esa-sceva/satcom-chunk-collection huggingface dataset:
# Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide
###### Abstract  
In conventional photonic crystal systems, extrinsic scattering resulting from random manufacturing defects or environmental changes is a major source of loss that causes performance degradation, and the backscattering loss is amplified as the group velocity slows down. In order to overcome the limitations in slow light systems, we propose a backscattering-immune slow light waveguide design. The waveguide is based on an interface between a square lattice of magneto-optical photonic crystal with precisely tailored rod radii of the first two rows and a titled 45 degrees square lattice of Alumina photonic crystal with an aligned band gap. High group indices of 77, 68, 64, and 60 with the normalized frequency bandwidths of 0.444%, 0.481%, 0.485%, and 0.491% are obtained, respectively. The corresponding normalized delay-bandwidth products remain around 0.32 for all cases, which are higher than previously reported works based on rod radius adjustment. The robustness for the edge modes against different types of interfacial defects is observed for the lack of backward propagation modes at the same frequencies as the unidirectional edge modes. Furthermore, the transmission direction can be controlled by the sign of the externally applied magnetic field normal to the plane.  
delay-bandwidth products; group velocity dispersion; flat band; slow light; interfacial defects; backscattering-immune +
Footnote †: journal: Applied Physics Letters
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 4 Conclusions  
Aiming at the SOC estimation problem of electric vehicle power lithium-ion batteries, this paper proposes an SOC estimation method based on the HIF algorithm. Firstly, based on the EIS of the lithium-ion battery, a fractional-order model based on the dual polarization equivalent circuit model was established. Without reducing the accuracy of the model, it not only simplifies the model structure, but also reduces the amount of calculation. Then, the parameters of the fractional-order battery model are identified by the HPSO algorithm based on the genetic crossover factor. It solves the problem that the classic PSO algorithm easily falls into the local best, improves the global search ability of the parameter identification algorithm, and further improves the fitting degree of the battery model. Finally, the accuracy of the SOC estimation results of lithium-ion batteries using HIF and EKF are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within \(\pm\)0.02 in any case, and is slightly affected by environmental factors and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system.  
Figure 16: Comparison of SOC estimation results and errors under HPPC conditions.  
Figure 15: Current-voltage diagram under HPPC conditions.  
**Author Contributions:** Conceptualization: [PERSON]; methodology: [PERSON]; validation: [PERSON]; formal analysis: [PERSON] and [PERSON]; investigation: [PERSON], [PERSON], and [PERSON]; writing--original draft: [PERSON]; writing--review and editing: [PERSON] and C.F.; supervision: [PERSON] and C.F.; project administration: [PERSON] and [PERSON]; funding acquisition: [PERSON] and C.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China [No. 2018 YFB0106102], and the Major Program of Chongqing Municipality [No. cstc2018 jszx-cyztzxX0007].  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### X-Ray Diffraction (XRD)  
X-ray diffraction (XRD) analysis was performed to determine the crystalline structure of the thermally treated samples. Figure 11a,b display the XRD patterns of titanate nanopowders after heating at 900 \({}^{\circ}\)C for 2 h. As shown in Figure 11a, a single phase of La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) was obtained, according to PDF #00-070-0903.  
La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) is a layered perovskite [9; 15; 27; 34; 39] with a monoclinic structure and space group P\({}_{2}\)(4) [14; 15; 16; 64; 16].  
Table 5 summarizes the lattice parameters and crystallite sizes. The lattice constants of the sample LTA closely matched those in the PDF card #00-070-0903.  
\begin{table}
\begin{tabular}{c c c|c c c} \hline  & **LTA** & & & **LTA-Pt** & \\ \hline Element & Weight (\%) & Atomic (\%) & Element & Weight (\%) & Atomic (\%) \\ OK & 13.24 & 46.65 & OK & 14.29 & 50.24 \\ TiK & 23.51 & 27.68 & TiK & 19.80 & 23.27 \\ LaL & 63.24 & 25.67 & LaL & 64.07 & 25.96 \\  & & & PtL & 1.84 & 0.53 \\ \hline \end{tabular}
\end{table}
Table 4: Elemental composition in the weight and atomic percentage of the samples.  
Figure 8: TEM images of the nanopowders: (**a**) LTA and (**b**) LTA-Pt.  
Figure 11: XRD patterns of the powders thermally treated at 900 \({}^{\circ}\)C for 2 h: (**a**) LTA and (**b**) LTA-Pt.  
