esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Computational Mesh  
For both airfoils used in this study, the computational meshes were generated using GMSH software 4.11.1 [52]. The mesh for the NACA0021 airfoil is an O-type mesh, featuring a combination of structured and unstructured elements. Figure 3 provides an overall view of the mesh details.  
The mesh for the NACA0021 airfoil was created using a combination of structured and unstructured elements to accurately capture the flow characteristics around the airfoil. To maintain solution accuracy while reducing computational cost, the far-field boundary was meshed with larger elements, resulting in a total of 14,824 elements. As in the study by [53], the domain boundaries were placed 20 chord lengths away from the airfoil to prevent any influence on the flow field.  
A structured grid was applied near the airfoil's surface, particularly in the boundary layer region, to achieve precise resolution of the flow, especially close to the wall. The first cell height was determined to meet the minimum y+ requirements for the low Reynolds number turbulence model used in this study. A total of 27 structured layers with linear progression were employed to fully capture the boundary layer thickness; the details are shown in Figure 4. Additionally, transfinite lines were utilized to control point distribution along critical areas, such as the leading and trailing edges, enabling finer mesh refinement in these regions. Regarding the boundary conditions, a far-field boundary condition was applied where the flow enters, a pressure-outlet condition was used at the downstream boundary, and a no-slip boundary condition was imposed on the airfoil surface.  
For the SD7003 airfoil, a C-type mesh was employed. The overall configuration of this mesh is illustrated in Figure 5. The total number of elements is 149,120.  
Figure 4: Detailed view of the boundary layer mesh around the NACA0021 airfoil.  
Figure 3: NACA0021 computational mesh.  
The mesh is entirely structured, with enhanced refinement near the wall to accurately capture boundary layer characteristics. The size of the first cell adjacent to the wall was determined based on the y+ value calculated using the Reynolds number for this simulation. Details of the mesh near the airfoil boundary are shown in Figure 6.  
The boundary conditions used in this test are the same as the ones used for the NACA0021.
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 4 Method
### Parameter Settings  
ERO extends the selection strategy of the vanilla DDPG algorithm with a novel replay policy using a single replay memory. This study extends the ERO algorithm with an alternating transition selection control mechanism and a dual memory. Nonetheless, the configurations and hyperparameters conform to ERO and the OpenAI DDPG stable baseline [34]. To improve our findings, experiments are conducted. Two threshold values are adopted and applied to perform ablative and additive experiments: the sampling ratio for the ATSC of both frameworks and the storage threshold for only the TSC in the second framework.  
After performing preliminary experiments, TSC threshold values between \(-5\) and \(-1\) are identified and adopted. These values are negative because of the initial downward random position of the pendulum. The ATSC ratios are set to 90:10, 50:50, and 10:90 percentages stipulated in the DER experiment. The frameworks' learning stability is evaluated on multiple buffer sizes of \(1\times 10^{6}\) (1,000,000), \(1\times 10^{5}\) (100,000), \(1\times 10^{4}\) (10,000), and \(1\times 10^{3}\) (1000). The FIFO and the enhanced FIFO retention strategies are equally assessed. In all, the experiments are conducted on an Intel(R) Xeon(R) CPU E3-1220 v6 @ 3.00 GHz (4 CPUs) with 32 GB RAM and Windows Server 2012R2 operating system. The hyperparameters used are outlined in Table 3.
## 7 Results and Analysis
### Comprehensive Security Evaluation  
To holistically assess our framework's security posture, we introduce a Composite Security Score (CSS) that integrates six key metrics derived from O-RAN's unique threat landscape:  
* **Attack Success Rate (ASR)**: Percentage of successful adversarial attempts.
* **Cross-Layer Resilience (CLR)**: Robustness against multi-vector attacks (0-100 scale).
* **Anomaly Detection Performance (ADP)**: F1-score of threat detection.
* **Privacy Leakage Rate (PLR)**: Success rate of membership inference attacks.
* **Recovery Time (RT)**: Milliseconds to mitigate detected threats.
