esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 4 Results and Discussion
### Feature Selection Results and Discussion
Since the model has only two features, it becomes useful to use those ANFIS parameters that allow a higher tuning level in order to extract as much information as possible from the data available. That is why a 5-2 discretization (instead of the previous 3-2) and a linear output function (instead of the constant previously used) are the parameters that obtain better results. Increasing the granulation to 7-2 does not improve the models obtained. The computational time needed when using only the 2 features selected by FIR is in the order of minutes, not hours, as was the case when all 8 features were used. Specifically, the number of output parameters to be optimized has been reduced from 1296 to only 30. It is relevant to recall that a quick and accurate prediction model is of great importance for buildings design and decision making. Therefore, a second contribution of this research is to illustrate that FIR methodology is able to identify the key parameters (from a set of building design characteristics) governing the energy use of residential buildings. That is, the model identification process of FIR methodology has been revealed \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **\#Epochs** & **Partition** & **RMSE** & **MAE** & **Exec. Time (min)** \\ \cline{2-5}  & 50 & 3-2 & 2.57 & 2.13 & 0.1 \\  & 100 & 3-2 & 2.26 & 1.69 & 0.2 \\  & 500 & 3-2 & 2.16 & 1.68 & 0.7 \\
**Cooling Load** & 1000 & 3-2 & 2.16 & 1.68 & 1.3 \\  & 2000 & 3-2 & 2.15 & 1.68 & 2.8 \\  & 50 & 5-2 & 1.68 & 1.09 & 0.2 \\  & 100 & 5-2 & 1.69 & 1.09 & 0.4 \\  & 500 & 5-2 & 1.69 & 1.09 & 1.2 \\  & 1000 & 5-2 & 1.69 & 1.09 & 2.5 \\  & 2000 & 5-2 & 1.69 & 1.09 & 5.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction errors obtained by ANFIS for the CL, when the features selected by FIR are used as input variables, that is, RC and GA. The best prediction performances are highlighted. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **\#Epochs** & **Partition** & **RMSE** & **MAE** & **Exec. Time (min)** \\ \cline{2-5}  & 50 & 3-2 & 1.71 & 1.34 & 0.1 \\  & 100 & 3-2 & 1.28 & 0.90 & 0.2 \\  & 500 & 3-2 & 1.13 & 0.85 & 0.7 \\
**Heating Load** & 1000 & 3-2 & 1.10 & 0.83 & 1.3 \\  & 2000 & 3-2 & 1.09 & 0.81 & 2.6 \\  & 50 & 5-2 & 0.85 & 0.63 & 0.2 \\  & 100 & 5-2 & 0.55 & 0.40 & 0.3 \\  & 500 & 5-2 & 0.51 & 0.37 & 1.2 \\  & 1000 & 5-2 & 0.51 & 0.37 & 2.4 \\  & 2000 & 5-2 & 0.51 & 0.37 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prediction errors obtained by ANFIS for the HL, when the features selected by FIR are used as input variables, that is, RC and GA. The best prediction performances are highlighted. as an efficient feature selection approach which can be used by other modelling methodologies, such as ANFIS, to enhance its skills.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude
#### 3.4.5 Effect of S-Wave Information on Magnitude Estimation Accuracy  
In the study of earthquake estimation [25], the results of a support vector machine (SVM) trained using six features to predict earthquake location and magnitude, which include mean amplitude, signal peak absolute amplitude, root mean square amplitude, P-wave onset time, S-wave onset time, and P-S interval, showed that the S-wave onset time is a handy feature that can help to improve the estimation accuracy of the location and magnitude of earthquakes when only a single vertical component is used. Previous studies usually consider the information of epicenter distance and S-wave phase arrival time in estimating magnitude. However, waveform features, especially those after the S-wave phase, may have equally important impacts on the magnitude estimation. When waveforms are used as inputs to the model, the magnitude estimation may be less dependent on the propagation time or attenuation and more on the frequency characteristics of the waveforms and the fingerprinting characteristics of the location.  
