esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Low Power Cusped Field Thruster Development at HIT
### Cusped Field Thruster Downscaling in HIT
Increasing the expanding angle also avoids the low-current to high-current mode transition in a wider mass flow rate and anode voltage range. Despite the thrust degradation at low mass flow rate, the expanding configuration performs with lower plume divergency, higher acceleration efficiency and better stability [72]. Figure 16: Differences among thrusters with different channel lengths: (**a**) propellant consumption and (**b**) ion acceleration efficiency. In (**c**), the results of the simulation of ion loss on channel walls for different channel lengths are presented. Under the guidance of the previous studies on component geometry, channel materials and variable cross-section configurations, a 4 mm exit diameter cusped field thruster was presented in 2018. The test results show that the thruster performs 1.8 uN to 112.7 uN thrust with mass flow rate from 0.15 sccm to 0.25 sccm and anode voltage from 150 V to 350 V. The power spectral density (PSD) of the thrust calculated by anode current and total ion current is obtained from the time variation of these parameters, as shown in Figure 18c. However, the uN-level cusped field thruster does not meet the target thrust noise and resolution requirement of typical gravitational wave detection missions. The size effect is still unavoidable for cusped field thruster downscaling, as it leads to problems such as wall loss, electron conduction difficulty and ionization inefficiency. Figure 17: Performance difference of thrusters with different expanding angle configurations; (**a**) Operation space, (**b**) ion current density distribution and (**c**) thrust. CFTK0, 4 and 8 are cusped field thrusters with different channel expanding angles (0\({}^{\circ}\), 4\({}^{\circ}\) and 8\({}^{\circ}\), respectively).
# Mechanical Properties of DNA Hydrogels: Towards Highly Programmable Biomaterials
## 5 Dissecting the Mechanical Properties of DNA Hydrogels
### Modeling/Simulations  
Computer simulation of DNA hydrogels at the atomic scale is currently beyond the limits of computational power, thus coarse grained models such as oxDNA [110; 111] have been developed. oxDNA represent nucleotides as single oriented rigid bodies, accounts for electrostatic interaction via the Debye-Huckel approximation, and accounts for sequence-dependent interactions in accordance with the SantaLucia model [112]. Small gel volumes (\(\sim\)100 nm box length) of tetravalent DNAs have been simulated using oxDNA into realistic times of a few \(\upmu\)s [113; 114]. [PERSON] et al. used oxDNA simulations of tetravalent DNAs gels to study the temperature and concentration-dependent formation dynamics to yield an accurate phase diagram [113]. Furthermore, using oxDNA simulation of DNAns gels, [PERSON] et al. found that DNAns gels will never crystallize owing to their arm flexibility, thus reducing the free energy of the gel state below that of the crystalline state [114; 115]. However, these simulations were run at high salt concentration of 0.5 M, prior to the inclusion of variable salt-dependence within the oxDNA model in 2015 [111]. The oxDNA model utilizes a cutoff distance of three times the Debye-Huckel Length and at lower, physiological, salt concentrations the electrostatic potential becomes long-range and significantly increases the computational cost due to the increased number of interactions. The oxDNA model of a single DNAns at low-salt concentrations was, however, shown to match with experimental results measured by small-angle neutron scattering (SANS) [116]. In 2018, [PERSON] et al. developed a coarse-grained simulation for DNA hydrogels by representing a trivalent DNAns with seven rigid bodies, a center and two bodies per arm, with patchy ends representing interunit hybridization [117]. This model was used to study the temperature and concentration-dependent degree of association, structural organization, and to dissect the rheological response, which will be discussed further in the next section. More recently, [PERSON] et al. used a similar model in conjunction with oxDNA simulations of a single DNAns and microrheology experiments to study the role of linker flexibility in DNA hydrogels [66]. Using oxDNA the probable angle for DNA sticky ends with rigid or flexible linkers was determined. This angle was incorporated as the range of motion for the patchy ends within the coarse-grained model and the rheological behavior acquired through simulation was validated with microrheological results, demonstrating a significant increase in the crossover frequency when flexible linkers are present, causing the gel to behave as a fluid at low frequencies [66].
