esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Solving Methods
### Construction Heuristic  
Since the proposed meta-heuristics are based on the VNS which is a single solution based meta-heuristic, it is necessary to build an initial solution as a starting point for the proposed hybrid meta-heuristics. Therefore, a construction heuristic (Algorithm 1) is proposed to find an initial grouping of PI-containers and then the assignment and scheduling of the outgoing trucks at the docks. The first step of the heuristic is to group the PI-containers in the trucks after sorting them in a decreasing order depending on their lengths while taking into consideration the destination of each PI-container. In the second step, the average position of each truck is calculated depending on the position of the PI-containers that are assigned to that truck. This average position will be used later to find the dock on which the truck will be assigned for loading the PI-containers. The next step is to group and assign the PI-containers to the trucks by destination. If a truck does not have enough space or has a different destination, a new truck must be selected. The last step is to calculate the schedule of the trucks (starting/ending time of processing).  
```
1:Input : Problem data and parameters
2: Sort PI-containers in decreasing lengths
3: Group PI-containers by destination
4: Set: \(L_{i}\) length of each PI-container (\(i\))
5: Set: \(A_{h}\) available length in each truck (\(h\))
6: Set: \(A_{h}=Q\) initial capacity of the truck (\(h\))
7: Set: \(F_{h}\) average position of a truck (\(h\)) at the docks area
8: Set: \(Dist=0\) (Sum of distances for the truck (\(h\)))
9:for each PI-container's destination \(d\)do
10:for each truck (\(h\))do
11:if truck (\(h\)) is a new empty truck then
12: Assign destination (\(d\)) to the truck (\(h\))
13:endif
14:if\(L_{i}\leq A_{h}\) and PI-container (\(i\)) and truck (\(h\)) have same destination then
15: PI-container (\(i\)) is assigned to the truck (\(h\))
16:\(A_{h}=A_{h}-L_{i}\)
17: Move to the next PI-container
18:endif
19:endfor
20:endfor
21:for each truck (\(h\)) do
22:\(Dist=0\)
23:for each PI-container (\(i\)) do
24:if PI-container (\(i\)) is in truck (\(h\)) then
25:\(Dist=Dist+PI\)-container's position in the train
26:endif
27:endfor
28:\(A_{h}=Dist\) / Number of PI-containers in truck (\(h\))
29: Assign the truck (\(h\)) to the closest dock to the average position \(A_{h}\)
30: Calculate the starting/ending time for loading truck (\(h\))
31:endfor
32:Return : Initial Solution (\(S_{0}\))
```  
**Algorithm 1** Overview of the heuristic (\(H_{0}\)) algorithm for the initial solution
## 3 Results and Discussion
### Surface Morphology
The location and environmental conditions of the 4 th compartment resulted in a large proportion of loose flocculent sludge and the small particle size of the granular sludge. The plug flow pattern of mABR made the front compartment have a high organic load and sufficient microbial nutrition, and with the degradation of organic matter along the way, only limited nutrients were able to be obtained by the microorganisms in the 4 th compartment, which greatly affected the metabolism thus slowing down the process of sludge granulation [23]. The images of activated sludge in each compartment were captured after 45d of start-up (OLR = 3.0 kg-COD/m\({}^{3}\) d). It was found that the particle size of granular sludge increased significantly, and the degree of granulation also increased. As shown in Figure 2d, the sludge granulation degree of the 1 st compartment was the highest due to the location where the indoor microorganisms could obtain adequate nutrition and grow fast. However, the 1 st compartment was at the front end of the acid production stage and the effect of gas production was strong, so that the surface of the granular sludge was relatively rough, irregularly shaped, and had a milky white color. The granular sludge in the 2 nd compartment had a good structure and uniform particle size, its shape was mainly spherical and ellipsoid, and the color was milky white. The growth rate of granular sludge in the 3 rd compartment was relatively fast, both the average particle size and the granulation degree were higher than those in the 2 nd compartment, and the surface of the granular sludge was smooth, clear, and black. The average particle size of the granular sludge in the 4 th compartment was still the smallest in the reactor, showing the lowest degree of granulation and a black color. By observing the images, it could be found that the interior of the granular sludge had spaces inside, which indicated that the large particles were composed of small particles gradually in polymerization. These spaces inside the granular sludge were also the important channels for microorganism energy metabolism, including internal gas release, metabolite exclusion, and nutrient entry. With the progress of sludge granulation, the sludge concentration in the reactor increases stably and the impact load resistance increases. The reactor enters the stable operational stage after successful start-up.
