esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory
### Long Short-Term Memory Network  
The fundamental concept of LSTM networks is to regulate the flow of information through the implementation of a gating mechanism, which is essential for capturing long-range dependencies in time series data [40]. The primary components of LSTM include the cell state \(C_{t}\), the hidden state \(h_{t}\), and three gating structures that govern the flow of information. At each time step, the LSTM updates both the cell state and the hidden state through a series of defined steps.  
Firstly, the sigmoid function computes the forgetting gate, mapping the output to the interval \([0,1]\), thereby facilitating the retention or abandonment of historical information. The formula is presented as follows:  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{20}\]where \(x_{t}\) is the weight matrix; \(h_{t-1}\) is the hidden state of the previous time step; \(W_{f},b_{f}\) is the weight and bias of the forgetting gate.  
The input gate \(i_{t}\) controls the update of the new information, and the candidate cell state \(\overset{\sim}{C}_{t}\) calculated by the tanh activation function is then provided with the candidate value of the new information:  
\[i_{t}=\sigma(W_{t}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{21}\]  
\[\overset{\sim}{C}_{t}=\tanh(W_{c}\cdot[h_{t-1},x_{t}]+b_{c}) \tag{22}\]  
After completing the above steps, the cell status is updated through the forgetting gate \(f_{t}\) and the input gate \(i_{t}\):  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\overset{\sim}{C}_{t} \tag{23}\]  
where \(f_{t}\cdot C_{t-1}\) is retained old memory; \(i_{t}\cdot\overset{\sim}{C}_{t}\) is newer memory.  
Finally, through the contribution of the output gate \(O_{t}\) to output hidden state information, the information transmitted from cell state to hidden state is determined:  
\[O_{t}=\sigma(W_{o}\cdot[h_{t-1},X_{t}]+b_{o}) \tag{24}\]  
\[h_{t}=O_{t}\cdot\tanh(C_{t}) \tag{25}\]  
Due to its robust capability for capturing long-term dependencies and its efficient processing of sequential data, LSTM networks are adept at extracting critical information from time series data and diagnosing fault modes in fault diagnosis applications, even in the presence of extended data lengths or noise.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
###### Abstract  
The increasing importance of spatial audio technologies has demonstrated the need and importance of correctly adapting to the individual characteristics of the human auditory system, and illustrates the crucial need for humanoid localization systems for testing these technologies. To this end, this paper introduces a novel feature analysis and selection approach for binaural localization and builds a probabilistic localization mapping model, especially useful for the vertical dimension localization. The approach uses the mutual information as a metric to evaluate the most significant frequencies of the interaural phase difference and interaural level difference. Then, by using the random forest algorithm and embedding the mutual information as a feature selection criteria, the feature selection procedures are encoded with the training of the localization mapping. The trained mapping model is capable of using interaural features more efficiently, and, because of the multiple-tree-based model structure, the localization model shows robust performance to noise and interference. By integrating the direct path relative transfer function estimation, we propose to devise a novel localization approach that has improved performance in the presence of noise and reverberation. The proposed mapping model is compared with the state-of-the-art manifold learning procedure in different acoustical configurations, and a more accurate and robust output can be observed.  
binaural localization; HRTF; feature learning; Spatial Hearing Model; random forest +
Footnote †: This paper is an extended version of our paper published in the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), entitled “Spatial feature learning for robust binaural sound source localization using a composite feature vector”.
## 3 Research Methods
### Inclusion and Exclusion Criteria  
The exclusion criteria are supported in the research database functions presented in Figure 2 and on criteria that comprehend the reading of the abstract or full content of the publication. Publications published in a year before 2000 (inclusive), in a non-peer-reviewed scientific journal, conference proceedings, or other source (for example, news, report, or electronic resource), or not written in English or Portuguese are excluded.  
Publications not accessible or those that are only meta-data are excluded. Publications with BC considerations unrelated to the research question or with no results that support, streamline, or guide BCP implementation are also excluded.  
\begin{table}
\begin{tabular}{c c c} \hline
**PICOC** & **Keyword** & **Synonym** \\ \hline Intervention I & business continuity plan & disaster recovery plan \\ Intervention II & guideline & guidance \\  & & strategy \\  & & approach \\ Comparison & framework & methodology \\  & & procedure \\  & & program \\ Outcome & streamline & agile, optimise, improve \\  & & encourage, bolster, support \\  & & boost, expedite, speed up, increase \\ \hline \end{tabular}
\end{table}
Table 1: Keywords and synonyms.  