# A More Efficient Conditional Private Preservation Scheme in Vehicular Ad Hoc Networks
## 5 The Improved More Efficient Protocol
### Safe Message Sending  
1. Signing: When a vehicle \(i\) wants to send message \(M\) to other surrounding vehicles, it signs on message \(M\) with the short-time anonymous public-key certificate \(Cert_{i}\) and the private key \(x\) before sending it out. * _Step 1_. Compute \(R=g_{1}^{r}\in G_{1}\) where \(r\in Z_{p}^{*}\) is a random number, and sign the message \(s_{r}\equiv r+x\cdot h(M,R)\,(\text{mod }p)\). * _Step 2_. Set signature \(Sig_{M}=(R,s_{r},Cert_{i})\).
2. Verification: Once receiving the message, the receiver is firstly checking the validity of \(T_{i}\) and \(Cert_{i}\) like Step 3 in Section 5.2. If invalid, the verification process aborts. Otherwise, the receiver verifies the signature \(Sig_{M}\) by checking the equation \(g_{1}^{s_{r}}=R\cdot Y^{h(M,R)}\). If it holds, the message is true and can be accepted, otherwise neglected, since \[R\cdot Y^{h(M,R)} =g_{1}^{r}\cdot g_{1}^{x\cdot h(M,R)}\] (4) \[=g_{1}^{r+x\cdot h(M,R)}\] \[=g_{1}^{s_{r}}\]
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 2 Experimental Program
### Loading and Measurement  
Static-loading experiments were conducted using a 5000 kN hydraulic pressure-testing machine, which is shown in Figure 4. The static pressure load was applied onto the column specimens step by step. At the beginning, each load step was \(1/10\) of the ultimate load of the column, and then adjusted to \(1/20\) of the ultimate load of the column when close to the ultimate load. The load and displacement of column top were detected by the data-collection system and recorded in a computer. With the load being applied step by step, the relationship of load and longitudinal displacement was planned by the computer, which could be used to monitor the experimental process and determine the yield-load and ultimate-load values.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
## 4 Discussion  
In this study, the multipath error was analyzed by using the actual data of dam deformation monitoring. Through the comparison with the SF method, some meaningful conclusions were obtained. Although the improvement effect in kinematic mode is not very obvious in this study (>30%), we hope to provide a research reference for multi-constellation analysis with different periods of BDS-2 and a practical engineering application. However, the multipath error caused by the reflection of the water surface was not considered in this study. The next step is to consider the reflection of the water surface or adding a water surface reflection suppression plate. Furthermore, the observation data is 12 days in total. In addition to MHGM modeling, the actual data processed for verification were only DOY 51-55, which is insufficient for actual dam deformation monitoring.  
Compared with the SF method, the MHGM can not only improve the effectiveness of multipath error, but also effectively provide a graphical display of multipath error interference around the stations. Because there are fewer MEO satellites and fewer double-difference observations between GEO/IGSO and MEO in BDS-2, the improvement of the DD residuals and relative positioning is not very obvious. The MHGM avoids the multipath reference problem caused by different orbital repetition periods, which makes it more suitable for the combined multipath modeling of BDS-2 and BDS-3. It also has distinct potential for improving the data processing accuracy of IGS stations, and further improving the accuracy of IGS products in the future.
## 3 Classification Based on Function
### Life-Detection Sensors
Radar life-detection technology is one of the most mature and widely studied life-detection technologies at present. It was used extensively in the 2008 Wenchuan earthquake in Sichuan, China, and in the 2023 Turkiye-Syria earthquake. The common radar life-detection system is divided into Continuous Wave (CW) and Ultra-Wide Band (UWB) radar life-detection systems. A CW radar transmits a monophonic continuous wave signal to demodulate the phase change of the reflected wave and obtain the breathing and heart rate of the person [19]. This is because the phase change of the reflected waves is linearly proportional to the displacement of the chest caused by cardiopulmonary activity [20]. The UWB radar life-detection system emits pulsed microwave beams on the biological body. The beam reflects the echo pulse according to the circular sequence modulated by biological activity that extracts the parameters of the life signal through the digital signal processing system [21]. However, due to the radiation effect of electromagnetic waves on the human body and interference caused by the simultaneous use of multiple radar life detectors at the earthquake site, radar life-detection systems still have some limitations in their use. Volatile organic compound (VOC) detection technology refers to identifying characteristic compounds in the exhaled air, blood, and urine of trapped individuals by determining the type and content of VOCs in the environment [22]. Breathing is considered a unique feature that can determine if a trapped individual is still alive [23] by detecting CO\({}_{2}\) and O\({}_{2}\) levels. Ion Mobility Spectrometry (IMS) and electronic sensors are common VOC life-detection instruments [24], where IMS separates these volatile organic compounds according to the difference in the drift velocity of the product ions in the inert buffer gas under the influence of an electric field [25]. The electronic sensor (also known as the electronic nose) uses an array of gas sensors to simulate animal olfactory organs to recognize odors [26]. VOC life-detection technology has some limitations, such as interference from dust and other particles at the rescue site, different VOCs of different groups of people (especially the VOCs of people trapped for a long time, with a lack of water and food), and the insufficient miniaturization of equipment [27].
