esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The key innovations and advantages of the developed surface acoustic wave (SAW)-based NO\({}_{2}\) sensor system are as follows: For the first time, a monolayer 2D sensing material has been integrated into SAW technology to create an NO \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gas** & **Formula** & **10-Day SMAC ppm** & **6-h SMAC ppm** \\ \hline Hydrogen & H\({}_{2}\) & 3000 & 12,000 \\ Carbon dioxide & CO\({}_{2}\) & 7000 & 13,000 \\ Ethanol & CH\({}_{3}\)CH\({}_{2}\)OH & 240 & 1000 \\ Methane & CH\({}_{4}\) & 5000 & 5000 \\
2-propanol & CH\({}_{3}\)CH[OH]CH\({}_{3}\) & 50 & 500 \\ Dichoromethane & CH\({}_{2}\)Cl & 25 & 100 \\ Toluene & C\({}_{6}\)H\({}_{5}\)CH\({}_{3}\) & 20 & 100 \\ Carbon monoxide & CO & 20 & 50 \\ Trichoroethylene & CHCl CCl\({}_{2}\) & 12 & 50 \\ Ammonia & NH\({}_{3}\) & 25 & 25 \\ Benzene & C\({}_{6}\)H\({}_{6}\) & 2 & 10 \\ Nitric dioxide & NO\({}_{2}\) & 0.5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main hazardous gases and their respective allowable concentration limits within the spacecraft. sensor. Our SAW sensor platform eliminates the need for direct current or voltage applied to the sensing material, thereby preventing Joule heating, atomic evaporation from the surface, and degassing from the sensor itself. The sensor demonstrates exceptionally rapid response and recovery times by utilizing a monolayer graphene/MoS\({}_{2}\) heterostructure within SAW technology. Most importantly, it operates effectively in both oxygen-free and zero-gravity environments, thanks to the use of the 2D sensing materials. The sensor capitalizes on both conductivity changes and mass loading, resulting in exceptionally high sensitivity. Additionally, the sensor's interface electronics incorporates a built-in compensation feature that mitigates the effects of external perturbation factors, such as temperature, humidity, and UV-C radiation, ensuring stable performance under varying conditions.
## 3 Results and Discussion
### Direct Impacts of Gasifier Uptake on Household Livelihoods
#### 3.2.4 Improved Kitchen Conditions  
Ninety-eight percent of households reported that the gasifier helped maintain kitchen cleanliness, especially of the cooking pots. This is attributed to the reduced smoke production by the gasifier compared with an open fire as reported by all the households. As a result, the users noted less soot on the cooking pots and anticipated less soot will stick to the kitchen walls and roof with the use of the gasifier. Consequently, less time is spent on cleaning pots after cooking. Being a portable stove, gasifier users were trained to light it outside, this further reducing smoke inside the kitchen. Even though this contributes to outdoor air pollution, air movement reduces the concentration compared with, for example, lighting a stove inside the kitchen. In a study in rural India, it was found that cleaning the soot-laden pots due to use of inefficient stoves, when added to other work done by women like cooking and fuel collection, brought the women's time spent on these activities to around three hours per day [32]. Households in Idifu village in Tanzania cited reduction in smoke from improved cook stoves compared with three-stone fires as the main advantage of the former [30].  
For 95.8% of the households using the gasifier, they had been asked about it by their friends, with some referring to it as wood gas. These households are probably seen as much better by owning the gasifier, which has lower emissions than the three-stone open fire. This agrees with the claim that ownership of efficient and less-polluting stoves raises a woman's prestige, directly by being a sign of wealth and indirectly through reduced exposure to smoke [32]. Improvements in cleanliness of the gasifier may also encourage men to participate in cooking, as has been found following the adoption of biogas in Uganda [33].
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 4 Results and Discussion
### Effect of Different Parameter Sharing Strategies (RQ4)  
In this subsection, we evaluate the effect of different parameter-sharing strategies: layer-shared and all-shared. We report the performance and efficiency of the two strategies, as depicted in Table 5. To make a fair comparison, ATRec methods with two sharing strategies only differ in sharing strategy. We notice that in most cases, all-shared strategy is better than layer-shared strategy, both in performance and efficiency, except in the baby dataset. Therefore, we can conclude that in most cases, the layer-shared strategy with ATRec is enough for IRS policy representation. The unshared tree structure in TPGR is not efficient in learning, as it contains redundant parameters.  
