esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Digital Image Analysis for Assessment of Star Content and Distribution in Potatoes
###### Abstract  
This study presents the possibility of using digital image analysis for the assessment of the starch content and distribution in potatoes. Tubers of six cultivars that were stored for 3 months in contrasting conditions (4 \({}^{\circ}\)C vs. \(-15\)\({}^{\circ}\)C) were used in the experiment. The starch distribution in the potato tubers was assessed on the basis of histograms of the pixel values along four lines in the tuber cross-sections. Next, the basic statistics were calculated and used for the analysis of variance. The applied method allowed more precise distinguishing between the studied potato cultivars than comparing the total starch content alone. The new method also clearly distinguished potatoes stored in a freezer from those kept in a cold store.  
digital image analysis; _Solanum tuberosum_; potato; starch distribution in tubers 202  
others [15; 16]. Thanks to computer technology, data analysis is less expensive and tests are faster, repeatable, recorded automatically, and can be performed in difficult conditions which were inaccessible to humans in the past [15].  
Imagej software is a tool for image analysis used in many fields of research [16; 17]. It was first released in 1997 by [PERSON] of the National Institutes of Health in the USA [14]. It is an open-source tool that runs on Linux, Mac OS X, and Windows, in both 32-bit and 64-bit modes. It has many functions typical of graphic software, but also some untypical ones, e.g., the designating of regions of interest (ROIs) in linear or other shapes and calculating the mean, standard deviation, and other statistics of the pixel values in the ROI [16]. Additionally, the software is easy to use for both amateurs and programmers, so it is now commonly used by scientists at various stages of their careers [17; 18].  
Like specialists in other fields of research, food technologists also attempt to simplify some procedures to save time, labor, and money. Because of the growing popularity of ImageJ software in other disciplines and the limited number of reports on the use of ImageJ software for starch content assessment in potatoes, we decided to verify the possibilities of its application for this purpose [19]. More precisely, this study aimed to determine the usefulness of ImageJ software for the assessment of the concentration and distribution of starch in potato tubers. Additionally, we checked the possibility of using this method to distinguish between the potato cultivars and to detect changes in the starch distribution in potatoes during storage at contrasting temperatures.
## 2 Results and Discussion
### Catalytic Tests
In our study, the Raman spectra of the recovered materials (Figure 16) proved such a point, where an increase in the \(F_{2g}\) mode intensity of CeO\({}_{2}\) after each cycle confirmed the change in the cerium coordination state towards non-framework species. It was also noticeable in the Ce-MCM-Cl spectrum that the intensity of this band doubles itself after the second cycle, while for Ce-MCM-NO\({}_{3}\), it happens to a lesser degree. Such an observation is consistent with the steadier loss in catalytic performance observed in Ce-MCM-NO\({}_{3}\), indicating that this catalyst is more resistant towards deactivation. This effect is possibly a consequence of the CeO\({}_{2}\) coating mentioned earlier in this work, since pore walls were thicker for Ce-MCM-NO\({}_{3}\) (3.64 nm) than for Ce-MCM-Cl (3.43 nm), making the former less susceptible towards possible etching effects from TBHP. The protective effect described is not usually highlighted in the literature for metal-incorporated MCM-41 materials and shows how relevant the choice of metal precursors is to attain not only active materials, but more stable ones towards unfavorable processes inherent of catalytic applications. Overall, the best conditions for the BzOH liquid-phase oxidation were the ones in run 8 for Ce-MCM-Cl (TBHP, molar ratio of 1:1, 10 wt.% of catalyst, and 80 \({}^{\circ}\)C), while the conditions of run 10 were best for Ce-MCM-NO\({}_{3}\) (TBHP, molar ratio of 1:1, 5 wt.% of catalyst, and 80 \({}^{\circ}\)C). In situations where better BzOH conversions, BzD yields, and heteroelement incorporation are aimed for, cerium(III) chloride precursor affords the ideal material. In situations where BzD selectivity and catalytic endurance is the objective, cerium(III) nitrate precursor might be the best choice of synthetic precursor.