## 4 Methods
### Node Aggregation and Update
#### 4.4.1 Edge-Level Attention  
Assume that the target node \(i\in\mathcal{V}\) and the neighboring node \(j\in\mathcal{V}\) are connected by an edge \(e_{ij}^{m_{1}}\), where \(m_{1}\) denotes the edge type. We compute the edge-type attention weight \(\alpha_{i,j}^{m_{1}}\) to measure the importance of node \(j\) to target node \(i\) through the edge \(e_{ij}^{m_{1}}\). The edge-type attention is based on improvements made in GAT and GATv2 [68]. While GAT performs excellently on homogeneous graphs, it cannot be directly applied to heterogeneous graphs because it ignores the types of nodes and edges. GATv2 improves upon this by optimizing the attention mechanism and adjusting the order of calculating attention coefficients, with scalar mappings placed last, thereby enabling dynamic attention and enhancing expressive capacity. In this paper, by extending the original graph attention mechanism and integrating improvements from GATv2, we introduce edge-type attention to more finely aggregate information in heterogeneous graphs. Specifically, edge-type information is incorporated into the attention calculation. The calculation is given by the following Equation (12):  
\[\alpha_{i,j}^{m_{1}}=\frac{\exp(\mathrm{  
and user-place edge (focusing on behavior preference), and capture the social relationship between users and the diversified interaction between users and places more accurately.  
Here, \(\alpha_{i,j}^{m_{1}}\) represents the attention weight between node \(i\) and node \(j\) connected by the edge of type \(m_{1}\), \(n\in N_{i}^{m_{1}}\) denotes the set of neighboring nodes connected to target node iii via edges of type \(m_{1}\), \(h_{i}\),\(h_{j}\),\(h_{n}\in\mathbb{R}^{d_{node}}\) represent the feature vectors of nodes \(i\),\(j\),\(n\), respectively, \(h_{e_{ij}^{m_{1}}}\in\mathbb{R}^{d_{node}}\) represents the feature vector of the edge \(e_{ij}^{m_{1}}\), and \(h_{e_{in}^{m_{1}}}\) represents the feature vector of the edge \(e_{in}^{m_{1}}\) connecting target node \(i\) and node \(n\), with edge type \(m_{1}\). \(\alpha^{T}\in\mathbb{R}^{d}\) is the learnable edge-type attention vector, and \(W_{h}\in\mathbb{R}^{d\times(2d_{node}+d_{edge})}\) is the learnable edge-level attention weight matrix. The symbol \(||\) indicates concatenation, and \(LeakyReLU(\cdot)\) is the activation function.
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 2 Proposed Method
### Deep Learning Model
#### 2.1.2 Loss Function (LF)  
The use of appropriate loss functions that can guide in 'learning' the features correctly is important. An LF is a type of region-based loss function--the focal Tversky loss function.  
In our case, due to the thinness of microcracks, the average area of each crack is less than 0.01% of the total area of the specimen. Thus, a region-based loss function (LF) based on the Dice Coefficient (DC) [48], Equation (1), is employed to maximize the overlap between the segmentation map of these cracks and the ground truth. For this problem, the impact of the incorrect positive assignment of a pixel (\(\rho_{i}^{\varepsilon}\)\(G_{i}^{\varepsilon}\)) and the negative assignment of a pixel (\(\rho_{i}^{\varepsilon}\)\(G_{i}^{\varepsilon}\)) is not equal. With this consideration, this LF is used with \(\beta\) as the weighing factor for the relative importance of \(\rho_{i}^{\varepsilon}\)\(G_{i}^{\varepsilon}\) and \(\rho_{i}^{\varepsilon}\)\(G_{i}^{\varepsilon}\) and probabilistic network output (with probability (\(\rho_{i}\)) of the ith pixel). In the PLF, irrespective of the number of pixels, each class is treated equally.  
\[LF=\left(1-\frac{\sum\limits_{i=1}^{N}\rho_{i}^{\varepsilon}G_{i}^{\varepsilon} }{\sum\limits_{i=1}^{N}\rho_{i}^{\varepsilon}G_{i}^{\varepsilon}+(1-\beta) \sum\limits_{i=1}^{N}\rho_{i}^{\varepsilon}G_{i}^{\varepsilon}+\beta\sum \limits_{i=1}^{N}\rho_{i}^{\varepsilon}G_{i}^{\varepsilon}}\right)^{\gamma} \tag{1}\]  
Here, N is the total number of pixels in the image, G is the ground truth of the image, c corresponds to class c, and \(\hat{c}\) corresponds to not being in a class.  
