esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Research  
In this paper, a performance-based life-cycle cost optimization method of SRHPC frame structures was studied, and the following conclusions obtained:  
1. A fuzzy mathematics and performance-based optimization method for the life-cycle cost of SRHPC frame structures was proposed that can not only obtain good economic benefits, but also guarantee the performance of the structure so that the structure design can achieve a balance between economy and reliability.
2. After considering the complexity of the design optimization of SRHPC frame structures, a two-stage optimization calculation method was proposed. This method can effectively reduce the number of design variables and constraints in the optimization process as well as simplify the optimization process.
3. The fuzzy reliability theory was proposed to consider the fuzziness existing in the failure of the SRHPC frame. Fuzzy mathematics was adopted to develop the fuzzy seismic response spectra to consider the fuzziness existing in seismic intensity and site classification.
4. The proposed optimization was programmed by MATLAB, and an example was used to verify the optimization method where the life-cycle cost of the structure dropped by 27.31% after optimization. The results show the effectiveness and feasibility of the optimization method.  
However, due to various limiting factors, relevant follow-up studies are necessary, and can be summarized as follows:  
1. In this study, the SRHPC frame columns' test data were considered the main object of analysis to quantify the performance index of the SRHPC frame structures; the impact of other components on the SRHPC frame structures was not considered.
2. The actual initial cost of a structure includes not only the cost of materials, but also the machinery costs, labor costs, and so on. This study only considered the loss of the initial material costs of a structure. The impact of the loss of the non-structural and maintenance costs of the structure were not considered.  
This project is sponsored by the National Key Technology R&D Program (Grant No. 2013 BAJ08B03), the National Natural Science Foundation of China (Grant No. 51678475), and the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20136120110003).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cost** & **Initial Cost/RMB** & **Failure Loss Expectation/RMB** & **Life-Cycle Cost/RMB** \\ \hline Before optimization & 13,377 & 4227 & 17,604 \\ After optimization & 9261 & 3535 & 12,796 \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison of the costs before and after optimization.  
Figure 7: The optimization result of life-cycle cost.  
**Author Contributions**: [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] analyzed the data of the SRHPC frame columns and quantified the seismic performance indices; [PERSON] and [PERSON] wrote the MATLAB optimization program and analyzed the optimization results.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
# Shedding Light on the Volatile Composition of _Broa_, a Traditional Portuguese Maize Bread
## 3 Results and Discussion
### Identification of Volatile Compounds in Maize Flour and Broas
For peak 99, two compounds can be proposed: geranylacetone and arylacetone, with close similarity indexes. This peak was tentatively identified as geranylacetone, since it has been widely described in several maize-based products [24; 35; 36]. The families of compounds identified in _proas_ were mainly aldehydes (19% of total chromatogram area), alcohols (16%) and hydrocarbons (14%). Ketones (7%), furans (5%), pyrans (3%), lactones (3%), esters (3%), acids (3%) and pyrazines (1%) were also present. All these compounds have been described in other baked cereal products [11; 20]. As in maize flour, it was not possible to confirm the identification of some compounds due to the absence of LRI bibliographic values for a DB-5 column, but tentative identifications were made. Peak 29 showed a mass spectrum with a base peak at \(m/z\) 88, and other fragments at \(m/z\) 43, 60, 70, 99 and 115. A structure corresponding to ethyl hexanoate, with a similarity index of 85%, was suggested. This compound has been reported in baked cereal products [11]. Peak 35 presented a spectrum similar to that of 3-ethyl-2-methyl-1,3-hexadiene, with a molecular ion at \(m/z\) 124, a base peak at \(m/z\) 67 and fragments at \(m/z\) 39, 41, 55, 95 and 109. This compound has been