposes on the demand and supply side. Tourists, for instance, could use LLMs to obtain information concerning destinations, attractions, traffic, hotels, restaurants, local culture/customs, and language translation. Travelers can also plan their entire itineraries by asking for suggestions from LLMs based on available time, interests, and preferences. For travel-related companies, on the other hand, LLMs could be used to optimize both front-end and back-end operations at technical and management levels regarding new products, targeted advertising, and staff training; see Table 3 for an overview. Applications of LLMs in the tourism industry are promising to induce a whole range of transformations [57], creating value for both customers and companies by changing ways for tourists to search for information and make decisions, as well as patterns of companies' offerings of products and and services [60; 61; 69]. From the perspective of customers, LLMs can be utilized during pre-trip, en-route, and post-trip stages [70]. Ref. [58] found that people held positive views on the usability of LLMs in trip planning, without obvious differences in attitudes between first-time users and those who had previously used LLMs. Nevertheless, the decision making of travelers will be influenced by the technology usage experience, prominence and type of incorrect information from LLMs and user interface [64; 65; 68]. From the perspective of companies, the incorporation with LLMs can offer advances in multilingual communication, personalized services, content creation, product development, operational efficiency, and customer loyalty [59; 62; 66]. In the travel industry, LLMs empower service individualization and internalizing information, leading to positive effects on service value co-creation. In particular, ChatGPT could significantly moderate the relationships between service individualization and internalizing information/service value co-creation [54]. To examine the personalized travel recommendation offered by ChatGPT, ref. [52] conducted a lay theory study and an in-depth analysis based on the data collected from 102 panelists on Prolific and 344 respondents via Amazon MTurk, respectively. They revealed that relevance, credibility, usefulness, and intelligence played significantly positive roles in the perceived trust of travelers, which further contributed to behavioral intentions. Tourism education and research will also go through reforms with wider application of LLMs. Universities should reevaluate their teaching/research modes and assessment
## 4 Results and Discussion
### Fit of Durations Simulation
#### 4.2.3. Sopladora Hydroelectric Project  
The evaluation was performed in months 10, 20, 30, and 35 after starting the simulated execution of the project, and in months 10, 8, 5, and 1 before the simulated completion of the project. Figure 4 presents the time evolution of how the EVM and CTCR methods fit in their forecasts of the final completion time of the Sopladora project, according to the ten thousand scenarios of the simulation model. It is apparent that from the first months of execution, the CTCR methodology presents a greater probability of fit than the EVM method. This indicates that CTCR predicts the period to completion of the Sopladora project more efficiently than EVM. It is apparent that in month 10 before completion of the project, CTCR has a probability of 57.3% of correctly predicting the project's period of completion, while in the same period, EVM achieves a probability of only 28.5%. Similarly, in month 5 prior to conclusion, CTCR shows a probability of 81.7% compared with 51.8% for EVM.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 2 Related Work
### Heterogeneous Graph Neural Networks  
Recently, researchers have begun to pay a large amount of attention to applying GNNs for heterogeneous graph learning. [PERSON]. [22] proposed R-GCN to use relation-specific projection matrices for aggregation among different relation edges. [PERSON]. [2] presents HAN by adopting a hierarchical attention mechanism to capture both node-level and semantic-level information through multiple human-designed metapaths. MAGNN [7] enhances HAN by considering the intermediate nodes in metapaths and using several encoders for both intra-metapath aggregation and inter-metapath aggregation. Het-GNN [23] uses a random walk strategy to sample neighbor sequences from different distances and then adopts specialized Bi-LSTMs to aggregate information. [PERSON]. [24] develops HetSANN with type-specific graph attention layers to handle messages from various relations. Motivated by the architecture of Transformer [25], [PERSON]. [8; 25] introduces HGT to characterize the heterogeneous information with meta-relation-specific attention modules, rather than metapaths. Although the above methods have explored a lot and achieved great success, they rely highly on the quality of the original graph structure and node features. Therefore, these existing methods cannot handle the structural and informational noise of heterogeneous graph data in reality.  
Considering the drawbacks of the above-mentioned methods, we believe that a good solution of HGNN should handle the heterogeneity in a natural and automatic manner and be robust to any type of potential noise in realistic graph data. This principle guides us to the design of different components in HGPN.
## 3 Results  
### Deconvolution of Simulated _NuSTAR_ Images  
The _NuSTAR_ PSF was used to simulate two nearby point sources. The sources were placed at various distances and the deconvolution algorithm was tested for efficiency. An example is shown in Figure 2, where a 4\({}^{\prime}\) offset PSF was used for both sources separated by 10\({}^{\prime\prime}\). Offset ranges from 0 to 7\({}^{\prime}\) have been tested with separations starting from 30\({}^{\prime\prime}\) to 6\({}^{\prime\prime}\), decreasing in steps of 2\({}^{\prime\prime}\).  
