esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. A unified flow and heat transfer model is established for gas injection and extraction process. This model innovatively incorporates the convective heat transfer of hydrogen or natural gas in the wellbore, radial heat transfer, the temperature-pressure coupling effects within the cavity, and the turbulent heat transfer effects within the cavity.
2. The temperature and pressure parameters of hydrogen and natural gas within the chamber and wellbore were compared. Under identical injection and withdrawal conditions, the temperature of hydrogen in the chamber was 10 \({}^{\circ}\)C higher than that of natural gas, and 16 \({}^{\circ}\)C higher in the wellbore. The pressure of hydrogen in the chamber was 2.9 MPa greater than that of natural gas, and 2.6 MPa higher in the wellbore.
3. A comparative analysis was conducted on the impact of surrounding rock's horizontal and numerical distance on temperature during hydrogen and natural gas injection processes. As the distance from the cavity increases, from 5 to 15 m, the temperature fluctuation in the surrounding rock diminishes progressively, with the temperature effect in the hydrogen storage chamber extending at least 10 m.
4. The influence of rock thermal conductivity parameters on temperature during the processes of hydrogen injection and natural gas extraction is compared. The better the thermal conductivity, the deeper the thermal effects penetrate the rock layers, with the specific heat capacity having the most significant impact.  
Conceptualization, Z.L.; methodology, Y.L.; validation, Z.W. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by National Key Research and Development Program of China (2023 YFB4005500), Basic Research Funding for Central Universities of China (2024 KYJD1010), National Natural Science Foundation of China (52404103), and Natural Science Foundation of Jiangsu Province (BK20241658, BK20220025).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
###### Abstract  
This article introduces a small microwave remote sensing satellite weighing 310 kg, operating in low earth orbit (LEO). It is equipped with an X-band synthetic aperture radar (SAR) antenna, capable of a maximum imaging resolution of 0.6 m. To achieve the objectives of lower cost, reduced weight, minimized power consumption, and enhanced temperature stability, an optimized thermal design method tailored for satellites has been developed, with a particular focus on SAR antennas. The thermal control method of the antenna is closely integrated with structural design, simplifying the thermal design and its assembly process, reducing the resource consumption of thermal control systems. The distribution of thermal interface material (TIM) in the antenna assembly has been carefully calculated, achieving a zero-consumption thermal design for the SAR antenna. And the temperature difference of the entire antennas when powered on and powered off would not exceed 17 \({}^{\circ}\)C, meeting the specification requirements. In addition, to ensure the accuracy of antenna pointing, the support plate of antennas requires stable temperature. The layout of the heaters on the board has been optimized, reducing the use of heaters by 30% while ensuring that the temperature variation of the support board remains within 5 \({}^{\circ}\)C. Then, an on-orbit thermal simulation analysis of the satellite was conducted to refine the design and verification. Finally, the thermal test of the SAR satellite under vacuum conditions was conducted, involving operating the high-power antenna, verifying that the peak temperature of T/RM is below 29 \({}^{\circ}\)C, the temperature fluctuation amplitude during a single imaging task is 10 \({}^{\circ}\)C, and the lowest temperature point of the support plate is 16 \({}^{\circ}\)C. The results of the thermal simulation and test are highly consistent, verifying the correctness and effectiveness of the thermal design.  
small satellite; SAR; thermal control; optimal design; thermal test +
Footnote †: journal: _Aerospace_
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 3 Methodology  
We proposed a novel MOTS framework based on masks and an enhanced multi-hypothesis tracking algorithm (MHT). The framework integrated a mask-conditioned feature fusion network and a occlusion-aware mask propagation network in congestion scenes. Our framework first extracted spatial-temporal features from video sequences via mask-guided foreground-background separation. Foreground features captured the target geometry such as contour and area and background features encoded the scene context. These features were then processed by a hierarchical hypothesis tree that dynamically weighted appearance, motion, and IoU based on the real-time occlusion state. Eventually, a memory-enhanced decoder synthesized the masks of the occlusion targets by correlating historical non-occlusion features with the current frame query to achieve pixel-accurate trajectory recovery.