Other exclusion criteria are based on results not transposable to organisations with ICT-based processes:  
* Publication for a specific industry;
* Publication for a specific disaster or incident;
* Publication for a specific system;
* Publication for specific software.  
Publications from before 2015 are excluded. Finally, publications can be included if they present relevant results that streamline, support, or guide the BCP implementation. Duplicate publications must be excluded to avoid bias in the results.
## 4 Conclusions  
In this study, a promising model in the accuracy improvement of the ability to quantitatively analyze soybean oil-adulterated biodiesel based on Raman spectroscopy and machine learning methods was presented. The D1-MSC-siPLS correction model was constructed after optimizing the input variables using a D1-MSC preprocessing method and the siPLS feature variable selection method. Compared with the iPLS and biPLS variable selection methods, results elucidate that the D1-MSC-siPLS calibration model is superior in the quantitative analysis of adulterated biodiesel. The cross-validated R\({}^{2}\)CV of the siPLS calibration set improved from 0.9738 to 0.9842, and the RMSECV decreased from 0.1912% (\(v/v\)) to 0.1329% (\(v/v\)). Moreover, the number of variables was significantly reduced from 1044 to 378, reducing the modeling time. The model's predictive performance also improved, with an R\({}^{2}\)P of 0.9899 and RMSEP decreasing from 0.1654% (\(v/v\)) to 0.1367% (\(v/v\)), and MREP decreasing from 9.51% to 6.31%. These results demonstrate the feasibility of using Raman spectra combined with the PLS calibration model for the quantitative analysis of biodiesel in adulterated diesel/biodiesel blends.  
Conceptualization, T.Z. and Y.S.; methodology, T.Z. and H.T.; software, Y.S.; validation, C.Y. and M.L.; formal analysis, M.L. and C.Y.; investigation, T.Z. and H.T.; resources, H.L.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, M.L. and T.Z.; visualization, Y.S. and M.L.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number (22173071, 22303066); Natural Science Basic Research Program of Shaanxi, grant number 2023-JC-QN-0169; and Scientific Research Program Funded by Shaanxi Provincial Education Department, grant number 22 JP064.  
Not applicable.  
We are grateful to [PERSON] of HBIS Institute of Materials Technology for her contribution to this manuscript.  
The authors declare no conflict of interest.
# A Study on Shock Absorption Characteristics of Honeycomb-Inserted Bollards
###### Abstract  
Lack of shock absorption capability of conventional steel bollards causes significant vehicle damage and, consequently, high repair costs. This research studies a solution to reduce vehicle damage by inserting polylactic acid (PLA) honeycomb structures. A honeycomb-inserted bollard was designed based on numerical simulations using LS-DYNA, which yielded the bollard designed for actual vehicle-bollard collision experiments. Simulation efforts were focused on calculating the acceleration characteristics when a vehicle collides with steel and honeycomb-inserted bollards. Compared to the simulated steel bollards, 20 MPa yield-strength honeycomb-inserted bollard showed 0.017 s delay in the maximum acceleration occurrence time, reduction of the maximum acceleration of 37.4% of that of steel bollards, and a 13.1% reduction in the B-pillar maximum acceleration. Actual vehicle-bollard collision experiments, with a gyro-sensor installed at the test vehicle front bumper frame, also proved improved shock absorption characteristics of the honeycomb-inserted bollards. An experiment with honeycomb-inserted bollard showed a 0.783 s delay in the maximum acceleration occurrence time, a significant delay when compared to steel bollards. The maximum acceleration measured by the gyro-sensor was \(0.35\times 10^{3}\) m/s\({}^{2}\) when the simulation predicted it to be \(0.388\times 10^{3}\) m/s\({}^{2}\), proving the similarity in the simulations and experiments. Thus, this study of shock absorption characteristics promised reduced damage to vehicles and lower repair cost.  
