esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Alternative Fat from Adipose-Derived Stem Cell from Bovine in 3D Culture
## 3 Results  
### Characterization of Bovine ADSC  
After isolating SVF from the initial 2 g of fat tissue, 1 \(\times\) 10\({}^{5}\) cells were seeded and cultured in vitro. A culture of adipose tissue-derived SVF (1 \(\times\) 10\({}^{5}\) cells) of bovine origin resulted in the formation of spindle-shaped, colony-forming cells within 4 days at passage 0 (Figure 1A,B). Starting with the culture with 1 \(\times\) 10\({}^{5}\) cells, the cumulative cell count reached approximately 3.0 \(\times\) 10\({}^{8}\) within a span of three weeks (Figure 1C). The assessment criteria for verifying MSCs include the expression of CD73, CD90, and CD105, coupled with the absence of CD45, as outlined in the IST guidelines. An attempt was made to assess the expression of MSC markers in bADSCs. As shown in Figure 1D, bADSCs showed a pronounced expression of CD73 (99%), CD90 (99%), and CD105 (99%), and was negative to CD45 (0.01%) (Figure S1). To investigate the cellular localization, immunofluorescence staining was conducted for CD73, CD90, and CD105, revealing strong positivity on the cellular surface of bADSCs, as shown in Figure 1E. This expression was consistently confirmed by Western blot analysis (Figure 1F).  
The data suggest that bADSCs exhibit a spindle-shaped morphology and possess the capacity to repopulate and form colonies, while concurrently expressing specific markers associated with ADSCs. This observation meets the criteria necessary for classifying these cells as mesenchymal stem cells from adipose tissue. Moreover, bADSC culture in this setting exhibits considerable potential for generating abundant stem cells in vitro.
# Hierarchical Resource Management for Mega-LEO Satellite Constellation
## 6 Approaches and Algorithms
### Game-Based Spectrum Planning
After SBSs reach the Nash equilibrium, the RRSC adjusts its own price through iteration according to the demand strategy distribution of the SBSs. The changing rate of the RRSC's price is expressed by the After the SBSs reach the Nash equilibrium, the RRSC adjusts its price through an iterative process based on the demand strategy distribution of the SBSs. The rate of change in the RRSC's price is determined by the marginal utility, a concept from microeconomics that represents the additional benefit gained from consuming one more unit of a good or service. The specific equation used to iterate the RRSC's price is as follows:  
\[p(t+1)=p(t)+\mu\frac{\partial G(p(t),q(t))}{\partial p(t)} \tag{7}\]  
where \(\mu\), which is greater than 0, represents the adjustment step of the RRSC's pricing strategy. This parameter plays a crucial role in determining the convergence rate, which is the speed at which the system reaches the equilibrium state. In other words, the value of \(\mu\) affects how quickly the pricing strategy adjusts to achieve the desired equilibrium. The marginal utility of the RRSC can be determined by calculating the change in revenue resulting from a change in price. This can be mathematically expressed as follows:  
\[\frac{\partial G(p(t),q(t))}{\partial p(t)}\approx\frac{G(\ldots,p(t)+\epsilon,\ldots)-G(\ldots,p(t)-\epsilon,\ldots)}{2\epsilon} \tag{8}\]  
where \(\epsilon\) is a very small value to calculate partial derivatives. Through the iterative process, the system enables SBSs and the RRSC to achieve an optimal resource demand strategy \(\mathcal{Q}^{*}\) and price strategy \(p^{*}\), respectively. Since both SBSs and the RRSC reach the Nash equilibrium, the two-stage single-master multi-slave Stackelberg game achieves the Nash equilibrium \((p^{*},\mathcal{Q}^{*})\). The iteration process is as follows:  
(1) At every moment \(t\), the RRSC adjusts its pricing strategy based on the marginal utility derived from Equations (7) and (8). (2) Once the SBSs become aware of the RRSC's pricing strategy, they adjust their demand strategy based on Equations (5) and (6) until their utility reaches a maximum value and the total revenue of all the SBSs reaches an equilibrium. (3) If the revenue of the RRSC reaches an equilibrium, the iteration process stops. Otherwise, at the next time step \(t+1\), the RRSC adjusts its pricing strategy based on the resource demand strategy of the SBSs and repeats step 1.