We have also tested various numbers of iterations starting with 50 and ending with 200 cycles; above 200 iterations, the noise increases considerably, and visible artifacts are introduced in the deconvolved images. The experiments demonstrated that two sources placed at a separation of 10\({}^{\prime\prime}\) are clearly resolved in the final image when the _NuSTAR_ PSF is implemented in the code.  
Figure 1: Central 100 \(\times\) 100-pixel regions of the _NuSTAR_ PSF at different off-axis angles ranging from 0\({}^{\prime}\) to 7\({}^{\prime}\). Each panel represents the PSF corresponding to a specific off-axis angle, highlighting the variation in spatial distribution due to the telescope’s optics. The color scale indicates relative intensity, and the images are shown in a logarithmic scale for a better view of the PSF structure.
## 3 Results
### Problem Formulation and Optimization  
The equations of the state space of a generalized plant G provide more details on the method with Hifoo.  
Figure 8: (**a**) The block diagram in Matlab Simulink for the smart beam disturbance rejection with Hifoo controller and sinusoidal disturbance. (**b**) The block diagram in Matlab Simulink for the smart beam with sinusoidal noise (percentage \(\pm\) 2% of the disturbance).  
\[\dot{x}(t)=Ax(t)+B1w(t)+B2u(t), \tag{20}\] \[z(t)=C1x(t)+D11w(t)+D12u(t),\] \[y(t)=C2x(t)+D21w(t)+D22u(t)\]  
and state-space realization of the controller \(K\)(s) is:  
\[\dot{x}K(t)=AKxK(t)+Bky(t), \tag{21}\] \[u(t)=CKxK(t)+Dky(t)\]  
the matrices in our problem with the cantilever smart structure are  
\[A_{K=}\begin{bmatrix}840&-5044\\ 208.0&-2500\end{bmatrix}\]  
\[B_{K=}\begin{bmatrix}412.8&911.6&1716&2810\\ -164.9&-637.2&-1348&-3207\end{bmatrix}\]  
\[C_{K=}\begin{bmatrix}1557&-916.7\\ 7013&-592.3\\ 627&-597.9\\ 144.3&-82.59\end{bmatrix} \tag{22}\] \[D_{K=}\begin{bmatrix}96.8&6.8&487.9&468.3\\ 23.5&87.69&186.5&303\\ 12.12&44.12&93.39&354.3\\ 8.204&12.53&86.92&883.51\end{bmatrix}\]  
where \(A\in R^{n\times n}\), \(D_{12}\in R^{p1\times m2}\), and \(D_{21}\in R^{p2\times m1}\) have additional matrices of equivalent dimensions, and \(A_{K}\in R^{nK\times nK}\), with \(B_{K}\), \(C_{K}\), \(D_{K}\), \(A_{K}\), and the size of the generalized plant matrices are comparable. This constant order of control makes it identifiable to the designer. The measured inputs (or sensor inputs), control inputs, external inputs (such as disturbances and instructions), and controlled outputs are represented by the signals (z, w, y, and u) in that order. The symbol represents the transfer function \(\mathrm{T}_{\mathrm{zw}}\) from the input w to the output z (see [23] for further details). The optimal infinity controller design can be represented by minimizing the closed-loop \(\mathrm{H}_{\infty}\) characteristic function [41; 42; 43; 44].  
\[\mathrm{Inf}\,\|\mathrm{T}_{\mathrm{zw}}\|_{\infty},\]
## 3 Results and Discussions
### Savings  
Table 2 summarizes the key technical findings, along with the annual savings after the project comes to execution. On replacing the conventional refrigeration system with a solar-powered refrigeration system, total energy savings of 10.6 MWh per day is achieved. It is worth mentioning that this savings is just for a single refrigeration unit. If multiple units are to be used depending on the refrigeration demand, this number will get multiplied accordingly. Similarly, energy savings per year per unit is 3.9 GWh. In addition, considering capital cost and operation and maintenance cost, the Levelized Cost of Energy (LCOE) from the solar system is calculated as USD 0.06/kWh. This energy is achieved at the cost of 1180 Kilo liters of diesel with a diesel generator running at an efficiency of 33%. The calorific value of diesel considered is 35.9 MJ/liter [48]. With global average diesel price [52], and including transportation and storage costs, total savings from fuel is USD 0.95 million per annum. Unit cost of diesel per liter is USD 0.81 and the cost of energy from diesel generator is calculated as USD 246 per MWh. Moreover, electricity generated from diesel plants is ineffective and comes at the cost of significant environmental risks. These risks may be associated with diesel particulate matter, vapor, and greenhouse gases. Although the effect of particulates and vapor is not accounted for, the GHG emission effect is evaluated. A total of 2.63 kg of CO\({}_{2}\) is released per liter of diesel combustion [16] and a total emission of 3103 tonne is saved in penetrating solar system to run a refrigerator. With a carbon tax rate of USD 35 per tonne of CO\({}_{2}\), total carbon savings is computed to USD 108k per annum.  
