esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The usable floor area is one of the key parameters when appraising residential property. In Poland, valuers have to base their analysis on data from the Real Estate Price Register (RCN) in order to value a property. Unfortunately, these data often turn out to be incomplete, especially with regard to floor area, which makes the selection of reference properties difficult and can lead to erroneous valuation results. To address this problem, a study was conducted that used linear models, non-linear models and machine learning algorithms to calculate the floor area of buildings with complex multi-pitched roofs. The analysis was conducted using data sourced from the Database of Topographic Objects (BDOT10k). Three key factors were identified to provide a reliable estimate of usable floor area: the covered area, the height of the building and, optionally, the number of storeys. The results show that the linear model based on the design data achieved an accuracy of 88%, the non-linear model achieved 89% and the machine learning algorithms achieved 93%. For the existing building data from the city of Koszalin, the best model achieved an accuracy of 90%. The estimated values of the usable area of the building designs for the best model on the test set differed on average from the true ones by 8.7 m\({}^{2}\), while for the existing buildings, the difference was 9.9 m\({}^{2}\) on average (in both cases, the average relative error was about 7%).  
real estate appraisal; neural networks; urban remote sensing; GIScience; linear and non-linear regression; mathematical modelling; machine learning +
Footnote †: journal: Applied sciences
## 3 The Application of Conventional ML Algorithms
### Hidden Markov Model (HMM)
ROC curve can reflect the discrimination ability of the model. In 2015, [PERSON] et al. proposed a novel ensemble HMM classification method that is based on clustering sequences in the log-likelihood space [27]. The eHMM uses multiple HMM models and fuses them for final decision making. Whether compared with discrete HMM or continuous HMM, the ROC performance of eHMM is superior. Another work about the application of HMM in GPR combined HMM with multiple-instance learning (MIL) [28]. The depth bins below and above the target had different characteristics and this multiple-instance HMM (MiHMM) method could incorporate the ambiguity in the individual sample labels. It was presented in this study that MiHMM had better ROC performance than standard EM-HMM and nonparametric Bayesian multiple-instance learning (NPBMIL) on discrete features, while NPBMIL outperformed the other two methods on HOG features.
# Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase
## 2 Literature Review
However, some items, such as 'Perimeter of the Building' or 'Exterior Finish Area', which may fluctuate significantly with design changes in the 'Schematic Design' stage, were also included. The study was focused on using influencing factors that could change frequently according to design changes, such as'Perimeter of the Building' or 'Exterior Finish Area', rather than factors that do not change much in the 'Schematic Design' stage, such as 'Total Area' and 'Building Area'. [PERSON] [9] proposed a cost estimation system for low-rise steel structures using ANN, and defined area, circumference length, height, and load as variables for estimating construction costs. When ANN was used compared to the result of using regression, the error rate was reduced by about 4%. Although ANN performs better than simple regression methods in cost estimation, its accuracy may vary depending on the configuration of the neural networks [10]. [PERSON] et al. [11] stated that the ANN method was inspired by the process in which the human brain operates. [PERSON] [1] stated that ANN finds the correlation weight of the hidden layer in the relationship between the input layer and the output layer, and that it is a method of predicting the result value through the calculated weight. [PERSON] [12] emphasized the importance of economic feasibility and cost analysis in the early stages of architectural planning, highlighting their impact on project management and implementation. The study proposed a novel cost estimation model that combines Building Information Modeling (BIM) and parametric methodology. Unlike traditional cost estimation methods, this model takes into account the interactions between architectural elements, enabling more accurate cost prediction. The effectiveness of the proposed model has been demonstrated through its application in real construction projects. [PERSON] et al. [13] discussed the feasibility and adoption of Building Information Modeling (BIM) for cost estimation in the construction industry. The paper proposed methods for performing cost estimation using BIM and presented a process schema for this purpose. It also provided case studies illustrating how BIM is currently being utilized in the field of cost estimation. Furthermore, the document discussed measures necessary to maximize the utilization of BIM.
