esa-sceva/satcom-chunk-collection huggingface dataset:
# Review Rating Prediction Based on User Context and Product Context
## 1 Introduction
believe that a product has a collection of product-specific words suited to evaluate it [15]. For example, people prefer using \"sleek\" and \"stable\" to evaluate a smartphone, while like to use \"wireless\" and \"mechanical\" to evaluate a keyboard. [PERSON] et al. believe that the same words may have different or even opposite sentiment meanings in the reviews of different products [16]. For example, \"easy\" is a positive sentiment in \"this digital camera is easy to use\", however, it may have a negative sentiment meaning in \"the ending of this movie is easy to guess\". The main problem with the existing RRP methods that are based on review content is that the user context dependency and product context dependency of the sentiment word cannot be processed very well based only on the review content information. So, we propose a review rating prediction method based on user context and product context (RRP + UC + PC). Our method firstly models the user context dependency of sentiment word, and it proposes a review rating prediction method that combines the review content and the user context. Then, the product context dependency of sentiment words is modeled, and a review rating prediction method based on review content and product context is proposed. Finally, a review rating prediction method that is based on user context and product context is proposed. The main contributions of this paper can be summarized as:  
1. We propose a review rating prediction method based on user context and product context. 2. Aiming at the problem of user context dependency of sentiment words, a review rating the prediction method based on review content and user context is proposed. Aiming at the problem of product context dependency of sentiment words, we proposed a review rating prediction method based on review content and product context. 3. We conduct comprehensive experiments on four large-scale datasets for RRP. We find that our methods significantly outperform the state-of-the-art baselines. The remainder of this paper is organized as follows: we describe the related works in Section 2. Our methods are described in Section 3. In Section 4, we describe experimental setup and discuss the performance evaluation results. Section 5 concludes the paper and it outlines future research directions.
## 2 Design of the Proposed DDL-TDC
### Calibration of TDL-TDC  
As the code density test is adapted, the time distribution of each delay cell can be expressed as follows:  
\[t_{1} =\frac{h_{1}}{\mathbb{C}}T, \tag{1}\] \[t_{2} =\frac{h_{2}}{\mathbb{C}}T,\] (2) \[\vdots\] \[t_{M} =\frac{h_{M}}{\mathbb{C}}T, \tag{3}\]  
where \(h_{i}\) indicates the number of hits for the \(i^{\text{th}}\) delay cell, \(C\) is the total hits of this calibration run, \(T\) is the range of the calibration signal, and \(M\) delay cells are allocated in this chain. The time conversion is calculated from the cells when the signal is reached. As the signal reaches the \(N^{\text{th}}\) cell, the time conversion result \(T_{N}\) can be expressed as follows:  
\[T_{N} = \sum_{j=1}^{N}(t_{j}). \tag{4}\]  
The TDC conversion output can be further expressed through the quantization operation \(Q(\bullet)\):  
\[TDC_{c} = Q\left(\sum_{j=1}^{N}(t_{j})\right). \tag{5}\]  
Based on Equation (5), the time conversion can be obtained according to the hit distribution of each cell, which is called the code density statistical calibration. Because the statistical bin calibration requires considerable time to obtain the time distribution of each delay cell in the chain, two delay lines are adopted in the proposed DDL-TDC to reach run-time calibration. Thus, the calibration can alternate between these two delay lines. These two delay lines are assigned to dedicated fast lookahead carry logic, referred to as CARRY4 cells in the Xilinx Vertix-5 FPGA [32], in two vertical lines by using the commands RLDCand LOC of the Xilinx ISE 14.7 software (XilinX Int., San Jose, CA, USA) tool. The dedicated fast lookahead carry logic (CARRY4) has a small time delay in the Vertix-5 FPGA logic resource, which can obtain higher time resolution than other logic resources. In each CARRY4, four delay cells are implemented, and 512 delay cells are allocated in each vertical delay line. Figure 2 illustrates the layout of the two delay chains in the FPGA chip and the details of the CARRY4 cells.