Figure 10: Particle size distribution histograms for (**a**) LTA sample and (**b**) LTA-Pt sample.  
Figure 9: Platinum particles across the surface of the particle agglomerates in sample LTA-Pt.  
According to Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) data, the LTA sample contains a titanium oxide-based phase. However, no secondary phases were detected in the X-ray diffraction (XRD) pattern of the LTA sample. This absence may be attributed to the secondary phase being present in an amorphous state, to the possible overlap of its reflections with those of the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) phase, or to its concentration being below the detection limit of the instrument.  
The crystalline structure of the LTA-Pt sample was also analyzed. All the reflections in the XRD diffractograms of the doped sample became sharper and higher after the impregnation with PtCl\({}_{4}\) aqueous solution for 24 h and the subsequent drying at 80 \({}^{\circ}\)C for 5 h. The modified sample (LTA-Pt) also contained single-phase La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\). No platinum-based compounds were detected. X-ray photoelectron spectroscopy (XPS) data indicated that platinum was present in the sample either in its metallic form or as an oxide. If platinum compounds were to crystallize, their diffraction line would completely overlap with those of the La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\) phase in the X-ray diffraction (XRD) pattern. The crystallite size slightly increased to 15 nm after platinum impregnation.  
The unit cell parameters of the LTA-Pt sample (listed in Table 5) are larger than those of the LTA sample. This increase can be attributed to the incorporation of platinum, which likely partially entered the interlayer spaces between the perovskite slabs.
## 3 Robot Functionalities: People Perception, Human Following and Pick and Placing
### Pick and Placing
#### 3.4.1 Target Object Localization  
Although traditional visual-servoing implementations use artificial markers on the target object, recent advances in image recognition allow for regular objects to be detected without markers. We use a YOLO v3 [44] Convolutional Neural Network, trained on the COCO dataset containing images of 80 object classes. The network outputs a 2D bounding box around detected objects in the camera frame (as shown in Figure 4a) at a high refresh rate (\(>\)10 Hz).  
To estimate the 3D pose of an object, the central portion of the bounding box is sampled, and its depth values are obtained. The 25 th percentile depth value is selected as representative of the object depth \(d\). This percentile was chosen to remove potential outliers. A ray is then projected from the camera lens and intersects the center of the YOLO bounding box. This ray is obtained through a pinhole camera model and is represented by unit vector \(\mathbf{r}\). The 3D object pose \(\mathbf{\hat{\xi}^{c}}\) (Figure 4b) is obtained by multiplying this direction vector by the depth value: \(\mathbf{\hat{\xi}^{c}}=d\cdot\mathbf{r}\).
## 2 Materials and Methods
### Bivariate Statistical Analysis  
Landslide susceptibility modelling was conducted using the bivariate statistical approach that relies on the correlation between landslide population and the landslide conditioning factors. Each conditioning factor is subdivided into several classes, and a weight is calculated for each class from its statistical spatial relationship with landslide distribution [5; 12; 51; 52].  
The method proposed by [53] involves calculation of the natural logarithm of landslide density of each class within each factor divided by the overall landslide density in the area expressed as the Equation (4).  
\[W_{i}=\ \ln\left(\frac{\textit{Densclass}}{\textit{Densmap}}\right)=\ln\left( \frac{\frac{\textit{Npix}(S_{i})}{\frac{\textit{Npix}(N_{i})}{\sum\textit{Npix }(S_{i})}}}{\frac{\sum\textit{Npix}(N_{i})}{\sum\textit{Npix}(N_{i})}}\right) \tag{4}\]  
where, \(W_{i}\) = the weight given to a certain factor class. _Densclass_ = the landslide density within the factor class. _Densmap_ = the landslide density within the entire map.  
Figure 3: Map of the landslide conditioning factors on distance to lineaments (**a**), distance to roads (**b**), topographic position index (**c**), and surface roughness (**d**).  
\(Npix(S_{i})=\) the number of pixels with landslide occurrence in a certain factor class. \(Npix(N_{i})=\) total number of pixels in a certain factor class.  
The weight indicates the correlation of the class factor with the landslide occurrence and refers to landslide density of each factor class. Negative weights indicate that the landslide density is lower than average, while a positive value signifies that it is higher. A zero weight is obtained when there is an absence of landslide occurrences within a parameter class [53]. Since the analysis is based on statistical calculation of the pixels in a map, raster maps are required for the processing. Vector maps in the form of polygon shapefiles were converted into raster files for further processing.  