## 2 Materials and Methods
### Characterization
#### 2.3.6 Thermal Analysis  
Thermal conductivity (k) and thermal diffusivity (\(\alpha\)) were measured through the transient plane heat source method according to ISO 22007-2 [30]. To carry out the measure the instrument (TPS2500S, Hot Disk AB, Waltham, MA, USA) was equipped with a sensor (5465, Kapton sensor, Hot Disk AB, Waltham, MA, USA) with 3.189 mm of radius. Two samples, for each formulation, were taken in contact with the sensor and they were placed in a container with a temperature controller (Haake AC200, Thermo Scientific Inc., Waltham, MA, USA), which set the temperature at 23 \(\pm\) 0.01 \({}^{\circ}\)C. The measure time was 2 min, and it started after the impulse of 30 mW. The samples had parallelepiped shapes with the following sizes: 20 \(\times\) 20 \(\times\) 4 mm\({}^{3}\) (length \(\times\) width \(\times\) thickness).  
The measures performed with Hot Disk were compared with analysis through a thermal imaging camera (FLIR A315/A615, INPROTEC IRT, Milano, Italy) with thermal sensibility < 0.05 \({}^{\circ}\)C with 10 Hz of frequency to record the temperature. The temperature of a hot plate was set at 90 \({}^{\circ}\)C and the thermal imaging camera was placed in a way that it could record temperature variation on the plate surface, then the samples (dimension 55 \(\times\) 55 \(\times\) 0.5 mm\({}^{3}\)) were positioned on it, ensuring a perfect contact between plate and sample. To evaluate an average trend of temperature, four points for each sample were considered and the measure was repeated three times.
# The Application of Numerical Ductile Fracture Simulation in the LBB Evaluation of Nuclear Pipes
###### Abstract  
The leak-before-break (LBB) concept is widely used in the design and estimation of piping systems of nuclear power plants, which requires considerable test work to obtain the fracture resistance (J-R) curves of nuclear pipes. The application of numerical ductile fracture simulation can effectively limit the test work. In this study, an extended stress-modified critical strain (SMCS) model is applied to simulate the crack growth behaviors of full-scale nuclear pipes (SA312 TP304L stainless steel) with a circumferential through-wall crack under a four-point bending load. The LBB evaluation is performed based on the J-R curves of CT specimens and full-scale pipes obtained from fracture resistance tests and numerical simulations. It shows that due to the high crack-tip constraint effect, CT specimens may cause lots of conservatism in the LBB evaluation of nuclear pipes, while the application of numerical ductile fracture simulation can largely reduce the conservatism.  
LBB; nuclear pipe; J-R curve; ductile fracture; numerical simulation Article
# Dimensioning of Reactive Power Compensation in an Autonomous Island System
## 3 Needs for Reactive Power Compensation
### Reactive Power Consumption Requirements in Rhodes Power System  
In order to investigate whether reactive power compensation is justified, the amounts of reactive power that need to be consumed by the generators in the system of Rhodes are first calculated by means of power flow analysis. The power flow model of the transmission system shown in Figure 1 is enhanced with TPP step-up transformers, the system's HV/MV distribution transformers, equivalent models for loads and PV generation per HV/MV transformer, load compensation capacitors (out-of-service at low load conditions), and an equivalent feeder and generator for wind power plants, as shown in Figure 3.  
The maximum reactive power consumption requirements occur under minimum load conditions (35 MW), which appear during autumn and spring nights. The equivalent load power factor is high due to a relatively extended MV cable network. Thus, a conservative assumption of unity power factor is made for the equivalent load under minimum demand conditions. WPPs are also modeled with unity p.f., as is the standard practice to minimize energy losses. Certain WPPs could consume reactive power, but the island system control center is capable of dispatching only active power setpoints. Moreover, according to available data, the installed inverters of both wind and PV power plants are not capable of operating in StatCom mode, i.e., to absorb reactive power at zero active power conditions.  