## 3 Results and Discussion
### Degree of PS Degradation in Ocean, Determined from Rate Constant of ST Formation
In Equation (3), \(3.240\times 10^{-7}\) (year\({}^{-1}\)) is the rate of ST formation at 30 \({}^{\circ}\)C that equals to the rate of SOs formation. Figure 5: TIM-GC/MS chromatograms of the sample extracted from pebble by Showa base, Antarctic continent, 60\({}^{\circ}\)00\({}^{\prime}\) S, 39\({}^{\circ}\)35\({}^{\prime}\) E, 1984. Equation (2) was given by multiplying Equation (1) by the share of PS 7% and inflow ratio 3%. Equation (3) is based on the kinetic parameters of ST formation at 30 \({}^{\circ}\)C.  
ST gradually decomposed to generate SM and SD and SD generated SM [22]. PS had been considered non-degradable in the nature, but kinetically PS, which contains no additives such as stabilizers, has been shown to degrade under the environmental temperature range to produce SOs. SOs formation would be enhanced under the severer conditions in the nature such as UV irradiation and exposure to oxygen in the air. There has been the common misconception that plastic is stable and does not decompose at ambient temperatures. Accordingly, plastics have been considered to remain permanently intact in oceans. However, this study has clearly indicated this not to be the case, based on careful examination of PS degradation. In this study, even purified PS decomposition was shown to start even at 30 \({}^{\circ}\)C. Not only dose drifting lumps of PS break into micro/nano pieces, but also subsequently degrades into basic structure units. It is widely known that the reaction rate doubles with a temperature increase of 10 \({}^{\circ}\)C. Tropical and subtropical ocean temperatures reach 30 \({}^{\circ}\)C and coastal sand at these latitudes reach 60 \({}^{\circ}\)C. The amounts of SO in the world oceans should thus be considered to be significantly higher than this PS value, suggesting ocean contamination to intensify as a result of plastics. Increasing amounts of various polymers go into the ocean and coastal area. Intensive studies are required to evaluate the biologic impacts of SOs from PS and the other chemical species possibly derived from various plastics.
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 3 Results and Discussion
### Flexural Strength of Concrete  
The evolution of the relative flexural strength of the T3, T7, T14, and T21 specimens with the dry-wet cycles is shown Figure 7 and Table 4. It is apparent that throughout the attack process, the relative flexural strength did not change with different dry-wet cycle periods, but the damage and deterioration caused by a single cycle notably differed. The relative flexural strength of T3 peaked after 28 cycles, while that of T21 peaked after only  
Figure 5: Visual inspection of concrete with dry–wet cycles. (**a**) Before sulfate erosion; (**b**) after sulfate erosion.  
Figure 6: Crystallized sodium sulfate.  
about four cycles. Therefore, the longer the dry-wet cycles, the great the relative flexural strength of the concrete, and the more obvious the effects of sulfate attack. Moreover, the variation in T14 and T21 was similar throughout the attack process, which shows that the excessive extension of the dry-wet cycle period had little effect on the flexural strength.  
After peaking, the relative flexural strengths in the four groups all rapidly decreased as the number of dry-wet cycles increased. The strength subsequently started to rebound at about 7, 10, 18, and 42 cycles, respectively, more so in T3 than the other groups. The rebound was caused by certain swelling products generated by the sulfate attack such as ettrinigite filling the concrete pores and cracks that resulted from the early attack [8; 31]. At the same time, the hydrated calcium silicate gel (C-S-H) continued to dissolve, leading to the fluctuation in the flexural strength.  
The single-attack intensity increased and the relative flexural coefficient decreased more quickly as the dry-wet cycle periods were increased in the terminal stage. The relative flexural strength of the concrete for T21 decreased the fastest: by 5.6% after each dry-wet cycle on average--higher than the 4.2% for T14, 2.7% for T7, and 1.05% for T3. T7 showed the largest general decrease; its relative flexural strength decreased by about 18% and 57% from the initial and peak values, respectively. The decreases for T14 and T21 were similar. This shows that the damage caused by sulfate attack with different dry-wet periods was not only related to the damage caused by a single cycle, but also related to the number of cycles. Therefore, both the degree of damage induced by a single dry-wet cycle and the number of cycles are important parameters determining the most unfavorable conditions for concrete.  