The raster factor maps were reclassified by assigning the weight to all the factor classes. The landslide susceptibility is a summation of the reclassified factor maps. The susceptibility classes can be defined based on the landslide density [54,55]. The classification was carried out manually based on the percentage of landslide points within each class. The classes are very high (>50%), high (20-50%), moderate (10-20%), low (2-10%) and very low (<2%) susceptibility, respectively.
## 1 Introduction
The results suggested that the addition of these hemp by-products in a proportion greater than 1% caused unfavorable changes in the quality of the bread [26]. Gluten-free bread, as a healthy alternative to wheat bread, may also include hemp flour and hemp protein concentrate, which are natural nutritional and structure-forming gluten-free bread ingredients. Hemp flour was used to make sourdough, and it was tested as an addition to gluten-free bread. The bread's staining was reduced by using hemp sourdough. Hemp flour has improved the acceptability of gluten-free corn/rice bread [27]. In a past study, gluten-free bread with 5% hemp flour did not differ significantly from the control bread and was accepted by consumers [28]. [PERSON] et al. [29] found that replacing some of the starch with hemp flour made bread dough weaker and more likely to deform, while a 20% share of hemp protein concentrate made bread dough stronger. The level of fiber increased from 15.2 to 61.0 g\(\cdot\)kg\({}^{-1}\), and the level of dietary fiber increased from 29.3 to 90.0 g\(\cdot\)kg\({}^{-1}\). Hemp protein supplementation improved the color of the crumb, reducing its brightness from 62.3 to 40.8 and increasing the bread volume from 633 to 878 mL. Hemp flour and hemp protein concentrate are the most commonly used ingredients in gluten-free bread production. Although various hemp products have been added to gluten-free bread, hemp inflorescance has not been added to this type of bread. Due to the growing number of people suffering from the consumption of cereals containing gluten, there is a demand for gluten-free bread on the market. Most often, rice and corn flour are used for their production. However, consumers perceive gluten-free bread to be less appealing than traditional wheat bread due to its unsatisfactory texture and taste. Gluten-free bread also has a lower nutritional value. Various additives are used to improve the quality of gluten-free bread and they affect the physical properties, taste, and content of bioactive substances [30; 31; 32; 33; 34; 35]. As previously stated, hemp inflorescance is a valuable raw material in terms of health and it has not previously been used in bread production, which is why it was proposed for inclusion in gluten-free bread. The scope of the work included determining the changes in the physical and sensory properties as well as the antioxidant potential of gluten-free bread with added hemp infloresence. Furthermore, we aim to recommend the most nutritionally valuable and acceptable recipe for customers.
# Utilization of Waste Clay-Diatomite in the Production of Durable Mullite-Based Insulating Materials
## 1 Introduction
This characteristic microstructure prevents deformation at high temperatures (shape retention) and ensures excellent long-term dimensional stability at temperatures above 1500 \({}^{\circ}\)C. However, a potential drawback arises when excessive amounts of the glassy phase accumulate at triple points, which can negatively affect mechanical performance. Engineering the microstructure, for example, by introducing closed spherical pores, can reduce thermal conductivity while improving mechanical performance and high compressive strength due to controlled porosity [19]. In addition, natural and waste-derived materials often contain alkali, alkaline earth and transition metal oxides (such as FeO, Fe\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), MnO, MgO, CaO, Na\({}_{2}\)O, K\({}_{2}\)O and P\({}_{2}\)O\({}_{5}\)), as well as rare earth elements, all of which significantly influence the properties of the final product. These components promote anisotropic grain growth and thus increase the strength and toughness of the resulting interlocked microstructure. This microstructure typically consists of intertwined orthorhombic acicular mullite grains, needles or whiskers with an aspect ratio of over 30 [14; 19; 20]. Furthermore, the high content of glassy phases in naturally occurring hydroxy-aluminosilicates (kaolinite, halloysite, pyrophyllite, sercite), alumosilicates (kyanite, sillimanite), mineral sources (albite, mining waste, kaolinite) and processing conditions (e.g., precipitation of aluminum oxide on kaolinite) is a significant factor; precipitation of aluminum oxide on the surface of waste sand particles) and the temperature of mullite formation (mullitization) could be reduced to the range of 1200-1400 \({}^{\circ}\)C [21; 22]. This study aims to determine the suitability of waste clay-diatomic powder from mine tailings as a precursor, coupled with the Al(NO\({}_{3}\))\({}_{3}\cdot\)9H\({}_{2}\)O, in order to obtain durable mullite-based ceramics that could be applied as thermal insulators and/or refractory materials. The qualitative phase identification, microstructure, pore size distribution, porosity percentage content, bulk density, relative density, open porosity, linear shrinkage, and compressive strength of the resulting mullite ceramics were investigated. Following the simple preparation of precursor powders and earlier investigations [23] where mullite compacts were pressed at 2 MPa and sintered at 1300, 1400, and 1500 \({}^{\circ}\)C for 2 h [24], further research has been undertaken. In the present study, the prepared powder was pressed into green compacts at 50 MPa and sintered at three different temperatures (1300, 1400 and 1500 \({}^{\circ}\)C) for 2 h.