# Three-Dimensional Human Pose Estimation with Spatial-Temporal Interaction Enhancement Transformer
## 3 Method
### Feature Extraction
#### 3.2.1 Spatial Feature Extraction Branch  
For the spatial domain, we propose SFE composed of the Transformer encoder to model the intrinsic structural relationships of the human joints for each frame (see Figure 3a,c). More specifically, we treated each joint of a 2D pose as a token and took a linear projection layer to embed it into high-dimension space: \(X^{i}\in\mathbb{R}^{J\times 2}\xrightarrow{embed}\overline{X^{S}}\in \mathbb{R}^{J\times C}\), where \(X^{i}\) is the \(i\)-th frame and C is the joint embedding dimension. Then, to save the spatial location information, we added a learnable spatial position embedding \(E^{S}_{pos}\in\mathbb{R}^{J\times C}\) with this high-dimensional feature, and it became \(Z^{Si}_{0}\in\mathbb{R}^{J\times C}\). Finally, \(Z^{Si}_{0}\in\mathbb{R}^{J\times C}\) were fed into the SFE to extract spatial structural information across all joints. These processes can be described as:  
\[Z^{Si}_{0}=\overline{X^{Si}}+E^{S}_{pos}, \tag{4}\] \[Z^{Si^{\prime}}_{l}=Z^{Si}_{l-1}+MSA(LN(Z^{Si}_{l-1})),\] \[Z^{Si}_{l}=Z^{Si^{\prime}}_{l}+MLP(LN(Z^{Si^{\prime}}_{l})),\]  
Figure 2: Structure of attention mechanism. (**a**) Scaled Dot-Product Attention. (**b**) Multi-head self-attention (MSA). (**c**) Multi-head cross-attention (MCA).  
where \(LN(\cdot)\) denotes the layer normalization layer and \(l\in[1,\cdots,L_{1}]\) is the index of the FE layers. After going through the \(L_{1}\)-layer FE module, the output of frame \(i\) on the SFE branch will be \(Z_{L_{1}}^{St}\).
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 3 Finite Element Verification of TVMS
### Tooth Profile Modification  
Figure 10 demonstrates that the TPM involves selectively removing material from the crest of the tooth, thereby enhancing the gear pair's meshing action. This refinement leads to a smoother transition between engagements of single and paired teeth, which effectively smooths out the'stepped' characteristics in the meshing stiffness curve. Nevertheless, applying the TPM reduces the proportion of double-tooth contact during a full meshing cycle, which, consequently, diminishes the gear's load-carrying capacity. This reduction in contact area, due to material removal from the gear teeth, inherently weakens the gear's load-bearing strength.  
Therefore, the tooth profile modification length should not be chosen to be excessively large or too small. A considerable modification length can significantly reduce the gear's load-carrying capacity if it exceeds the transition point between single and double-tooth meshing on the gear. Modifying the single-tooth contact region can significantly compromise the gear's strength, which should be avoided in engineering. Conversely, selecting a modification length that is too small will not effectively reduce meshing impact phenomena.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**k-Error-Max (MN/m)**} & \multicolumn{3}{c}{**Mean-k (MN/m)**} & \multicolumn{3}{c}{**TIME**} \\ \hline  & **a.A** & **a.B** & **b.A** & **b.B** & **a** & **b** & **a** & **b** \\ \hline CMFEM & 48.61 & 38.23 & 190.2 & 141.1 & 46.81 & 170.6 & 6.12 (h) & 6.25 (h) \\ CMTAM & 51.91 & 39.72 & 198.1 & 134.5 & 45.50 & 167.2 & 3.82 (s) & 2.79 (s) \\ error & 6.36\% & 3.75\% & 3.99\% & 4.68\% & 2.80\% & 1.99\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of CMFEM and CMTAM.  