## 3 Results and Discussion
### Physicochemical Properties of Sausages
#### 3.1.4 Texture Profile Analysis (TPA) of Cooked Pork Sausages  
The overall cohesiveness of SS sausages was significantly influenced by the fat and salt levels, and a fat \(\times\) salt interaction was observed, and chewiness by the fat and salt levels. The springiness of both SS and IW sausages was affected by the salt level (Table 5). The hardness and springiness values of SS sausage treatments were not different (\(p\) > 0.05) from the control. The SS20f/1.0s treatment had significantly lower cohesiveness, while SS10f/1.5s had significantly higher chewiness values than the control. The overall hardness and chewiness of IW sausages were significantly affected by the fat and salt levels, and a fat \(\times\) salt interaction was observed. The hardness and chewiness of IW sausage treatments: 20f/1.5s, 20f/1.0s, 15f/1.5s, and 10f/1.5s, were higher (\(p\) < 0.05) than the control (Table 5). The springiness and cohesiveness of IW seaweed sausages were similar (\(p\) > 0.05) to the control.  
[PERSON] et al. [42] and [PERSON] et al. [9] observed increasing hardness and chewiness and decreasing springiness by adding sea tangle in low-salt gel/emulsion meat systems and sea spaghetti in pork meat batter. However, some reports have observed softer parties being generated by adding wakame seaweed [35]. The variances in research findings can most likely be attributed to the seaweed type, the seasonality of the seaweed harvesting, and the gross composition of the seaweed utilised, etc. Also, the reduction in fat in meat products such as meat batters and sausages can yield harder products [31; 44]. Salt also acts as a solubilising agent in emulsified products, stabilising the fat within the protein matrix (protein-fat interaction) and contributing to juiciness and texture [45]. According to [PERSON] et al. [46], reduced amounts of sodium can decrease the ability of protein to retain water, which leads to changes such as tougher and chewier products. Changes in the texture profile (hardness and chewiness) of SS and IW sausages, when compared to the control, may have an impact on consumer sensory acceptance.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps from Other Data Sources
#### 3.5.2 Pan-Tropical Forest Biomass Map at 1 km Resolution for the 2000 s  
[PERSON] et al. [19] and [PERSON] et al. [18] produced pan-tropical AGB maps using similar methods that integrated field data, GLAS data, and optical imagery. [PERSON] et al. [103] combined both tropical forest biomass maps with a pan-tropical AGB map at 1 km resolution using the bias removal and weighted linear averaging method. They initially derived a high-quality biomass reference dataset from abundant field observations and high spatial resolution reference maps, and then estimated forestbiomass on the basis of reference data and additional covariates. This fusion approach was applied in areas (strata) with homogenous input error patterns ([PERSON] and Baccini) maps. The fused map was validated by independent reference data, and results showed that the RMSE was 15%-21% lower than that of the input maps.
## 3 Results and Discussion
### Antioxidant Activity Determination  
An antioxidant is any chemical that, when present at lower concentrations than those of an oxidizable substrate, considerably slows or inhibits its oxidation. Natural antioxidants and their health advantages have received a lot of attention in recent years. Antioxidant-based medication compositions are used to prevent and cure a wide range of diseases and ailments. Plants are a significant source of natural antioxidants; they produce a diverse spectrum of secondary metabolites with antioxidative activity and therapeutic potential [29]. The antioxidative activity are mostly based on two reaction mechanisms such as hydrogen atom transfer (HAT) and single electron transfer (SET) [30].  
Currently, there is no unique method that has been standardized for antioxidant activity determination due to the various reaction features, mechanisms as well as variable phase localizations that are frequently involved in the process [30]; however, while extracting valuable bioactive compounds from various plants, it is very important to select the appropriate method and optimize the extraction conditions, since this has a direct impact on the production of extracts with high activity. In this study, the assessment of antioxidant activity of _Laminum album_ flower extract was conducted by two free radical-scavenging methods, the DPPH free radical scavenging assay and the ABTS radical cation decolorization assay; and by ferric reducing antioxidant power assay (FRAP) in order to have a better estimation on the antioxidant capacity of the _Laminum album_ extract. These assays were conducted using a UV-VIS spectrophotometer.  
The antioxidant activity of the sample extracts was examined, and the findings were slightly different. However, all of the assays could be used to determine the antioxidant activity of _Laminum album_ flower extract obtained by SC-CO\({}_{2}\) with methanol as a co-solvent because they all yielded similar rankings among the extracts and demonstrated that extracting the antioxidants at 50 \({}^{\circ}\)C and 250 bar was the best condition in this study. The results are summarized in Table 2.
