esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Hardened Properties of OMGPC
#### 3.2.4 Splitting Tensile Strength  
The result of the splitting tensile strength (STS) of OMGPC containing recycled aggregates is shown in Figure 11. The control specimen exhibits a maximum STS of 2.7 MPa and 2.8 MPa at the 28-d and 90-d of curing, respectively. The STS values decrease with the addition of RESIN8, with an insignificant increase observed with curing age. Notably, at the 7-d of curing, the control specimen of OMGPC achieved 98% of its 28-d STS. The failure pattern of STS specimens is shown in Figure 12.  
Figure 10: Flexural strength of OMGPC containing RESIN8 and FWG.  
Compared to the control specimen, incorporating R5% aggregate reduces the STS substantially, with a 34.4% and 36.9% decrease at the 28-d and 90-d of curing, respectively. Similarly, the inclusion of R10% content resulted in a significant STS reduction, with 39.1% and 40.1% reductions at the 28-d and 90-d of curing, respectively.  
These reductions are ascribed to the lower stiffness, weak ITZ around plastic aggregate, and higher porosity of the plastic aggregate in the concrete mix. However, compared to the compressive strength of OMGPC reported in Figure 9, the STS of OMGPC at the 28-d is relatively lower. The STS values are 9.6%, 7.3%, 7.7%, 8.7%, and 8.9% of their respective compressive strengths for 0%, R5%, R10%, G5%, and G10% specimens. However, according to [PERSON] [79], the splitting tensile strength of concrete is often around 8-14% of the compressive strength. Hence, the STS of the OMGPC incorporating RESIN8 is below the range.  
In contrast, the STS values of OMGPC with FWG aggregates were similar to the control specimens. The inclusion of FWG aggregate did not lead to significant changes in the concrete's STS. Notably, the STS of the control specimen was well preserved, and the FWG aggregate demonstrated compatibility with the concrete mix. The inclusion of FWG aggregates did not significantly impact the STS, indicating its potential as an alternative and compatible aggregate material for OMGPC application. This report agrees with the findings of [PERSON] et al. [55], showing that the inclusion of FWG up to 10% has no significant difference compared to the control specimen. However, [PERSON] et al. [57] reported that adding 25% of FWG in FA-based geopolymer concrete resulted in a decrease in the STS, but an increase in the FWG from 25% to 75% improved the STS of the composite. Hence, they attributed the improvement of STS to the participation of FWG in the polymerisation reaction.  
Figure 11: Splitting tensile strength of OMGPC containing RESIN8 and FWG.  
Figure 12: Failure pattern of STS specimens.
# Multi-Scale Indoor Scene Geometry Modeling Algorithm Based on Segmentation Results
## 3 Methods
### Recognition and Modeling of Regular Geometric Shapes  
The surfaces of objects in real indoor scenes are mostly composed of geometric primitives such as planes, cylinders, cones, and spheres. The Random Sampling Consistency Algorithm (RANSAC) can be used to search for the basic geometric primitives mentioned above in 3D point clouds, as well as to extract parameters from specified types of geometric primitives. RANSAC is a hypothesis- and validation-based method that generates hypothesis model parameters based on the minimum number of sample points, and uses all data points to validate and update model parameters. Compared to the process of fitting model parameters using all data points using the least squares method, the model parameters estimated by RANSAC using the minimum subset and local points method are more robust, especially suitable for processing point cloud data with more outliers.  
For known geometric primitive types, first the minimum subset required for fitting is determined, such as determining at least three non collinear points on a plane in space. Then, the minimum subset is randomly selected to estimate the parameters of geometric primitives. By determining whether all other data points comply with the current model, the data points are divided into local and external points. Update the model parameters using local points and continue to iterate the above process for the remaining points until the local points are no longer amplified and meet the set threshold requirements [19]. At this point, the optimal parameters of the geometric primitive model are obtained. If the optimal model parameters cannot be obtained in the end, it indicates that the current patch does not match the specified type, thus achieving verification of geometric primitive type judgment.  