Figure 9: TVMS considering the LCR of Straight Bevel Gears. (**a**) gear pair 1. (**b**) gear pair 2.  
Therefore, reasonably choosing the gear tooth modification length is essential based on the specific circumstances.  
Table 5 provides a comparison of the errors and computational efficiency between TPMFEM and TPMTAM:
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program
### Materials
#### 2.1.3 Sintered Gypsum  
For this study, gypsum rock was obtained from a field as raw material and was crushed using the Los Angeles apparatus. Subsequently, the material was passed through sieve number 120, which allows only particles smaller than 0.125 mm. The prepared gypsum was then subjected to a sintering process in an oven, conducted at 1050 degrees Celsius for 24 h. After the sintering process, the material underwent an additional sieving to separate any undesirable particles that may have accumulated in the gypsum during the sintering operation.
# A High-Utility Differentially Private Mechanism for Space Information Networks
## 1 Introduction
### Related Works  
In distributed communication systems, for example Space Information Networks, the deluge of data generated by the terminal poses a serious threat to its privacy. The distributed communications systems have advantages that every entity can control its own data. Distributed communication systems also need to consider privacy protection in their foundations so that they can provide user-centric support for privacy [5].  
Many technologies have been applied to distributed communication systems to solve privacy issues. For example, the secure multi-party computation was used to protect the privacy of transform protocols [6; 7]. Blockchain technology was introduced into satellite communication networks to solve privacy issues [8]. Homomorphic encryption was used by the nodes in distributed communication systems to perform integer operations (additions only or additions and multiplications) in a privacy-preserving manner to calculate the average of their integer initial values [3]. Deep learning of layer-partitioned training with step-wise activation functions was used to safeguard sensitive information [9].  
Compared with cryptography tools such as secure multi-party computation, differential privacy is a promising technology, which can solve privacy problems of distributed communication systems with the lower power consumption. Differential privacy was proposed by [PERSON] et al. from a sequence of papers [10; 11; 12]. Then, differential privacy was extended to local differential privacy [13] so that it can be used in a distributed architecture. However, local differential privacy suffered from low data utility [14]. To improve the utility, many new concepts were proposed such as elastic sensitivity [15], record sensitivity [16]. Furthermore, many other technologies were combined to differential privacy to improve its data utility. Anonymous communication such as Mix Networks [17] was combined to differential privacy to improve data utility [18; 19]. Sampling technology was introduced into differential privacy by [PERSON] et al. [20]. [PERSON] et al. introduced personalized sampling into differential privacy to further improve the data utility of differential privacy [21; 22].  
Compared to the previous work, we extend differentially private mechanisms based on personalized sampling to distributed communication systems such as Space Information Network. On the one hand, through comparing with other mechanisms, the proposed mechanism has better data utility. On the other hand, the proposed mechanism has a hierarchical privacy guarantee.
## 2 Background of WUE Estimation by Remote Sensing
### Indirect WUE Estimation by Measuring GPP and ET at the Ecosystem Level
#### 2.2.3 Advances in Indirect WUE Estimation by Remote Sensing  
Remote sensing techniques, especially using MODIS data, has produced GPP and ET products with high spatial and temporal coverage [69; 70; 71], because both photosynthesis and ET are closely related to physiological characteristics and meteorological factors [72; 73]. From there, ecosystem WUE across time and space can be calculated using MODIS-based GPP and ET products under the classical definition of WUE [74]. MODIS GPP and ET products are the most widely used and unique high resolution remote sensing data products for the indirect estimation of WUE. Although these products have been used to analyze the temporal and spatial patterns of global WUE for terrestrial ecosystems, the discrepancy with tower-based WUE at 8-day time scales still exist at specific sites [68; 75].  
