esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Bioactive Component Analysis
#### 2.4.3. Sualene Analysis  
Initially, 300 \(\upmu\)L of a squalane internal standard solution (1 mg/mL in hexane) was added to a 250 mL tube and dried with nitrogen. Subsequently, 1.5 g of the sample oil and 50 mL of potassium hydroxide solution (1 mol/L in ethanol) were added to the tube, and the reaction was allowed to proceed for 50 min in an 80 \({}^{\circ}\)C water bath. Afterward, 50 mL of distilled water was added to terminate saponification. Hexane was employed for three washes, using 50 mL, 30 mL, and 20 mL, successively, after cooling to room temperature. The hexane extracts were amalgamated into a 250 mL separatory funnel, and 25 mL of ethanol was utilized for washing until the lower effluent reached a neutral state. Then, the washed hexane extracts were passed through an anhydrous sodium sulfate column to eliminate water and dried with nitrogen. Finally, hexane was used to dissolve the residue, and the volume was adjusted to 10 mL for GC analysis [40].  
Sualene analysis was conducted using an Agilent 7890 Gas Chromatograph (Santa Clara, CA, USA) equipped with a hydrogen flame ionization detector (FID) and a HP-5 capillary column (30 mm \(\times\) 0.32 mm, 0.25 \(\upmu\)m; Agilent Technologies, Palo Alto, CA, USA). High-purity nitrogen served as the carrier gas, with a lateral pressure of 16 psi and a split ratio of 1:10. The injection and detector temperatures were set at 250 \({}^{\circ}\)C and 300 \({}^{\circ}\)C, respectively. The oven conditions were programmed as follows: the oven temperature increased from 160 \({}^{\circ}\)C to 220 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min and held for 2 min; then raised to 280 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for 20 min; finally reaching 300 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and held for 2 min. The content of squalene in the oil sample was calculated using the standard curve, wherein the peak area ratio of squalene to squalene served as the abscissa, and the mass ratio served as the ordinate (Y = 1.0257X \(-\) 0.0042; R\({}^{2}\) = 0.9998).
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 2 Materials and Methods  
For this study, we built the dataset by collaborating with a lumber mill company in Tamaulipas, Mexico. We analyzed the company's history for five products: dining room, interior door mod1, interior door mod2, entrance door mod1, and entrance door mod2. We selected these products because these were the most significant ones, in terms of sales, for the lumber mill. The data provided by the company covered five years, from 2019 to 2023. In addition, the company handles sales reports for products every week. Thus, the data instance had 261 records for each product considered in this study.  
The following variables constituted the instance:  
* Year: Year extracted from the Date column.
* Sine_Week: Value of the sine of the week of the year, representing the weekly periodicity.
* Cosine_Week: Cosine value of the week of the year, complementing the cyclical representation.
* Event: Indicated whether there were any events during the week.
* Sales: Number of units sold in the week.  
As the weeks of the years were cyclic, we characterized them as the sine and cosine functions. Figure 1 shows a characterization for some weeks for product 1.  
Furthermore, since we observed that sales of the different products considered had different behaviors among themselves, we decided to have one instance for each product. Table 1 presents a partial part of the data instance obtained for product 1: dining room. We used this structure for each of the instances created.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Record** & **Year** & **Sine\_Week** & **Cosine\_Week** & **Event** & **Sales** \\ \hline
1 & 2019 & 0.120537 & 0.992709 & 0 & 32 \\
2 & 2019 & 0.239316 & 0.970942 & 0 & 12 \\ \hline \end{tabular}
\end{table}
Table 1: Data instance of product 1: dining room.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 4 Analytical Models with Different Outlier Filtering Criteria
### Analytical Models  
This section proposes models to analyze the relationships of location error and availability to different outlier filtering thresholds \(t\).