* **Model Accuracy (MLA)**: Accuracy under adversarial conditions.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 3 IEKF Algorithm Based on LM
### Formulation of the State Space Equation of the System  
According to the dynamic equations of the second-order Thevenin model expressed in Equations (2) and (3), the working current \(I_{L}\) is regarded as the input quantity of the Kalman  
Figure 4: Process of the LM-IEKF algorithm.  
filtering process, and the terminal voltage \(U_{t}\) is regarded as the observation quantity. The state equation and output equation of the system can be obtained as follows.  
\[x_{k}=\begin{bmatrix}0&0&0\\ 0&-\frac{1}{R_{1}C_{1}}&0\\ 0&0&-\frac{1}{R_{2}C_{2}}\\ \end{bmatrix}\begin{bmatrix}SoC\\ U_{t1}\\ U_{t2}\end{bmatrix}+\begin{bmatrix}-\frac{1}{Q_{n}}\\ \frac{1}{C_{1}}\\ \frac{1}{C_{2}}\end{bmatrix}I_{L} \tag{26}\]  
\[z_{k}=U_{t}=\begin{bmatrix}1&-1&-1\end{bmatrix}\begin{bmatrix}\begin{bmatrix} \text{OCV}(SoC)\\ U_{t1}\\ U_{t2}\end{bmatrix}-R_{0}I_{L} \tag{27}\]  
As the result to be estimated in the state matrix, the relationship between SoC and OCV is not linear, so the state space equation needs to be approximately linearized according to the description in Section 2.2. Combined with Equation (1), the discrete state equation after processing can be obtained as follows.  
\[\begin{bmatrix}SoC_{k+1}\\ U_{t1_{k+1}}\\ U_{t2_{k+1}}\end{bmatrix}=A\begin{bmatrix}SoC_{k}\\ U_{t1_{k}}\\ U_{t2_{k}}\end{bmatrix}+BI_{L_{k}}+w_{k} \tag{28}\]  
The sampling time \(T_{s}\) is set to 1 s, the time constant \(\tau_{1}=R_{1}C_{1}\), \(\tau_{2}=R_{2}C_{2}\) is defined, and the state matrix after discretization can be expressed as follows.  
\[A=\begin{bmatrix}1&0&0\\ 0&\text{e}^{-T_{s}/\tau_{1}}&0\\ 0&0&\text{e}^{-T_{s}/\tau_{2}}\end{bmatrix} \tag{29}\]  
The control matrix of the system can be expressed as follows.  
\[B=\begin{bmatrix}-\frac{\eta T_{s}}{Q_{n}}&R_{1}\Big{(}1-\text{e}^{-T_{s}/\tau _{1}}\Big{)}&R_{2}\Big{(}1-\text{e}^{-T_{s}/\tau_{2}}\Big{)}\end{bmatrix}^{ \text{T}} \tag{30}\]  
By bringing the equivalent circuit parameter values identified experimentally by HPPC into the above matrix, the state-space equation can be calculated.  
Similarly, the discretized observation equation can be expressed as follows.  
\[U_{t_{k+1}}=H\begin{bmatrix}SoC_{k+1}\\ U_{t1_{k+1}}\\ U_{t2_{k+1}}\end{bmatrix}-R_{0}I_{L_{k}}+v_{k} \tag{31}\]  
The observation matrix \(H\) can be expressed as follows.  
\[H=\begin{bmatrix}\begin{array}{cc}\frac{\partial(\text{OCV}_{k+1})}{ \partial(\text{SOC})}&-1&-1\end{array}\end{bmatrix} \tag{32}\]  
Combined with the discrete system state space equation obtained from the battery equivalent model, it is brought into the iterated process of the LM-IKEF algorithm, and the SoC of the battery can be estimated.
# An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data
## 2 Related Work
### Safety Incident Prediction  
The research of flight safety incident prediction mainly aims to establish forecasting models which can be utilized as a warning before a safety incident occurs. For a hard landing, [PERSON] et al. [17] and [PERSON] et al. [8] took advantage of a BP neural network and an SVM to predict the incident, respectively. Because both of them are relatively early works, their prediction accuracy is unsatisfactory. Then, [PERSON] et al. [18] tried to use the RBF neural network and K-means clustering algorithm to predict a hard landing. With the rise of deep learning and in order to capture time-series features, [PERSON] et al. [9] proposed a model based on the Long Short-Term Memory (LSTM) network to predict a hard landing. The same model was also used to address the landing speed prediction problem [19] and the tail strike risk prediction problem [20]. [PERSON] et al. [21] further proposed a deep sequence-to-sequence model based on LSTM and an attention mechanism to improve the landing speed prediction accuracy. These deep learning-based methods not only take advantage of the information at the feature level but also capture the temporal information from these time-series flight parameters. Hence, the LSTM-based methods have achieved a good prediction performance. Similarly, for the long landing incident, [PERSON] et al. [22] investigated the correlation between different QAR parameters and a long landing through the analysis of variance method and utilized the logical regression and linear regression models for the long landing risk prediction. Recently, [PERSON] et al. [23] utilized a deep sequence-to-sequencemodel for long landing prediction, which further improved the prediction accuracy by incorporating an attention mechanism. Predicting aviation safety incidents can enable proactive warnings before safety incidents occur, but these methods cannot help uncover the reasons for safety incidents.
