esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In response to problems concerning the low autonomous localization accuracy of mobile robots in unknown environments and large cumulative errors due to long time running, a spatial location representation method incorporating boundary information (SLRB) is proposed, inspired by the mammalian spatial cognitive mechanism. In modeling the firing characteristics of boundary cells to environmental boundary information, we construct vector relationships between the mobile robot and environmental boundaries with direction-aware information and distance-aware information. The self-motion information (direction and velocity) is used as the input to the lateral anti-Hebbian network (LAHN) to generate grid cells. In addition, the boundary cell response values are used to update the grid cell distribution law and to suppress the error response of the place cells, thus reducing the localization error of the mobile robot. Meanwhile, when the mobile robot reaches the boundary cell excitation zone, the activated boundary cells are used to correct the accumulated errors that occur due to long running times, which thus improves the localization accuracy of the system. The main contributions of this paper are as follows: 1. We propose a novel method for constructing boundary cell models. 2. An approach is presented that maps the response values of boundary cells to the input layer of LAHN (Location-Adaptive Hierarchical Network), where grid cells are generated through LAHN learning rules, and the distribution pattern of grid cells is adjusted using the response values of boundary cells. 3. We correct the cumulative error caused by long-term operation of place cells through the activation of boundary cells, ensuring that only one place cell responds to the current location at each individual moment, thereby improving the positioning accuracy of the system.  
Keywords:boundary cells; grid cells; place cells; environmental characterization; brain-inspired computing +
Footnote †: journal: Applied
###### Abstract  
One of the new trends of personal care industry is the use of organic ingredients derived from nature, in particular, from food-processing residues with proven efficacy. Lycopene is a carotenoid responsible for the red color of several fruits, namely tomato, whose antioxidant and photoprotective effects have been studied. Methods: Lycopene-enriched extracts (LEE) were obtained from tomato waste using supercritical CO\({}_{2}\) extraction, incorporated in microemulsions and macroemulsions for topical use, and characterized through GC-MS for the identification of volatile compounds. The aim of this work was to evaluate the impact of the presence of lycopene-enriched extracts developed emulsions through the identification of volatile compounds and by a sensory analysis to assess the odor and color perception and the acceptability of such semi-solid systems as cosmetic products. Results: Volatile compounds were identified in the extract and in the formulations containing the extract. Preliminary data show that the odor of both LEE-loaded microemulsions and LEE-loaded macroemulsions was classified as undefined. The information about the composition did not modify the odor perception but increased the acceptability of some cosmetic products. LEE conferred a yellowish color to formulations, and the information about the formulation composition increased the likelihood of different cosmetic products with this color being bought. Conclusions: The commercialization of personal care products does not only dependent on the associated organoleptic properties, but is influenced by the information about the composition, namely by the presence of an antioxidant compound.  
Keywords:antioxidant skin supplementation; lycopene extracts; supercritical fluid extraction methods; personal care; sensory analyses +
Footnote †: journal: Applied Sciences
## 3 Results
### Statistical Analysis  
The ANOVA evaluates whether there is a statistically significant difference between each pass reading and how the RFID reader detects tags at variable speeds. The ANOVA parameters are the source of variation, Sum of Squares (SS), degree of freedom (df), Mean Squares (MS), F statistical (F), (\(p\)-value) a measure of the probability of the test results gathered on how likely that the same tag readings would occur by random chance, and F critical (F crit). Furthermore, F, \(p\)-value, and the F crit values are the values that would determine the significance level of the source of variation. The value F > F crit is the same as \(p\)-value < alpha, which rejects the null hypothesis. In this experiment, the source of variation is the number of passes and the drone speeds. The SS, df, MS are values used to compute the F, F crit, and the \(p\)-value. The SS is derived by getting the count for each speed reading multiplied by the difference between the speed and grand mean. The grand mean is the average of all the readings, adding the total new tag readings divided by the total count. In addition, the df is the difference between groups minus 1 (8 passes \(-\) 1 = 7). The MS is the square of the difference between the mean, and the data collected, while the F results from the mean square of the number of passes divided by the mean square in each pass. The \(p\)-value of 0.000126 measures the probability that an observed difference could occur is computed using F, df between groups as the numerator and df within groups as the denominator. F crit is computed using three arguments, the alpha value, numerator df between groups (between each pass), and the denominator df within each pass.  