## 3 Experiments and Results  
### Computing Environment Configuration  
The experiments in this paper were conducted based on the deep learning framework TensorFlow using the word segmentation tool jieba and the feature vector training tool GOOGLE Gensim Word2 Vec 3.8.3. The Sac-BiLSTM algorithm was built using Python language. The experimental environment and settings are shown in Table 2.
## 3 Materials and Methods
### Data Forwarding Method
#### 3.3.2 \(Q\)-Learning Model  
\(Q\)-Learning is a model-free reinforcement learning technique, based on agents taking actions and receiving rewards from the environment in response to actions [11]. Each action is evaluated a \(Q\)-value due to its fitness. In the learning process, the agent calculates the reward of each potential action and updates the \(Q\)-value by which the real action can be determined. \(Q\)-Learning has been widely adopted in wireless ad hoc communications. The main challenge is the modeling of the \(Q\)-Learning process and the definition of \(Q\)-values.  
Given the set \(X=\{x_{1},x_{2},...,x_{t},...,x_{T}\}\) of states of an agent, a reward \(r_{t}(a_{t})\) is received in state \(x_{t}\) after the agent takes action \(a_{t}\in A\) at time slot \(t\).  
To evaluate how good an action is at a state, the \(Q\)-value of action \(a_{t}\) at time slot \(t\), \(Q(x_{t},a_{t})\), is updated as follows:  
\[Q(x_{t},a_{t})=r_{t}(a_{t})+\gamma\sum_{x_{t+1}\in X}P^{a_{t}}_{x_{t}\to x_{t+ 1}}Q(x_{t+1},a_{t+1}) \tag{10}\]  
where \(r_{t}(a_{t})\) is the reward of taking action \(a_{t}\) at time slot \(t\), \(Q(x_{t+1},a_{t+1})\) is the expected fitness at time slot \((t+1)\), \(\gamma\) is the learning discount factor and \(P^{a}_{x_{t}\to x_{t+1}}\) represents the transition probability from state \(x_{t}\) to \(x_{t+1}\).  
In order to determine the optimal action, the action with the highest \(Q\)-value from state \(x_{t}\) to \(x_{t+1}\) at time slot \(t\) can be acquired as follows:  
\[a_{t}^{*}=\arg\max_{a_{t}\in A}Q(x_{t},a_{t}) \tag{11}\]  
For each state \(x_{t}\in X\), the optimal action \(a_{t}^{*}\) can be greedily acquired by updating the \(Q\)-value.
## 3 Results and Discussion
### Color Correlation Analysis  
Figure 2 shows a color correlation diagram between seven parameters of independent variables and the responses of microorganisms' viability in molasses. The values of the correlation coefficients between the two tested parameters are visually presented by color (blue for positive and red for negative correlation) and the size of the circles.  
The results of the correlation analysis show a high level of positive correlation between all the responses of the microorganisms' viability in molasses (number of: _E. coli_, _L. monocytogenes, Salmonella_ spp., Enterobacteriaceaeae, and TPC), while there is a high level of negative correlation between the independent variable of exposure time and all the tested microorganisms' viability responses. The effect of the osmolality level is shown to have a lower negative correlation on all the microorganisms' viability responses, with the highest negative correlation on the _Salmonella_ spp.'s viability response.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & _E. coli_ & _L. monocytogenes_ & _Salmonella_ & **Enterobacteriaceae** & **TPC** \\ \hline \(\beta_{0}\) & \(8.103405\) * & \(-1.90308\) & \(5.372118\) * & \(5.768105\) * & \(5.468519\) * \\ \(\beta_{1}\) & \(-0.450115\) * & \(0.52251\) * & \(-0.143098\) * & \(-0.276613\) * & \(-0.351648\) * \\ \(\beta_{11}\) & \(0.063844\) * & \(-0.00506\) & \(0.027177\) * & \(0.040088\) * & \(0.045015\) * \\ \(\beta_{2}\) & \(-0.000890\) & \(0.00219\) & \(-0.000066\) & \(-0.000072\) & \(0.000079\) \\ \(\beta_{22}\) & \(0.000000\) & \(-0.00000\) & \(0.00000\) & \(0.00000\) & \(-0.00000\) \\ \(\beta_{12}\) & \(-0.000037\) & \(-0.00017\) * & \(-0.000018\) * & \(-0.000017\) * & \(-0.000013\) * \\ \hline \end{tabular}
* Statistically significant at level of \(p<0.05\).  