described in the crust of whole-meal wheat bread and other maize-based foods [14; 56]. A tentative identification for peak 47 was methylentyfuran, with a molecular ion at \(m/z\) 110 and a base peak at \(m/z\) 95; however, it was not possible to confirm this identification since no LRI value was described in the literature. For peak 51, two compounds were suggested: 3.5-octaiden-2-one (\(E\),\(E\)) and 1-octyn-3-ol, as they presented similar spectra. However, the compound 3,5-octaiden-2-one was selected, due to the presence of the base peak at \(m/z\) 95 and the molecular ion at \(m/z\) 124. Compound 74 was tentatively identified as 2-hexyl-1-octanol, due to the high-spectrum similarity index (90%), and it has been described in other cereals, such as rice [57; 58]. Since rye and wheat flours were also included in the recipe of _broa_, these samples were analyzed in the same conditions as maize flours. The chromatographic profiles corresponding to these flours are presented in Figure 1. Compounds such as acetoin (peak 2), hexanoic acid (27), 3-octen-2-one (37) and 2,4-decadienal (\(E\),\(E\)) (84) were detected in _broas_ and rye and wheat flours, but not in maize flours. Other compounds, such as 2(3H)-furanone-5-heptyldihydro (peak H) and linalyl acetate (peak F), were detected only in wheat and rye flours. 2,4-Decadienal (\(E\),\(E\)) (84) was particularly abundant in the commercial wheat flour. Rye was distinguished by the presence of acids, such as octanoic (peak C) and nonanoic (G) acids, and terpenes, such as \(\alpha\)-terpinolene (A), camphor (B), 4-terpineol (D) and \(\alpha\)-terpineol (E).
## 2 Motivation and Justification  
This new approach has been developed to optimise current maintenance techniques for thermal installations in buildings. Nowadays, the maintenance of these systems, both in centralised and individual facilities, is mainly aimed at ensuring the operational efficiency, safety, and durability of thermal generation equipment, especially generators. The standard maintenance procedure comprises the following activities:  
* Visual inspection and review of components: to identify possible leaks, corrosion, wear and tear, or blockages in the equipment.
* Cleaning of the equipment: in order to remove sediments, limescale, and residues that may compromise heat transfer.
* Analysis of the combustion in the boiler: using a gas analyser to ensure complete and efficient combustion.
* Checking the control and safety system: ensuring the operability of protection devices, overheating, and leaks.  
In addition, the current maintenance usually follows a predefined preventive schedule, set both by the manufacturer and by local safety and energy efficiency regulations. Consequently, the approach is individual (performed at a component scale), intermittent, and does not consider the dynamic interactions between the components of the thermal system.  
However, the methodology proposed in this work incorporates continuous and real-time maintenance, eliminating the dependence on a predefined schedule. Furthermore, it addresses maintenance from a holistic perspective, considering the interactions between all the components of the thermal system, which allows for a more global and accurate view of the system's operation.  
According to the literature, techniques capable of analysing a thermal system as a whole are still relatively limited and are rarely applied in the maintenance of thermal systems in real buildings. These methodologies, instead of focusing solely on the analysis of components in isolation, are based on tools supported by software-assisted dynamic simulations. Among the most relevant techniques are the ones mentioned in the introduction section: FDD methodologies and thermoeconomic diagnosis. These techniques allow us to evaluate the behaviour of the entire system, considering variables such as the variability of thermal demand, fluctuations in the outside temperature and other factors that have an impact on the performance of the system at each moment of operation. However, the application of these approaches in real installations remains limited, being mainly restricted to research through simulation models using private licenses.  
Therefore, the key motivation for developing this work is related to reducing the associated costs and dependence on commercial software. Thus, it is proposed to develop dynamic ML-based models using free and open-source software platforms for diagnosis application.