## 2 Proposed Approach
### Generator \(Q\)-Limit Index  
Figure 5a depicts \(\left|Z^{\mathrm{L}}\right|\) and \(\left|Z^{\mathrm{th}}\right|\) as a function of load increase without violations of generator \(Q\) limits. The solid line shown in Figure 5a denotes the function curve for extrapolation with cubic splines. Observations from extensive simulations on power systems ranging from small to large power grids indicate that, using the approach above, the estimated \(\lambda^{\mathrm{es}}\) (which is close to but a little less than the exact \(\lambda^{\mathrm{max}}\)), can be acquired.  
For those cases with generator \(Q\) limit violation during load increase, \(Z\) in Equation (5) must be updated based on the change of bus type status (\(PV\) bus to \(PQ\) bus) before the estimation of \(\lambda^{\mathrm{es}}\). An illustrative example of \(Q\) limit violation is depicted in Figure 5b, where the sharp jump in \(\left|Z^{\mathrm{th}}\right|\) coincides with the generator reaching its capacity limits. In this study, a \(Q\)-limit index \(Q_{\mathrm{Gi}}^{\mathrm{ex}}\) proposed in [20] is used to measure the extreme \(Q\) for each generator based on the given load increase direction (LID). The process for identifying generator \(Q\) limit violation is summarized in Algorithm 1. According to [20], the approximation quadratic model of \(\lambda-Q_{\mathrm{G}}\) curve can be described as \(\lambda=\alpha_{i}Q_{\mathrm{Gi}}^{2}+\beta_{i}Q_{\mathrm{Gi}}+\gamma_{i}\), where \(\alpha_{i}\), \(\beta_{i}\), and \(\gamma_{i}\) can be obtained by utilizing three sets of \(\lambda\) and \(Q_{\mathrm{Gi}}\). Once the coefficients are determined, the extreme value of \(Q_{\mathrm{Gi}}\), denoted by \(Q_{\mathrm{Gi}}^{\mathrm{ex}}\), can be estimated. When \(Q_{\mathrm{Gi}}^{\mathrm{ex}}\leq Q_{\mathrm{Gi}}^{\mathrm{min}}\) or \(Q_{\mathrm{Gi}}^{\mathrm{ex}}\geq Q_{\mathrm{Gi}}^{\mathrm{max}}\), generator \(Q\) limits occur at \(PV\) bus \(i\), in which \(Q_{\mathrm{Gi}}^{\mathrm{min}}\) and \(Q_{\mathrm{Gi}}^{\mathrm{max}}\) are the lower and upper limits of the \(i\)th generator. Otherwise, there is no generator \(Q\) limit violation at \(PV\) bus \(i\). After the execution of Algorithm 1 for \(\left\{Q_{\mathrm{Gi}}\right\}_{i=1}^{n_{\mathrm{G}}}\), a list of \(Q\) limit violations at \(PV\) buses is obtained.
## 3 Results and Discussion
### Effect of Neutralization on Thermophilic Solid-State Anaerobic Digestion  
The cumulative methane yields and daily methane yields of the reactors for 30 d for thermophilic SS-AD are shown in Figure 4. The cumulative methane yields of all reactors with different neutralization treatments ranged between 23.1-232.7 mL\({}_{\text{N}}\cdot\text{gVS}_{\text{feed}}{}^{-1}\). Meanwhile, the reactors treated by WW had much lower cumulative methane yield of 23.1 mL\({}_{\text{N}}\cdot\text{gVS}_{\text{feed}}{}^{-1}\), which was approximately 88-90% less compared with reactors treated by others and RM (see Figure 4a). In fact, the cumulative methane yield of the WW treatment nearly halted from the 9 th day (Figure 4b), thereby exhibiting a typical acidification phenomenon, which was confirmed by the measurement of pH (<5.8) on day 30. In the experiments described herein, a large amount of tap water was consumed to lower the pH of the lime-treated CS to 8.0, which could simultaneously reduce the alkalinity of the substrate. At the same time, our experiments were performed at lower inoculation ratios (S/I = 6, VS basis) compared to those reported for SS-AD at an S/I of 1-4 [38; 39], which further increased the risk of over-acidification. Moreover, AD of WW has not failed both in the BMP test and the L-AD, seemingly due to the higher ratio of inoculum, lower TS, and intermittent physical stirring providing many microbes and a more efficient mass transfer environment.  