Figure 2: PV size, Levelized Cost of Energy (LCOE), and area variation with solar intensity.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 3 Connectivity Requirements for Drones
### Multimedia Transmission  
Some UAV-based systems handover data to ground stations, such as live multimedia/video streaming or data analysis, in order to save time. Advanced multimedia services such as truly immersive XR, 3D holograms, and 360-degree ultra-high image/video quality shoots (4K and 8K videos) must be eventually realized in the future. Furthermore, XR experiences such as AR, VR, and MR services necessitate higher data rates at higher Gbps levels. The 6G network can meet a high-bandwidth data connection requirement in the UTM. A sufficient bandwidth must be ensured for the improved data transmission capabilities that come with 6G technology, so that the drones do not constantly drop the connection and can transmit high-quality live videos. Sixth Generation is expected to deliver a data rate of up to 10 Gbps to support multimedia transmission [56].
## 4 Experimental Results
### Performance Evaluation and Discussion
#### 4.3.1 Performance Evaluation on SCUT-FBP  
The distribution of beauty scores in the SCUT-FBP dataset was found to be approximately Gaussian [45; 47]. However, this non-perfect normal distribution of beauty scores suggests that relying solely on the traditional L2 loss function may not be optimal in certain scenarios. Table 6 exhibits the performance of three pretrained CNNs on the SCUT-FBP dataset under five-fold cross-validation, using various loss functions. Based on the evaluation metrics, L1 loss produced slightly higher PC values and lower error rates than L2 loss for the three investigated pretrained CNNs. Similarly, the Log-cosh loss function outperformed both the L1 and L2 loss functions among the utilized networks because it provides a more balanced error calculation between large and small residuals. The proposed ensemble loss functions yielded better results for all three fine-tuned CNN models. Specifically, AlexNet achieved a PC value of 0.903758, MAE of 0.26348, and RMSE of 0.348266. On the other hand, VGG16-Net, with more layers than AlexNet, achieved a slightly higher PC value of 0.905851 and lower error rates of 0.222954 and 0.292028 for MAE and RMSE, respectively. FIAC-Net, unlike pretrained object classification models such as AlexNet and VGG1-Net, was trained specifically for facial aesthetic classification, making it better suited to estimating the beauty scores of faces. The proposed ensemble loss function yielded the best performance with FIAC-Net, achieving a PC value of 0.9100582, the lowest MAE of 0.185949, and an RMSE of 0.259156. Model prediction on test data samples from SCUT-FBP using fine-tuned FIAC-Net is demonstrated in Figure 5. In order to evaluate the effectiveness of the proposed model, Figure 6 visualizes the predictions in a scatter plot. It plots the predicted values on the \(x\)-axis against the ground-truth values on the \(y\)-axisbased on the utilized loss functions, with different pretrained CNNs, namely (a) AlexNet, (b) VGG16-Net, and (c) FIAC-Net, implemented on the SCUT-FBP dataset. The results clearly indicate that the integration of the ensemble loss into FIAC-Net resulted in the most effective scatter-plot representation.
###### Abstract  
For the vehicular platoon consisting of connected automotive vehicles, time delays degrade both the internal stability and string stability. In this study, the internal stability and string stability of the vehicular platoon suffering from sensing delay and communication delay are investigated. In the internal stability analysis, the necessary and sufficient internal stability condition is obtained and the exact time delay margins (ETDMs) are derived via the cluster treatment of characteristic root (CTCR) paradigm. A Dixon resultant matrix-based method is proposed to determine the kernel and offspring hypersurfaces of the CTCR paradigm, and then the computational burden of deriving the ETDMs is reduced significantly. In the string stability analysis, we first propose the string stability conditions for the situation no matter how large the frequency of the leader vehicle's maneuver is. Furthermore, the more practical string stability conditions are studied by considering only the region of low frequency, where most of the energy of the spacing errors exists. Then, a lower bound of the time headway is deduced to enhance road capacity, so the potential of the vehicular platoon is fully motivated. Numerical simulations are provided to illustrate the effectiveness of the theoretical claims.  
vehicular platoon; sensing delay; communication delay; internal stability; string stability; CTCR; time headway Article