## 3 Results
### Standard Curve and Sensitivity  
After the dosages of CmAP-IgG and MPs-IgG were optimised, a series of NT-proBNP standard samples (0.0 ng/L, 5.7 ng/L, 40.7 ng/L, 65.7 ng/L, 136.0 ng/L, 696.0 ng/L, 2310.0 ng/L, 6450.0 ng/L, and 21,700.0 ng/L) were used. A standard curve was obtained through 4-parameter logistic curve fitting. The concentration of NT-proBNP was significantly correlated with the chemiluminescence value, as shown in Figure 4 (R\({}^{2}\) = 1). Twenty times of the blank sample were determined (Table S1), and then, the standard deviation was calculated. (SD = 289.6). The sensitivity (limit of detection (LoD)) of the assay was defined as 3 SD/\(K\)[19; 20], where \(K\) is the slope of the standard curve fit by the first 8 points, except the concentration of 21,700.0 ng/L (\(K\) = 1489.1, R\({}^{2}\) = 0.999). Consequently, the sensitivity of this method was 0.58 ng/L. The value was sufficiently low for NT-proBNP diagnosis.  
Figure 3: Influence of the dosage of MPs-IgG and the dilution ratio of CmAP-IgG studied using the standard N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentration of 696 ng/L. The seven curves correspond to a series of dilution ratios of CmAP-IgG (i.e., 1:20, 1:50, 1:100, 1:200, 1:300, 1:400 and 1:500). All tests were performed at 37 \({}^{\circ}\)C.
## 2 Layout of PSF Measurement  
The proposed PSF measurement configuration consisted of a movable assembly (PSF calibration module) and a static assembly (light source and Z-axis position measurement charge-coupled devices (CCDs); Figure 2). The core component was a dual position-sensitive-detector (PSD)-based unit [15] that was within the PSF calibration module. The dual PSD-based unit was used to obtain the incident beam direction vector. Here, two PSDs were replaced by two CCDs that could obtain two light spots from two different light routes. In this way, the dual PSD-based unit was able to obtain two incident beam light direction vectors at the same time. When a light beam reached one receiving plane, the intensity distribution of the light spot acted as a form of a Gaussian surface, and the plane position of the light spot was directly obtained by the PSD. The intensity distribution of the light spot was imaged by the CCD, and a Gaussian function was applied to fit the intensity distribution; then, the plane position was obtained by fitting.  
Figure 1: The measured object plane is perpendicular to the optical axis of the microscopic system.  
The PSF calibration module was integrally fixed on a six-degree-freedom macro/micro displacement platform. The slope face of the rectangular prism containing the polarization refractor plane and polarization splitter plane was parallel to the Y-Z plane of the coordinates, as determined by the dual PSD-based unit. The essential characteristics of the proposed PSF measurement setup included the following (Figure 2):  
1. A linearly polarized beam of light originated from a fiber-coupled laser (mounted on an adjustor). The linearly polarized beam light could go through the polarization refractor plane and was divided in half by the polarization splitter plane.
2. The light beam from the laser was the excitation light of a fluorescent particle. The cover slip acted as an emission filter [16] that only allowed the fluorescent particle's emission light to travel into the microscope.
3. When the linearly polarized light beam reached the Z-position measurement CCD (mounted on an adjustor) from the beam shifting corner cube (CC), after going through the quarter wave plate, it changed into circularly polarized light. A part of this light was reflected by the semi-transparent mirror parallel to the Z-position measurement CCD's light-sensitive surface. Another part was directed into the Z-position measurement CCD. When the reflected part again went through the quarter wave plate, its linearly polarized direction was perpendicular to the original. This portion was reflected by the polarization refractor plane and directed into the dual PSD-based unit.