## 3 Software and Image Processing
### Multivariate Statistical Analyses
#### 3.1.1 Unsupervised Methods  
Unsupervised methods give qualitative information of the analyzed data. Without any prior assumption, they allow to (i) group samples sharing similar features, (ii) identify outliers or anomalous data trends, and (iii) identify correlations among variables and interactions between samples and variables. Principal component analysis (PCA) is one of the most common unsupervised methods applied to HSI to extract useful information. PCA seeks a linear combination of the original variables (wavelength) to produce new variables (the principal components, PCs) orthogonal to each other which account for the variance of the original ones in a new space of projection. Furthermore, it describes the sample distribution highlighting trends and groups, it permits to understand any variable correlation, and to sort and select variables according to relative importance [13]. Instead, other methods find potential clusters in the data in a nonhierarchical or hierarchical  
Figure 6: Spectra of samples labeled according to the percentage of moisture content (MC%): (**a**) Raw spectra, (**b**) SNV transformed spectra, (**c**) MSC transformed spectra, (**d**) Savitzky–Golay first derivative transformed spectra, (**e**) Savitzky–Golay first derivative transformed normalized with mean centering.  
manner [22]. The former includes k-means clustering that divides the observations into a predetermined number of groups (\(k\) clusters), and a sample is placed in a cluster with the nearest barycenter. On the contrary, hierarchical techniques create a hierarchy of clusters using the distance between pairs of samples, and it is typically displayed as a dendrogram [4].
## 4 Sensitivity Analysis  
In order to study the GNT model to variation of its constants, the sensitivity of reduction in diameter of specimen with respect to each constant of GTN damage model was studied in this investigation. To do this, the reduction in diameter of the specimen due to the change of one parameter was evaluated by performing the simulation keeping the other parameters unchanged. The results of this evaluation for all four parameters are illustrated in Figures 16-19.  
Figure 16: Sensitivity of diameter reduction vs. \(f_{0}\).  
Figure 15: The location of voids’ initiation and coalescence predicted by GTN Model.  
For better understanding the sensitivity of reduction in diameter of specimen, \(\Delta d\), with respect to the constants of Gurson damage model, the absolute valve of Variation of the \(\Delta d\%\) due to 20% change in any of the four constant is presented in Figure 20. As it is seen, a small variation in each constant,  
Figure 19: Sensitivity of diameter reduction vs. _f._  
Figure 17: Sensitivity of diameter reduction vs. _f._  
Figure 18: Sensitivity of diameter reduction vs. _f._  
gives rise to significant change in \(\Delta d\). Therefore, from the sensitivity analysis it may be concluded that the values of the constants obtained for GTN model in this work are reliable. The reason is that for false constants, the change in \(\Delta d\) would not be sensitive to small variations in the constants.  
As Figure 20 indicates, \(f_{n}\) and \(f_{f}\) show the highest and the lowest sensitivity of \(\Delta d\) to 20% variation in the constants, respectively.
# Magnetic Localization Method for Vehicles Based on Transformer
## 4 Experiments and Discussions
### Ablation Study  
To further illustrate the importance of each module in the proposed method, we conducted ablation experiments in this section.  
First, to verify the role of Transformer, it was replaced with an LSTM module [39] that has achieved success in magnetic localization tasks. The first and second columns in Table 5 demonstrate the maximum errors achieved with Transformer and LSTM, respectively, with a 7-m-long sequence as the input. It can be seen that their results were sometimes similar. However, for some tracks, the maximum errors of LSTM were larger than 10 m, which significantly influenced its positioning performance, such as that attained for tracks 1, 2, and 4. Figure 11(a) shows the positioning results yielded by LSTM for track 1. According to the comparison shown in the first plot of Figure 10(a), our method yielded more accurate and more stable results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{Proposed method} & \multicolumn{5}{c}{**Methods**} \\ \cline{2-8}  & \multicolumn{2}{c}{\begin{tabular}{c} Traditional MAGCOM method \\ using the MAD metric \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} Method in [12] \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} Method in [27] \\ \end{tabular} } \\ \hline
52.63 ms & 0.52 ms & 27.03 ms & 6.85 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average testing time (ms) induced by different methods.  
Figure 12: Comparison between the positioning results obtained with (a) LSTM and (b) the method removing the equal distance interval division module when using a 7-m-long sequence for track 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Sequence Length**} & \multicolumn{5}{c}{**Evaluation Data**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline Proposed method & 7 m & 2.69 & 4.20 & 1.84 & 3.50 & 3.28 & 3.22 & 4.63 & 1.82 \\ \hline LSTM & 7 m & 22.89 & 11.82 & 1.45 & 12.02 & 3.59 & 1.70 & 8.55 & 1.62 \\ \hline Without the equal distance interval division module & \(\approx\)7 m (2 s) & 32.83 & 31.19 & 75.06 & 31.67 & 29.99 & 30.75 & 53.49 & 74.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The maximum errors (m) induced by different methods.  
