esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Correction of Seasonal Systematic Error  
For a more precise comparison, we selected the seasonal glacier surface elevation changes in this region from 4790 m to 4810 m above sea level and calculated the mean value of glacier surface elevation change in this region to be \(-\)1.908 m (Figure 9). The glacier mass balance was estimated using an ice density of 900 \(\pm\) 17 kg m\({}^{-3}\), which resulted in \(-\)1717.2 mm w.eq. [PERSON] et al. conducted field measurements at 4800 m above sea level on the Parlung No. 4 glacier. According to these field measurements, the surface mass balance was \(-\)3900 mm w.eq. between 25 May 2009 and 14 August 2009 [6]. The main reason for the discrepancy between these two results is that the bi-static InSAR results are the sum of spring accumulation and summer ablation, whereas the field measurements only captured summer ablation. To further validate the accuracy of the seasonal correction, we combined our results with satellite altimetry ICESat and satellite gravity GARCE data from [PERSON] et al. [43]. These results indicate the strong seasonality of the overall annual mass balance signal of glaciers in the SETP between 2003 and 2009, with 2000 mm w.eq. winter accumulation (February-May) and 4000 mm w.eq. summer ablation (May-September). These independent measurements are consistent with the seasonal glacier mass balance estimated by the TanDEM-X bi-static InSAR, so we adopted 1.908 m as the seasonal systematic error correction value. The main reason for the seasonal error correction is that most glaciers in the SETP region are spring-accumulating glaciers, and the maximum ablation occurs in summer, with seasonal fluctuations in glacier mass balance estimation. Therefore, it is necessary to correct the glacier surface elevation results of the above six pairs of bi-static InSAR images with this seasonal error correction value, and corrected our elevation change results that were obtained at the end of the melt season accordingly (Table 3).
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 2 LSA Pointing Dynamics
\begin{array}{c}\boldsymbol{I}\ddot{\boldsymbol{\theta}}_{a}+\sum \limits_{i=1}^{m}\boldsymbol{B}_{roti}\ddot{\boldsymbol{\eta}}_{i}-\boldsymbol{I }\ddot{\boldsymbol{\theta}}_{\Delta}=\boldsymbol{T}+\boldsymbol{T}_{d}\\ \ddot{\boldsymbol{\eta}}_{i}+2\xi_{i}\boldsymbol{\Lambda}_{i}\dot{\boldsymbol{ \eta}}_{i}+\boldsymbol{\Lambda}_{i}^{2}\boldsymbol{\eta}_{i}+\sum\limits_{i=1 }^{m}\boldsymbol{B}_{roti}\tilde{\boldsymbol{\theta}}=0\\ \mathrm{i}=1,2,...,m\end{array} \right. \tag{6}\]  
where \(\boldsymbol{\theta}_{\Delta}=\boldsymbol{L}\boldsymbol{\phi}_{\mathrm{Ia}} \boldsymbol{\eta}_{a}+\boldsymbol{\theta}_{\mathrm{the}}+\boldsymbol{\theta}_{ \mathrm{ins}}+\boldsymbol{\theta}_{\mathrm{rep}}\). **Remark 1**. : _If we define the pointing angle \(\mathbf{\phi}=[\mathbf{\varphi}\ \mathbf{\vartheta}\ \sigma]^{\mathrm{T}}\) in the antenna body coordinate system, then \(\mathbf{\phi}\) can be obtained by:_  
\[\mathbf{\phi}=\mathbf{C}\mathbf{\theta}_{a} \tag{7}\]  
_where \(\mathbf{C}\) is the transformation matrix from the satellite body coordinate system to the antenna body coordinate system. The rotation order is \(Z\)-\(X\)-\(Y\) and rotation angles are \(\theta_{z}\), \(\theta_{x}\) and \(\theta_{y}\); then, \(\mathbf{C}\) is given by:_  
\[\mathbf{C}=\left[ \begin{array}{cc}\mathrm{c}\theta_{y}\mathrm{c}\theta_{x}- \mathrm{s}\theta_{y}\mathrm{s}\theta_{x}\mathrm{s}\theta_{x}&\mathrm{c}\theta_ {y}\mathrm{s}\theta_{x}\mathrm{s}\theta_{x}+\mathrm{s}\theta_{y}\mathrm{s} \theta_{x}\mathrm{c}\theta_{x}&-\mathrm{s}\theta_{y}\mathrm{c}\theta_{x}\\ -\mathrm{s}\theta_{x}\mathrm{c}\theta_{x}&\mathrm{c}\theta_{x}\mathrm{c} \theta_{x}&\mathrm{s}\theta_{x}\\ \mathrm{s}\theta_{y}\mathrm{c}\theta_{x}+\mathrm{c}\theta_{y}\mathrm{s} \theta_{x}\mathrm{s}\theta_{x}&\mathrm{s}\theta_{y}\mathrm{s}\theta_{x}- \mathrm{c}\theta_{y}\mathrm{s}\theta_{x}\mathrm{c}\theta_{x}&\mathrm{c}\theta _{y}\mathrm{c}\theta_{x}\end{array} \right] \tag{8}\]  
_where \(\mathrm{c}\) represents cosine function and \(\mathrm{s}\) denotes sine function._
## 6 Case Study
### Realization of Optimization
#### 6.2.2 Determination of Genetic Parameters  
The success of the genetic algorithm optimization is dependent on the setting of various parameters, including population size, iteration times, crossover rate, and mutation rate.  