Keywords:bollard; honeycomb; polylactic acid (PLA); LS-DYNA; shock-absorption; in-plane +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Characterizations  
To evaluate the lap shear strength (LSS), a Shimadzu AG-X universal testing machine (Kyoto City, Japan) with a 50 kN load cell was used, operating at a speed of 1.5 mm/min, in accordance with ASTM D1002:19 [20]. Five samples were tested for each experiment (18 experimental conditions, with 17 corresponding to treated samples and 1 used as a control), totaling 90 samples analyzed. After statistical analysis and determining the highest lap shear strength value, the coating that yielded this result was evaluated using scanning electron microscopy (SEM) with a ZEISS microscope, model EVO LS15 (Jena, Germany). The thickness of the oxide coatings was measured using an INSTRUTHERM non-metallic film thickness gauge, model ME 250 (Sao Paulo, Brazil), employing the eddy current concept. Each sample underwent 10 measurements, with an accuracy of 0.1 \(\upmu\)m. The contact angle of the untreated and treated samples was measured using a Rame-Hart goniometer (ADVANCED 300 F-1, Succasunna, NJ, USA), applying 10 drops to each surface randomly.  
To evaluate the corrosion resistance of the alloy and after PEO treatment, the samples were subjected to linear polarization electrochemical techniques using the AUTOLAB potentiostat/galvanostat model PGSTAT302N from Metrohm Autolab(Utrecht, The Netherlands). The tests were conducted in a horizontal electrochemical cell with a 3.5% NaCl solution and equipped with three electrodes: treated AA2024 alloy (working electrode), platinum (counter electrode), and Ag/AgCl (saturated KCl--reference electrode). One replicate was performed for each sample, with each sample being immersed in the solution for approximately 86,400 s (at open circuit potential). The linear polarization test was performed at a scan rate of 0.33 mV/s, with an analysis range of +700 to \(-\)100 mV relative to the open circuit potential (OCP) value.
## 4 Results and Discussion
### Wear Resistance of Cladding Coating  
The wear resistance properties were measured by a pin-on-disc wear testing machine, at room temperature. The coatings were cut into standard specimens of 30 mm \(\times\) 7 mm \(\times\) 6 mm; next, the surface of the coating was polished with SiC paper (grade 1200) and the sample was fixed on the wear testing machine. The countermaterial was the GCr15 steel after quenching with the normal load of 200 N; the diameter and thickness of the disc friction pair were, respectively, \(\Phi\)40 mm and 10 mm; the surface roughness of the friction pair was 6.3 \(\upmu\)m; and the sliding velocity was 200 r/min with an unidirectional way. The loss of the mass was measured every 40 min, and the results were shown in Figure 13. After 160 min, the wear mass of the coating was 21.08 mg, which was less than the 9.94 mg of the substrate. The main reason for the difference was that the surface grain structure of the layer was refined (as shown in Figure 14); the size of the surface grain was in the range of 2 \(\upmu\)m-4 \(\upmu\)m, and there were many grain boundaries that could resist plastic deformation. In addition, the surface hardness was improved after laser cladding, and a high level of hardness improved the wear resistance of materials; therefore, the resistance property of the layer was better than the P20 steel.  
Figure 12: The result of thermal shock test (**a**) the top of layer before thermal shock test, (**b**) and (**c**) the top of layer after thermal shock test; (**d**) the side of layer before thermal shock test, (**e**) and (**f**) the side of layer after thermal shock test.