On the other hand, to indicate the effectiveness of the equal distance interval division strategy, it was removed before obtaining the positioning results. To conduct a fair comparison, the number of input points was set to 20 to ensure a sequence length of approximately 7 m when the average speed of the vehicle was 3.5 m/s and the magnetic data were sampled at 10 Hz. As shown in Figure 12b and the last column in Table 5, without the equal distance interval division module, the performance of the model drastically decreased.  
In summary, the use of Transformer and the equal distance interval division strategy are both essential for our method.
###### Abstract
However, the sparsity for the SAE network has not been fully exploited to further extract efficient features for classification, and the existing CNN based classifier do not utilize the multi-scale features of the compressed data. Due to these disadvantages, the crop classification performance still cannot achieve a level for practical use. Therefore, the main purpose of this study is to propose a new method to improve the performances of crop classification for better application in agricultural monitoring. Firstly, we adopted various coherent and incoherent scattering decomposition algorithms to extract particular parameters from multi-temporal PolSAR data. Secondly, a sparse auto-encoder network with non-negativity constraint (NC-SAE) was built to perform feature dimension reduction, which extracts the polarimetric features more efficiently. Finally, a classifier based on crop discrimination network with multi-scale features (MSCDN) was proposed to implement the crop classification, which greatly enhanced the classification accuracy. The main contributions of this paper were to propose a NC-SAE for data compression and a MSCDN for crop discrimination. The remainder of this paper is organized as follows. Section 2 devotes to our methodology, including the structure of PolSAR data, the polarimetric features decomposition and dimension reduction with proposed NC-SAE network, as well as the architecture of the proposed MSCDN classifier. In Section 3, the experimental results of crop classification for the proposed method are evaluated and compared with traditional method using simulated Sentinel-1 data. Finally, Section 4 concludes the study.
## 5 Discussion
's research, which showed that accuracy improved when segmenting smartphone acceleration into a larger window size for convolutional neural network training for a sidewalk type estimation [40]. If the recommended LSTM technique was to be applied in a real-world scenario, it would be essential to capture at least five seconds of data, since a typical stride time for pedestrians is approximately 1.10 s.  
Conventional practices for sidewalk condition assessments, which depend on trained experts from governmental agencies or the voluntary participation of residents or pedestrians, are subject to staffing and budget constraints. As a result, the time span between assessments becomes extended [8]. Besides being user-oriented, the practicality of the proposed deep learning approach was also evident, as it achieved a high classification performance in detecting irregular walking surfaces using just a single wearable sensor. In contrast to the shallow machine learning approach, the proposed deep learning approach had an inherent ability to automatically distill knowledge from a high dimensional feature set to extract a good representation, which is analogous to automated feature selection, while simultaneously enhancing the classification performance. This scalable deep learning method could be deployed as a tool to continuously analyze pedestrians' acceleration data to monitor the surface conditions of sidewalks. The system, by removing human biases during the evaluation, could introduce new perspectives to walkability assessments and minimize the time and expense needed for on-site inspections. Nonetheless, this study had its limitations. One of them was that the experiment was conducted in an experimental setting instead of real-world neighborhoods that vary in walkability. The subjects could have demonstrated distinct walking patterns in a real-world environment as opposed to the experimental setting. Another limitation was the small sample size of subjects. Therefore, we evaluated the approach by iterating through each  
Figure 15: Subject distribution based on VM and VMD features in scatterplot. subject to draw conclusions repeatedly. Another drawback of the proposed framework was its inability to track the subjects' locations due to the lack of global positioning system (GPS) data. Finally, the LSTM is inherently a sequential model that relies on recurrence. This sequential nature impeded parallelization during training [55] and could present challenges in capturing long-term dependencies for longer data sequences in future experiments. In the future, we plan to include more subjects to increase the number of subject samples and conduct our experiment in real-world walkable and less walkable neighborhoods. Furthermore, GPS data could be integrated in future works to locate participants and pinpoint the source of problematic sidewalk walking surfaces. To address the limitations of the LSTM and to further enhance the classification performance, we also plan to consider the use of a state-of-the-art deep network called the Transformer, which relies on a self-attention mechanism instead of recurrence.
## 1 Introduction
Thirdly, based on the measurement of the antenna pattern with the solar radiation, we extended the solar radiation method to online monitoring of the health automatic checks of the GMR system on a long-term observation. Moreover, the solar radiation method was applied to evaluate the stability and relative sensitivity of the receiving system. The experimental results show that the method of the GMR health checks based on solar observations has the advantages of simple operation and high accuracy.