## 5 Conclusions  
In order to solve the problem of subsidence prediction for an irregularly shaped coal mining area using PIM, a new DTM- and Monte Carlo integration-based method has been proposed. In Section 2, the fundamentals of PIM-based subsidence prediction are introduced. Details of the proposed method are provided in Section 3. In that section, DTM is used to firstly divide the irregularly shaped mining area into a series of triangular extraction elements. Then, the extraction elements within the calculation area are selected by comparison of the distance from the incenter of the extraction element to the mining area boundary and the deviation of the inflection point. Finally, based on the PIM described in Section 2, the Monte Carlo method is used to calculate the triangular extraction element-induced ground subsidence. The method could convert the complicated calculation of the double integral with a triangular domain into a simple multiplication operation. By summing each ground subsidence induced by triangular extraction elements within the calculation area, the final ground subsidence prediction of the irregularly shaped mining area can be obtained. The proposed method is evaluated comprehensively in Section 4 by making use of two data sets, including the simulation data and direct leveling-based subsidence measurements. The simulation results show that the prediction error of the proposed method is proportional to mesh size and inversely proportional to the amount of generated random points within the auxiliary domain. The difference in subsidence predictions between the proposed method and the traditional PIM can be ignored when the mesh size is smaller than 0.5 times the minimum deviation of the inflection point of the mining area, and the amount of random points within an auxiliary domain are greater than 800 times the area of the extraction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Observation Line** & **Mean (cm)** & **STD (cm)** & **RMSE (cm)** \\ \hline A & 2.38 & 1.06 & 2.61 \\ B & 2.35 & 1.39 & 2.73 \\ C & 2.47 & 1.14 & 2.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean, STD and RMS of the proposed method-based subsidence prediction errors.  
Figure 14: Scatterplot of the proposed method-based subsidence prediction error versus the distance from the surface point to the starting point of the corresponding observation line.  
element. The measurement results show that the root-mean-square error of the proposed method-based subsidence predictions is smaller than 3 cm, the average of the absolute deviations of the proposed method-based subsidence predictions is 2.49 cm, and the maximum of the absolute deviation is 4.05 cm, which is equal to 0.75% of the maximum direct leveling-based subsidence observation.  
Future research will focus on using the proposed method to predict other coal mining-induced ground movements, including ground tilt, curvature, horizontal displacement and deformation. Predicting dynamic ground movement using the proposed method is another future research focus.
## 2 Classical Topologies  
### Fundamental Concepts  
To facilitate the transmission of the desired active power in a wireless power transfer (WPT) system, a significant requirement arises for a substantial reactive power component. This necessity underscores the immediate need for compensation strategies, as elucidated in [9]. One of the most-straightforward approaches to achieve this compensation is by employing classical topologies, as discussed in [21]. In the context of the four topologies being examined in this study, the transmitting segment comprises a capacitance component (\(C_{1}\)) arranged either in series or parallel with a coil, characterized by its winding impedance, composed of an inductance (\(L_{1}\)) and a resistance (\(R_{1}\)). Similarly, the receiver section consists of a coil with a winding impedance that encompasses an inductance (\(L_{2}\)) and a resistance (\(R_{2}\)), with an associated capacitance (\(C_{2}\)) configured in series or parallel with the secondary coil. Regardless of the specific topology being considered, a load resistance (\(R_{L}\)) is positioned at the output of these circuits. The magnetic coupling between these coils is realized through mutual inductance (\(M\)). Visual representations of these four topologies are provided in Figure 1.  
Figure 1 confirms that topologies with series compensation on the primary side are powered from voltage sources (Figure 1a,b), which considerably simplifies the implementation. Topologies whose primary compensation occurs through parallel capacitance (Figure 1c,d) are powered by current sources. This makes the  
Figure 1: Classical compensation topologies: (**a**) SS; (**b**) SP; (**c**) PS; (**d**) PP.  
ous and, in most cases, leads to topological variations to achieve compatibility between a voltage source and the circuits in question [9].  
At frequency \(\omega_{r}\), the equivalent impedance (\(Z_{1}\)) must have its imaginary portion compensated for \(C_{1}\); consequently, all reactive demand is eliminated. For the four classical topologies, Table 1 summarizes the equations of \(C_{1}\), the theoretical efficiency (\(\eta\)), and the equivalent impedance (\(Z_{1}\)). It is interesting to note that, except for the SS topology, the primary compensation depends on mutual inductance. That is, considering that the inductances \(L_{1}\) and \(L_{2}\) do not change, \(C_{1}\) also depends on the coupling factor \(\kappa\). Another relevant fact is that the efficiency does not depend on the capacitive elements; however, for topologies with parallel resonance in the secondary, even for high frequencies, \(\eta\) will be affected by \(L_{2}\) and \(M\).