Figure 7: Variation of relative flexural strength among different dry-wet cycle periods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cycles**} & \multicolumn{2}{c}{**T3**} & \multicolumn{2}{c}{**Average**} & \multicolumn{1}{c}{**Relative**} \\ \cline{2-5}  & **1** & **2** & **3** & **Value** & **Value** \\ \hline
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Morphology and Size of MXene Flakes
#### 3.1.2 Flake Thickness  
The thickness of MXene flakes is another important consideration. Thinner flakes possess higher flexibility and are more susceptible to conforming to the surface irregularities  
Figure 1: TEM (**a,b**) images of functionalized Ti\({}_{3}\)C\({}_{2}\) nanosheets at various magnifications, showing sharp edges and interlayer gaps [42]. Reprinted from [PERSON] et al., JCOMC, copyright (2021), with permission from Elsevier.  
Figure 2: TEM images of (**a**) LPFD-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\), (**b**) S60-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) (60 s sonication), and (**c**) S360-Ti\({}_{3}\)C\({}_{2}\)T\({}_{\rm x}\) (360 s sonication) [44]. Reprinted with permission of [PERSON] et al. Adv. Sci., copyright (2022), with permission from John Wiley and Sons.  
of neighboring flakes, resulting in enhanced sticking. On the other hand, thicker flakes tend to have lower conformability and exhibit reduced sticking due to reduced inter-particle contact.  
In [45], a straightforward method to increase the yield of Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) few-layer flakes by reducing the precursor size has been proposed. By utilizing small 500 mesh Ti\({}_{3}\)AlC\({}_{2}\) powders as the raw material, obtained through a facile ball milling process, a high yield of 65% has been successfully achieved. Furthermore, the resulting small flakes also demonstrated improved pseudocapacitor performance due to their excellent electrical conductivity, expanded interlayer space, and higher oxygen content on the surface. The Ti\({}_{3}\)C\({}_{2}\)T\({}_{x}\) flakes in the TEM image (Figure 3) exhibit a transparent morphology, indicating that most of the received flakes are single-layer or few-layer.  
At a scan rate of 2 mV/s, this electrode exhibits the highest gravimetric capacitance of 364 F/g. However, as the flake sizes increase, the capacitance of the electrodes noticeably decreases, with the lowest capacitance of 296 F/g achieved for an electrode prepared using 300 mesh MXene flakes. Additionally, the Ti\({}_{3}\)C\({}_{2}\) 500 mesh electrode demonstrates stable cyclic performance, retaining 88% of its capacitance after 10,000 cycles.  
It has been proved that a single-flake device eliminates the conductivity loss caused by the electronic transitions between stacked MXene sheets in layered films. Thus, single-flake devices are more favorable for obtaining high conductivities [46].
## 3 Results and Discussion
### Method Validation  
To validate the method, figures of merit were determined and the results are presented in Table 3 below. The relative standard deviation (RSD) was found to be under 5% in all test samples. A good analytical method for metal analysis is generally considered to have an RSD of less than 20% [57; 58; 59]. However, much higher values are accepted for spotting techniques particularly for thin layer chromatography plates [60; 61].  
The accuracy of this method was tested using certified reference material TraceCERT(r) CRM Fe in water. The results of Fe analysis of the CRM are displayed in Table 4. Results show that this method was accurate as the mean from the silica device analysis of the CRM was statistically not different from the certified value at a 95% confidence level (\(p\) > 0.05). (Table 4). The Fe concentration in the CRM was measured to be 20.09 \(\pm\) 0.17 mg/L using FAAS and 20.73 \(\pm\) 1.25 mg/L using the silica device.  
\begin{table}
\begin{tabular}{c c c} \hline
**Certified** & **FAAS Analysis** & **Silica Device Analysis** \\ \hline
20.00 & 20.03 \(\pm\) 0.17 & 20.73 \(\pm\) 1.3 \\ \hline N = 6 & & \\ \end{tabular}
\end{table}
Table 4: Certified reference material (TraceCERT® CRM Iron in water (ISO/IEC 17025)) concentration determination with silica device compared to AAS analysis in mg/L.  