This paper mainly focuses on the study of simple geometric objects, including cuboids, cylinders, cones, and spheres. By determining the geometric type and extracting parameters of segmented objects, the modeling of regular objects in the original scene is achieved. The surface of a geometric object is composed of geometric primitives, and the Random Sample Consensus algorithm can be used to verify and extract parameters of known geometric primitives, providing a theoretical basis for subsequent geometric modeling. Based on the judgment results of internal faces of various objects mentioned earlier, a basic geometric model graph is established, categorizing geometric objects into planar and curved ones. For different types of geometric objects, their specific types are determined based on the combination information of internal face patches and the relationship of surface mean curvatures, combined with geometric primitive parameters and geometric shape parameters to perform the modeling.
## 4 Result and Discussion
### Aspect of Transition of Weights
The second graphs show that the stability numbers calculated by using the optimal initial weights determined by the HS algorithm are distributed within the range of 0 to 5 as they are in the observation. Case 1 shows much better correlation between calculation and observation than the Case 2, whereas the index of agreement of Case 1 is only slightly better than that of Case 2. The calculated stability numbers in Case 1 show strong correlation with the observed ones, but they are underestimated as a whole. The third graphs after further training by the BP algorithm show very strong correlation and good agreement between calculation and observation. Even Case 2 (the worst case) shows quite high value of index of agreement compared with the values for HMCR = 0.1 and PAR = 0.5 in Table 5. Note that the results in Figures 7 and 8 are for training data, whereas those in Table 5 are for test data.
# Metamorphic Testing of Relation Extraction Models
## 5 Results and Analysis
### Performance Comparison of Subject RE Models  
We first compare the overall performance of the three RE models. The Overall row of Table 6 reports the overall VR values across all MRs for individual RE models. A lower VR value indicates a better overall performance. According to the results, NCB (\(VR=21.7\%\)) exhibited the best overall performance, which was followed by LUKE (\(VR=22.5\%\)). BERT\({}_{\text{rst+MTB}}\) had the worst overall performance with respect to all of our MRs (\(VR=25.9\%\)).  
We next focus on individual RE models to understand their strengths and weaknesses. First, from Figure 3, we were disappointed to find that BERT\({}_{\text{rst+MTB}}\) did not perform the best with respect to any of the MRs.  
Another model, LUKE, exhibited the lowest VR value on MR-R\({}_{3}\) (\(VR=39.7\%\)) and MR-R\({}_{4}\) (\(VR=18.4\%\)), indicating that this model performed better than the other models for handling co-related entities with different entity types as well as coreferential entities. Meanwhile, relatively lower VR values were observed for MR-S\({}_{1}\) (\(VR=5.5\%\)) and MR-S\({}_{2}\) (\(VR=17.7\%\)), suggesting that LUKE is less sensitive to the order of entities. In addition, LUKE outperformed the other models for both MR-C\({}_{1}\) and MR-C\({}_{2}\) (with the two lowest VR values of 19.8% and 18.2%, respectively). Therefore, it can be inferred that LUKE performs better in correctly capturing the relations between new entity pairs, which are combined from two pairs of entities with different relations.  
Finally, for NCB, we found that the performance of the models varied widely for MR-R\({}_{1}\), MR-R\({}_{2}\), and MR-R\({}_{3}\), which all involved entity replacement. When the model achieved the best performance with the lowest VR value on both MR-R\({}_{1}\) (\(VR=4.2\%\)) and MR-R\({}_{2}\) (\(VR=15.6\%\)), NCB showed a large number of violations on MR-R\({}_{3}\) (\(VR=49.5\%\)) related to entity type changes. We consider that NCB may expose the potential problems of relying heavily on entity type information. To investigate further, we performed a white box analysis of NCB's source code. We learned that the model utilizes entity masking techniques that replace the head (subject) and tail (object) entities with their named entity types. For example, a short sentence such as \"_[PERSON] founded Microsoft_\" would become \"[subject-person]_ founded_ [object-organization]\" after the entity mask. Hence, in this case, changes in entity types were extremely likely to incur prediction failures. This explains the large difference in model performance between MR-R\({}_{1}\) (entity type unchanged) and MR-R\({}_{3}\) (entity type changed), with both involving entity replacement. This also further illustrates the effectiveness of our evaluation of RE.