Figure 4: Effect of different neutralization treatments on the (**a**) cumulative methane yield; and (**b**) daily methane yield during 30 days thermophilic SS-AD of lime-treated corn stover. Data are shown as mean \(\pm\) SD, \(n\) = 3.  
Compared to RM, the cumulative methane yield of CN was significantly (\(p<0.05\)) increased by 19.9%. [PERSON] et al. [7] also achieved higher methane yields when using NaOH for pretreatment, which was a larger increase (40.1%) than that in this study because [PERSON] et al. used a smaller size of corn stover as a substrate for AD. Moreover, the peak of CN's daily methane yield was delayed by four days (Figure 4b). In contrast, the peak of daily methane yield of LDN was 1 and 5 days earlier than that of RM and CN, respectively (Figure 4b). This finding can be potentially explained by the fact that the microorganisms in LD facilitate the disruption of the dense structure of CS. At the same time, the inhibition of the AD process driven by the pretreatment by-products disappeared, probably because the extrusion step had removed some of the by-products, thereby resulting in the concentrations of these inhibitors below the threshold of inhibition. However, the extrusion step removed not only the fermentation inhibitors but also the dissolved fermenttable fractions, so that the cumulative methane yield was not significantly different from that of RM (Figure 4a).
## 3 Results and Discussion
### Flexural Properties  
Naturally, the glass-ceramic specimens produced behaved as brittle materials. The stress-strain curves of batch 8v are represented in Figure 9. These were a representative example of flexural tests after load deflection curves were translated into stress-strain using the measured average dimensions of each specimen's transverse section. The remaining experimental data can be found in the Supplementary Materials associated with this research.  
To globally analyze the impact of the process parameters in the behavior of SPD specimens, a simplification was made, only displaying the average transverse rupture strength (\(\sigma_{TRS}\)) and the average modulus of elasticity in bending (\(E_{Bend}\)), noting that in some cases, the rupture strain was very variable. Irregularities such as down-steps happened in all types of specimens. These down-steps corresponded to times at which sections partially broke. These down-step-shaped irregularities are not rare in stress-strain graphs obtained in bending tests with similar materials. One likely primary source of error in the flexural assessments was related to the visual warping of each specimen. Considering this average of \(\sigma_{TRS}\) and \(E_{Bend}\) between batches, Figure 10 illustrates the global results obtained in the bending tests.  
When it came to the specimens with 20% sand mixed with the glass, the down-steps became bigger and more frequent. This behavior was predicted and could be related to the fact that the sand particles did not sinter and therefore were merely held together due to the sintered glass microspheres. The sections containing bigger percentages of mixed sand were more susceptible to breaking, making the specimens weaker. Figure 11 shows an example of the fracture surfaces of a pure glass specimen and a specimen with 60% sand.  
Figure 10: Average transverse rupture strength (\(\sigma_{TRS}\)) and modulus of elasticity in bending (\(E_{Bend}\) ). Darker bars indicate the average \(E_{Bend}\), and lighter-colored bars indicate the average \(\sigma_{TRS}\).  
Figure 9: Flexural stress–strain curves of the specimens of batch 8v.  
Looking at the specimens printed with 100% glass microspheres, the horizontally printed specimens sustained the higher flexural stresses, from 25 MPa to 33 MPa, while the maximum flexural stress for the vertically printed specimens was below 20 MPa.  
Despite undergoing a sintering stage that could minimize the layer effect, the results on build orientation still revealed the natural tendency of layered processes. In the horizontal specimens, the layers were aligned with the bending plane, perpendicularly to the direction of the load. In vertical specimens, layers were colinear with the load direction, which made them more susceptible to breaking. When considering the specimens with 20% sand, the orientation of the specimens did not significantly impact the maximum stress, as both types only sustained flexural stresses up to 13 MPa.