Recently, various studies have shown a strong linear relationship between GPP and SIF using satellite and ground measurements for GPP estimation [76; 77]. Global SIF products have also been retrieved with several time and space resolutions from spaceborne spectrometers, such as the Orbiting Carbon Observatory2 (OCO-2), the TROPOspheric Monitoring Instrument (TROPOMI), the Global Ozone Monitoring Experiment 2 (GOME2), and the Greenhouse Gases Observing Satellite (GOSAT) [78; 79; 80]. A previous study has developed an algorithm of GPP estimation using SIF products, which have global coverage and can be used in all weathers [81]. Studies have also shown the empirical and mechanistic relationship between T and SIF to improve the T estimation [82; 83; 84]. Due to weak signal strength of SIF and a weak relationship between T and SIF, T produced by SIF needs further evaluation and research considerations [85]. However, these valuable products with GPP and ET algorithms provide new opportunities to estimate the terrestrial WUE at large scales. Further work is necessary to compare the WUE estimated from different remote sensing products.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 3 Case Study
### System Scenarios  
Two 24 h optimizations were conducted to simulate the minimum load scenario (case 1) and the maximum load scenario (2). The 24 h load profile was taken from one year of historical data from the Kinmen grid. The day with the least power and the day with the highest power were used as case 1 and case 2, respectively. However, for both cases, though the PV was also taken from measured data, no cloud condition was used. Therefore, the solar PV peak at 11 a.m. reached a peak of about 10 MW. Furthermore, regular maintenance of three heavy oil units (Plant1_1, Plant1_8, Plant1_9) is conducted during the summer, so these plants were considered shutdown for case 2.  
Figure 3a shows the load and PV profiles for the minimum load scenario that had a minimum load of 17 MW occurring at 4:00 a.m. This specific day took place in the winter season where most of the load occurred at night due to electric heaters being turned on by island dwellers. There was no district heating present since the average winter temperature was 16 \({}^{\circ}\)C during the day, and the lowest night temperature was around 3 \({}^{\circ}\)C.  
Figure 3b shows the load and PV profiles for the day with a maximum load of 62 MW occurring at 1:00 p.m. This load profile occurred in the summer. The increased energy usage was due to switching on air conditioning units, especially during the day where the temperature can reach 33 \({}^{\circ}\)C.  
Figure 2: The (**a**) cost curve of the generators, and (**b**) average cost curve.  
For both case studies, a fixed reserve was used. The up-spinning reserve limit was set to 10 MW while the down-spinning reserve was 2 MW (based on a 20 MW minimum load).
# A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
## 2 Materials and Methods
### Data Used to Verify the Model  
This study used the fatigue data of 2024-T4 aluminum alloys in the Metal Material Performance Development and Standardization Manual (MMPDS) [27] to verify the predictive ability of each model. The aluminum alloy sample is a V-notched bar. Different sizes of the notch produce different sizes of stress concentration factor Kt, and different sizes of the stress ratio (R) are used for fatigue test loading. Detail dimensions of the specimen are shown in Table 2. The numbers of the samples are shown in Table 3.  
Two sample sets (Kt = 2.4, R = 0.06) and (Kt = 3.4, R = 0.74) are taken as test samples. The test samples do not participate in the subsequent training to verify the extrapolation performance of the PGNN. In order to verify the predictive ability of the model in small training samples, the remaining samples are divided into four groups in a similar form to K-fold cross-validation. Each case takes one group as the training sample and the remaining three groups as the verification sample. Therefore, there are four cases in total. The training samples in each case account for 25%, and the verification samples account for 75%. The verification samples do not participate in the training and are used to evaluate the prediction ability of the model. All samples are shown in Figure 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Hyperparameters** & **SVR** & **PGNN** & **ANN** \\ \hline Penalty coefficient & 35 & — & — \\ The influence domain of a single vector & 0.16 & — & — \\ Batch size & — & 5 & 5 \\ Number of epochs & — & 2000 & 2000 \\ Learning rate & — & 0.001 & 0.001 \\ Activation function & — & ReLU & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hyperparameters of SVR, PGNN and ANN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Kt** & **Gross Diameter/mm** & **Net Diameter/mm** & **Notch Root Dadius/mm** & **Flanj Angle/\({}^{c}\)** \\ \hline
1.6 & 11.43 & 10.16 & 2.54 & 60 \\
2.4 & 12.70 & 10.16 & 0.81 & 60 \\
3.4 & 11.43 & 10.16 & 0.25 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail dimensions of the specimen.  