The number of passes made by drone scanning new/unique tags has an influence on the number of tags read (\(p\)-value = 0.000126); however, the three speeds used for this experiment are not significant (\(p\)-value = 0.941182) (see Table 3).
## 5 Analyses and Results
### Results of Measurement Model Assessment
#### 5.1.1 Model Fit Criteria  
The fulfillment of at least one fitness index from absolute, incremental, and parsimonious fit categories and factor loading of more than 0.5 were applied to test overall measurement model fit. The model evaluation revealed that the measurement model with Relative Chi-Sq (\(\leq\)3) = 1.975; CFI (\(\geq\)0.9) = 0.867; RMSEA (\(\leq\)0.08) = 0.063 did not meet the model fit requirements, because none of the Incremental fit indices met the requirements. Moreover, all factor loadings were more than 0.5 except for Ext6 (factor loading = 0.07), Int8 (factor loading = 0.36), and Con5 (factor loading = 0.37).  
Accordingly, the deletion should be made on Ext6, Int8, and Con5, respectively. After each deletion, we ran a new measurement model. It revealed that the modified measurement model; with Relative Chi-Sq (\(\leq\)3) = 1.972; CFI (\(\geq\)0.9) = 0.883; RMSEA (\(\leq\)0.08) = 0.063, still did not meet the model fit requirements. As all factor loadings exceed 0.5, we look for items associated with highest modification index. Items Ext3 (occurring 10 times) and Ext4 (occurring 11 times) show the highest MI (29.766). The occurrence for item Ext4 is the highest; thus, we delete item Ext4. The results of this step showed the modified measurement model with some improvement on fit indices (Chi-Sq (\(\leq\)3) = 1.878; CFI (\(\geq\)0.9) = 0.896; RMSEA (\(\leq\)0.08) = 0.063) but still does not meet the model fit requirements.  
Consequently, a second round of modification index checks is conducted. Items QA10 (occurring 8 times) and QA 11 (occurring 11 times) show the highest MI (27.136). The occurrence for item QA11 is the highest; thus, we delete item QA11. As a result, the respective measurement model fit to the data showed improvement where Relative Chi-Sq (\(\leq\)3) = 1.814; CFI (\(\geq\)0.9) = 0.905; IFI (\(\geq\)0.9) = 0.906; RMSEA (\(\leq\)0.08) = 0.058. These results of the model fit indices and factor loadings indicate that the model fits well, meeting all the required criteria. Figures present all rounds of measurement model modifications through CFA are available in the Supplementary Materials.
# Urban Free-Space Optical Network Optimization
## 4 Results  
This section presents the experiments conducted to evaluate the suggested GA optimization framework by modeling large-scale urban visibility graphs and solving the underlying FSO optimization problems using simulation. This section reviews the tools used to implement the visibility graph model presented in Section 2. The GA and implementation discussed in Section 3 are also presented here, followed by the algorithm results on randomly generated graphs and real-world-data graphs.
# Aerospace
## 5 Single Small Satellite to Venus Orbit as Secondary Payload
### Effect of Atmospheric Uncertainties and Target EFPA Selection  
As discussed in Section 4.5, the effect of atmospheric mean density uncertainties must be considered when selecting the target EFPA. Table 9 shows the corridor bounds using a representative minimum, average, and maximum mean density profile from VenusGRAM.  
Figure 19 shows the effect of atmospheric density variations on the Venus aerocapture entry corridor. The blue, green, and red rectangles indicate the a  
Figure 18: Nominal Venus small satellite aerocapture trajectory for a vehicle with \(\beta_{1}=20\) kg/m\({}^{2}\), \(\beta_{2}/\beta_{1}=7.5\), \(R_{N}=0.235\) m entering at atm. relative to EFPA \(=-5.20\) deg. Target apoapsis \(=2000\) km.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Overshoot limit EFPA, deg. & \(-5.10\) \\ Undershoot limit EFPA, deg. & \(-5.53\) \\ TCW, deg. & \(0.43\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Venus SmallSat aerocapture entry corridor.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Min. Density** & **Average** & **Max. Density** \\ \hline Overshoot, deg. & \(-5.171\) & \(-5.109\) & \(-5.057\) \\ Undershoot, deg. & \(-5.582\) & \(-5.530\) & \(-5.483\) \\ TCW, deg. & \(0.410\) & \(0.421\) & \(0.427\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Effect of density variations on Venus aerocapture corridor.  