HV transmission lines operate as net reactive power producers due to their significant capacitance and low loading levels. In the absence of reactive power compensation, the synchronous generators need to absorb excess reactive power, which may reach up to 27 MVAr at minimum load conditions. Excess VAr are reduced with increasing load levels, e.g., to 24 MVAr at 65 MW of demand. In the UC-ED model, the relationship between excess reactive power and system load is assumed to be linear for simplification purposes.  
Due to the short distances and the autonomous nature of the local power system, sizing the reactive power compensation is not an issue of voltage drop/rise but of reactive power balance over the entire system. Therefore, the location of compensating devices does not make a significant difference in this respect, even though the installation location and the breakdown into multiple smaller compensating facilities will have to be determined with other considerations in mind, including ensuring connectivity at N-1 conditions, so that reactors remain in service in case of disturbances leading to HV circuit outages.
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 3 Novel Monomers Synthesized from Renewable Materials
### Others  
[PERSON] et al. [59] synthesized a multifunctional methacrylate (HEMA-DDSA) monomer from HEMA and 2-dodecen-1-ylsuccinic anhydride (DDSA) via esterification (Figure 9). The resulting monomer had relatively high molecular weight so that the volatility is insignificant. TMPTA has superior reactivity than styrene for its three unsaturated sites. Combination of HEMA-DDSA with TMPTA as co-crosslinker for UV-cured resins would accelerate the curing process [77].  
Levulinic acid (LA) could be synthesized from hexose, which is hydrolyzed from biomass such as starch or cellulose. Levulinic acid contains a carboxylic group and a keto group, which could react with vinyl acetate via transesterification to form vinyl levininate (VL) for use as a RD in UPE resins (Figure 10). However, the replacement of styrene with VL is limited due to its low reactivity and residual components that plasticize resin, hence weakening the crosslinking density and mechanical properties of the resulting resins [60].  
Figure 8: Synthesis of TAHAs from tannic acid via grafting with GMA and CE10 [58].  
Figure 10: Synthesis of VL from levulinic acid and vinyl acetate [60].  
Rosin acid with rigid hydrogenated phenanthrene ring is beneficial for improving strength and modulus of the resulting resins [78]. Two kinds of rosin acid derivatives, i.e., malayipimaric anhydride and acrylicipimaric acid, were used to react with allyl bromide to obtain novel comonomers with divinyland trivinyl functionalities, respectively (Figure 11) [61]. The novel monomers were used as RDs for AESO resin, but their application is limited by their low reactivity. By contrast, a preferable curing system would be achieved by the reactions between rosin acid and GMA [78].  
A series of biore renewable RDs from vanillin and eugenol were synthesized as possible styrene replacements in biobased VE thermosets [79]. In their routes, malonic acid was introduced into vanillin through piperidine-mediated addition, then the intermedia was obtained by an in situ double decarboxylation. After further methylation, the target compound 4-vinylvertrole was synthesized. In another route, vanillin was firstly allylated in solvents followed by aromatic-Claisen rearrangement. Similarly, the intermedia after rearrangement went through piperidine-mediated addition, decarboxylation and methylation to prepare 3-allyl-5-vinylvertrole. Following similar procedures, 3,5-diallyl-1,2-dimethoxybenzene was synthesized from eugenol.