Figure 2: The structure of ANN.
# On the Performance of Underlay Device-to-Device Communications
## 4 Performance Analysis
### Average Sum Rate of D2D Networks  
Having obtained the average rate of each D2D transmission, we now compute the average sum rate of the whole D2D networks under the \(s\) scheme (denoted by \(\overline{\mathcal{R}}_{\Sigma}^{s}\)) as follows:  
\[\overline{\mathcal{R}}_{\Sigma}^{s}=\sum\limits_{k=1}^{N}\overline{\mathcal{ R}}_{k}^{s}. \tag{16}\]
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 3 Key Design of SRSS/ILMO
### Fault Detection and Automatic Isolation Design  
SRSS/ILMO adopts the overcurrent protection scheme consisting of digital processing logic and fast comparator to realize automatic fault isolation of external sensors or other devices. The fast comparator is used to monitor the power supply current according to the threshold set by the CPLD or FPGA. The digital processing logic integrates the current detection signal and gives the system fault judgment while guaranteeing the normal power-on process and power supply of the system. Once the system fault judgment is given, the digital processing logic will cut off the power supply of external equipment. The fault detection and isolation system diagram is shown in Figure 9.  
Figure 8: Two-stage anti-crash mechanism.  
Figure 9: Fault detection and isolation design.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 3 Extraction and Purification Methods
### Precipitation methods  
Precipitation is a technique that is based on the addition of a range of precipitating agents: (1) salts, (2) non-ionic polymers, (3) organic solvents (ethanol, propanol, methanol, ketones, etc.). An alternative method is based on pH change, which alters the nature of the solution.  
Organic solvents have been used successfully for bromelin purification. In fact, ethanol (30-70%) has been used for extraction, leading to a recovery of 98% of the initial enzymatic activity [9]. Ethanolic extraction gave higher yields than sulphate and isoelectric precipitation [49]. [PERSON] et al. have compared different precipitation agents; ethanol, (NH4)2 SO4, and PEG. The most effective agent was ethanol, which gave higher enzyme recovery (30-70% vs. 20-40%) than ammonium sulphate, whereas PEG failed to precipitate bromelin [9].  
These methods have also been extensively combined with other purification techniques (e.g., chromatography) to remove impurities and contaminants, as well as to improve enzyme purity and specific activity [5; 8; 20; 35].
Recombinant DNA technology is a well-known methodology used to produce about 90% of the enzymes on the market as a whole [50]. The production of recombinant cysteine proteases, such as papain from Pichia pastoris [50] and glycyl endopeptidase from E. coli [51], has been reported in the literature. The first attempt to obtain recombinant bromelain was carried out by [PERSON] et al. who cloned the BAA1 gene that encodes fruit bromelain in Brassica rapa [51]. The stem-bromelain gene, from Ananas comosus, has been cloned by [PERSON] et al. and gave higher enzymatic activity than commercial bromelain, with the optimum temperature and pH being 45 \({}^{\circ}\)C and 4.6, respectively [50]. Recombinant bromelain from Ananas comosus showed higher thermal stability, as highlighted in a study by [PERSON] et al. [52]. Recombinant stem bromelain also showed higher potential as an anti-microbial agent (in particular vs. _E. coli_ O111), compared to the commercial product [53].