\begin{table}
\begin{tabular}{c c c} \hline
**No. of Deposits** & **R2** & **Calibration Equation** \\ \hline
1 & 0.9984 & y = 4.91x + 0.91 \\ \hline
2 & 0.9576 & y = 7.43x + 9.45 \\ \hline
3 & 0.8829 & y = 8.41x + 18.0 \\ \hline \end{tabular}
\end{table}
Table 2: Effect of increasing the number of deposits per detection point showing coefficients of determination (R2) and calibration equations (each deposit was 0.3 \(\mu\)L of standard).  
The limit of detection and limit of quantification were determined as 0.2 ng and 0.6 ng of Fe, respectively. This LOD is less than that previously reported using the same method on a paper device [22]. A similar study also reported accuracy within a 95% confidence level in detection of Fe in river sediments, concluding such a method would be suitable as a green alternative option for field testing [62].
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Baseline Models  
Some of the 2D scene graph methods using a priori knowledge and existing 3D scene graph generation frameworks were selected as the baseline models. We compared them mainly with the best-performing baseline models, where the same training environment and parameter settings were used for the experiments on irrelevant factors.  
SGPN [11]: A scene graph prediction network was proposed--the first end-to-end network for point cloud 3D scene graphs. The SGPN uses two parallel PointNets for feature extraction to obtain the object and edge features and uses the GCN for the relational inference.  
Priori knowledge-based methods [8,9]: KERN and Schemata are 2D scene graph methods that use a fused prior knowledge approach. In the experiments, in order to facilitate comparisons with the 3D scene graph method, PointNet was used directly to replace the image feature extraction module in the original model. Such methods were introduced into the 3D scene graph task as a baseline model for comparison. Among them, the knowledge-embedded routing network (KERN) represents the statistical correlations between the objects appearing in the image. Their relationships are explicitly demonstrated using a structured knowledge graph and prior knowledge. Schemata first proposed the idea of prior knowledge learning to learn a set of weighted feature vectors associated with a category.  
KSGPN [14]: As the best baseline model so far, referring to the ideas of KERN and Schemata, based on SGPN, this method of fusing prior knowledge into a 3D scene graph prediction task was used for the first time. It proposes that prior encoding knowledge and the embedding vectors' fuse point cloud features significantly improve the prediction effect of the 3D scene graph.
## 8 Simulation and Evaluation
### Delay Analysis  
In the simulation, all the source and destination nodes of each data stream are chosen randomly and all the packets are generated randomly. Figure 5a,a' show the comparison of delay distributions of 220 packets between NCR and ANCR in the case of two data streams. Because there are only two data streams, the network coding opportunities are fewer. ANCR degenerates to NCR, which makes NCR and ANCR have a similar delay distribution of 220 packets. Figure 5b,b' show the comparison of delay distributions of 660 packets between NCR and ANCR in the case of five data streams. From the figures, we could find the packets with a longer delay in ANCR are slightly fewer than those in NCR. In spite of this, the fewer data streams still result in the fewer coding opportunities, so NCR and ANCR also have a similar delay distribution of 660 packets. Figure 5c,c' show the comparison of delay distributions of 880 packets between NCR and ANCR in the case of twenty data streams. Intuitively, with the number of data streams increasing, both the number of intersections of data streams and the network coding opportunities are also raising. In addition, compared with NCR, ANCR would select a route with a high transmission success rate and coding opportunities, which can reduce the number of retransmission. Moreover, the coding technology can decrease the queuing delay of packets. All these reasons make ANCR bring about lower delay. The two figures also prove this conclusion. The center position of the distribution in NCR is about 2 us, however, that in ANCR is about 1 us. Therefore, compared with NCR, ANCR can reduce the network delay by about 50%.  
Figure 4: Three main cases dealt with by the algorithm. (**a**) Alternative local path strategy under poor quality of input link. (**b**) Alternative local path strategy under poor quality of output link. (**c**) Alternative local path strategy under current node without coding opportunities.