In this paper, we explored whether the S-wave seismic phase improves the accuracy of magnitude estimation after introducing S-wave information (Figure 10). After viewing the S-wave information without S-wave arrivals, we compared the magnitude estimation errors with the source distance. These were analyzed as follows: Gray dots indicate that only the P-wave information is available. In contrast, black dots indicate that both the P-wave and the S-wave seismic phases are included in the information. Starting from the 2 nd second, the S-wave arrival information of the station gradually becomes involved in the magnitude estimation. From the 3 rd second onwards, the mean magnitude estimation errors of the stations containing S-wave information are significantly lower than those using only P-wave. After the 13 th second, the mean error of the stations containing S-wave information tends to be more stable and close to 0, while the overall error fluctuates somewhat. Overall, introducing S-wave information can effectively improve the accuracy of magnitude estimation, and this finding provides new ideas for future earthquake estimation research.  
Figure 9: Error with magnitude at 1–20 s (all events in gray; average error per magnitude class in blue error bars).
## 2 Theoretical Analysis
### The Second Part  
As shown in Figure 6a,b, the second part of the presented dielectric rod antenna is a solid dielectric rod embedded with several tapered printed loops. In this design, the spacing between the loops is chosen to be the same as the helix turns of 0.21 \(\lambda\). Thus, for the total length of 3 \(\lambda\), the number of the printed loops is 14. These loops are arranged uniformly along the axis of the dielectric rod with their radius tapered linearly from 2.5 mm to 1.2 mm. These printed loops are fed by the mutual coupling helix in the first part. These embedded printed loops significantly increase the gain and decrease the side lobe level due to the long periodic feeding profile inside the dielectric rod. The side lobe levels are further suppressed by tapering the radius of the printed loops.  
Figure 5: (a) Return loss of tapered helix antenna with covering of different dielectric constant. (b) Schematic of Tapered-helix in Uniform cover (c) FBR with and without tapered dielectric cover.  
Figure 6: **(a,b)** The schematic of embedded printed loops dielectric rod (**c**) Gain and SLL comparison first part and combining first and second part (**d**) Radiation pattern comparison of tapered and uniform printed loop.  
To show the effect of the second part on the radiation pattern of the first part, Figure 6c is helpful. It gives a comparison of the radiation patterns between the first part alone and the combination of the first and second parts. It can be seen that this part further improves 3.58 dB gain and a \(-3\) dB side lobe level suppression. Figure 6d shows the radiation pattern comparison of tapered and uniform printed loops. It can be seen that printed tapered loops further suppressed \(-3\)dB side lobe levels, and 4 dB FBR is also improved.
## 5 Biofilm and Heterogeneity of _A. baumannii_ Populations
### Formation of A. baumannii Biofilms
It was reported that enhanced cAMP levels, caused by the lack of cAMP phosphodiesterase, lead to the inhibition of surface-associated motility and pellicle formation [104]. C-di-GMP is synthesized by diguanylate cyclase activity of GGDEF domain-containing proteins, while degradation of c-di-GMP into two GMP molecules is catalyzed by the phosphodiesterase activity of EAL domain-containing proteins [144; 145]. Most c-di-GMP-dependent signalling pathways regulate the bacteria ability to interact with abiotic surfaces or with other bacterial and eukaryotic cells. Eleven genes for GGDEF/EAL proteins in the genome of the _A. baumannii_i 17,978 strain were identified, and most of the predicted proteins were enzymatically active [118]. It was demonstrated that distinct panels of these enzymes promote biofilm formation, macro-colony growth, and surface-associated motility [118]. The (p)ppGpp alarmone is produced by the RelA/SpoT proteins in response to amino-acid starvation and other stresses. (p)ppGpp triggers the stringent response resulting in the downregulated transcription of most metabolic genes and the upregulation of genes responsible for amino-acid biosynthesis [146; 147; 148]. The stringent response was linked to biofilm formation in a range of pathogens, including _Acinetobacter_ spp. [149; 150; 142]. The formation of biofilms is impaired [151; 149] or enhanced [152; 153] in (p)ppGpp-deficient bacteria. Recent studies revealed the interplay among the stringent response, QS, motility, and biofilm/pellicle formation in _A. baumannii_i, but the exact mechanisms remain unclear (Figure 2B). It was reported that the formation of _A. baumannii_i biofilms can be inhibited by a synthetic dodecapeptide 1081, which triggers degradation of (p)ppGpp [149]. However, the activity of peptide 1081 and its link with the stringent response were recently questioned [154]. The _\(\Delta\)relA_ mutation in _A. baumannii_i results in a hypermotile phenotype, as well as in the overproduction of AbuR and acinetin-505. Acinetin-505 is a 505-Da lipopeptide that may act as a surfactant promoting surface-associated motility [127], biofilm formation, and virulence [121]. Numerous studies indicated that (p)ppGpp is involved in the formation of dormant persister bacteria, which are implicated in biofilm tolerance to antibiotics [127; 155].