\end{table}
Table 3: Second-order polynomial regression coefficients for five selected microorganisms in molasses.
# Lightweight Deepfake Detection Based on Multi-Feature Fusion
## 3 Proposed Fusion Model
### HOG Features  
The HOG is a feature descriptor widely used for texture analysis and object detection [59]. This method is particularly suitable for tasks requiring robust edge and gradient-based analysis, such as detecting structural inconsistencies in deepfake images. HOG divides an image into smaller spatial regions, known as cells, and computes a histogram of gradient directions within each cell. This process can be summarized into three steps: gradient calculation, cell histogram generation, and feature vector construction:  
* Gradient Calculation: For each pixel in the image, the gradients along the \(x\)- and \(y\)-axes are calculated using Sobel filters: \[G_{x}=I(x+1,y)-I(x-1,y),\quad G_{y}=I(x,y+1)-I(x,y-1)\] The magnitude \(M\) and direction \(\theta\) of the gradient are computed as: \[M=\sqrt{G_{x}^{2}+G_{y}^{2}},\quad\theta=\arctan\left(\frac{G_{y}}{G_{x}}\right)\]
* Cell Histogram Generation: The gradient magnitudes \(M\) are binned into orientation histograms, where the direction \(\theta\) is quantized into a fixed number of bins (e.g., 9 bins for 0\({}^{\circ}\)-180\({}^{\circ}\) or 18 bins for 0\({}^{\circ}\)-360\({}^{\circ}\)). To improve invariance to illumination and contrast changes, the histograms are normalized within overlapping spatial blocks. Given a block \(B\), normalization can be performed as: \[\text{HOG}_{\text{norm}}(B)=\frac{\text{HOG}(B)}{\sqrt{\|\text{HOG}(B)\|^{2}+ \epsilon}}\] where \(\epsilon\) is a small constant to prevent division by zero.
* Feature Vector Construction: The normalized histograms obtained from all the blocks are concatenated to form a single feature vector representing the image. HOG captures fine-grained details about edge orientations and their distribution, making it suitable for identifying subtle spatial distortions caused by deepfake manipulations. In this study, the following HOG parameters were used:
* Cell Size: \(8\times 8\) pixels;
* Block Size: \(2\times 2\) cells;
* Number of Orientation Bins: 9 (0\({}^{\circ}\)-180\({}^{\circ}\));
* Step Size: 50% overlap between blocks.  
HOG is an effective choice for resource-constrained deepfake detection due to these parameters, which maintain a balance between descriptive strength and processing efficiency. HOG can be used in combination with other robust feature descriptors, such as KAZE, to improve its sensitivity to high-level semantic adjustments to improve deepfake detection.
# Accuracy of Code GNSS Receivers under Various Conditions
## 5 Conclusions
For smartphones, the maximum deviations in the N component ranged from 15 to 19 m, while for the E component, it was from 5 to 13 m. The error of cell phones during the linear measurement ranged from 8 to 11 m for the N component, and for the E component, it was about 2 m. On the other hand, during the surface measurement, the error values of NE components were less than 6 m. The G_1 (GPS) and G_2 (GPS+GLONASS) receivers determined the coordinates with an accuracy of 3 m for the NE components. The difference in height determination with the handheld receiver operating in the GPS mode in comparison to GPS+GLONASS reached values of up to 40 m. However, when comparing only the heights obtained using the Garmin receivers, only measurements on the point (Figure 7) gave similar, comparable results. In the case of line (Figure 10) and surface (Figure 13) measurements, the accuracy of the receiver operating in GPS+GLONASS mode was significantly worse than the receiver operating in GPS mode. This is most likely due to imperfections in the computational algorithms for the dual-system receiver, which is underdeveloped in these receivers.