###### Abstract  
The need for energy and environmental sustainability has spurred investments in renewable energy technologies worldwide. However, the flexibility needs of the power system have increased due to the intermittent nature of the energy sources. This paper investigates the prospects of interlinking short-term flexibility value into long-term capacity planning towards achieving a microgrid with a high renewable energy fraction. Demand Response Programs (DRP) based on critical peak and time-ahead dynamic pricing are compared for effective demand-side flexibility management. The system components include PV, wind, and energy storages (ESS), and several optimal component-sizing scenarios are evaluated and compared using two different ESSs without and with the inclusion of DRP. To achieve this, a multi-objective problem which involves the simultaneous minimization of the loss of power supply probability (LPSP) index and total life-cycle costs is solved under each scenario to investigate the most cost-effective microgrid planning approach. The time-ahead resource forecast for DRP was implemented using the scikit-learn package in Python, and the optimization problems are solved using the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm in MATLAB(r). From the results, the inclusion of forecast-based DRP and PHES resulted in significant investment cost savings due to reduced system component sizing.  
demand response program (DRP); photovoltaic system (PV); pumped heat energy storage (PHES); critical peak pricing (CPP) DRP; time-ahead dynamic pricing (TADP) DRP; loss of power supply probability (LPSP); energy storage system (ESS); Multi-Objective Particle Swarm Optimization (MOPSO) +
Footnote †: journal: Accepted: 11 September 2019; Published: 13 September 2019
## 3 Method
### Light Compensation
#### 3.1.2 An Illumination Compensation Mechanism Based on Luminance Adjustment and Gamma Correction
After extracting the luminance components, the two-dimensional gamma correction function Equation (11) is constructed according to image luminance distribution. \[\gamma=(\frac{1}{2})^{\frac{B_{W}-B_{0}(x,y)}{B_{W}}} \tag{11}\]  
The contrast effect of the two parts of the image is improved by the method of nonlinear tone editing. Equation (12) is used to obtain the gamma-corrected image brightness value in HSV space, after which the image brightness \(B(x,y)\) in RGB space and the image \(\overset{\sim}{I}(x,y)\) are obtained via spatial conversion. \[B_{f}(x,y)=255(\frac{I^{\prime}{}_{V}(x,y)}{255})^{\gamma} \tag{12}\]  
\(B_{w}\) is the average luminance value of the luminance estimate. \(B_{f}(x,y)\) is the V-channel value in HSV space, which can be obtained from the image \(\overset{\sim}{I}(x,y)\), according to the spatial transformation (\(HSV\to RGB\)). \[I_{f}=b\times I(x,y)+c\overset{\sim}{\times}{\overset{\sim}{I}(x,y)} \tag{13}\]  
Finally, the brightness adjustment of the target region and the gamma-corrected image are obtained by Equation (13), where \(\overset{\sim}{I}(x,y)\) is the gamma-corrected image, and \(b\) and \(c\) are the weight values of the input image \(I(x,y)\) and the corrected image \(\overset{\sim}{I}(x,y)\). The use of light compensation mechanisms can improve the visibility of images captured under low lighting conditions or images with strong lighting differences without changing the clarity of the image. By adjusting the brightness and contrast of the image, the dark areas can be clearer and the details more abundant, with a reduction in the incompleteness of the image after the reflection removal operation and the error recognition rate of obstacles. This method can be applied to real-time image processing or large-scale image dataset processing to provide real-time image information of the vehicle's operating environment after reflection removal. But adding a lighting compensation mechanism cannot solve all lighting problems through simple brightness adjustment and gamma correction. For more complex lighting scenes, this mechanism may not be able to effectively suppress lighting differences or restore details. Secondly, an excessive increase in brightness or contrast may lead to loss of details or overexposure of the image, thereby reducing image quality. Finally, this mechanism is a global adjustment method and cannot handle different regions individually. Therefore, the applicable scenarios set in this paper are only tested and validated for the feasibility of the method under the conditions of relatively uniform lighting. In response to the issue of inability to adjust the region, this paper adds a reflection region extraction process, which involves local reflection region recognition and extraction through MSER region matching.