# Image-to-Image Translation Using a Cross-Domain Auto-Encoder and Decoder
## 4 Results
### The Effectiveness of the Feature Mapping Layer  
Table 2 shows the effectiveness of adding the feature mapping layer between the auto-encoder of the source domain and the auto-decoder of the target domain on the photo \(\rightarrow\) label translation of the cityscapes dataset. We experiment using different feature mapping layers with various settings: without feature mapping layer, \(1\times 1\) convolutional layer, \(3\times 3\) convolutional layer, \(7\times 7\) depth-wise convolutional layer, \(1\times 1\) convolutional layer with skip-connection, and \(3\times 3\) convolutional layer with residual connection for our CycleGAN-based model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Label \(\rightarrow\) Photo**} \\ \cline{2-7}  & **SSIM**\(\uparrow\) & **PSNR**\(\uparrow\) & **MAE**\(\downarrow\) & **MSE**\(\downarrow\) & **IS**\(\uparrow\) & **FID**\(\downarrow\) \\ \hline Baseline (Pix2 pix) & 0.2863 & 12.8684 & 41.7085 & 3547.5539 & 2.3955 \(\pm\) 0.1610 & 101.04794 \\ \hline Baseline (CycleGAN) & 0.3416 & 14.5333 & 35.4496 & **2558.5918** & 2.3421 \(\pm\) 0.2216 & 75.3792 \\ \hline UNIT & 0.3526 & **14.6102** & **35.4176** & 2653.8875 & 2.3304 \(\pm\) 0.2486 & 82.7469 \\ \hline Ours + 3*3 convolutional layer (on CycleGAN) & **0.3571** & 14.2083 & 36.1383 & 2714.3276 & **2.4554 \(\pm\) 0.1582** & **69.1041** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of baseline models and our model on the cityscapes dataset (label \(\rightarrow\) photo). Higher value is better in SSIM, PSNR, IS. Lower value is better in MAE, MSE, FID.  
Figure 9: Comparison of the UNIT model and our model on paired datasets. On the cityscapes and night2 day datasets, UNIT generates images with overly sharp textures, while our model generates a smoother, more natural texture. Additionally, UNIT results on the maps dataset show a missing “park” (green class in the input) section and a lower quality of building details than our method.  
Our model with a \(3\times 3\) convolutional layer shows high overall performance, especially in terms of FID score, which is considered an important indicator. The \(1\times 1\) convolutional layer also shows better overall performance than most other experiments in FID. Using only a pre-trained auto-encoder and decoder shows the best performance in IS but shows poor results with respect to all other metrics. These results indicate that our feature mapping layer helps with learning to map latent space when used with the suitable pre-trained encoder and decoder, which results in improving the translation quality significantly.  
Adding skip-connection in the feature mapping layer shows even worse results compared to the baseline model. We think that connecting encoder and decoder of two different domains interferes with learning correct translation.
## 3 Results and Discussion
### Effect of the Enzyme Treatment on the Sensory Quality of the Rock Tea Extract
#### 3.1.3 Sensory Effects of the Synergistic Action of Enzymes on Tea Extract  
Tannase, tyrosinase, and lactose have a notable effect on improving the taste of tea, prompting further exploration of the combined action of these three enzymes. From the perspective of sensory scores for complex enzymes, the TAN-TYR and TAN-TYR-LAC combinations exhibited the best taste improvement, although no significant difference between the two in terms of improvement level (_p_ > 0.05) was observed. The overall flavor score increased by 42.7% and 40.7% compared with ck, respectively, and the aftertaste score increased from 5.61 to 7.52 and 7.6. The overall flavor score of TAN-TYR was also 22.8% higher than that of the most effective single enzyme (TAN). The improvement effect of TAN-LAC on the tea extract was slightly inferior to that of TAN-TYR. Compared with the best enzyme combination, TYR-LAC showed no significant improvement in the taste of the tea extract (_p_ > 0.05) (Figure 3). Combining the effects of single and combined enzymes on the extract revealed that the enzyme treatment significantly contributes to taste improvement, not only in bittermess and astringency but also in aftertaste. This phenomenon may be  
Figure 2: Sensory scores of tea extract for different enzyme treatment concentrations. a, b, and c represent tannase (**a**), tyrosinase (**b**), and lactose (**c**), respectively. ck represents the sample that has not been treated with the enzyme. The error bar represents the standard error of the mean, and by the LSD test, the average of different lowercase letters indicates a significant difference (_p_ < 0.05).  