the minimum, average, and maximum density atmospheric profiles, respectively. Initially, a target EFPA \(=-5.3\) deg. was selected, but it was found that the mean density of the atmospheric profiles used for the Monte Carlo simulation was slightly higher than those used to compute the corridor bounds in Figure 19. This required adjusting the nominal target EFPA to \(-5.2\) deg. (slightly more shallow), which is used for the Monte Carlo simulations.
# Attention-Based Hypergraph Neural Network: A Personalized Recommendation
## 5 Conclusions  
In this study, we proposed a heterogeneous hypergraph and attention mechanism-based online course recommendation algorithm (HHOCR) and compared it with several existing recommendation algorithms (POP, BPR, GRU4 Rec, BERT4 Rec, Light-GCN, TP-GNN, IRS-GCNet) through comprehensive experiments. The experimental results demonstrate that HHOCR outperforms other algorithms in recommending the top 5, top 10, and top 15 items.  
Specifically, in the case of recommending the top 5 items, HHOCR achieved an NDCG@5 of 0.7965, F1-Score@5 of 0.6945, and Recall@5 of 0.7102; in recommending the top 10 items, NDCG@10 was 0.8593, F1-Score@10 was 0.7813, and Recall@10 was 0.7514; in recommending the top 15 items, NDCG@15 was 0.8938, F1-Score@15 was 0.8092, and Recall@15 was 0.7698. Notably, HHOCR's performance in the NDCG metric stood out.  
The superior performance of HHOCR is primarily attributed to its unique heterogeneous hypergraph structure and attention mechanism. The heterogeneous hypergraph is capable of capturing complex higher-order relationships between users and courses, while the attention mechanism dynamically focuses on important features and relationships, thereby enhancing the personalization and precision of recommendations. Additionally, by utilizing the Bayesian hyper parameter optimization library Optuna, we were able to find an optimal set of hyper parameter configurations, further boosting the model's performance.  
In summary, the HHOCR algorithm demonstrates significant advantages in online course recommendation, achieving optimal recommendation results across multiple evaluation metrics. This provides new insights for future online education and personalized course recommendation systems.
## 3 Materials and Methods
### Microstructural Analysis  
All samples were polished with 200 to 2400-grit silicon carbide papers and with a 0.25 \(\upmu\)m diamond suspension and a 0.04 \(\upmu\)m colloidal suspension. These samples were observed with an optical microscope (OM; Eclipse LV150N, Nikon Metrology Inc., Tokyo, Japan) and a field emission scanning electron microscope (FE-SEM; MIRA3, Tescan.--Brno, Czech Republic) with an acceleration voltage of 5.0 kV. Energy dispersive spectroscopy (EDS, EDAX, Mahwah, NJ, USA) was also performed to analyze the chemical composition of IMCs. Chemical quantitative analysis was performed using an electron probe microanalyzer (EPMA; JXA-8230, JEOL Ltd.--Tokyo, Japan) with an acceleration voltage of 15.0 kV. The area fractions of the IMCs and microstructures were measured using an image analyzer (I-solution DT software).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Heat Treatment**} & \multirow{2}{*}{
\begin{tabular}{c} **Temperature** \\ **(K)** \\ \end{tabular} } & **Holding Time** & **Heating Rate** & **Cooling** \\  & & **(K)** & **(h)** & **(K/min)** & **Rate (K/min)** \\ \hline \multirow{2}{*}{ST} & E & & & 1.1 \\  & WQ & 773 & 4 & 4.16 & 1340 \\ \multirow{2}{*}{AG} & & 473 & 4 & 1.67 & 1750 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Heat treatment condition of A356 alloys (based on at 298 K).  