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
###### Abstract  
Slag is increasingly unified for the production of sustainable concrete. This paper presents a procedure with which to analyze the property development of slag composite concrete. Experimental studies of the hydration heat and compressive strength development and simulation studies using a kinetic hydration model and a thermodynamic model were performed. First, we performed an experimental study of the isothermal hydration heat of cement-slag blends. Based on the results of the experimental study on cumulative hydration heat, the reaction degree of slag was determined. We found that the reaction degree of slag decreased as the slag content increased. Second, the reaction degree of slag and cement were used as the input parameters for the Gibbs energy minimization (GEM) thermodynamic equilibrium model. Moreover, the phase assemblage of hydrating cement-slag was determined. The trends of calcium silicate hydrate (CSH) are similar to those of strength. Based on the CSH content, the strength of hardening cement-slag blends was determined. In addition, the calcium hydroxide (CH) content resulting from the thermodynamic model shows good agreement with the experimental results. In summary, the integrated kinetic-thermodynamic model is useful for analyzing the properties of cement-slag blends.  
slag; hydration; kinetic model; thermodynamic equilibrium model; heat; strength Article
## 2 Evaluation Study and Design
### Electromagnetic Simulations  
The purpose of the electromagnetic simulations, conducted using CST Microwave Studio 2024 (Dassault Systemes, Velizy-Villacoublay, France), was to assess the distribution of the EM field within the laboratory office based on the placement of devices. This evaluation aimed to determine the optimal positioning of the device within the actual reference site.
# Wavelength Conversion Enhancement Achieved by Using Resonance in an Array of Nanocylinders
## 2 Upconversion Material Nanocylinder
### Periodic Structure of UC Nanocylinders  
To exploit a UC nanocylinder in a usable device, we consider an array of UC nanocylinders arranged in a square lattice with a period of 420 nm, as shown in Figure 3a. The height and diameter of the nanocylinder are 400 nm and 400 nm, respectively. We calculated the spectra for reflectance, transmittance, and absorbance in the nanocylinder array structure in Figure 3b. The absorbance has a peak of 0.56 at a wavelength of 730 nm. In terms of the wavelength, the electric field vector flows of each nanocylinder are similar to those of the MD of a single nanocylinder (Figure 1d), and the overall vector flows for the entire surface of an array of nanocylinders is directed along the x-direction in phase, as shown in Figure 3c. Magnetic dipole resonance in each nanocylinder and the coupling  
Figure 2: Normalized scattering cross section (Q\({}_{\text{Scat}}\)) as a function of wavelength for a nanocylinder with (**a**) a fixed diameter (\(d\)) of 400 nm and different heights (\(h\)) of 360 nm, 380 nm, and 400 nm; and (**b**) a fixed height (\(l\)) of 400 nm and different diameters (\(d\)) of 360 nm, 380 nm, and 400 nm. The resonant wavelengths of ED and MD increase with increasing \(h\) and \(d\).  
between cylinders contribute to absorption enhancement. Because of the coupling between cylinders, a concentrated electric field, called an electric hotspot, is induced along the direction of the electric field of the incident lights. We are able to use this MD resonance of a single cylinder and the coupling effect of MDs in an array of nanocylinders by tuning the resonant wavelength of the resonance to the absorption wavelength of the material.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 2 Works on Fault Detection
Packet losses have been identified by the distributed filtering mechanism with a nonlinear system in WSN by [PERSON] This technique has also been used for classification. The sensor nodes that failed in the WSN are identified and segregated by Bernoulli distribution. In addition to this, the Lyapunov functional technique is used for performance analysis on fault-finding stability. Heterogeneous faults are also present in the WSN, and they have to be identified and rectified to increase the performance of WSN. It has three stages of operations which are used to find out hard permanent, soft permanent, intermittent and transient faults. Energy consumption is a major problem in WSN. The clustering approach has decreased this energy expenditure. [PERSON] has used a time-out stats signal mechanism to find the fault nodes. Analysis of Variance (ANOVA) is used to check the sensor nodes that have equal values on parameters.