## 3 Results  
The sample size of this study was composed of 74 subjects separated into two different population groups. One group comprised subjects with plantar fasciopathy with more than six months evolution (\(n\) = 31) and a mean age of 47.58 years (SD: 6.23). The control group comprised healthy subjects without any lower limb pathology at the time of the study (\(n\) = 43), and a mean age of 50.03 (SD: 7.69). The mean BMI was 23.76 kg/m\({}^{2}\) (SD:4.16) for the injury group, and 26.31 kg/m\({}^{2}\) (SD: 3.98) for the control group. The mean foot size for the whole group was 40.29 (SD: 3.3); the mean for the injury group was 40.04 (SD: 2.84, and for the control group, it was 40 (SD: 3.62) (Table 1). The spatio-temporal gait parameters in people with plantar fasciopathy that had statistical significance (_p_ < 0.001) when compared to the control group were step length, contact phase, double support, response to load, pre-swing, gait cycle, stride, speed, cadence, and propulsion phase (Table 2). The analysis of the correlation levels between the spatio-temporal parameters in both the injury group and the control group are shown in Table 1 and Figure 1. Swing phase, and response to load and step length in the injury group presented a strong positive correlation (1), with unipedal support, pre-swing, and the stride of the control group. The regression model coefficients are significant (_p_ < 0.001), except for the acceleration variable (_p_ 0.925). This indicates that the lesion is linearly related to the spatio-temporal parameters, with the exception of acceleration. The R-squared determination indicates a 0.6154 goodness of fit; consequently, the regression explains 61.54% of the injury variable variability, which is a moderately high value. The most significant results when analyzing the relationships of the spatio-temporal parameters in both groups were shown using scatter plots (see Figure 3). Logistic regression is represented by a dashed line in the screen plots. This regression shows the categorization of the results obtained in both groups. With regard to cadence, and step and speed, the healthy subjects had a higher number of steps and speed when walking. Additionally, gait cycle, contact phase, and propulsion presented lower values th \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Total (SD)** & **Injured (SD)** & **Healthy Control** & **Kolmogorov** \\  & **N = 74** & **N = 31** & **(SD)** & **Smirnov** \\  & & & **N = 43** & **(_p_-Value)** \\ \hline
**Age (years)** & 48.52 (7.01) & 47.58 (6.23) & 50.03 (7.69) & 0.006 \\ \hline
**Height (cm)** & 170.55 (9.2) & 170 (8) & 171 (9) & 0.305 \\ \hline
**Body mass (kg)** & 73.32 (17.13) & 69.5 (16.45) & 78 (16.24) & 0.426 \\ \hline
**BMI (kg/m\({}^{2}\))** & 24.99 (4.27) & 23.76 (4.16) & 26.31(3.98) & 0.352 \\ \hline
**Foot size** & 40.29 (3.3) & 40.04 (2.84) & 40 (3.62) & 0.015 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Anthropometric characteristics of both sample groups.
###### Abstract  
External bonding of carbon fiber sheets has become a popular technique for strengthening concrete structures all over the world. Epoxy adhesive, which is used to bond the carbon fiber sheets and concrete, deteriorates rapidly when being exposed to high temperatures. This paper presents a high-temperature-resistant modified magnesia-phosphate cement (MPC) with the compressive strength that does not decrease at the temperature of 600 \({}^{\circ}\)C. The bond properties of both the modified MPC and the epoxy adhesive between externally bonded carbon fiber sheets and concrete were evaluated by using a double-shear test method after exposure to elevating temperatures from 105 \({}^{\circ}\)C to 500 \({}^{\circ}\)C. The results showed that the bond strength of the modified MPC at room temperature (RT) is much higher than that of the epoxy resin. Full carbonation with almost 0 MPa was detected for the epoxy sample after the exposure to 300 \({}^{\circ}\)C, while only 40% reduction of bond strength was tested for the modified MPC sample. Although the modified MPC specimens failed through interlaminar slip of fiber strips instead of complete debonding, the MPC specimens performed higher bond strength than epoxy resin at ambient temperature, and retained much higher bond strength at elevated temperatures. It could be concluded that it is feasible to strengthen concrete structural members with externally bonded carbon fiber sheets using the modified MPC instead of epoxy adhesive. Furthermore, the use of the modified MPC as the binder between carbon fiber sheets and concrete can be less expensive and an ecologically friendly alternative.  
strengthening concrete; carbon fiber sheets; epoxy resin; modified magnesia-phosphate cement (MPC); resistance to elevated temperatures
# Robust Structural Damage Detection Using Analysis of the CMSE Residual's Sensitivity to Damage
## 4 Numerical Simulation
### Robustness Performance Investigation  
A parametric study is conducted to investigate the effects of location and extent of damage on the robustness performance of the proposed damage identification scheme against noise. In order to consider noise interference, the measurements of the \(i\)-th polluted frequency and mode displacement at the \(v\)-th DOF of the damaged structure, denoted by \(\hat{\omega}_{j}^{*}\) and \(\hat{\Phi}_{v,j}^{*}\), respectively, are simulated by adding a Gaussian random error to the corresponding true values:  
\[\hat{\omega}_{j}^{*}=(1+n_{\omega}\varsigma_{\omega})\omega_{j}^{*} \tag{38}\]  
\[\hat{\Phi}_{v,j}^{*}=\Big{(}1+n_{\phi}\varsigma_{\phi}\Big{)}\phi_{v,j}^{*} \tag{39}\]  
where \(\varsigma_{\omega}\) and \(\varsigma_{\phi}\) are two Gaussian random numbers both with zero mean and unit standard deviation, and \(n_{\omega}\) and \(n_{\phi}\) are the modal noise levels for natural frequency and mode shape, respectively. Previous studies [34] suggest that mode shape estimates have error levels as much as 20 times greater than those in the natural frequency estimates. Therefore, in this study, the modal noise levels were set as \(n_{\phi}=20n_{\omega}\). In the following, the mentioned noise level refers to \(n_{\phi}\).