## 2 Materials and Methods
### Model Inputs  
Three types of input data are needed for Biome-BGC model, namely, meteorological data, general stand information, and eco-physiological parameters. The meteorological data included daily maximum temperature (Tmax), daily minimum temperature (Tmin), daily mean temperature (Tavg), vapor pressure deficit (VPD), daily precipitation (Prcp), daylenth, and shortwave radiation (Srad). The stand information data refers to soil texture, soil depth, altitude, slope, aspect, albedo, and so on. The eco-physiological data involved 34 parameters, such as annual turnover rate, carbon/nitrogen (C/N) ratio, phenology, and so on. The climate dataset for the research period was developed by integrating the six-hourly Climate Forecast System Reanalysis produced by the National Centers for Environmental Prediction [30; 31; 32]. The vegetation type map was combined by the vegetation map of China (10 \(\times\) 10 km in resolution) and the vegetation map of the five Central Asia states (25 \(\times\) 25 km in resolution) [33]. GlobCover 2009 land cover dataset (due.esrin.esa.int/globcover/) was used to describe the distribution of irrigated cropland, rain-fed cropland, and settlements areas (Figure 1). The soil maps (i.e., volumetric content of sand and clay, bulk density, and soil pH) were downloaded from the Harmonized World Soil Database [34] with a resolution of approximately 1 km.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation
### Comparison of Travel Route Length  
Generally, the mobile collector is deployed in a converted intelligent car and its velocity is limited by its physical features. Therefore, the scheduled route length has a significant influence on the performance of the network especially network latency. We define the round as the period which denotes the time for the mobile collector to visit all the SPs for data gathering. Different algorithms are executed in many rounds respectively to compare the scheduled route length. The simulation result is described in Figure 12. LEACH generally adopts a static sink to gather information, and we set its travel route length is zero. The travel route length in TRP-MC is constant because the coverage rate will not change once the network model and transmission range are determined. Therefore, TRP-MC only needs to be executed once and the energy consumption used for control messages is greatly decreased. SHDGP plans the longest travel route path in each round because it constructs a tree structure to traverse all the polling points instead of a loop and many paths have to be visited repeatedly.  
Figure 11: Comparison of network lifetime between different algorithms.  
Figure 12: Comparison of travel route length between different algorithms.
## 2 Methods
### Related Work
#### 2.1.2 Discrete Cosine Transform  
The discrete cosine transform (DCT) [26] technique has been employed for image tasks. Ref. [12] investigates the convolution theorem of the DCT and proposes a faster spectral convolution method for CNNs. DCT convolution kernels are employed to transform the input feature map into the frequency domain, allowing them to execute the novel convolution operation in the spectral domain, inspired by simple spatial convolution. This technique is then used to reshape high-resolution images in the frequency domain, instead of resizing them in the spatial domain, for super-resolution and other reconstruction purposes. The DCT (discrete cosine transform) convolutional kernel applies the principles of the DCT to perform convolutional operations on image data. Unlike traditional convolutional kernels that operate in the spatial domain, DCT convolutional kernels operate in the frequency domain, focusing on capturing frequency components of the input image. With the DCT convolution, the characters of HSI data can be fully used and lead to a more efficient representation. Ref. [27] regards channel attention as a compression problem and introduces the DCT in channel attention and compress more information with multiple frequency components of the 2D-DCT.
## Nomenclature  
\(\varepsilon(t)\)Signal Error  
\(\Phi\)Phase  
AmAmplitude  
ANNArtificialNeuralNetworks  
BRBBrokenRotorBar  
DASDataAcquisitionSystem  
fFrequency  
\(fc(t)\)EstimatedFundamentalComponent  
\(f_{\text{FC}}\)FrequencyoftheFundamentalComponent  
FFCFundamentalFrequencyComponent  
FFTFastFourierTransform  
\(f_{\text{LSH}}\)FrequencyoftheLeftSideHarmonic  
FPGAFieldProgrammableGateArray  
\(f_{\text{RSH}}\)FrequencyoftheRightSideHarmonic  
\(G_{\text{1}}\)BestMemberoftheCurrentPopulation  
GAGeneticAlgorithms  
\(G_{\text{c}}(i)\)i-thIndividualoftheCurrentPopulation  
\(G_{n}(i)\)i-thIndividualoftheNextPopulation  
\(i(t)\)TimeDomainCurrentSignal  
\(la\)Currentfromphasea  
IAEIntegraloftheAbsoluteError  
\(lb\)Currentfromphaseb  
\(lc\)Currentfromphasec  
IMInductionMotors  
\(J(x)\)ObjectiveFunction  
LSHLeftSideHarmonic  
\(maxF(x)\)Maximumvalueof\(a\)fitnessfunction  
MCSAMotorCurrentSignatureAnalysis  
MUSICMultipleSignalClassification  
NFNotchFilters  
PLLPhase-LockedLoop  
\(pm\)MutationProbability  
RSResidualSignal  
RSHRightSideHarmonic  
\(s\)InductionMotorSlip  
VFDVariableFrequencyDrive
## 2 Data Analysis and Features Extraction
### Health Features Extraction  
As shown in Figure 1, the SOH of the battery decreases nonlinearly with the increase in cycle count. This curve illustrates the fluctuations and partial rejuvenation characteristics, which are due to the natural recovery process of LIBs in a stationary state. During this process, unstable compounds near the electrodes re-decompose, releasing lithium-ion, which then recombines with the electrode's active material, leading to a temporary capacity recovery in certain cycles [27]. The nonlinear relationship between battery capacity and cycle count has a considerable impact on the accuracy of SOH estimation. Therefore, it is crucial to extract reliable and accurate HFs from the battery's external parameters to effectively predict its SOH. To accurately describe the battery SOH, this paper extracts four types of HFs from the charging process, namely time HFs, energy HFs, similarity HFs, and second-order HFs.