Figure 5: Hit rate@30 of different state modeling methods over different episode lengths. (a) Instant Video; (b) Musical Instruments; (c) Baby.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Metric** & **All-Shared** & **Layer-Shared** \\ \hline \multirow{8}{*}{Instant Video} & Reward@10 & **0.15478** & 0.11970 \\  & HR@10 & **0.15151** & 0.17416 \\  & MRR@10 & **0.08836** & 0.05442 \\ \cline{2-4}  & Reward@30 & **0.24399** & 0.22471 \\  & HR@30 & **0.28041** & 0.25890 \\  & MRR@30 & **0.09431** & 0.06133 \\ \cline{2-4}  & Average learning time per-step & **0.05439** & 0.07010 \\  & Average decision time per-step & **0.00939** & 0.01216 \\ \hline \multirow{8}{*}{Musical Instruments} & Reward@10 & **0.05500** & 0.05191 \\  & HR@10 & **0.06776** & 0.06305 \\ \cline{1-1}  & MRR@10 & **0.02333** & 0.02208 \\ \cline{1-1} \cline{2-4}  & Reward@30 & **0.09998** & 0.10186 \\ \cline{1-1}  & HR@30 & **0.12205** & 0.12486 \\ \cline{1-1}  & MRR@30 & **0.02647** & 0.02556 \\ \cline{1-1} \cline{2-4}  & Average learning time per-step & **0.06201** & 0.08463 \\ \cline{1-1}  & Average decision time per-step & **0.01135** & 0.01593 \\ \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different feature extraction methods.
## 2 Materials and Methods
### Total Volume Flow Calculations via the Direct Method  
Calculations of total volume flow (Q) m\({}^{3}\) h\({}^{-1}\) via the direct method were based on the fluid continuity equation, which identifies airflow based on the following formula:  
\[Q_{tot}=3600\cdot\sum_{i=1}^{n}\cdot R_{velocity,i}\cdot A_{i}. \tag{1}\]  
where \(R_{velocity}\) is the perpendicular air velocity to the face of the opening in m s\({}^{-1}\), \(n\) is the number of partial areas in the barn, and \(A_{i}\) describes an partial area of the opening in the side wall in m\({}^{2}\). Q\({}_{tot}\) is the total volume flow which is flowing through the whole barn, and Q\({}_{i}\) is the volume flow which is flowing through an area section A\({}_{i}\) on the side wall.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \begin{tabular}{c} **Number of Cows** \\ **[Number]** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **[Kg]** \\ \end{tabular} & \
\begin{tabular}{c} **Milk Yield** \\ **[Kg Day\({}^{-1}\)]** \\ \end{tabular} &
\begin{tabular}{c} **Days of Pregnancy** \\ **[Day]** \\ \end{tabular} \\ \hline Min & 353 & 518 & 34.5 & 89 \\ \hline Mean & 387 & 667 & 36.7 & 94 \\ \hline Max & 413 & 800 & 38.6 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Animal parameters used for CO\({}_{2}\) balance method.  
Figure 2: Floor plan of the experimental barn. The colored lines represent gas sampling lines, red lines measure inside, green lines outside the barn. Each black “x” represents a critical orifice (detailed view in the yellow frame), which ensure homogeneous gas sampling rates along each sampling line. The alignment of the longitudinal axis of the barn is shown with the compass. Grey stars mark the positions of the ceiling fans. The yellow shaded area marks the feeding alley. The blue shaded areas mark the scraping floors, and areas with grey bars mark the lying cubicles.  
The entire side opening was equipped with a net that acted as a wind shield. This net reduces the opening area, which must be taken into account with an adjusted \(A\) in Equation (1). The different permeability between sections is due to past construction. Each net grid in the old part of the building is 27 by 29 mm, and in the new part, 17 by 29 mm. Based on the sizes and number of grids, we reconstructed the full opening area and used this value as a permeability coefficient (\(K_{per}\)) in calculations of the parameter \(A\) (the area of the opening in side wall, m\({}^{2}\)).  