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 3 GLS Encryption Using Physical Networked Dynamic
### Relay Selection & Weight Computation  
To implement the GLS node pair encryption and communication in Equations (4) and (5), one is required to select the appropriate relays and compute their weights. We do so by maximising the secrecy rate \(R_{\text{Tx,Rx}}\) formulated in Equation (8). To be specific, for each (Tx,Rx) communication pair, we compute the optimal weight vector \(\mathbf{\alpha}^{(Tx,Rx)}\in\mathbb{R}^{N}\) by solving the following optimisation problem:  
\[\begin{split}\max_{\mathbf{\alpha}^{(Tx,Rx)}}&\left[ \log_{2}\left(1+\frac{\mathbb{E}(\mathbf{s})^{2}}{\|\mathbf{X}^{T}\cdot\mathbf{\omega} ^{(\text{Tx,Rx})}\|_{F}^{2}+\|\mathbf{\alpha}^{(\text{Tx,Rx})}\|_{2}^{2}\cdot \sigma^{2}}\right)\right.\\ &\left.-\max_{j\in\{Tx,Rx\}}\log_{2}\left(1+\frac{\mathbb{E}( \mathbf{s})^{2}}{\|\mathbf{\alpha}_{j}^{(Tx,Rx)}\cdot\mathbf{X}_{j,\cdot}\|_{2}^{2}} \right)\right]^{+}.\end{split} \tag{9}\]  
As such, the relay selection is converted to the weight computation problem whereby the non-zero weights account for the selection of the corresponding nodes.  
In Equation (9), two challenges remain. First, the physical dynamic \(\mathbf{X}\) is unknown, which makes Equation (9) difficult to solve. Second, the objective function in Equation (9) is non-concave (for maximisation) with respect to \(\mathbf{\alpha}^{(Tx,Rx)}\), although the feasible region is convex. To address the above challenges, we use a data-driven GFT operator as a surrogate matrix for \(\mathbf{X}\), and then transform the non-concave maximisation problem into an approximated convex optimisation. We elaborate these two in the following.
## 3 Results and Discussion
### Mechanisms of Action of PAW-N and PAW-P on Different Hair Types  
The distinct chemical profiles and oxidative capacities of PAW-N and PAW-P lead to markedly different effects on hair fibers, especially when considering intrinsic hair characteristics such as porosity, curl pattern, and cuticle density. As synthesized in Table 5, PAW-N, dominated by high nitrate content (-500 mg/L), elevated ionic strength (6244 uS/cm), and low pH (2.15), creates an aggressive oxidative environment that promotes disulfide bond cleavage, lipid peroxidation, and significant protein denaturation. These effects are particularly detrimental to porous hair types, such as wavy (Type 2) and coily/kinky (Type 4) hair, which exhibited pronounced morphological damage and molecular degradation. In contrast, PAW-P--characterized by high hydrogen peroxide concentration (>25 mg/L), lower ionic content (1926 \(\upmu\)S/cm), and moderate acidity (pH 2.54)--induces milder oxidative effects. The FTIR and SEM data collectively show that PAW-P preserves cuticle integrity, reduces lipid loss, and maintains the keratin structure more effectively than PAW-N. All hair types benefited from PAW-P's gentler profile, with straight hair (Type 1) being particularly resilient, showing minimal alterations in both morphological and molecular signatures. Coily hair, although generally more susceptible to oxidative stress, demonstrated partial structural reorganization under PAW-P, possibly due to its high surface area and dense cuticular architecture that moderate the penetration of reactive species. To provide a clearer molecular basis for the observed effects, representative reaction pathways involving key biomolecules are outlined below:  
(1) Disulfide bond (cystine) oxidation in keratin [30]:  
\[\text{R-S-S-R}^{\prime}+\text{ROS}\to 2\text{R-SOH}\to\text{R-SO}_{2}\text{H}/ \text{R-SO}_{3}\text{H} \tag{1}\]