## Appendix B  
In this appendix, we will introduce the Newton iterative method to solve the nonlinear GTLR-BiSAR range-Doppler geolocation equations. Assuming that the position and velocity vectors of LEO receiver are \(\vec{S}_{L}=(S_{Lx},S_{Ly},S_{Lz})\) and \(\vec{V}_{L}=(V_{Lx},V_{Ly},V_{Lz})\), separately. Correspondingly, \(\vec{S}_{G}=(S_{Gx},S_{Gy},S_{Gz})\) and \(\vec{V}_{G}=(V_{Gx},V_{Gy},V_{Gz})\) stand for the position and velocity vectors of the GEO transmitter, respectively. Then we suppose the range vector from the LEO receiver to the target as \(\vec{S}_{LT}=\vec{S}_{T}-\vec{S}_{L}=(x,y,z)\), where \(x\), \(y\) and \(z\) are the three-axis components of the vector. Moreover, the range vector from the GEO transmitter to the target is assumed as \(\vec{S}_{GT}=\vec{S}_{T}-\vec{S}_{G}=\vec{S}_{T}-\vec{S}_{L}+\vec{S}_{L}-\vec {S}_{G}=(x+S_{Lx}-S_{Gx},y+S_{Ly}-S_{Gy},z+S_{Lz}-S_{Gz})=(x+\Delta x,y+\Delta y,z+\Delta z)\), where we introduce \(\vec{S}_{LT}\) and the vector from the GEO transmitter to the LEO receiver. \(\Delta x\), \(\Delta y\) and \(\Delta z\) are the three-axis components of the vector from the GEO transmitter to the LEO receiver. Finally, we can change the GTLR-BiSAR range-Doppler geolocation equations into the following functions:  
\[ \begin{cases}f_{1}(x,y,z)=R_{e}-\frac{(x+S_{Lx})^{2}+\left(y+S_{Ly}\right)^{ 2}}{R_{e}}-\frac{R_{e}(z+S_{Lx})^{2}}{R_{e}^{2}}\\ f_{2}(x,y,z)=\left(xV_{Lx}+yV_{Ly}+zV_{Lz}\right)\sqrt{(x+\Delta x)^{2}+(y+ \Delta y)^{2}+(z+\Delta z)^{2}}\\ \qquad\qquad\qquad\qquad+\left((x+\Delta x)V_{Gx}+(y+\Delta y)V_{Gy}+(z+\Delta z )V_{Gz}\right)\sqrt{x^{2}+y^{2}+z^{2}}\\ \qquad\qquad\qquad\qquad-\Delta f_{de}\sqrt{x^{2}+y^{2}+z^{2}}\sqrt{(x+\Delta x )^{2}+(y+\Delta y)^{2}+(z+\Delta z)^{2}}\\ f_{3}(x,y,z)=R_{R}+R_{L}-\sqrt{x^{2}+y^{2}+z^{2}}-\sqrt{(x+\Delta x)^{2}+(y+ \Delta y)^{2}+(z+\Delta z)^{2}}\end{cases}  \tag{12}\]  
\[\text{Supposing that }X= \begin{bmatrix}x\\ y\\ z\end{bmatrix} ,f(X)= \begin{bmatrix}f_{1}(x,y,z)\\ f_{2}(x,y,z)\\ f_{3}(x,y,z)\end{bmatrix} \text{ and }X_{k}\text{ is an approximate zero point of }f(X)\). \(f(X)\) is approximated by the linear part of the Taylor expansions (the first two terms) at this zero point:  
\[f(X)\approx f(X_{k})+f^{\prime}(X_{k})(X-X_{k}) \tag{13}\]According to the Newton iterative method, we make the above equation equal to 0. Moreover, \(X_{k+1}\) is an new approximate zero point of \(f(X)\). Then we have the iterative formula:  
\[X_{k+1}=X_{k}-B^{-1}f(X_{k}) \tag{121}\]  
where \(B\) is the Jacobi matrix, \(B= \begin{bmatrix}\frac{\partial f_{1}}{\partial x},\frac{\partial f_{1}}{ \partial y},\frac{\partial f_{1}}{\partial z}\\ \frac{\partial f_{2}}{\partial x},\frac{\partial f_{2}}{\partial y},\frac{ \partial f_{2}}{\partial z}\end{bmatrix}
## 3 Results Analysis
### Effect of Expansion on Slope Displacement  
The final displacement contour maps considering swelling under various rainfall conditions are shown in Figure 5. Table 3 presents the maximum displacement values considering swelling and no swelling for each rainfall condition. The maximum displacements considering swelling are one order of magnitude larger than those without considering swelling, for the same rainfall condition.  
Figure 4: Image depicting the saturation distribution of the slope after the rainfall has ended (condition 4).  
Figure 5: Contour map of displacement considering expansion effect (unit: m): (**a**) Condition 1, (**b**) Condition 2, (**c**) Condition 3, (**d**) Condition 4.  
Considering the factor of swelling deformation, the potential sliding surface of the slope may traverse the entire slope surface, located at the interface between the weathered layer and the un-weathered layer, and tangent to the interface. The maximum displacement of the slope gradually increases with rainfall intensity increase. Shallow layer sliding first occurs at the toe of the slope, and with the increase in rainfall duration, the sliding area gradually expands upward along the slope. The depth of the sliding surface deepens with an increase in rainfall duration, ultimately stopping at the interface between the weathered layer and the un-weathered layer. Considering that the slope failure of expansive soil mainly occurs in the shallow layer and exhibits a cohesive nature, the upward expansion of the sliding area is due to the sliding of the soil at the toe of the slope, which reduces the support to the upper part soil and consequently decreases its shear strength, leading to its movement. Therefore, the influence of moisture-induced swelling cannot be ignored when calculating slope stability.