The size of the population is positively correlated with the length of the chromosome. However, an appropriate population size should not only ensure the diversity of individual genes but also consider the time cost of program operation.  
In this study, iteration times are set as the termination condition of the genetic algorithm, and this parameter dramatically affects the time required to obtain the optimization results. The setting of iteration times should strike a balance between effect and efficiency. If the iteration times are too small, the total penalty value may not reach the minimum before the algorithm terminates, and if it is set too large, the running time cost would be too high.  
The crossover rate determines the global search ability of the genetic algorithm, and the mutation rate determines the local search ability of the genetic algorithm. Hence, the appropriate combination rate and mutation rate significantly affect the optimization performance of the genetic algorithm. However, there is yet to be a complete and feasible theory to guide how to determine the combination rate and mutation rate. The most reliable method is to determine the rates by parameter adjustment tests.  
Although 34 components are divided into three subgroups according to the thickness, the optimization of these subgroups was not independent and co-occurred. Therefore, when optimizing the assembly sequence of a subgroup of components, the influence of previous subgroups must be taken into consideration. While the calculation of the weight penalty and the occupied space penalty is independent of previous subgroups, the interference penalty is influenced by all the assembled components, especially those in the earlier subgroups. As a result, this penalty requires greater attention. The parameters of the three subgroups and the group with total components are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subgroup**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{\(P_{weight}\)} & \multirow{2}{*}{\(P_{space}\)} & \multirow{2}{*}{\(P_{layout}\)} & \multirow{2}{*}{\(\begin{bmatrix}\text{Population}\\ \text{Size}\end{bmatrix}\)} & \multirow{2}{*}{\(\begin{bmatrix}\text{Iteration}\\ \text{Times}\end{bmatrix}\)} & \multirow{2}{*}{\(\begin{bmatrix}\text{Crossover}\\ \text{Rate}\end{bmatrix}\)} & \multirow{2}{*}{\(\begin{bmatrix}\text{Mutation}\\ \text{Rate}\end{bmatrix}\)} \\ \hline
270 mm & 13 & 1 & 1 & 10 & 100 & 500 & 0.6 & 0.3 \\
200 mm & 15 & 1 & 1 & 20 & 300 & 1000 & 0.6 & 0.3 \\ \hline
100 mm
# SR-GNN: A Signed Network Embedding Method Guided by Status Theory and a Reciprocity Relationship
## 3 A Directed Signed Network GNN Model
### GNN Model for Directed Signed Networks
#### 3.4.1 GNN Convolutional Layer Design  
Each convolution of layer \(l\) (\(l\in[1\sim K]\)) consists of two parts:  
1. Sending (\(x_{i(\rightarrow)}^{(l)}\)): aggregates information from neighbors where node \(i\) is the sender, and its own features.
2. Receiving (\(x_{i(\leftarrow)}^{(l)}\)): aggregates information from neighbors where node \(i\) is the receiver.  
Figure 2: The overall structure of the model.  
A plausible analogy for these two parts can be represented in the context of social networks: receptive-based status aggregation is used to characterize the status that a user is endorsed by the others (e.g., the popularity of the user), while generative-based status aggregation is for capturing the extent to which a user is willing to endorse the others (e.g., the deference of the user to the others).  