## 3 Methodology and Details of Data Analysis
### Results and Discussion
In terms of accuracy, it is evident from the results of the first case study that static observations with the GPS technique remained insufficient to resolve the deformations observed by the inclination sensors because of the error sources that affect GPS observations [19]. However, from the application point of view, using inclination sensors and GPS together in structural monitoring and analysis yields improved insight into deformation phenomena. To identify the response of the structure to environmental loading as static, quasi-static or resonant, the separate contributions of GPS and inclination sensors are both notable. In other words, the static application of GPS helps to identify the static or quasi-static deformations of a structure at the centimeter level. On the other hand, using inclination sensors provides input for clarifying the amplitude and frequency of periodic movements, even at the sub-millimeter level. 4. In addition to its static application, the GPS technique can be used for continuous monitoring. In the second case study, the structural deformations of the pillar were analyzed using time series by continuous GPS solutions. The periodic components of the deformation signal were similarly clarified in inclination data evaluation. The daily movement of the structure is clear from the spectral analysis results of the GPS data. 5. The other important output of this study is methodological. We confirmed and demonstrated the previously mentioned advantages of the LSSA technique in the spectral analysis of irregularly sampled data. Practical data handling and analysis strategy of the technique provided benefits in the evaluation of data and the interpretation of the results.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Evaluation Criteria  
We adopted four evaluation criteria, namely accuracy, precision, recall, and F1-score in a bid to accurately analyze and fairly compare the effectiveness of the LTS-SEI method in addressing the long time span SEI problem. For each individual category, these criteria can be separately calculated as follows:  
\[accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{27}\]  
\[precision=\frac{TP}{TP+FP} \tag{28}\]  
\[recall=\frac{TP}{TP+FN} \tag{29}\]  
\[F1_{score}=\frac{2\times precision\times recall}{precision+recall} \tag{30}\]  
where \(TP\) represents the number of samples with both real and predicted categories being positive, \(TN\) the number of samples with both real and predicted categories being negative, \(FP\) the number of samples with the real category being negative and the predicted category being positive, and \(FN\) the number of samples with the real category being positive and the predicted category being negative. For the multiclassification task in this study, we can obtain multiclassification evaluation criteria using the averages of accuracy, precision, recall, and F1-score:  
\[acc=\frac{1}{N}{\sum\limits_{i=1}^{N}accuracy_{i}} \tag{31}\]  
\[pre=\frac{1}{N}{\sum\limits_{i=1}^{N}precision_{i}} \tag{32}\]  
\[rec=\frac{1}{N}{\sum\limits_{i=1}^{N}recall_{i}} \tag{33}\]  
\[f1=\frac{1}{N}{\sum\limits_{i=1}^{N}F1_{score_{i}}} \tag{34}\]
## 5 Model Evaluation
### Model Experiment 1: Speed Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Speed (km/h) Northbound**} \\ \cline{2-10}
**Prediction** & \multicolumn{2}{c}{**BiLSTM**} & \multicolumn{2}{c}{**Uni-LSTM**} & \multicolumn{2}{c}{**RNN**} & \multicolumn{2}{c}{**Elman**} & \multicolumn{2}{c}{**DLBP**} \\ \cline{2-10}
**Horizons** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** & **MAPE** & **Accuracy** \\  & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\ \hline
15 min & 0.07\% & 99.93\% & 8.63\% & 91.37\% & 10.77\% & 89.23\% & 10.88\% & 89.12\% & 11.16\% & 88.84\% \\ \hline