## 2 Materials and Methods
### Characterization  
To analyze its adsorption mechanism for fluoride removal in an aqueous solution, the surface morphologies, elemental compositions, crystalline structures, and chemical properties of WA and fluoride-adsorbed WA (F-WA) were investigated using various methods. The surface morphologies of WA and fluoride-adsorbed WA (F-WA) were investigated using field scanning electron microscopy (FE-SEM; S-4700, Hitachi, Chiyoda, Japan). The specific surface area, pore volume, and pore size of the WA were analyzed using an AutosorbiQ 2 ST/MT analyzer (Quantachrome, Boynton Beach, FL, USA). The elemental compositions and crystalline structures of WA and F-WA were investigated using X-ray fluorescence spectroscopy (XRF; S8 TIGER, Bruker AXS, Madison, WI, USA) and X-ray diffraction spectroscopy (XRD; Smartlab, Rigaku, Tokyo, Japan), respectively. The XRD results were analyzed based on the X-ray intensity of a standard material (\(\alpha\)-Al\({}_{2}\)O\({}_{3}\)), using PDXL2 software (version 2.8, Rigaku, Japan) coupled with the ICDD PDF-2 (Release 2015) data, to calculate the individual mineral content. X-ray photoelectron spectroscopy (XPS; K-Alpha+, Thermo Fisher Scientific, Winsford, UK) was conducted to validate the chemical properties of WA and F-WA, with the binding energies for the high-resolution spectra adjusted by setting C1s to 284.6 eV. The point of zero charge (PZC) of WA was determined by measuring the pH changes (\(\Delta\)pH) in 0.1 M NaNO\({}_{3}\) solutions with varying initial pH values. The initial pH of the 0.1 M NaNO\({}_{3}\) ranged from 3 to 15, adjusted using 0.1 M HNO\({}_{3}\) and NaOH. WA of 5 g/L was reacted for 24 h at varying initial pH (pH\({}_{i}\)) levels, after which the final pH (pH\({}_{i}\)) was measured. The PZC was calculated by plotting pH\({}_{i}\) against \(\Delta\)pH (\(\Delta\)pH = pH\({}_{i}\) - pH\({}_{i}\)).
# A Systematic Review of Osteotomies to Correct Hallux Valgus in the First Metastasis
## 3 Results
### Proximal Spherical Metatarsal Osteotomy
#### 3.5.2 Follow-up and Biomechanical Analysis  
The osteotomy site was fixed with small screws and Kirschner wires. Medial soft tissue reconstruction was performed at the same time. Post-operatively, a dressing was applied without plaster immobilization. Weight bearing on the heel was allowed one day after surgery. Although 81% of patients expressed satisfaction with the results, it was found that 50% of those with a pre-operative HVa angle of 50 degrees or greater had a post-operative HVa angle of 20 degrees or greater. In these cases, the correction of the primus varus metatarsus was adequate, but the valgus deviation of the hallux was only reasonable. The average correction towards plantar flexion was 1.5 degrees, and no increase in arch height was observed.  
The effectiveness of the treatment was assessed using the AOFAS Hallux Metatarsorpha langeal-Interphalangeal Scale. Pain levels, specifically at the first metatarsophalangeal joint and metatarsalgia, were examined independently using the pain subscale of the AOFAS scale, categorized as 'no pain','mild pain','moderate pain', or'severe pain'. The active and passive motion ranges at the first MTP joint were measured following the AOFAS guidelines. Satisfaction levels were gauged utilizing [PERSON]'s methodology. Results were classified as 'excellent' if complete alleviation of symptoms and deformity was achieved, as assessed by both the patient and surgeons. 'Good' outcomes were reported when the patient was content but experienced mild symptoms. Conversely, outcomes were deemed 'unsatisfactory' if a significant flaw persisted, such as metatarsalgia, notable recurrence, or hallux varus. Additionally, patients were surveyed regarding their willingness to undergo the procedure again.  
Proximal spherical osteotomy has consistently demonstrated satisfactory results for patients with HVa angles less than 50 degrees. However, corrections were less effective in more severe cases of deformities. Additionally, plantar flexion in the osteotomy area was compensated for by the displacement of the first tarsometatarsal joint.
# Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis
###### Abstract  
Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed.  
deep learning; attention mechanism; sparsity; fault mechanism; fault diagnosis Article