Parameter \(A\) (the value ranging from 19.72 m\({}^{2}\) until 32.91 m\({}^{2}\)) is a reconstructed opening area without the net. In Equation (1), \(R_{velocity}\) was obtained from vector decomposition and adjusted according to the barn's position. Figure 1 shows a view of the southern wall with the location of ultrasonic anemometers in each section. We obtained values of \(Q\) for each section and then summed all volume flow values over nine sections to identify the total \(Q\) for the barn.
## 3 Results and Discussion
Figure 12 shows that the increase in the EBF and EABF values is low at small values of penetration depth, whereas the EBF and EABF increase greatly at high values of penetration depth (especially for penetration depths higher than 20 mfp). Increasing the penetration depth forces the incident photons to stay for longer inside the fabricated samples and perform many collisions with the surrounding electrons. Therefore, the number of photons accumulated inside the material increases with increasing penetration depth. Another observation from Figure 12 is that raising the Bi\({}_{2}\)O\({}_{3}\) concentration in the fabricated composites reduces the number of accumulated photons inside the material. This is ascribed to the increase in the probability of PE interaction with increasing Bi\({}_{2}\)O\({}_{3}\) concentration at the studied energy of 60 keV. Thus, the photon's energy is consumed totally during the interaction (the energy is consumed to eject one of the boundary electrons from the atoms). Thus, the increase in Bi\({}_{2}\)O\({}_{3}\) concentration is accompanied by a significant reduction in the EBF and EABF values at low energy intervals. Figure 3.2 Variation in the EBF of the fabricated polyepoxide-reinforced Bi\({}_{2}\)O\({}_{3}\) composites versus the emitted \(\gamma\)-photon energy and Bi\({}_{2}\)O\({}_{3}\) concentration. Figure 3.3 Variation in EBF and EABF values versus the penetration depth.
## 3 Capacity Optimization Model
### Constraints  
The constraints mainly include the capacity constraints of equipment, electricity balance, heating balance, and cooling balance.Decision parameters and their range of variations are listed in Table 1.  
The heat storage tanks and batteries have some constraints in the specific operation.  
\[\left\{\begin{array}{c}H_{lst,chr}^{min}\leq H_{lst,chr}(t)\leq H_{lst,chr}^{ max}\\ H_{lst,dis}^{min}\leq H_{lst,dis}(t)\leq H_{lst,dis}^{max}\\ SOC_{min}\leq SOC(t)\leq SOC_{max}\\ P_{b,in}(t)\leq P_{b,in}^{max},P_{b,out}(t)\leq P_{b,out}^{max}\end{array}\right. \tag{19}\]  
where \(H_{lst,chr}(t)\) and \(H_{lst,dis}(t)\) are the heat storage and heat release power in the thermal storage tank, respectively; SOC(\(t\)) is the charged state of the battery at time \(t\); \(P_{b,in}(t)\) and \(P_{b,out}(t)\) are the charging and dischargingpower of the battery, respectively [32].  
Theelectrical power balance is as follows:  
\[E_{pv}(t)+E_{mt}(t)+E_{grid,in}(t)+E_{b,out}(t)=E_{grid,out}(t)+E_{b,in}(t)+E( t)+E_{ec}(t) \tag{20}\]  
where \(E_{pv}(t)\) and \(E_{mt}(t)\) represent the electric energygenerated from the photovoltaic power generation system and micro-gas turbine at time \(t\), respectively. \(E_{grid,in}(t)\) and \(E_{grid,out}(t)\) represent the electricity purchased and sold from/to the grid at time \(t\), respectively. \(E_{b,in}(t)\) and \(E_{b,out}(t)\) are the charging and discharging power of the battery at time \(t\), respectively. \(E(t)\) represents the power load demand at time \(t\). \(E_{ec}(t)\) represents the energy consumed by the electric refrigerator at time \(t\).  