The combination of sending and receiving features through a linear transformation is computed as follows:  
\[x_{i(\rightarrow)}^{(I)}=\gamma\left(x_{i}^{(I-1)},\underset{j\in N_{e}(I)}{ \oplus}\phi\left(x_{j}^{(I-1)},h_{e_{i-j}}^{(I)}\right)\right) \tag{1}\]  
\[x_{i(\leftarrow)}^{(I)}=\gamma\left(x_{i}^{(I-1)},\underset{j\in N_{i}(I)}{ \oplus}\phi\left(x_{j}^{(I-1)},h_{e_{i-j}}^{(I)}\right)\right) \tag{2}\]  
\[x_{i}^{(I)}=W^{I}\cdot\left[x_{i(\leftarrow)}^{I}\middle|\left|x_{i(\rightarrow )}^{I}\right|+b^{I}\right. \tag{3}\]  
\(x_{i}^{(0)}\in\mathbb{R}^{d}\) is the initial feature vector of any node \(i\) in the directed signed network \(G\). \(h_{e_{i-j}}^{(I)}\) and \(h_{e_{i-j}}^{(I)}\) are the feature vectors of the edges \(h_{e_{i-j}}^{(I)}=W_{e_{i-j}}^{(I)}\cdot\left[Sign_{e_{i-j}},Reciprocity\right]\), \(h_{e_{i-j}}^{(I)}=W_{e_{i-j}}^{(I)}\cdot\left[Sign_{e_{i-j}},Reciprocity\right]\), where \(W_{e_{i-j}}^{(I)}\) and \(W_{e_{i-j}}^{(I)}\) are the linear transformation parameters, \(Sign_{e_{i-j}}=\{-1,0,1\}\) is the sign of the edge in \(G\), and \(Reciprocity=\{0,1\}\) indicates whether there is reciprocity--0 for non-reciprocal, 1 for reciprocal.  
The function \(\phi\) is the concatenation operator (\(\mid 1\)), combining the edge features with the node features; \(\oplus\) is the mean aggregation function with permutation invariance, aggregating information for node \(i\) in both sending and receiving directions; function \(\gamma\) defines how a node passes its own features to its neighbors, here \(\gamma\) is taken as the concatenation operation.  
This design ensures that the model explicitly differentiates between outgoing and incoming edges, capturing the asymmetric nature of directed signed networks.
## 3 Methods for Precise Relative Orbit Determination
### National University of Defense Technology (NDT) Approach  
The PROD results of NDT were generated by the National University of Defense Technology Orbit Determination Toolkit (NUDTTK) software, which has been successfully used in POD and PROD of LEOs, such as Tiangong-2 [24], GRACE [30], and GRACE-FO [31]. The NDT solutions of POD and PROD are described in detail by [PERSON] et al. [30].  
The NDT adopts a reduced-dynamic approach and a batch least-squares estimation method to perform POD and PROD. The TH-2B satellite keeps the result of POD unchanged; POD processing uses a zero-difference ionosphere-free code and carrier phase combination. TH-2A adopts a double-difference (DD) ionosphere-free carrier phase combination, where the DD ambiguity needs to be fixed in order to obtain high-precision PROD results. First, the DD wide-lane ambiguities were solved by analyzing the Melbourne-Wubbena linear combination. Then, the wide-lane ambiguities were introduced as known in order to resolve the narrow-lane ambiguities. Both the wide-lane and narrow-lane ambiguities used the LAMBDA method [32; 33; 34] to search for integer values. In order to maintain orbit and formation, three maneuvers are performed by TH-2A per day during 04:00-07:00; all maneuvers must be considered to obtain continuous and high-precision PROD results. In the NUDTTK software, each orbit maneuver is modeled as constant acceleration in the R, T, and N directions over a predefined thrust interval, an 11 th-order Adams-Cowell multi-step integrator for normal arcs, and an eighth-order Runge-Kutta single-step integrator around maneuver arcs [35]. The estimation orbit parameters are: initial states of TH-2A, Z component of the phase center offset (PCO), a solar pressure coefficient, piecewise constant atmospheric drag coefficients over 3 h intervals, piecewise linear empirical accelerations in T and N directions over 15 min intervals, and maneuver parameters.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 2 Literature Review
### Gradient-Based Estimations  
Gradient-based methods include explicit gradient descent (Simultaneous Perturbation Stochastic Approximation, SPSA [40, 41, 42, 43, 44, 45]) and implicit gradient propagation (Neural Networks, NNs [3, 12, 19, 46, 47, 48]). As the name suggests, SPSA simultaneously optimizes both the target variable (OD flows) and the observations (traffic count) by gradient descent. Thanks to its simple expression, SPSA can be conveniently deployed in different road networks. However, it is sensitive to OD flows with significant fluctuations, and thus convergence is not guaranteed under large-scale road networks where traffic demand varies significantly. To address this, [PERSON] et al. clustered OD flows according to their magnitude and assigned a set of hyper-parameters to each cluster [43], which was named c-SPSA. They further presented Robust SPSA to enhance the stability during the gradient process by employing a hybrid gradient strategy [44]. One of the earliest works using neural networks for OD estimation was the Hopfield Neural Networks used in Ref. [46]. [PERSON] et al. applied Multi-Layer Perceptron (MLP) to estimate OD matrix [19]. [PERSON] et al. proposed a multi-layered Hierarchical Flow Network to integrate multiple data sources and estimate travel demand [12]. [PERSON] et al. compared five machine learning methods on OD estimation problems: K-Nearest Neighbor, Random Forest, LightGBM, MLP, CNN [48]. However, as aforementioned, the distribution shift between data of the training set and test set makes the application of neural networks in the field of OD estimation still limited.