###### Abstract  
Accurate environmental sensing is pivotal for advancing automation in construction, particularly in autonomous excavation. Precise 3D representations of complex and dynamic site geometries is essential for obstacle detection, progress monitoring, and safe operation. However, existing sensing techniques often struggle with capturing irregular surfaces and incomplete data in real-time, leading to significant challenges in practical deployment. To address these gaps, we present a novel framework integrating curve approximation, surface reconstruction, and marching cubes algorithm to transform raw sensor data into a detailed and computationally efficient soil surface representation. Our approach improves site modeling accuracy, paving the way for reliable and efficient construction automation. This paper enhances sensory data quality using surface reconstruction techniques, followed by the marching cubes algorithm to generate an accurate and flexible 3D soil model. This model facilitates rapid estimation of soil volume, weight, and shape, offering an efficient approach for environmental analysis and decision-making in automated systems. Experimental validation demonstrated the effectiveness of the proposed method, achieving relative errors of 4.92% and 1.42% across two excavation cycles. Additionally, the marching cubes algorithm completed volume estimation in just 0.05 s, confirming the approach's accuracy and suitability for real-time applications in dynamic construction environments.  
Article  
Estimation in Construction Sites Using Marching Cubes Method  
2025  
26 June 2025  
2025  
surface reconstruction; marching cubes; sensing; autonomous excavation; construction site
# A Layer-Wise Strategy for Indoor As-Built Modeling Using Point Clouds
## 5 Conclusions  
This paper proposes a layer-wise method for indoor as-built spaces modeling using 3D planar primitives. The layer-wise strategy slices the point clouds into several pieces on the vertical directions which retains the structural changing in the walls and ceilings. By using 3D planar primitives, detailed structures such as the thickness of inner walls and protruding structures can be retrieved. Compared with the existing methods, our methods can automatically generate models with a higher level of details, and enhance the geometric representation of models. To our knowledge, few existing methods for indoor building modeling can deal with both mobile and terrestrial point clouds. The proposed reconstruction method provides a possible solution, which does not have the particular requirement for input point clouds, and does not rely on any extra information such as the line-of-sight record or the scanner trajectory. The assessment demonstrates the effectiveness and accuracy of our method, and the capability against sparse and noisy data.  
In order to enhance the details of models, in future work, we will focus on exploring more geometric primitives (e.g., sphere and cylinder) to deal with non-planar structures. We also intend to integrate the multi-story building detection (i.e., stairs) for modeling a multi-story building in one pipeline.  
Conceptualization, L.X.; methodology, L.X.; software, L.X.; writing-original draft preparation, L.X.; writing-review and editing, L.X., Z.M., D.C., R.W.; visualization, L.X., Z.M.; supervision, R.W.  
This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).  
The authors declare no conflict of interest.  
Figure 28: The comparison of different methods on mobile scanning data. (**a**) Reconstruction from our method, (**b**) reconstruction from decomposition and reconstruction of structural elements [17], and (**c**) enlarged detail of outputs.