# Unsaturated Hydraulic Conductivity in Composite Porous Media
## 2 Materials and Methods
### Saturated Hydraulic Conductivity Measurements  
The soil cores were subjected to saturation processes for 72 h before taking \(k_{\mathrm{s}}\) measurements. Measurements were made in a constant head permeameter at a fixed height of hydraulic pressure of 160 cm and in a variable head. In each piezometric head, the samples were replicated 1 to 5 times. The values obtained were normalized at a temperature of 20 \({}^{\circ}\)C. The practices were carried out in distilled water. The \(k_{\mathrm{s}}\) results were determined by employing a circuit of five soil replicas, where the proportion of additives was gradually increased, from 5% to 40%, following the procedural standardization regulated in the INV-E-130-13 for saturated specimens. However, for iron oxide (Fe\({}_{2}\)O\({}_{3}\)), the powder was calculated by weight at the same percentage, and it was mixed with Guamo sand since the oxidation processes in this compound can transform it to its insoluble state [45].  
Immediately after the measurements in the conventional permeameter, the saturated hydraulic conductivity, \(k_{\mathrm{s}}\), was determined for each soil sample using the KSAT device in constant and variable cycles. The KSAT practical guidelines are established by the Deutsches Institut fur Normung through DIN 19683-9 [46] and DIN 18130-1 [47]. A ring with a free overflow of the outflow was arranged for the variable head system at the sample top. The assembly was assembled on the KSAT device and fixed with a screw cap. The water was supplied from higher than the outlet level, using a buret connected to the soil core (Figure 1c). The KSAT View software automatically calculates the \(k_{\mathrm{s}}\) data using the variation in the recorded hydraulic head. The infiltration rates in the KSAT mechanism are theoretically obtained by reversing [PERSON]'s law [48], where the flux density in an event corresponding to laminar flow can be considered proportional to the hydraulic gradient (Equation (1)).  
\[q=\frac{V}{A\ t}=-k_{\mathrm{s}}\frac{H}{L}\ \therefore\ \ \ k_{\mathrm{s}}=\frac{L\ V}{H\ A\ t} \tag{1}\]  
\(V\), volumetric flow [cm\({}^{3}\)]; \(A\), sample area [cm\({}^{2}\)]; \(t\), time [s]; \(L\), sample length [cm]; \(H\), hydraulic head [cm].  
For the correct evaluation of the samples, it is vital to show manifest integrity. They cannot have openings, fissures, or cracks that allow the passage of water through these imperfections. Due to the automatic nature of the equipment, the real challenge is to obtain genuinely representative samples and not in the measurement itself [49]. Different issues of the literature established valid values for \(k_{\mathrm{s}}\). In the current case study, the materials' hydraulic conductivity characteristics, according to [47], can be found in Table 2.  
Figure 4: Particle size distribution of diatoms, ferric oxide, and calcium carbonate.