## 6 Conclusions and Future Work  
With the rapid development of XR technology (VR, AR, and MR, etc.), its range of possible applications in an industrial environment have greatly expanded. Construction machinery is an important infrastructural tool within the field of equipment manufacturing. The application of MR technology in the manufacturing of construction machinery and equipment will help the development of traditional industries.  
This paper proposes a holographic visualization verification platform for a digital prototype of construction machinery based on virtual terminal equipment. This equipment consists of six modules, including virtual-real integration design assembly verification and man-machine verification, remote multi-user collaborative review, complete machine modeling verification, factory production line layout, digital twin visualization of production line, virtual simulation and motion simulation of construction machinery equipment. The platform is designed to cover the whole life cycle of construction machinery. This study shows that the visual verification platform is a feasible, low-cost, and scalable solution that can be easily applied to the field of construction machinery. In addition, it not only expands the application of mixed reality technology in industrial fields, but also further advances intelligent manufacturing in traditional industries.  
Further exploration of computer vision and machine learning techniques is needed to improve existing methods. For future research, the team will focus on optimizing the existing modules, developing more functional modules, and ameliorating the existing solutions using 3D reconstruction and data visualization technologies.  
Conceptualization, M.D., L.L., Y.L., X.Z., L.X., Y.T. and D.G.; methodology, L.L., C.T. and F.M.; software, L.L., C.T. and F.M.; validation, L.L., C.T. and F.M.; formal analysis, M.D., L.L., Y.L., L.X. and Y.T.; investigation, M.D., L.L. and Y.T.; resources, M.D., L.L., Y.L. and L.X.; writing--original draft preparation, L.L.; writing--review and editing, M.D., L.L., Y.T. and D.G.; project administration, X.Z., Y.T. and D.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hebei Province, grant number F2022203015 and the Innovation Capability Improvement Plan Project of Hebei Province, grant number 22567637H.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction  
Most of the world energy needs are met through fossil fuels. However, there is a demand to look for a novel, renewable and sustainable alternative energy source, such as microalgal biofuels [1]. These microorganisms show a great potential to complement fossil fuels to satisfy the energy demand since they have a high growth rate, 100 times faster than conventional biomass, and do not compete with food crops [2, 3, 4, 5].  
Microalgae can be easily converted into liquid biocrude through advanced thermal conversion technologies such as the hydrothermal liquefaction process (HTL) [6, 7, 8]. HTL is a promising technology for wet biomass due to the reduction of microalgae drying energy costs. In addition, HTL does not require an extraction step, and converts the main macromolecules of the microalgal biomass (lipids, proteins and carbohydrates) into products of interest [9]. In contrast, biodiesel can be obtained only from the lipid fraction of the microalgae. HTL is usually carried out at a temperature range of 200 to 350 \({}^{\circ}\)C and at pressures between 5 and 25 MPa, with or without catalysts to produce an upgradable biocrude, together with gas, aqueous and solid phases that can be used within a biorefinery either for energy production (biocrude) or in the microalgae culture thereof [10, 11, 12].  
HTL biofuel research is currently focused on optimising the yield, quality and chemical properties of biocrude obtained with microalgae. Biocrude from microalgal HTL shows heating values in the range 30-50 MJ/Kg (HHV), although microalgal biocrude is more viscous and presents higher oxygen and nitrogen contents (1-20 wt% and 1-8 wt%, respectively) [13]. The biomass is hydrolys into smaller molecules, which repolymerise to form the biocrude phase. This phase is a complex mixture of compounds with broad molecular weight distribution, mainly including linear and branched aliphatic chains, carboxylic acids, esters, aromatic and phenolic compounds, and nitrogen within rings or linear structures [14]. The high nitrogen and oxygen content makes the HTL biocrude not directly suitable for storage and use as a transportation fuel, and it requires subsequent hydrothermal steps [15]. The yield and composition of the biofuel are affected by operating conditions such as temperature, reaction time, biomass concentration, pressure or the presence of a catalyst [8; 10; 13; 16].  