Theheat power balance is as follows:  
\[H_{re}(t)+H_{gb}(t)+H_{lst,out}(t)=H_{lst,in}(t)+H_{he}(t)+H_{ac}(t) \tag{21}\]  
where \(H_{re}(t)\) is the waste heat recovered by RE at time \(t\). \(H_{GB}(t)\) is the heat generated by the gas boiler at time \(t\). \(H_{lst,in}(t)\) and \(H_{lst,out}(t)\) are the heat energy absorbed and released by the heat storage tank at time \(t\), respectively. \(H_{he}(t)\) is the heat energy consumed by the heat exchanger at time \(t\). \(H_{ac}(t)\) is the heat energy consumed by the adsorption chiller at time \(t\)[28; 32].  
Thecold power balance is as follows:  
\[Q_{ac}(t)+Q_{ec}(t)=Q(t) \tag{22}\]  
where \(Q_{ac}(t)\) is the cold energy generated by the adsorption chiller at time \(t\); \(Q_{ec}(t)\) is the cold energy generated by the electric chiller at time \(t\); \(Q(t)\) is the cooling load demand at time \(t\).
## 5 Results and Discussion
### Fault Prediction Phase  
In the fault prediction stage, model optimization should be carried out. The criterion of model optimization is based on the prediction evaluation index, and the object of model optimization includes the algorithm optimizer, model structure, and neural network type. After selecting and determining the neural network model, fault prediction is carried out to predict the operating parameters of the machine tool, and the predicted operating parameters of the machine tool are used to prepare for the subsequent fault diagnosis process.
## 2 Preliminaries
### Terrain Model Generation  
We resort to the use of the publicly available digital elevation models (DEMs) to prepare the simulation setup for the real-life terrains under study. To this end, we modeled the natural terrain as a graph. Nodes are scattered in the area of consideration, where some node pairs are connected via traversable links. A UGV will be moving from its source node, traversing a sequence of links until it reaches its desired target node. Similar to [22], a visual representation of a sample terrain graph is given in Figure 3, where each node is connected to eight other neighboring nodes via links. The details on the distribution and allocation of nodes and links will be elaborated on later in Section 4.2. Each link's energy cost, distance cost, and soil trafficking will play an important role in the developed energy-efficient shortest path routing algorithm. In what follows, we describe the calculation of the used link metrics: distance and energy cost.  
Figure 3: A visual representation of the graph that represents the simulated terrain.  
Figure 2: RCI values plotted against different humidity conditions for the three soil types found in the real-life terrain considered in this paper.
## 6 Conclusions  
In this study, we proposed an ISDTSVM algorithm. Compared to the conventional SDTSVM algorithm, the classification order was determined by the sum of the Euclidean distances between the multi-class sample centers and the parameters were optimized by the inertia weight PSO algorithm. Experiments were conducted on the multi-class UCI data sets and the steel cord conveyor belt defects signals using the ISDTSVM algorithm and the conventional SDTSVM algorithm. For the Vowel, the Zoo and the Wine data sets, as well as the steel cord conveyor belt defects, the ISDTSVM algorithm improved the classification accuracy by 3%, 3%, 1% and 4% respectively, compared to the conventional SDTSVM algorithm. The classification accuracy of the steel cord conveyor belt defects reached 94% using the proposed ISDTSVM algorithm. The experiment results showed that the inertia weight PSO algorithm could optimize the SVM parameters quite well and the proposed ISDTSVM algorithm could improve the classification accuracy significantly. It could be applied efficiently to the classification of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & \
\begin{tabular}{c} **Number of Three** \\ **Types of Samples** \\ \end{tabular} & **IR** & **Accuracy** &
\begin{tabular}{c} **Balanced** \\ **Accuracy** \\ \end{tabular} & **F1** \\ \hline Steel cord conveyor belt defects & 20,20,20 & 2 & 97\% & 98\% & 95\% \\ Steel cord conveyor belt defects & 74,20,74 & 7.4 & 94\% & 97\% & 80\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The classification contrast results of the steel cord conveyor belt defects with different imbalanced ratio using the ISDTSVM algorithm.  
the steel cord conveyor belt defects. It has an important significance for preventing fracture accidents of steel cord conveyor belts.  
Compared to the conventional SDTSVM algorithm, although the proposed ISDTSVM algorithm could improve the classification accuracy by considering the distribution characteristics of the samples. The algorithm has limitations for outliers, noise and imbalanced data sets. In the future, we will study the ISDTSVM algorithm combined with the FSVM algorithm and how the algorithm works for imbalanced data sets.  