# Regeneration of Articular Cartilage Using Membranes of Polyester Scaffolds in a Rabbit Model
## 3 Results  
### Destruction of PLCA Materials in Membranes Analyzed by Means of GPC  
The weight average (M\({}_{\mathrm{w}}\)) and number average (M\({}_{\mathrm{n}}\)) of molar masses of the initial PCLA copolymer introduced to the membranes were 138,200 g/mol and 78,100 g/mol, respectively. We examined the copolymer material extracted after 8 and 16 weeks of implantation from 16 rabbit joints in total. The average molar masses were measured by means of GPC and calculated with respect to PS standards for all samples. Each sample contained several populations of macromolecules present at fractions. Since the molar mass distributions partially overlapped to facilitate statistical analysis, we decided to calculate the mean values for M\({}_{\mathrm{w}}\) and M\({}_{\mathrm{n}}\) for whole multimodal macromolecular distribution of all materials from each sample separately (Table 1). Exemplary, selected molar mass distributions are shown for illustration in Figure 5. Finally, we calculated the mean values of M\({}_{\mathrm{w}}\) for each type of membrane and implantation period. The data with standard deviation bars are presented in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Kind of Membrane** & **Time of Implantation (Weeks)** & **Sample Nr** & **Mean Mw** \\ \hline PVP & 8 & 1 & 5155 \\ \hline PVP & 8 & 2 & 4816 \\ \hline PVP & 8 & 3 & 4737 \\ \hline PVP & 8 & 4 & 1896 \\ \hline PVP 1  & 8 & 5 & 24,238 \\ \hline PVP & 16 & 1 & 2387 \\ \hline PVP & 16 & 2 & 2176 \\ \hline PVP & 16 & 3 & 2088 \\ \hline Z & 8 & 1 & 3678 \\ \hline Z & 8 & 2 & 3167 \\ \hline Z & 8 & 3 & 3022 \\ \hline Z & 8 & 4 & 2490 \\ \hline Z & 8 & 5 & 1432 \\ \hline Z & 16 & 1 & 2139 \\ \hline Z & 16 & 2 & 2127 \\ \hline Z & 16 & 3 & 1663 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the GPC examination.  
We performed the GPC examination on sections collected only from cavities to prove that the membranes stayed at the primary site of implantation. Because in every sample we detected fractions of polymers, we concluded that membranes remained in the cavities. This confirms that the surgical technique developed by us and used in the study meets the requirements.  
Figure 5: Exemplary molar mass distributions of extracted “PVP” membrane residue.  
Figure 6: Average values of M\({}_{\mathrm{w}}\) of PLCA contained in origin membranes and residue materials.
# Electronic Nose Analysis to Detect Milk Obtained from Pasture-Raised Goats
## 3 Results and Discussion
### Relationship between Sensory and Instrumental Data  
PLSR analysis was applied to summarise the results and investigate the correlation between the odour sensory descriptors and instrumental data (volatile compounds and E-nose analysis) in the differentiation of P and S milk samples (Figure 4).  
Figure 3: Heat map of the diversity of volatile compounds in Pasture (P) and Stall (S) milk samples of the five milking periods. Volatile compounds were plotted on the Y axis, while the X axis had the milk samples from each period. Results are displayed as negative (\(-1\)) to positive (\(+1\)) correlation of the volatile compounds in the permuted matrix for the respective milk samples.  
The instrumental data were selected as the X variables and the sensory response values as Y variables. The results show a good separation of the two milk (P and S) samples. Feeding the goats on pasture has given milk different odours, such as sweet aromatic, grassy, butterscotch and cream. These olfactory descriptors are well correlated to the presence of terpen, some acids, \(\delta\)-dodecalactone, 2-nonanone, methyl octanoate and heptanal and mostly agree with the odour descriptors reported in the literature for these molecules (Table 4).  
S milk was positively correlated to volatile compounds such as maltol, [Z]-2-heptenal, 2,3-pentanedione and hexane, with pungent, buttery, fresh milk and caramel odours (Table 4), well correlated to the \"Feed/malty/silage\" odour descriptor.  
The E-nose sensors that allowed discriminating P milk samples were WSS, W2W and W1W. These MOS sensors had stronger responses to the terpene compounds and are in line with the results reported by [PERSON] et al. [59], who obtained a positive correlation of some terpen, such as \(\alpha\)-pinene, \(\beta\)-pinene and limonene, with WSS, W2W and W1W sensors, as well as the W1S and W2S sensors, which also in our study were positively correlated to P milk samples (Figure 4). Still in agreement with [PERSON] et al. [59], the W1S and W2S sensors also had higher responses for the chemical class of free fatty acids, which are more present in P milk samples (Figure 4).  