## 5 Performance Evaluation
### Results
#### 5.3.4 Ratio of Average Cost to Average Revenue  
The ratio of average embedding cost to average revenue must be less than 1 in order for the NFVI provider to get the financial benefit. As we can see in Figure 11, the proposed ILP-P and DcPSM show the best ratios of embedding cost to revenue in all scenarios while the DSBM shows the worst ratios. Due to the proposed path mapping approach, ILP-P and DcPSM overcomes all compared schemes in terms of the ratio of average cost to revenue, as shown in Figure 12. As for the DSBM, it shows the worst ratio of average cost to revenue due to the high possibility of clustering virtual nodes. The clustering of nodes might reduce the embedding cost but it might also lead to request rejection since it may select a service decomposition with high number of edges and paths, as in Figure 8.  
Figure 10: Average embedding cost for service requests with: (**a**) specific number of nodes and specific number of edges; (**b**) specific number of paths and specific number of edges.  
Figure 9: Average embedding cost for: (**a**) Simple-Small, (**b**) Simple-Large, (**c**) Multiple-Small, and (**d**) Multiple-Large scenarios. The shaded background behind each curve represents the 95% confidence interval on the reported average values.  
For all solved service requests, we found that the proposed path mapping schemes are more profitable for the NFVI provider if compared to the state-of-the-art schemes. The total number of solved requests, the total of acceptance ratio and the ratio of average cost to revenue have been compared for the studied schemes as in Table 5. The \(\textbf{R}_{c/r}\) for ILP-P was 1.5% better than ILP-A, and ILP-P shows approximately the same ratio in both small and large scales. For the heuristic schemes, DSBM shows a small profit margin equals to 0.09% in small-scale, while in the large-scale, its \(\textbf{R}_{c/r}\) ratio is greater than one which means no profit can be achieved. Despite that the DSBM shows higher acceptance ratio (8.75%) compared to the DcPSM; however, the DcPSM shows lower cost to revenue ratio equals to 71.46% compared to the DSBM, which is about 8 folds of the acceptance ratio difference.
# Thermophilic Methane Production from Hydrothermally Pretreated Norway Spruce (_Picea abies_)
## 4 Discussion  
### Effect of Sugars  
Hydrolysate pretreated at 170 \({}^{\circ}\)C had a higher concentration of sugars and COD values compared to hydrolysate pretreated at 140 \({}^{\circ}\)C due to increased solubilization of macromolecular organic compounds (e.g., hemicelluloses) that hydrolyzed into small molecular matter like oligosaccharidescharides and monosaccharide (e.g., glucose and xylose). A similar trend has been observed by others [39, 40]. Higher methane yield for hydrolysate produced at 140 \({}^{\circ}\)C than at 170 \({}^{\circ}\)C is also observed by others [11, 36]. Contrary to different studies, that higher sugar content in the hydrolysate is best for  
Figure 5: Methane content of H170 at different OLs over time in syringes.  
Figure 6: Methane content of H140 at different OLs over time in syringes.  
\begin{table}
\begin{tabular}{c c c} \hline
**OL (gCOD/L)** & **H170** & **H140** \\ \hline
6 & 73.3 \(\pm\) 0.3 & 70.1 \(\pm\) 0.4 \\
10 & 69.4 \(\pm\) 0.4 & 65.7 \(\pm\) 0.1 \\
20 & 69.5 \(\pm\) 0.8 & 66.4 \(\pm\) 0.3 \\
30 & 68.2 \(\pm\) 1.4 & 64.9 \(\pm\) 0.8 \\ \hline \end{tabular}
\end{table}
Table 9: Weighted average methane content in the produced biogas in syringes.  
methane production [20], it shows that it is not necessarily true. The reason is assumed to be the formation of sugar and lignin degradation products (e.g., furfural, HMF, phenol, cresol) which inhibit the AD process [2; 19]. Therefore, sugar content should not be the sole objective to be considered if methane production is the main goal, and the amounts of inhibitors and soluble lignin in the hydrolysate needs to be considered [19; 41]. The severity index during the pretreatment appears to be a useful control parameter to obtain fewer inhibitors, solubilize lignin and cellulose in the hydrolysate and to obtain a solid residue ideal for other purposes, like making bio-char.  
Usually, glucose is present in the hydrolysate due to hydrolysis of cellulose but as the HWE is not severe enough to cause dissolution of cellulose, it is expected to have originated from hemicelluloses or extractives [6; 42].