In this work, the biocrude production by HTL from _Nannochloropsis gaditana_ L.M. Lubian was investigated because of its high potential to produce biofuels [17]. A 2\({}^{3}\) factorial design of experiments (three factors and two levels) was carried out, and the effects of temperature, reaction time and microalgae concentration were assessed to maximise the yield of biocrude and minimise the nitrogen and oxygen content.
## 2 Principles of Rainfall Field Retrieval by ESLs and HMLs
### Rainfall Field Retrieval by Combined ESLs and HMLs  
When multiple ESLs and HMLs exist within an area, the rain rates \(R(x_{i},y_{i})\) at the location \((x_{i},y_{i})\) of the ESLs and HMLs can be measured. The rain rates at these different locations reflect part of the distribution of the rain rates \(R(x,y)\) over the whole area. Therefore, we can combine the ESLs and HMLs in the area to obtain highly accurate rainfall fields by means of the reconstruction algorithm. In this paper, the OK algorithm and RBF neural network are applied to accomplish the rainfall field retrieval.
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results
### DPU Side Effect Prediction  
Data splitting procedure: We split all given drug pair samples labeled as adverse events into training, validation, and test datasets to evaluate prediction performance for DPU inputs. Specifically, single drugs were first divided into five groups for five-fold cross validation. Then, at each iteration, labeled drug pairs consisting only of drugs belonging to four groups were used as training data, while labeled drug pairs containing at least one drug belonging to the remaining group were divided into half for validation and half for test data. Moreover, virtual drug pairs generated based on the given 284 drugs were added to the training data to include all possible multidrug combinations that were not originally present in the training data.  
Performance evaluation: The performance of the proposed model was evaluated in two aspects. The first aspect relates to the effectiveness of additionally using labeled single drug data to train the model, and the second aspect relates to the appropriate range of data used for regularization when training the model. As an option for the second aspect, only the multidrug combinations included in original training dataset were used, or virtually generated multidrug combinations were used in addition to the given multidrug combinations. Table 1 shows changes in performance of model prediction with respect to these two aspects. Here, each of the model characteristics is observed to be highly effective in improving the generalizability for multidrug side effects prediction. Although virtually created drug pairs do not always seem to help improve predictive performance, they are observed to significantly improve performance if single drug label data are used together with drug pair label data for learning. Therefore, it is recommended to simultaneously use single drug and drug pair data for model training, with regularization based on all given drug pairs and virtual drug pairs.  
Comparison with existing methods: For comparative study, we applied two existing methods (Decagon [7] and DistMult [18]) for model training with our data. This is because, to the best of our knowledge, the prediction performance for DPU input data has not been reported in previous studies using knowledge graphs. As results show in Table 2, the proposed model showed the highest performance with AUC of 0.708 compared to Decagon with AUC of 0.531 and DistMult with AUC of 0.577. This performance improvement demonstrates the effect of using labeled single drug data and a regularization term in the loss function. Moreover, even at the baseline model using no labeled single drug data and no regularization, our model showed better performance as AUC of 0.618 than the models based on existing methods. However, this may be because existing methods need to learn far more parameters than our model, and thus can overfit the training data. Using more extensive data for training might help to improve their performance.
## 4 Experiments
### Benchmark Comparisons on Public Datasets
#### 4.6.3 Parameter Analysis  
To assess the lightweight nature of our model, We benchmarked the DRFAN against 12 mainstream SR methods on the Urban100 dataset under \(\times\)4 scaling. The compared methods include IMDN, RFDN, LatticeNet, LAPAR-A, EMASRN, SMSR, DeFiAN, ShuffleMixer, SAFMN, DIPNet, GASSL-B, and DVMSR. As depicted in Figure 9, the DRFAN achieves the highest PSNR with minimal parameter growth, outperforming existing methods by 0.03-0.22 dB while maintaining a competitive model size.  
Figure 8: Error map visualization for Urban100 samples corresponding to Figure 7. DRFAN exhibits the least red region, demonstrating enhanced pixel-level accuracy and structural consistency.  
Figure 9: Visual comparisons of PSNR and parameter count between our model and mainstream SR models on the Urban100 dataset under a \(\times\)4 scale factor.