## 3 Results and Discussion
### Calculation Function
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Moisture Content** & \multirow{2}{*}{**Porosity (\%)**} & \multirow{2}{*}{**\(\mathbf{5}\)**} & \multicolumn{4}{c}{**Temperature (\({}^{\circ}\mathbf{C}\))**} \\ \cline{3-6}
**(\%w.b.)** & & **5** & **12.5** & **20** & **27.5** & **35** \\ \hline \multirow{4}{*}{14.7} & 38.4 & 0.93 & 1.35 & 1.89 & 2.54 & 3.32 \\  & 41.5 & 0.7 & 1.05 & 1.51 & 2.1 & 2.8 \\  & 44.6 & 0.64 & 0.92 & 1.31 & 1.82 & 2.45 \\ \hline \multirow{4}{*}{17.6} & 38.4 & 1.64 & 2.07 & 2.63 & 3.3 & 4.1 \\  & 41.5 & 1.36 & 1.73 & 2.21 & 2.81 & 3.53 \\  & 44.6 & 1.26 & 1.55 & 1.96 & 2.49 & 3.14 \\ \hline \multirow{4}{*}{20.55} & 38.4 & 2.63 & 3.09 & 3.67 & 4.37 & 5.19 \\  & 41.5 & 2.29 & 2.68 & 3.19 & 3.82 & 4.56 \\  & 44.6 & 2.12 & 2.44 & 2.88 & 3.43 & 4.1 \\ \hline \multirow{4}{*}{23.5} & 38.4 & 3.5 & 3.99 & 4.59 & 5.31 & 6.16 \\  & 41.5 & 3.11 & 3.53 & 4.06 & 4.71 & 5.48 \\  & 44.6 & 2.89 & 3.32 & 3.69 & 4.27 & 4.97 \\ \hline \multirow{4}{*}{26.4} & 38.4 & 4.38 & 4.89 & 5.51 & 6.26 & 7.12 \\  & 41.5 & 3.94 & 4.37 & 4.93 & 5.6 & 6.39 \\ \cline{1-1}  & 44.6 & 3.66 & 4.03 & 4.51 & 5.11 & 5.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obtained capacitance of corn samples in different moisture contents, temperatures, and porosities. In order to verify the accuracy and rationality of Equation (24), parameters, such as moisture content, temperature, and porosity were randomly set as the conditions of the validation experiments, which are listed in Table 5. The moisture contents of paddy were 15.2%, 20.6%, and 25.4% w.b., respectively. The calculated capacitance obtained by subbing moisture content, temperature, and porosity into Equation (24) was taken as the predicted value, and the measured capacitance obtained by the developed instrument was taken as the measured value for error analysis. Table 5 shows the results of the validation. Compared with the measured values of capacitance, the relative difference between the model predictions and the measured values was in the range of 3.42% to 8.11%, and the average relative difference was 5.83%. The results of the validation experiments demonstrated that the capacitance predicted by measurement device and the function of capacitance was in good agreement with the measured value, and the feasibility of predicting function of corn capacitance was thus validated.
## 2 Materials and Methods
### Antibiofilm Evaluation of L. dentata Essential Oil Against the Selected Strains  
For biofilm formation, 100 uL of BHI broth and 100 uL of each standardized bacterial suspension (10\({}^{8}\) CFU/mL in physiological solution) were added to 96-well microtiter plates. The plates were then incubated at 37 \({}^{\circ}\)C for 48 h without shaking, under static conditions, with the culture medium replaced every 24 h to maintain adequate nutrient availability. After incubation, the wells were washed three times with sterile saline to remove non-adherent bacteria. Cells that remained adhered to the surface were considered true biofilms.  