With focal parameter \(\gamma\)[49], the change in the LF for the unit improvement of accuracy for pixels at different accuracies was explored, as shown in Figure 3; \(\gamma\) was selected such that the change in the PLF has higher values when accuracy is lower to facilitate the detection of more difficult regions in the HRCF network.  
Figure 3: Effect of gamma on change in LF at different levels of accuracy.
## 3 Datasets and Methodology
### Methodology
#### 3.2.2 TEC Data Pre-Processing
The area under the curve marked in red corresponds to a monthly mean SSN number higher than 50 SSN; the area under the curve marked in green corresponds to the monthly mean SSN number lower than 50 SSN. The 9.1 Tohoku earthquake occurred on 11 March 2011, marked in a red vertical line. Figure 7: An example of a randomly picked normalized quiet day (3 November 2013) from the quiet days corresponding to the 9.1 Tohoku earthquake that occurred on 3 November 2011. As can be seen, within a two days time window (before the earthquake event), the normalized ionospheric TEC values exceeded four times the standard deviation of the randomly chosen normalized quiet day. Additional case studies for other large earthquake events compared with the corresponding randomly picked normalized quiet days are shown in Appendix A.
## 4 Calculation of Measurement Time  
The calculation time data were presented in the form of a histogram of the average time spent per frame for FastICA and PSO-ICA. As the initial values of the hybrid ICA matrix were random, the initial values affected the difficulty of the calculation and led to higher time consumption in calculation. Therefore, the time needed for the computing of five iterations via the two algorithms using the same initial value was compared.  
According to Figure 12, the method proposed in this study could shorten the calculation time under initial values with a certain complexity, whereas the FastICA method had no advantage over the method proposed in this study. It is also worth noting that the total time taken to measure all physiological parameters once was 30 s for FastICA and 10 s for PSO-ICA, indicating a total time saving of 33%.
## 2 Results and Discussion
### (\(\pm\))-Ectoine Enantiomers Separation
Temperature effect on separation and resolution (methods 29-32) are shown in the chromatograms in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Mobile Phase \({}^{*}\)**} & \multirow{2}{*}{**Flow Rate** [mL/min]} & \multicolumn{2}{c}{**Column**} & & & \\  & & & **Temperature** & \(t_{\text{R1}}\) [min] & \(t_{\text{R2}}\) [min] & \(k_{\text{1}}\) & \(k_{\text{2}}\) & \(\alpha\) & \(R_{\text{S}}\) \\  & & & [°C] & & & & & & \\ \hline
**16** & MeOH & 1 & 30 & 11.7 & 42.2 & 5.9 & 24.0 & 4.1 & 5.6 \\
**20** & MeOHH\({}_{2}\)O = 9:1 & 1 & 30 & 9.2 & 26.3 & 4.3 & 14.1 & 3.3 & 9.4 \\
**21** & MeOHH\({}_{2}\)O = 9:1 & 1 & 35 & 8.6 & 23.6 & 3.9 & 12.6 & 3.2 & 9.6 \\
**22** & MeOHH\({}_{2}\)O = 9:1 & 1 & 40 & 7.9 & 21.1 & 3.6 & 11.1 & 3.1 & 9.8 \\
**23** & MeOHH\({}_{2}\)O = 7:52.5 & 0.5 & 30 & 9.9 & 24.6 & 3.2 & 9.3 & 2.9 & 10.3 \\
**24** & [PERSON]\({}_{2}\)O = 7:52.5 & 0.5 & 40 & 8.9 & 20.0 & 2.7 & 7.5 & 2.8 & 10.2 \\
**25** & [PERSON]\({}_{2}\)O = 7:52.5 & 1 & 40 & 4.2 & 9.2 & 2.6 & 6.8 & 2.6 & 10.0 \\
**26** & [PERSON]\({}_{2}\)O = 7:52.5 & 1.2 & 40 & 2.9 & 7.0 & 2.1 & 6.4 & 3.1 & 7.8 \\
**27** & [PERSON]\({}_{2}\)O = 1:1 & 0.5 & 40 & 5.7 & 10.3 & 3.8 & 7.6 & 2.0 & 8.2 \\
**28** & [PERSON]\({}_{2}\)O = 1:1 & 0.75 & 40 & 3.7 & 6.6 & 2.1 & 4.5 & 2.1 & 8.1 \\
**29** & [PERSON]\({}_{2}\)O = 1:1 & 1 & 30 & 3.0 & 5.7 & 3.9 & 8.5 & 2.2 & 8.4 \\
**30** & MeOHH\({}_{2}\)O = 1:1 & 1 & 35 & 2.8 & 5.2 & 3.6 & 7.5 & 2.1 & 8.3 \\
**31** & MeOHH\({}_{2}\)O = 1:1 & 1 & 40 & 2.7 & 4.7 & 3.5 & 6.9 & 2.0 & 8.0 \\
**32** & MeOHH\({}_{2}\)O = 1:1 & 1 & 45 & 2.6 & 4.4 & 3.4 & 6.3 & 1.9 & 7.7 \\
**33** & MeOHH\({}_{2}\)COOH & & & & & & & & \\  & (25 mL, pH = 3.0) = 7:3 & 1 & 40 & 3.0 & 6.1 & 0.8 & 2.6 & 3.2 & 7.1 \\
**34** & MeOHH\({}_{2}\)COOH & & & & & & & & \\  & (25 mL, pH = 3.0) = 1:1 & 1 & 40 & 2.3 & 3.7 & 0.4 & 1.3 & 2.9 & 5.5 \\
**35** & ACN:H\({}_{2}\)O = 82 & 1 & 30 & 7.6 & 13.2 & 3. In terms of the resolution (\(R_{\text{s}}\)), the best result was obtained with method 23 (MeOH:H\({}_{2}\)O = 7.5:2.5; \(R_{\text{s}}\) = 10.3), but the method 30 (MeOH:H\({}_{2}\)O = 1:1; \(R_{\text{s}}\) = 8.3) was chosen because it uses less MeOH, the flow rate is higher, and the peak shapes are better. This method is optimal for the Chiral-T column in terms of its properties and characteristics. The chosen method is also suitable for a scale-up. Figure 3 shows the identification of (+)- and (-)-ectoine enantiomers compared to standard, natural (+)-ectoine using the selected method 30.
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 3 Related Work
### Time Series Prediction Algorithm
#### 3.2.2 Large-Scale Time Series Model  
Large-scale Time Series Models (LTSMs) represent a significant leap in the application of advanced neural architectures, originally designed for natural language processing, to the realm of numerical time series forecasting. By leveraging the capabilities of pretrained large language models (LLMs), LTSMs aim to bridge the gap between text generation and time series forecasting. The foundational concept behind LTSMs involves utilizing the inherent pattern recognition abilities of LLMs, initially trained on vast amounts of temporal data, and applying these skills to forecast time series with minimal or no additional training.  
Several groundbreaking works have propelled the field forward, showcasing diverse approaches to integrating LLMs with time series forecasting. Among these, LLMTime [23] exemplifies the zero-shot forecasting capability of pretrained LLMs through a novel tokenization scheme that transforms real-valued data into string representations, enabling forecasts from models like GPT-3 without further tuning. Following this, GPT4 TS [24] was created by adapting a pretrained GPT-2 model specifically for time series tasks, focusing on fine-tuning positional embeddings and layer normalization parameters while employing patch embeddings for efficient processing. In contrast, TimesFM [25] introduces a decoder-only foundation model tailored for time series forecasting, featuring an increased output patch size for faster decoding. This approach emphasizes scalability and efficiency by pretraining the model on extensive Google Trends and Wiki pageview data, alongside open-source datasets. Meanwhile, Time-LLM [26] pushes the boundaries by aligning time series patches with textual prototypes and prompting frozen LLMs, though it requires domain-specific fine-tuning for optimal performance. Lastly, Chronos [27] represents a unique strategy, training language models from scratch on large-scale time series datasets using scaling and quantization techniques, thus avoiding reliance on pretrained LLMs altogether.  
In summary, LTSMs highlight the transformative potential of adapting LLMs for time series forecasting, offering innovative solutions ranging from zero-shot prediction to specialized fine-tuning methods. Despite notable advancements, challenges such as computational costs and the necessity for domain-specific adjustments persist, motivating continuous research towards developing more efficient and versatile models capable of handling diverse datasets with enhanced accuracy and scalability.