30 min & 0.14\% & 99.86\% & 9.24\% & 90.76\% & 12.74\% & 87.26\% & 12.72\% & 87.28\% & 12.85\% & 87.15\% \\ \hline
45 min & 0.11\% & 99.89\% & 9.21\% & 90.79\% & 12.78\% & 87.22\% & 12.67\% & 87.33\% & 16.17\% & 83.83\% \\ \hline
\begin{tabular}{c} Accuracy \\ (\%) \\ \end{tabular} \\ \hline
15 min & 0.13\% & 99.87\% & 11.24\% & 88.76\% & 13.94\% & 86.06\% & 14.06\% & 85.94\% & 16.06\% & 83.94\% \\ \hline
30 min & 0.14\% & 99.86\% & 12.35\% & 87.65\% & 17.05\% & 82.95\% & 17.33\% & 82.67\% & 17.86\% & 82.14\% \\ \hline
45 min & 0.16\% & 99.84\% & 12.46\% & 87.54\% & 17.67\% & 82.33\% & 18.07\% & 81.93\% & 18.37\% & 81.63\% \\ \hline
60 min & 0.20\% & 99.80\% & 12.59\% & 87.41\% & 18.41\% & 81.59\% & 18.77\% & 81.23\% & 17.11\% & 82.89\% \\ \hline \hline \end{tabular} Note: The green cells represent the best-performing models, while the yellow cells represent the worst-performing models.  
\end{table} 
Table 3: Speed prediction accuracy results for northbound and southbound directions. Figure 9: Speed prediction results for 15-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation.
## 4 Conclusions  
This paper presents a method for the CNEP problem that minimizes the difference between investment and operation costs and flexibility benefit according to optimal power flow equations and planning constraints of different equipment. At first, the linear formulation of the AC optimal power flow model is used in the proposed strategy. Stochastic programming according to RWM-based scenario generation and Kantorovich method-based scenario reduction is used in this paper to model the forecasted error uncertainty of the load, energy price, and RES. Numerical results show that the proposed configuration structure by using MINLP and MILP models with linear AC and DC power flows is the same. The difference between the three models is in the network parameters, such as bus voltages and losses, the produced active and reactive power, and the operation cost. This difference is caused by the zero loss in MILP with AC power flow, and also the zero loss and reactive power and the 1 p.u. voltage in the MILP model with DC power flow. The difference in the first model is lower than in the second one. Also, two of the models have very  
Figure 8: (**a**) Flexibility benefit, (**b**) operation cost in _FC_.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum voltage drop from 1.05 per unit (p.u.) & 0.064 \\ \hline System flexibility & 19.4 \\ \hline Calculation time (s) & 21 \\ \hline Solution situation & Optimal \\ \hline \hline \end{tabular}
\end{table}
Table 9: Convergence, operation, and flexibility results.  
short computation time and optimal solutions, while the MINLP model has a very long computation time and its solution is not an optimal one. The mentioned items indicate the superior performance of the MILP model by using linear AC power flow compared with the two other models. Thereby, the proposed CNEP strategy can improve the network indices such as the voltage profile, and it can allow high flexibility in the test system.  
M.R.A., conceptualization; S.P., validation and writing--original draft; M.K., methodology; A.N., edit and writing--original draft; M.B., supervision, analysis and interpretation of data. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge financial support from the Universiti Teknologi Malaysia (Post-Doctoral Fellowship Scheme grant 05E09, and RUG grants 01M44, 02M18, 05G88, and 4B482).  
Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study.