## 2 Materials and Methods
### Chemical Analysis  
The methods for determining moisture, ash content, and cooking loss have been described in [30]. The results of these analyses are presented in the Supplementary Materials.  
Metal content was determined using inductively coupled plasma mass spectrometry (ICP-MS). Sample preparation for the analysis involved wet digestion in a microwave system, Mars Xpress (CEM Corporation, Matthews, NC, USA). Each sample (0.5 g) was digested with 4 cm\({}^{3}\) of concentrated nitric acid (CAS: 7697-37-2, Suprapur, Supelco, Bellefonte, PA, USA). The digestion was conducted in Teflon vessels under the following conditions: the temperature was raised to 210 \({}^{\circ}\)C over 25 min, followed by a 15 min hold at 210 \({}^{\circ}\)C, using a magnetron power of 800 W.  
In our study, each experimental group (i.e., each combination of heat processing method and the presence or absence of skin) consisted of three biological replicates, corresponding to independent goose breast muscle (_Pectoralis major_) samples collected from different birds. The total of \(n=6\) per heat treatment resulted from combining 3 samples with skin and 3 without skin, as described in Section 2.1. Each biological sample was analyzed twice for trace element content using ICP-MS.  
After digestion, the solutions were quantitatively transferred to 50 mL volumetric flasks and diluted to the mark with demineralized water with a conductivity of 0.055 \(\upmu\)S/cm (Direct-Q\({}^{\circledR}\), Millipore, Darmstadt, Germany).  
The clear digests were analyzed using an inductively coupled plasma mass spectrometer (ICP-MS, Varian MS-820, Palo Alto, CA, USA). Plasma was generated using argon gas with a purity of 99.999%. A collision/reaction cell (CRI) was not employed during the analysis. The operational parameters of the ICP-MS system were as follows: plasma gas flow rate--16 L/min, nebulizer gas flow rate--0.98 L/min, RF power--1.38 kW, and sampling depth--6.5 mm.  
Quantification was performed using the external calibration method with multielement standards from Ultra Scientific (purity 99.999%, Billerica, MA, USA).  
The reliability of the measurements was assured by implementing quality control procedures, including the analysis of a blank sample, a duplicate sample, and a certified reference material (NIST-1577c Bovine Liver, National Institute of Standards and Technology, Gaithersburg, MD, USA).  
The validation parameters used in the analysis are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Element**} & **LOD** & **LOQ** & **Precision** & **Recovery** & **Uncertainty** \\  & **[mg/kg]** & **[mg/kg]** & **[\%]** & **[\%]** & **[\%]** \\ \hline Cr & 0.003 & 0.008 & 3.15 & 98 & 7 \\ I & 0.005 & 0.012 & 7.80 & 96 & 10 \\ Mn & 0.002 & 0.005 & 2.10 & 101 & 5 \\ Br & 0.006 & 0.014 & 6.50 & 97 & 11 \\ \hline \end{tabular}
\end{table}
Table 1: Validation parameters used in the analysis.
# Geometric Similarity Measurement Method for Micro Scene Generalization
## 4 Conclusions  
In this study, we examined the geometric similarity of micro scenes from the perspective of generalization. The geometric similarity changes in the micro scene were described from three aspects: shape similarity, direction similarity and position similarity. This study's findings contribute to existing research on cartographic generalization by analyzing the geometric features and the generalization process.  