The responses of W1C, W3C and W5C sensors were relatively higher in S milk samples (Figure 4). It was indicated that W1C, W3C and W5C sensors were sensitive to hydrocarbons, aromatic compounds [60] and some ketones [59], and the S milk samples had higher amounts of volatile compounds such as hexane and 2,3-pentanedione.  
Figure 4: PLS-loading plot (t1 vs. t2) for volatile organic compounds (black circle), E-nose sensors (grey circle) and olfactory sensory descriptors (blue circle) of Pasture goat’s milk (P; green square) and Stall goat’s milk (S; red triangle). Numbers 1 to 5 indicate the milking period (April 2021 to June 2021).
## 2 OFDM Based Multicarrier Modulation Schemes
### Non-Contiguous OFDM  
Cognitive radios need transceiver designs which use non-contiguous portions of the spectrum for transmission [20]. In OFDM based cognitive radio systems, the transmission bandwidth must be large in order to support high data rates. However, a large contiguous bandwidth may not be available for opportunistic transmission of CR. In addition, in a dynamic spectrum access (DSA) network, multicarrier-based cognitive radio transceivers need to deactivate some of their subcarriers to avoid causing interference to primary users, as shown in Figure 1. To provide high data rates while avoiding interference with incumbent user transmission, a variant of OFDM called non-contiguous OFDM (NC-OFDM) was proposed in [21; 22]. The schematic of an NC-OFDM transceiver is shown in Figure 2. Selective subcarrier allocation (SSA) is performed at the transmitter side, where zeroes are assigned for deactivated subcarriers and data symbols are assigned for active subcarriers, as shown in Figure 3. Similarly, selective subcarrier retrieval (SSR) is performed at the receiver side. This modulation method achieves high data rate transmission through collective utilization of non-contiguous blocks of subcarriers and also avoids interference with incumbent users by deactivating subcarriers overlapping with the primary user's signal spectrum. Figure 4 shows the spectrum of a NC-OFDM signal, in which the blue trace is the cognitive radio's environmental signal, the red trace is the OFDM waveform, and the green trace is the narrowband interferer, which can be seen in the middle of the OFDM spectrum.  
Figure 1: Adaptive subcarrier deactivation in the middle of \(U\) subcarriers.  
Figure 3: Selective Subcarrier Allocation (SSA) in NC-OFDM.  
Figure 2: Schematic of an NC-OFDM transceiver.
## 5 Driver Acceptance Evaluation Model
### Modeling of Driver Acceptance  
In this section, two driver acceptance models using two different parameter selection methods are presented: the statistical significance test and the suggested acceptance sensitivity. To derive the models, a specific set of data was considered: classified as driven by expert drivers, performing lane changes only as commanded, and lane changes with traffic. The formulations of the models are defined in Equation (5) in Section 4.2. The vectors of the \(p\) input variable \(\mathbf{x}^{T}\) and the \(p\) unknown parameters \(\mathbf{\beta}^{T}\) are also the same as those expressed in Equations (4) and (7), respectively. From the results of the parameter selection study, the two different input variable vectors are presented in Tables 2 and 3 in Section 5.1; as a result, 11 input variables and their coefficients were used for one model based on their statistical significances, and 38 input variables and their coefficients were used for the other model based on the results of the acceptance sensitivity analysis, which is the suggested method in this study. Then, the vectors of their coefficients (\(\mathbf{\beta}^{T}\)) were designed by the maximum log-likelihood method described in Section 4.2[27; 28]. Table 5 presents the modeling results of the driver acceptance evaluation models using the different parameter selection methods, the two different sets of the input variables derived by the statistical significance test and the acceptance sensitivity, and their respective coefficient values. In addition, the effective ratios of the selected parameters for each method were analyzed using a Pareto chart as shown in Figure 4. It was observed that the parameters which were positioned higher had a greater impact on each parameter selection method. Specifically, the sensitivity analysis indicated that approximately 72% of the selected variables were from the top four parameters for the significance test, while the effective ratios of the top five parameters accounted for about 60% of the overall effect in the sensitivity analysis.