The formed biofilms were treated with 100 uL of _L. dentata_ essential oil at the concentrations of 1\({}^{\circ}\)\(\times\) MBC, MBC, and MBC \(\times\) 2. In addition, 100 uL/well of a sterile liquid culture medium (BHI broth) was added. Sterility control with culture medium alone, a negative control with culture medium and bacterial suspension, and a 2% chlororekidine (CHX) treatment control (Bioformula Manipulacao, Sao Jose dos Campos, SP, Brazil) were included in the assay.  
Following treatments, for 30 min or 24 h, the antibiofilm activity of the essential oil was assessed by evaluating the metabolic activity of microorganisms using the MTT assay, in which the wells were washed once with saline solution, and 200 uL of MTT  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Bacterial Strain** & **AC** & **A + C** & **EM** & **AM** & **TC** & **CF** & **VM** \\ \hline _E. faecalis_ 1 & S & S & S & S & S & S & S \\ \hline _E. faecalis_ 2 & S & S & S & S & S & I & S \\ \hline _E. faecalis_ 3 & S & S & I & S & R & I & I \\ \hline _E. faecalis_ 4.1 & S & S & S & S & R & S & I \\ \hline _E. ficeium_ 4.2 & S & S & I & I & S & S & S \\ \hline _E. ficeium_ 7.3 & S & S & S & S & S & S & S \\ \hline \end{tabular} Legend: S: sensitive, I: intermediate, R: resistant, AC: Anoxicillin, A + C: Anoxicillin/Clavulanic acid, EM: Erythromycin, AM: Azithromycin, TC: Tetracycline, CF: Ciprofloxacin, VM:ancomycin.  
\end{table}
Table 1: The antibiotic resistance of the clinical strains.  
(3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolzolium Bromide) solution was added to each well. The plate was then incubated in the dark at 37 \({}^{\circ}\)C for 1 h. After incubation, the MTT solution was removed, and 200 \(\upmu\)L of Dimethylsulfoxide (DMSO) was added, followed by incubation at 37 \({}^{\circ}\)C for 10 min with additional shaking for another 10 min. Optical densities (OD) were then measured using a microplate reader at 570 nm and converted into the percentage of microbial metabolic activity using the formula:  
\[\%\;metabolic\;activity=\frac{OD\;treated\;group\times 100}{medium\;OD\;control\;group}\]
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 3 Results and Discussion
### Reactivity  
Data in Table 3 indicate the studied adhesive formulations exhibit sufficient reactivity at ambient temperature, as the gelling times between 10 and 15 min at 20 \({}^{\circ}\)C is a practical range for solid wood bonding in industry. As it was shown previously, the reactivity of the polyglycerol-based PUR systems can be easily controlled by the type and amount of catalyst used. Thus, set times as short as 1 min can be obtained [37]. However, highly reactive binders are useful only in automatic bonding lines to keep high production capacity--e.g., veneering in furniture industry.  
It is worth to mention that, the addition of the SiO\({}_{2}\) filler decreased the reactivity of PUR. The gelling times at ambient temperature were increased by 17% and 250% for the branched polyglycerol A and the sucrose-based polyol C, respectively. This was caused by the difference amount of silica added, since polyol A was doped with 2%, due to its innate high viscosity, while polyol C was added 6% silica. Apparently, strong H-bonding interactions between polyol molecule and SiO\({}_{2}\) might have been the main reason of slowing down in the gelling/curing. The observed slowing down of gelling is accordant to the literature data on the thermal effects of PUR curing in the presence of silica. It had  
Figure 3: 0.6-mm thick oak veneer surface: (**A**) adhesive overpenetration and staining (polyurethane (PUR) without SiO\({}_{2}\)), (**B**) avoided staining (PUR with SiO\({}_{2}\)).  
been proved that even small additions resulted in a few-fold decrease in enthalpy of curing, thus, the process was hampered and required higher temperature to full conversion [27]. Also, [PERSON] and co-workers demonstrated that nanosilica additions resulted in a decreased the rate constant for curing by ca. 60%, reduced thermal effect and a shift of heat flow peak in DSC curves to higher temperatures [42]. Curing kinetics of the polyglycerol-PMDI systems were described elsewhere [43]. The observed thermal effects depend on the abundancy of hydroxyl groups in a molecule and range between ca. 12 J/g and 50 J/g, while heat flow peak appears about 67-69 \({}^{\circ}\)C.  
Since veneering usually is performed at the temperature close to 100 \({}^{\circ}\)C, high reactivity of the PUR adhesives allows fast processing, and, makes possible to get one-cycle pressing time as short as 30-60 s under industrial conditions.