The findings illustrated that the three factors can quantitatively describe the geometric similarity changes in micro scene generalization. In the river + islands micro scene, the simplification operation caused the shape similarities to decrease from 0.923 to 0.818, and the delete and merge operations caused the position similarity to change from 0.769 to 0.692. In the river + lakes micro scene, the changes in position similarity occurred due to the merge operation, and the directional similarity dropped from 0.819 to 0.730. In the rivers + buildings and roads + buildings micro scenes, the change rates of directional similarity were 5.05% and 5.63%, respectively; the changes mainly came from the deletion and simplification operations.  
In general, the geometric similarities of micro scenes can be comprehensively calculated, even if they contain different types of geographic features. The proposed method effectively detects the changes in shape, direction, and position during generalization. In addition, it is able to quantitatively evaluate the preservation of geometric features between original micro scenes and their generalized counterparts; the calculation results were more in line with the actual situation.  
However, several issues should be investigated further. The proposed approach could be improved further by considering the constraints of spatial relationships, such as topological constraints, and the method should be extended to the similarity calculation of multiscale and multi-morphological micro scenes. In addition, automatic micro scene extraction and segmentation methods need to be further studied.  
[PERSON] helped conceive and design the study; [PERSON] carried out the method and wrote the manuscript; [PERSON] and [PERSON] offered significant contribution to result evaluation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 41930101, 41861060, 42161066, and 42174003), Local Science and Technology Development Fund Projects under the Guidance of Central Government, Gansu Province Natural Sciences Fund (No. 637020), and LZJTU EP (No. 201806).  
Not applicable.  
We sincerely appreciate the Editor's encouragement and the anonymous reviewer's valuable support.  
The authors declare that they have no conflict of interest.
###### Abstract  
Vehicle routing problems (VRPs) are challenging problems. Many variants of the VRP have been proposed. However, few studies on VRP have combined robustness and just-in-time (JIT) requirements with uncertainty. To solve the problem, this paper proposes the just-in-time-based robust multiobjective vehicle routing problem with time windows (JIT-RMOVRPTW) for the assembly workshop. Based on the conflict between uncertain time and JIT requirements, a JIT strategy was proposed. To measure the robustness of the solution, a metric was designed as the objective. Afterwards, a two-stage nondominated sorting ant colony algorithm with deep reinforcement learning (NSACOWDRL) was proposed. In stage I, ACO combines with NSGA-III to obtain the Pareto frontier. Based on the model, a pheromone update strategy and a transfer probability formula were designed. DDQN was introduced as a local search algorithm which trains networks through Pareto solutions to participate in probabilistic selection and nondominated sorting. In stage II, the Pareto frontier was quantified in feasibility by Monte Carlo simulation, and tested by diversity-robust selection based on uniformly distributed weights in the solution space to select robust Pareto solutions that take diversity into account. The effectiveness of NSACOWDRL was demonstrated through comparative experiments with other algorithms on instances. The impact of JIT strategy is analyzed and the effect of networks on the NSACOWDRL is further discussed.  
robust; VRP; multiobjective problem; ACO; DRL Article
## 6 Results of the Simulations
### Comparison of the Results Among the Pattern-Matching Methods
#### 6.6.2 Comparison of the Best Results Obtained That Minimize the Error Distance
A third observation is that aggregating the results in the fingerprint domain (with the methods 3-MultFingIn-PosOut and 4-MultFingIn-ClassOut) produces better results than aggregating the position results in the position domain (with the method 2-SingFingIn-AggPosOut). We observe in Figure 22 that pattern-matching method 4-MultFingIn-ClassOut has about 100 to 150 mm less error than the method 3-MultFingIn-PosOut at the 95 th percentile in the CDF of the error distances. However, looking at Table 16, the method 4-MultFingIn-ClassOut requires about 12.6 to 31.5 times more parameters than the method 3-MultFingIn-PosOut does. Thus, it should be evaluated whether for a specific use-case the gain produced using the method 4-MultFingIn-ClassOut is worth the cost in terms of the number of NN parameters required and the associated computing costs. It is noted that the column in Table 16 indicating the number of NN parameters for the method 2-SingFingIn-AggPosOut includes the number of instances of, or calls to, the NN model implemented in this method. The NN model is executed for each one of the \(N_{ST}\) sub-tiles in the tile. This is provided to indicate that for this method, the number of NN parameters alone is not representative of the computing cost of this method.