## 5 Mechanical Properties of the Natural Fibers Reinforced Epoxy Composites
### Impact Strength/Toughness  
The impact energy of the materials is the resistance against the high-speed stress or material's toughness. The composite's ability to store energy during plastic deformation is improved as additional fibers are added because more energy will be required to break fiber bundles [18]. A comparative study between treated and untreated banana/epoxy composites was performed by [PERSON] et al. [87]. The impact strength of treated banana/epoxy composites increased at 1% NaOH concentration. [PERSON] et al. [110] studied the effect of fiber loading (10, 20, 30, 40, 50, and 60 wt.%) and surface modification techniques such as alkali (6%), acrylic acid (1%), permanganate treatment (0.5%), and benzene-diazonium chloride on the impact strength of the abaca-reinforced epoxy composite. The benzene-diazonium chloride-treated fiber epoxy composites showed better impact characteristics. The untreated fiber composites showed poor impact characteristics, irrespective of fiber loading. The chemical treatment improved the fiber-matrix adhesion by removing non-cellulosic components and adding hydroxyl (-OH) groups. The 40 wt.% of fiber loading showed better distribution in the matrix composite, low fractures, and better load transfer from fiber to the matrix. However, beyond 40 wt.% fiber loading, the impact strength decreased due to poor bonding and less load transfer from the matrix to fibers (Figure 2). The effect of alkali and silanized woven sisal fibers on the mechanical properties of natural rubber/epoxy composites was investigated by [PERSON] et al. [111]. They observed that grafted depolymerized natural rubber with 1 wt.% methyl methacrylate/glycidyl methacrylate had a 163% greater impact stregth value than epoxy resin. [PERSON] et al. [101] performed a comparative study between groundnut shell and rice husk epoxy composites with different fiber loadings. The groundnut shell-reinforced epoxy composites showed superior mechanical characteristics compared to the rice husk-reinforced epoxy composites. Low-velocity impact behaviour of hep-basalt/epoxy-reinforced composites was studied by [PERSON] et al. [112]. The results indicated better impact behaviour of hybrid composites than hep and basalt-reinforced composites. The impact strength of NFRCs can be improved by proper chemical and physical treatments [113]. The fracture progresses along with the fiber-matrix interface in composites with poor interfacial bonding, causing debonding and substantially improving the composites' energy absorption capabilities. The good adhesion between the fiber and the matrix is responsible for better fracture resistance under impact loading [114]. Fiber loading can also improve the energy absorption capability of the fibers.
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 3 Methodology
### Sobol Sensitivity Analysis  
Numerous spectral indices have been developed to estimate key plant biophysical variables, including chlorophyll concentration, leaf area index, and biomass. This study employed Sentinel-2 satellite imagery to explore the potential of vegetation indices in assessing maize phosphorus status. A comprehensive analysis was conducted on 50 vegetation indices (Table 1; [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]). ### Sobol Sensitivity Analysis  
Numerous spectral indices have been developed to estimate key plant biophysical variables, including chlorophyll concentration, leaf area index, and biomass. This study employed Sentinel-2 satellite imagery to explore the potential of vegetation indices in assessing maize phosphorus status. A comprehensive analysis was conducted on 50 vegetation indices (Table 1; [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Formula** & **Ref.** \\ \hline Two-band vegetation indices & & \\ Ratio Vegetation Index 1 (RVII) & NIR/B & [24] \\ Ratio Vegetation Index 2 (RVIZ) & NIR/G & [25] \\ Ratio Vegetation Index 3 (RVI3) & NIR/R & [24] \\ Difference Index1 (DVII) & NIR –B & [24] \\ Difference Index2 (DVI2) & NIR –G & [24] \\ Difference Index3 (DVI3) & NIR –R & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multispectral vegetation index. Figure 2: Flowchart of this study. In this study, the Sobol sensitivity analysis was employed to assess the influence of input variables on the performance of the LIBSVM surrogate model. The Sobol method decomposes the variance of the output to calculate the first-order sensitivity index (S) and total effect index (ST) for each input parameter.