# Effects of Successive Annual Training on Young Swimmers' Strength Asymmetries and Performance
## 3 Results
### Strength Asymmetries Evaluation  
Strength asymmetries evaluated from the dryland (HG and ISO) and in-water (TF) tests were similar in Year-1 compared to Year-2 (Table 5) without any change at the pre-training period compared to the post-training period in both years (\(p\) > 0.05; Table 5). The \(\Delta\) values of asymmetries were similar for ISO, HG, and TF (\(p\) > 0.05; Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Year-1**} & \multicolumn{3}{c}{**Year-2**} \\ \cline{2-7}
**Swimming** & **Pre-1** & **Post-1** & **\%\(\Delta\)** & **Pre-2** & **Post-2** & **\%\(\Delta\)** \\ \cline{2-7}
**Tests** & \multicolumn{3}{c}{**SR (cycles min\({}^{-1}\))**} \\ \hline
50 m & \(51.8\pm 3.9\) & \(52.0\pm 5.2\) & \(0.3\pm 10.7\) & \(47.9\pm 3.5\) \(\dagger\) & \(50.2\pm 4.1\) \(\dagger\) & \(4.2\pm 7.5\) \\
200 m & \(43.9\pm 2.7\) & \(44.9\pm 4.2\) & \(1.8\pm 5.4\) & \(42.8\pm 3.1\) & \(43.3\pm 5.1\) & \(0.6\pm 0.3\) \\
400 m & \(44.6\pm 2.7\) & \(43.2\pm 2.3\) & \(1.2\pm 6.8\) & \(41.3\pm 3.2\) & \(41.8\pm 3.2\) & \(0.8\pm 7.4\) \\ \hline \multicolumn{7}{c}{SL (m cycle\({}^{-1}\))} \\ \hline
50 m & \(1.84\pm 0.16\) & \(1.86\pm 0.26\) & \(0.5\pm 8.1\) & \(1.98\pm 0.16\) \(\dagger\) & \(1.97\pm 0.19\) \(\dagger\) & -0.7 \(\pm\) 6.1 \\
200 m & \(1.75\pm 0.21\) & \(1.84\pm 0.26\) \(\dagger\) & \(4.9\pm 4.8\) & \(1.89\pm 0.22\) \(\dagger\) & \(1.94\pm 0.23\) \(\dagger\) & \(2.2\pm 2.9\) \\
400 m & \(1.75\pm 0.19\) & \(1.78\pm 0.25\) & \(1.1\pm 6.8\) & \(1.86\pm 0.22\) \(\dagger\) & \(1.89\pm 0.19\) \(\dagger\) & \(1.5\pm 6.6\) \\ \hline \multicolumn{7}{c}{SI (m\({}^{2}\)s\({}^{-1}\)cycle\({}^{-1}\))} \\ \hline
50 m & \(2.91\pm 0.44\) & \(2.98\pm 0.56\) & \(1.7\pm 5.8\) & \(3.14\pm 0.43\) \(\dagger\) & \(3.25\pm 0.46\) \(\dagger\) & \(3.2\pm 8.2\) \\
200 m & \(2.24\pm 0.47\) & \(2.53\pm 0.53\) \(\dagger\) & \(11.2\pm 8.1\) & \(2.56\pm 0.48\) \(\dagger\) & \(2.69\pm 0.45\) \(\dagger\) & \(5.1\pm 5.6\) \\
400 m & \(2.19\pm 0.39\) & \(2.30\pm 0.52\) \(\dagger\) & \(3.5\pm 8.5\) & \(2.39\pm 0.43\) \(\dagger\) & \(2.48\pm 0.38\)\(\dagger\) & \(4.2\pm 6.3\) \\ \hline \hline \end{tabular} \({}^{*}\)\(p\) = 0.05, Pre-1 vs. Post-1 and Pre-2 vs. Post-2; \(\dagger\
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 3 Domain Adaptation Tasks for EO Mission
### Pre-Deployment Model Training  
Prior to deployment and launch, \(f(\,\cdot\,;\theta)\) is trained on a labelled dataset \(\mathcal{D}^{s}=\{x_{i}^{s},y_{i}^{s}\}_{i=1}^{N}\), where each \(x_{i}^{s}\) is a preprocessed data cube and \(y_{i}^{s}\) is the corresponding ground-truth label.  