Figure 2: Schematic of gray cast iron–A356 hybrid brake disk after casting: investigated locations in brake disk are highlighted. Section A is closer to the center of rotation than section B.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 1 Introduction  
The scope of this review is limited to agricultural concrete and metallic structures exposed to a corrosive environment. The content of the review paper includes the following topics and subtopics: agricultural concrete and metallic structures (impact of poultry, cattle, and swine manure), specific impact of corrosion on steel structures, corrosion, and degradation of metallic structures (corrosion in the farm environment, activity of fungi and bacteria, durability of concrete and acid-induced corrosion, mechanisms of acid corrosion) and corrosion control measures. The main problem under investigation was the mechanisms that contribute to the degradation of agricultural buildings that are in contact with agricultural effluents, animal manure, and natural elements. Additionally, the most effective mechanisms to mitigate corrosion were explored; a higher emphasis was placed on the design for durability, silica fumes, and fly ash ratios in concrete, sulfur concrete, and fiber-reinforced polymers.  
Corrosion predicts the durability of structural materials used in farms and agricultural environments, which are characterized by bio-waste, agro-food effluents, silage juices, manure, and molasses, that originate from agricultural processes [1]. Other factors that enhance the risk of corrosion include high concentrations of acids (originating from spilled meal-water mixtures), salts, chlorine-containing sterilizing chemicals, aggressive gases, hydrogen sulfide (H\({}_{2}\)S), dust, and temperature [2]. In addition, microbes--such as fungi, yeast, and bacteria--contribute to microbial induced corrosion [2]. The aim of this review article is to contribute new knowledge concerning the durability of concrete structures exposed to aggressive corrosion linked to acids, moisture, bacteria, and other corrosion-inducing agents and the role of high strength concrete, special concretes, stainless steel and galvanized steel in the mitigation of corrosion and the environmental degradation of materials.
## 4 Discussion
### Identification of Transcription Factor in Response to SA and MeJA Treatments  
_WRKY70_ is a unique member of the WRKY transcriptional factor family controlling plant defense, senescence, and developmental processes [39; 40; 41; 42; 43]. [PERSON] et al. [44] reported that _WRKY70_ was a common component in salicylic acid and jasmonic acid mediated signal pathways, and played a pivotal role in determining the balance between salicylic acid-dependent and jasmonic acid-dependent defense pathway. In Arabidopsis, it was found that the expression of _WRKY70_ was activated by SA and repressed by MeJA. In citrus, however, the research on _WRKY70_ has not been reported. In the present study, differentially expressed genes in the SA and MeJA treatments were investigated by using microarray analysis. We found that the expression of _CitWRKY70_ was induced by the SA treatment, while suppressed by the MeJA treatment in the juice sacs in vitro. In our study, it was showed that the expression of key genes related to flavonoid and carotenoid biosynthesis (e.g., _CitCHS2_, _CitT3'H_, and _CitHYb_) was also induced by SA treatment, while suppressed by the MeJA treatment. Thus, it was deduced that _CitWRKY70_ could be involved in the regulation of flavonoid and carotenoid accumulation in response to the treatments with the two phytohormones. In the future, more in-depth researches on the function of _WRKY70_ in flavonoid and carotenoid biosynthesis are still needed, which would contribute to elucidating the regulation mechanisms of flavonoid and carotenoid accumulation in response to SA and MeJA treatments in citrus fruit.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 3 Algorithm Principle Explanation
### Attention Mechanism  
The core of the attention mechanism is to give the model the ability to dynamically select information so that when processing sequence data, it can independently decide which parts of the input should be focused on in the current step, rather than treating all information equally; that is, extracting the key value. First, we need to clarify a few concepts. Query is the feature representation of the current processing target (such as the hidden state of the decoder at the current moment). Key is the feature identifier of the input element, which is correlated with the query. Value is the actual content of the input element, which is weighted to generate a context vector. When receiving an input query, Attention will treat it as a query, then Attention will search for information related to the query in the given information. At this stage, it can be considered that the query is searching among many keys. After the query and a series of calculations, an output will be obtained. This involves finding the value according to the key and performing some calculation according to the value [37]. When Attention searches for keys related to the query, it uses three weight matrices to input information to construct a multi-dimensional semantic space, and it realizes dynamic information screening through differentiated feature mapping, thereby outputting the optimal value.  
\[Query=h_{j}W_{q} \tag{9}\]  
\[Key=h_{t}W_{K} \tag{10}\]  
\[Value=h_{t}W_{V} \tag{11}\]  
\(W_{k}\) is a learnable parameter for the key, used to project the key to be stored in the key space.  