## 2 Multi-State Lane-Changing Decision-Making System
### Design of Fuzzy Control System  
The process of the method is illustrated in Figure 1, which depicts the framework of the lane-changing decision-making system. In this system, the lane-changing decision process is modeled as a function:  
\[E_{steer}=f(\mu,v_{subj},v_{sur},a_{subj},a_{sur},d) \tag{1}\]  
where \(E_{steer}\) quantifies the decision threshold, referred to as the lane-changing decision factor, \(\mu\) is the adhesion coefficient of the road surface, \(v_{subj}\) is the velocity of the self-vehicle, \(v_{sur}\) is the velocity of the surrounding vehicle, \(d\) is the distance between the self-vehicle and the surrounding vehicle, \(a_{subj}\) is the acceleration of the self-vehicle, and \(a_{sur}\) is the acceleration of the surrounding vehicle. When the value of \(E_{steer}\) exceeds 0.5, the vehicle will execute a lane change; otherwise, it will continue moving straight ahead.  
In the first layer of fuzzy reasoning, the collision risk factor \(E_{r}\) is obtained by analyzing \(v_{subj}\) and \(\mu\). \(E_{r}\) can be expressed as:  
\[E_{r}=FIS_{1}(v_{subj},\mu) \tag{2}\]  
where \(E_{r}\) is the risk factor. The inference surface of the first layer structure is shown in Figure 2a.  
In the second layer of fuzzy reasoning, the risk avoidance factor \(E_{ra}\) can be calculated by the velocity of the self-vehicle, the velocity of the surrounding vehicle, and the distance between the self-vehicle and the surrounding vehicle. Then, \(E_{ra}\) and \(E_{r}\) are input together into the second layer of the system to obtain the lane-changing decision factor \(E_{steer}\):  
\[E_{steer}=FIS_{2}(E_{r},E_{ra}) \tag{3}\]  
The inference surface of the second layer structure is shown in Figure 2b.
## 3 Biosynthesis and Function of Glandular Trichome-Produced Compounds
### Flavonoids  
Like the phenylropenes, flavonoids are derivatives from the phenylpropanol pathway. The first step in flavonoid biosynthesis comprises the condensation of one molecule of 4-coumaroyl-CoA and three molecules of malonyl-CoA, catalyzed by the enzyme chalcone synthase (CHS), followed by a cyclization reaction. In subsequent reactions, the flavone basic structure can be further modified by reductases, isomerases, hydroxylases, and glycosyltransferases, thereby forming the various subclasses of flavonoids, such as flavones, flavonoids, flavandiols, anthocyanins, proanthocyanidins and isoflavonoids [105]. Accumulation of flavonoids in trichomes may serve to protect plants from UV-B [45] and there is evidence for sunlight-induced secretion of flavonoid glycosides by glandular trichomes of _Phillyrea latifolia_ plants to protect them against damage induced by UV-A [106]. In_S. habrochaites_, it was shown that type I, IV and VI glandular trichomes contain methylated forms of the flavonoid myricetin [107]. In the cultivated tomato, it was subsequently shown that the _hairless_ (_hl_) mutation, which causes alterations in the morphology of all trichome types, also decreased accumulation of quercetin-trisaccharide, rutin, kaempfer-rhammoside and 3-\(O\)-methylmyricetin in type VI glandular trichomes [25, 108]. These and related phenolic compounds can inhibit growth of lepidopteran larvae [109]. Interestingly, trichomes from _hl_ leaves were also deficient in various sesquiterpenes, but contained wt levels of monoterpenes and acyl sugars [25]. As suggested by [PERSON] _et al._[25], perhaps _hl_ disrupts a cellular function required for the biosynthesis of sesquiterpenes and flavonoids, which are both synthesized in the cytosol.