[PERSON] did modeling studies and analyzed the experiment results; [PERSON] and [PERSON] did the experimental studies; [PERSON] analyzed the experiment results; All authors took part in the paper writing.  
This research was supported by \"the Natural Science Foundation of China, grant numbers U1361121, 51505380\", \"the Green Manufacturing System Integration Program Funded for Ministry of Industry and Information Technology, grant number [2017]327\", \"the Scientific Research Program Funded by Shaanxi Provincial Education Department, grant number No.16 JK1508\", \"Shaanxi Province Innovative Talents Project, grant number(2018 TD-032)\" and \"Shaanxi Province Key Research and Development Project, grant number 2018 TD-ZDCXL-GY-06-04\". The first author is also particularly grateful to the Chinese Scholarship Council for funding his overseas study in the UK.  
The authors declare no conflict of interest.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 4 Results
### Reflectance-Based Results for UArizona and NASA GSFC at Iranpah Playa  
The joint UArizona-GSFC field deployment to Ivanpah Playa was successful, with clear skies and minimal wind, which can lead to airborne dust. Each team collected data within the predefined ROI using a portable spectroradiometer (UArizona) and CaTSSITTR (GSFC). Landsat 9 OLI results from the joint UArizona-GSFC collection at Ivanpah Playa during the tandem flight phase with Landsat 8 are shown in Figure 17. The uncertainty bars reflect the current uncertainty estimate for the reflectance-based approach of \(\pm 2.5\%\) (k = 1). The results from both teams agree to within the uncertainties of the reflectance-based approach.  
Figure 16: An example of the typical surface BRF at Railroad Valley (RRV), Ivanpah Playa, and S. Dakota State University (SDSU) as a function of wavelength.  
Figure 15: A summary of the SDSU radiometric calibration results for the Landsat 9 OLI using three comparisons with ground measurements using the reflectance-based approach (June 2022 to August 2022). The results are shown as the ratio of the TOA quantities for each data product (e.g., spectral radiance or reflectance) to the in situ field results.
# Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer
## 1 Introduction
[43] introduced the Transformer model with a self-attention mechanism. This model enables fast parallel computation and improves the ability to capture long-range dependencies. [PERSON] et al. [44] applied the Transformer model to computer vision and introduced the Image Transformer image generation model, which can fully replace the convolution process. [PERSON] et al. [45] proposed the Vision Transformer model, which directly applies the sequence of image blocks to the Transformer model and effectively extracts image features. [PERSON] et al. [46] proposed a semantic segmentation network based on the Vision Transformer. By introducing a decoding module, they were able to better realize the semantic segmentation task. [PERSON] et al. [47] proposed a Swin Transformer backbone network that improves the quality of extracting multiscale and higher-resolution target features while drastically reducing computational complexity. This network can be applied to various tasks, such as image classification, target detection, and semantic segmentation. This paper proposes an encoder-decoder model for semantic segmentation and detection of rail surface defects based on improvements made to the Swin Transformer network. The aim is to address the challenging problem of the intelligent detection of high-speed rail surface defects in small datasets characterized by scale variability in complex scenes. Ablation experiments were conducted, and the proposed model was compared with other classical semantic segmentation methods. The study demonstrates that the proposed method achieves a mean accuracy of 90.1% for semantic segmentation, a mean dice coefficient of 89.5%, and a segmentation speed of 37.83 FPS. These results surpass those of other network segmentation methods in terms of both accuracy and efficiency. The main contributions of the paper are as follows:  
1. In response to the public rail dataset, which is small and in black and white, this paper presents a comprehensive and colorful dataset of rail surface defects, derived from field photography, data tagging, and data expansion. 2. Adding two Local Perception Modules (LPMs) to the Swin Transformer coding network improves segmentation accuracy by providing local context information. 3. The decoding network now includes the Multiscale Feature Fusion Module (MFFM), which effectively fuses feature information of defects at different scales during the decoding process. This results in improved accuracy of multiscale defect segmentation. 4. The decoding network now includes the Spatial Detail Extraction Module (SDEM), which preserves spatial detail information and enhances the segmentation accuracy of small-scale defects.