closely related to changes in the amount of phenolic substances, such as gallated catechins, non-gallated catechins, theaflavins, and thearubigins [29], in tea extracts [4]. Therefore, it is speculated that the increase in aftertaste may result from changes in the gallic acid content. The addition of tannase and polyphenol oxidase increases the content of gallic acid and non-gallated catechins, which positively affect the soft taste and produce a pleasant aftertaste [11].
# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 3 Methodology
### Conveyor Belt Area Loss  
A key observation is that relevant foreign objects on the conveyor belt must lie within the belt region. If a predicted box appears outside this region, it is more likely to be a false detection. To address this, we introduce the Conveyor Belt Area Loss (CBAL), which penalizes detections lying outside the conveyor belt and rejects obvious false detections that fall outside the belt area, as shown in Figure 5. By incorporating this geometric constraint into training, the model gains a more robust understanding of the conveyor belt context and improves its adaptability to different camera viewpoints.  
Firstly, we manually annotate the conveyor belt area, \(Poly\), from different camera perspectives,  
\[Poly=[(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{n},y_{n})] \tag{9}\]  
where \((x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\) represent the vertices of the conveyor belt polygon. Next, we use the polygon area to evaluate whether the center points of the predicted bounding boxes fall within the conveyor belt area. Specifically, we calculate the number of intersection \(Num_{points}\) between a horizontal ray emitted from each prediction box center \((x_{c},y_{c})\) and the boundary of the polygon [34], as shown in Equation (10).  
Figure 5: CBAL enforces the focus on the conveyor belt region by penalizing predictions outside the defined belt area. Here, the large coal and iron bar on the belt are detected, while the three background boxes are ignored as they lie outside the polygon.  
\[Num_{points}=\sum_{i=1}^{n}\Bigg{(}(\text{sign}(y_{i}-y_{c})\
eq\text{sign}(y_{i+1 }-y_{c}))\wedge\bigg{(}x_{c}<\frac{(y_{c}-y_{i})\cdot(x_{i+1}-x_{i})}{y_{i+1}-y_ {i}}+x_{i}\bigg{)}\Bigg{)}, \tag{10}\]  
where sign represents a function to determine whether a number is positive or negative. The parity of \(Num_{points}\) indicates the positional relationship of the prediction box concerning the conveyor belt area. Specifically, when \(Num_{points}\) is odd, the prediction box \(j\) is located within the conveyor belt area, and when \(Num_{points}\) is even, the prediction box is outside the belt.  
Subsequently, we compute the loss for predictions outside the conveyor belt area using the loss defined in Equation (12).  
\[\text{Outside}(Pred_{j},Poly)=1-(Num_{points}\mod 2) \tag{11}\]  
\[L_{clul}=\frac{1}{m}\sum_{j=1}^{m}\text{Outside}\big{(}Pred_{j},Poly\big{)}, \tag{12}\]  
where \(m\) represents the total number of prediction boxes. The loss minimizes the number of prediction boxes incorrectly positioned outside the conveyor belt, as shown in Figure 5. When all prediction boxes are correctly placed within the conveyor belt area, the loss value becomes zero, indicating optimal performance. The analysis of error types and loss curves in Section 4 demonstrates that this module can reduce false detections of background regions outside the conveyor belt area, enhancing both the detection accuracy and the model's generalization ability.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Implementation Details
#### 4.1.2 Model Construction and Pre-Training  
Our method is implemented in Pytorch [54]. The transformer architecture is constructed by the deep learning library timm [55]. The transformer encoder, shown in Figure 2, is built by the transformer block, while the network depth is 12. The multi-head attention block consists of 6 heads. The network is pre-trained on ImageNet [56].