# A Review on the Importance of Microbial Biopolymers Such as Xanthan Gum to Improve Soil Properties
## 4 Xanthan Gum Biopolymer
### Effects of Xanthan Gum on Soil Properties for Geotechnical Engineering
#### 4.3.4 Other Properties  
Relative to the density of the sandy soil, the results reveal an increase in density with the increment of the xanthan gum concentration [29]. The increasing density can be explained based on the nature of xanthan gum, which reduces the friction between the particles, thus inducing a better arrangement of the soil particles. This viscosity can also be expected to increase as the molecular weight of the biopolymer increases because a crystallization in its macromolecule chain is more likely. This consequently leads to an increase in the degree of crosslinking within the soil matrix.  
In some works, it was observed that the optimum water content (OWC) increased with higher gum concentrations for all samples and this is due to the increase in the amount of water absorbed with the increasing concentration of the biopolymer [21; 24; 26].  
Some experiments have shown that the use of xanthan gum also changes the liquid limit (LL) of the biostabilised soil, and this effect depends on of two opposite effects, the formation of a viscous hydrogel in the voids and the aggregation of clay particles. When the latter effect is dominant, an increase in the LL is obtained for a higher content of xanthan gum. This was obtained with a clayed silt-sand soil, montmorillonite clay [25], a soft marine soil [24] and a mine tailing [38]. For a kaolinite clay, the LL has a peak for a low content of xanthan gum (close to 0.5%), followed by its decrease. Thus, during the first stage, the formation of the hydrogel is more significant, while after the peak, aggregation is the key factor [25; 47].
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 4 Results, Analysis, and Discussion
### Discussion  
#### 4.2.1 Advantages of the TFPF-SU Method in Extracting Slope Units  
Another advantage of the TFPF-SU method is its ability to better reflect and preserve terrain characteristics. This method divides the surface into regions with similar topographic attributes based on slope, aspect, and curvature. This means that each slope unit is uniform in these geometric characteristics, reflecting natural terrain variations and avoiding artificial boundaries or anomalous shapes that may be introduced by hydrological methods. The region-growing algorithm recursively expands adjacent pixels that conform to terrain features until the similarity conditions are no longer met. This recursive process captures the continuous changes in local terrain and ensures that the intrinsic terrain characteristics of each slope unit are consistent, thereby better preserving the complexity of natural landforms. Therefore, this method has significant advantages in preserving natural terrain features, especially for areas with significant terrain variations, where it can better capture and reflect terrain complexity.
## 2 Materials and Methods
### Case Study Definition and Realization
Because the laser source, optical, and powder feeding configurations of the two machines are similar, the same set of process parameters (laser power, laser spot diameter, scan speed, powder feed rate, shielding gas flow rate, carrier gas flow rate, standoff distance, hatch spacing, and \(Z\) step) are used on both machines. Because of this, any differences in geometrical accuracy and deformation of deposited components can be attributed to the kinematic mechanisms of the two machines. Furthermore, a standard set of process parameters (reported in Table 2) was used, resulting in a productivity/deposition rate of roughly 0.3 kg/h but a low heat load on the component since a relatively low level of laser power (875 W) was delivered on a large laser spot diameter (2.4 mm). The same laser spot was set on the two machines by adjusting the distance between the collimation lens and the process fiber outlet in the deposition heads. The operation was conducted with the aid of a Beamage Series USB 3.0 beam profiling camera by Gentec (Quebec City, QC, Canada) to measure the laser spot diameter on the working plane. The scan speed was also limited to reduce the molten pool vibrations and instabilities. The initial standoff distance was set to 8 mm at the beginning of the process, but it should be noticed that it may naturally slightly vary during the deposition due to the component growing more or less than expected. The hatch spacing is the spiral pitch in the scanning approach employed. Single tracks were produced on both machines using  
Figure 7: The adopted scanning strategy for (**a**) even and (**b**) odd layers. the chosen set of process parameters as preliminary research to examine the concordance of the depositions. The tracks were cut transversally and polished to reveal their cross sections. The images were captured using an optical microscope, and the width, height, and cross-section area were calculated using ImageJ software [31].