\(\mathcal{D}^{s}\) is called the **source dataset** as it is collected from a relevant **source domain**, e.g., from a previous EO mission. The details on building \(\mathcal{D}^{s}\) and training \(f(\cdot\,;\,\theta)\) are provided in Section 4 and Section 5, respectively. Note that the training is conducted on the ground, e.g., on a graphics processing unit (GPU) workstation. Once trained, we obtain a **source cloud detector**\(f(\cdot\,;\,\theta^{s})\) parameterised by **source weights**\(\theta^{s}\), which is then deployed onto the satellite and launched into orbit. We distinguish between two copies of the model: \(f^{d}(\cdot\,;\,\theta^{s})\) and \(f^{s}(\cdot\,;\,\theta^{s})\), i.e., the deployed and ground versions. Both versions are identical at the time of deployment. The steps from pre-deployment model training to launch are depicted in Figure 2.
## 1 Introduction
The inference mechanisms used are the Mamdani and Sugeno methods, with the Mamdani method being used more frequently due to its simplicity [34]. The chosen inference engine performs fuzzy inference based on fuzzy rules, while a defuzzification method is used to transform the fuzzy output. Considering the importance of this problem, numerous researchers deal precisely with the problem of machine selection. The process of machine selection is influenced by many factors, such as machining time, productivity, surface quality, accuracy and other technological features. Therefore, the process is specific to an individual company and cannot be generalized. The machine selection problem must be carefully evaluated using MCDM technique because of the many factors involved for a company in selecting a machine. Accordingly, the basic objective of the research in this paper refers to the technological analysis of the three machining processes, PAM, LBM and AWJM, used to cut various thicknesses steel sheets. Among the various technological aspects or technological criteria, the machine type, cutting speed, and thickness of the workpiece are the input factors. On the other hand, machining time, dimensional accuracy, kerf width, and surface roughness are used as output parameters. In order to create an intelligent decision-making system, a GFA is used to select cutting process from the aspect of the previously mentioned technological parameters. Reviewing the literature in the field of non-conventional machining methods, it was found that few studies are based on the analysis of machining performance as in this study. The main contribution to this research is reflected in the application of the intelligent decision system. In addition, the method for developing a fuzzy model, i.e., a rule base with more than three input variables, is presented. The advantage of the mirror method is the easy integration into any decision-making system and the updating of data without creating a new model. Based on the obtained results, the proposed methodology can be successfully applied to other machining methods.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 4 Establishment and Calibration of Numerical Model  
### Establishment of Numerical Model  
As shown in Figure 6, a FLAC\({}^{\text{3D}}\) numerical model was established based on the geological conditions of the CT301 panel at Chahasu Coal Mine. The model dimensions are 160 m \(\times\) 200 m \(\times\) 63.4 m. Displacement constraints were applied to the bottom and surrounding boundaries of the model, and a uniformly distributed load of 10 MPa was applied to the upper boundary.  
The Mohr-Coulomb model is based on the shear failure criterion of materials and uses parameters such as the internal friction angle and cohesion of the rock to effectively represent the deformation and failure characteristics of rocks under stress. This model aligns well with the mechanical properties of surrounding rock in practical engineering applications. Therefore, the Mohr-Coulomb model is applied to the surrounding rock of the mined-out area, with the mechanical parameters of the coal and rock mass shown in Table 1.  
After the weak rock reaches the peak stress, it experiences a rapid decrease in strength as deformation increases, which is referred to as \"strain softening\" [45; 46]. The stress-strain curves of the paste backfill material under different curing ages exhibits the same characteristics: elastic deformation before reaching the stress peak, followed by a rapid stress drop, demonstrating a clear strain softening phenomenon [47; 48]. In the FLAC\({}^{\text{3D}}\) numerical simulation, the Strain-Softening model was used to simulate the mechanical properties of the paste backfill material [49; 50].  
Figure 5: Age strength of filling materials at different ratios: (**a**) mass concentration; (**b**) fly ash proportion; (**c**) cementitious material proportion.  
After achieving initial equilibrium, the excavation of the CT301 headgate and tailgate was carried out. The roadway cross-section is 6 m \(\times\) 3.8 m, and excavation was performed step by step along the strike direction, with each step being 5 m. During coal seam extraction, simultaneous filling was applied to both gob and roadways.