\(W_{q}\) is a learnable parameter for query, used to project the vector to be queried into the query space.  
\(W_{V}\) is a learnable parameter for values, used to project the value to be stored into the value space.  
\(h_{t}\) is the hidden state of the encoder at time step \(t\).  
\(h_{j}\) is the hidden state of the decoder at time step \(j\).  
Before obtaining the output, the query and key will be analyzed for correlation. The size of the weight is the size of the correlation value, and the correlation score is normalized by softmax:  
\[\alpha_{i,t}=soft\text{max}(score(h_{j},h_{t}))=\frac{\exp(score(h_{j},h_{t}))} {\sum_{t^{\prime}=1}^{L}\exp(score(h_{j},h_{t^{\prime}}))} \tag{12}\]  
Then, the weighted sum of the values is used to obtain the output of Attention.  
\[c_{j}=\sum_{t=1}^{L}\alpha_{j,t}v_{t} \tag{13}\]  
where \(L\) represents the length of the encoder input sequence, and the desired result is finally obtained. The overall process mechanism of the three models is shown in Figure 4.
## 5 Numerical Study
### Uncertainty Analysis
#### 5.2.2 Stability Threshold under Uncertainty  
The mean values and the 95% confidence intervals of the run-up curve and the borderline are shown in Figure 10. It is observed that the run-up curve is more sensitive to the random parameters than the borderline. It is also found that an intersection of the run-up curve and borderline may occur at any point within the region bounded by their 95% confidence intervals. The bounded region is surrounded by black and red dashed lines, respectively. This represents the possible zone within which the stability threshold can fall.  
The PDF, mean value, and 95% confidence interval of the stability threshold are evaluated using the gPC expansion, as presented in Figure 11. The PDF matches well with that of the MC simulation. The mean values of the stability threshold obtained by the gPC expansion and by MC simulation are 212.46 rad/s and 212.53 rad/s, respectively. These values are close to the deterministic result. However, the mean value of the stability threshold lies outside the maximum probability region. The 95% confidence interval of  
Figure 8: PDF and mean values of maximum pressure of oil-film (unit: bar), attitude angle (unit: \({}^{\circ}\)), and Sommerfeld number at \(\varepsilon=\{0.2,0.4,0.6,0.8\}\) by gPC expansion and MC simulation.  
Figure 10: Mean values of run-up curve and borderline by gPC expansion and their 95% confidence intervals plotted with dashed lines as the boundary.  
Figure 9: Mean values of dimensionless dynamic coefficients by gPC expansion and their 95% confidence intervals plotted with dashed lines as the boundary.  
the stability threshold is from 210.69 rad/s to 215.96 rad/s, which means that the system should avoid this region during operation.
## 2 Theory and Calculation Model
### Model Parameters  
We assumed that the tip is made of gold and the substrate is a gold film. The metallic structures are assumed to have a Drude-type susceptibility with the parameters of Au  
\[\chi_{\text{Au}}(\omega)=\frac{1}{4\pi}\left[\epsilon_{\text{Au}}-\epsilon_{0}- \frac{(\hbar\omega_{\text{Au}})^{2}}{(\hbar\omega)^{2}+i\hbar\omega\Big{(} \gamma_{\text{bulk}}+\frac{\hbar V_{\text{F}}}{L_{\text{eff}}}\Big{)}}\right], \tag{4}\]  
where \(\epsilon_{\text{Au}}\) is the background dielectric constant of the metal; \(\epsilon_{0}\) is the dielectric constant of vacuum; \(\omega_{\text{Au}}\) is the bulk plasma frequency; \(\gamma_{\text{bulk}}\) is the electron relaxation constant of bulk gold; \(V_{\text{F}}\) is the electron velocity at the Fermi level; and \(L_{\text{eff}}\) is the effective mean free path of electrons, which is comparable to the size of the tip diameter. The following parameters were used for the gold structures: \(\epsilon_{\text{Au}}=12.0\), \(\epsilon_{0}=1.0\), \(\hbar\omega_{\text{Au}}=8.958\) eV, \(\gamma_{\text{bulk}}=72.3\) meV, \(\hbar V_{\text{F}}=0.922\) nm \(\cdot\) eV, and \(L_{\text{eff}}=60\) nm [25; 26].