###### Abstract  
Mosques, revered as sacred spaces, are distinguished by their sustainable architectural designs. A critical aspect of these designs is the implementation of daylighting strategies, essential for ensuring adequate light levels, particularly for reading the Holy Quran. Traditional daylighting typologies in mosques, such as multi-lateral lighting, clerestories, skylights, and shading devices, are widely recognized. However, the potential application of electrochromic (EC) switchable glazing within mosque architecture remains underexplored. This study delves into the effectiveness of various daylighting typologies, assessing their performance before and after the integration of EC switchable glazing in the Al-Shagroud Mosque located in Saudi Arabia. Employing comprehensive radiance simulations in accordance with the LEEDv4.1 standards, the study evaluates additional metrics including useful daylight illuminance (UDJ\({}_{300-3000\text{km}}\)), workplane illuminance (WPI), uniformity index (UI), and daylight glare probability (DGP) under multiple scenarios. The results demonstrate that clerestory lighting effectively meets LEEDv4.1 benchmarks but exhibits limitations in achieving uniform light distribution, particularly in the men's prayer hall. In contrast, the women's prayer hall benefits from a combination of clerestory lighting and shading devices, which improves light quality and mitigates glare issues. However, the use of skylights without adequate shading leads to excessive WPI and high DGP levels, compromising visual comfort. Moreover, managing daylight quantity and quality through the integration of different typologies, including hybrid models, presents substantial challenges. The integration of EC switchable glazing, while maintaining optimal WPI and reducing DGP, offers enhanced visual comfort in prayer areas. Nevertheless, this technology faces difficulties in ensuring even light distribution, as evidenced by lower uniformity index values. This research highlights the critical need for optimizing daylighting in mosques to balance sustainability and comfort, without detracting from their sacred ambiance.  
daylighting; clerestory; multi-lateral lighting; skylight; electrochromic; mosque +
Footnote †: journal: Applied Sciences
## 5 Case Study and Analysis
### Performance Analysis of Algorithm  
#### 5.3.1 Stability Analysis  
To test the stability of TSHM, we ran the instance 100 times with different initial solutions. Overall, the TSHM performed stably in this instance (see Figure 5). Figure 5 shows the relation between the cost of initial (\(X\)-axis) and final solutions (\(Y\)-axis). We found that the costs of most final solutions are clustered within an interval [4200, 4300], which validates the stability of final solutions obtained by TSHM.
# SNA: Sensitivity-Aware Neural Architecture Adaptation for Uniform Quantization
## 5 Conclusions  
In conclusion, we proposed a novel sensitivity-aware architecture adaptation method for uniform quantization. This technique automatically adapts neural architectures by splitting the channels of the convolution operations. Moreover, to quicken the fine-tuning process of the refined model, we formulated four initialization strategies, **Halving, Zero Padding, Averaging**, and **Small Int**, to reasonably initialize the newly expanded weights. The experiments on ResNet-50 and EfficientNet-B2 demonstrated that neural architecture adaptation can benefit both robust networks and sensitive networks, achieving better performance compared to uniform quantization without architecture adaptation. The sensitivity analysis further indicated that SANA is more suitable for redundant networks such as ResNet-50. For sensitive networks such as EfficientNet-B2, higher bitwidths and larger multipliers are needed. Overall, SANA effectively reduced the quantization  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Size (MB)** & **Multiplier** & **Precision** \\ \hline Expanded-ResNet50 & 78.460 & 195.6 & 1.414 & FP \\ \hline SANA & 77.984 & **24.4** & **1.414**-layerwise & W4A8 \\ SANA & 77.772 & **24.4** & **1.414** & W4A8 \\ \hline Mixed-Precision & 77.600 & 24.4 & 1.414 & MP4A8 \\ \hline Expanded-ResNet50 & 79.346 & 130.5 & 1.155 & FP \\ \hline SANA & 76.630 & **12.2** & **1.155** & W3A8 \\ \hline ResNet50 & 77.610 & 97.8 & 1 & FP \\ Naive-Q & 77.318 & 24.4 & 1 & W8A8 \\ Mixed-Precision & 76.534 & 12.2 & 1 & MP4A8 \\ Naive-Q & 76.210 & 12.2 & 1 & W4A8 \\ \hline Shrunk-ResNet50 & 74.710 & 48.9 & 0.707 & FP \\ Naive-Q & 74.860 & 12.2 & 0.707 & W8A8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model sizes and accuracies for different quantization strategies and baselines. Utilizing an expanded pretrained model with a smaller compression ratio yielded better results compared to utilizing a shrunken pretrained model with a larger compression ratio. For instance, when both methods were given the same model size of 12.2 MB, the expanded model combined with a low-bit approach achieved 76.630% accuracy, while the shrunken model paired with a high-bit approach only managed 74.710%.  
error and maintained high performance. Its automated features and uniform approach ensure that it is easy to deploy on various types of devices.  
Methodology, Z.D.; experiments, M.G. and Z.D.; writing (original draft preparation), Z.D. and M.G.; writing (review and editing), Z.D. and K.K.; investigation, M.G. and Z.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.