esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Proposed Energy Improvement  
All improvements have been designed in accordance with the limit values established in the Codigo Tecnico de la Edificacion (CTE; Spanish Building Technical Code) [40]. These limit values are listed in Table 2 and correspond to climate zone C, which applies to Granada.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Layer** & **s** & **\(\lambda\)** & **R** & **U** \\  & **[m]** & **[W/mK]** & **[(m\({}^{2}\)K)/W]** & **[W/(m\({}^{2}\)K)]** \\ \hline \multirow{4}{*}{Wall} & Cement mortar & 0.010 & 0.57 & \multirow{4}{*}{0.24} & \multirow{4}{*}{1.27} \\  & Perforated brick & 0.120 & 0.70 & & \\  & Air layer & 0.050 & & & \\  & Hollow brick & 0.120 & 0.70 & & \\  & Gypsum plaster & 0.010 & 0.57 & & \\ \hline \multirow{4}{*}{Flat roof} & Catalan tile & 0.015 & 1.00 & \multirow{4}{*}{0.16} & \multirow{4}{*}{1.93} \\  & Compression mortar & 0.030 & 1.00 & & \\  & Air layer & 0.150 & & & \\  & Reinforced concrete & 0.200 & 2.5 & & \\  & Gypsum plaster & 0.020 & 0.57 & & \\ \hline \multirow{4}{*}{Unoccupied sloping roof} & Clay tile & 0.080 & 1.00 & \multirow{4}{*}{3.31} \\  & Compression mortar & 0.050 & 1.00 & & \\  & Hollow brick & 0.020 & 0.80 & & \\ \hline \multirow{4}{*}{Floor} & Urea formaldehyde foam Cast concrete Roof screed & 0.132 & 0.04 & \multirow{4}{*}{1.00} \\  & Road screed & 0.070 & 0.41 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermal characterization of the building envelope.  
Figure 1: Selected pilot case study.  
Figure 2: Floor plans of the case study.  
To retrofit the building, three solutions have been proposed: (i) window replacements; (ii) adding insulation to the facade; and (iii) adding insulation to the roof. The selection of these measures is based on common practices in the energy retrofitting of buildings in Spain [41]. A brief description of the measures considered, along with their thermal properties, is provided below:
# SPARQL2 Flink: Evaluation of SPARQL Queries on Apache Flink
## 2 Background
### SPARQL Protocol and RDF Query Language  
SPARQL [29] is the W3C recommendation to query RDF. There are four query types: SELECT, ASK, DESCRIBE, and CONSTRUCT. In this paper, we focus on SELECT queries. The basic SELECT query consists of three parts separated by the keywords PREFIX, SELECT, and WHERE. The PREFIX part enables to declare prefixes to be used in IRIs to make them shorter; the SELECT part identifies the variables to appear in the query result; the WHERE part provides the Basic Graph Pattern (BGP) to match against the input data graph. A definition of terminology comprising the concepts of _Triple and Basic Graph Pattern_, _Mappings_, _Basic Graph Patterns and Mappings_, _Subgraph Matching_, _Value Constraint_, _Buill-in condition_, _Graph pattern expression_, _Graph Pattern Evaluation_, and SELECT Result Form_ is done by [PERSON] et al. in [28,30]. We encourage the reader to refer to these papers before going ahead.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 2 Image Denoising Method Based on Probability Statistics  
The basic principle of the image denoising method based on probability statistics proposed in this paper is shown in Figure 2. Based on the characteristic that the probability of noise is lower than the probability of an effective pixel block, the pixel block is screened by designing a frequency threshold. By identifying noise pixel blocks and valid pixel blocks, the information from valid pixel blocks can be retained. The mean value of the surrounding pixels is used to replace the noise points and achieve the effect of accurate noise filtering. The core technology of this method includes (1) reading and storing the original image, (2) modular processing of image data, (3) data frequency statistics, and (4) noise processing.
## 3 Optimum TMD Parameters Based on AFSA  
### Optimum TMD Parameters and Fitting Formulas  
In this section, the optimum TMD frequency ratio \(f_{opt}\), TMD damping ratio \(\zeta_{opt}\) and dynamic amplification factor \(N_{opt}\) are shown in Table 1.  
To illustrate this more clearly, the comparison of the optimum parameters of TMD systems for different mass ratios and damping ratios of the primary structure under external harmonic excitations are presented in Figure 2.  
It can be seen from Figure 2a that with the increasing of the TMD mass ratio, the optimum TMD frequency ratio decreases, and with the increasing of the structural damping ratio, the optimum TMD frequency ratio increases too. It is shown in Figure 2b that with the increasing of the mass ratio, the optimum TMD damping ratio also increases, and with the increasing of the structural damping ratio, the optimum TMD damping ratio has a small incensement. It is presented in Figure 2c that with the increasing of mass ratio, the optimum dynamic amplification factor decreases, and with the increasing of structural damping ratio, the optimum dynamic amplification factor decreases steeply for a small mass ratio and smoothly for a large mass ratio.  
The explicit expressions for the optimum TMD frequency ratio and optimum TMD damping ratio are given as follows, which result from data fitting through the least square method.  
\[f_{opt}=0.999-0.421\mu+0.0456\zeta_{p}-0.588\mu^{2}+1.2477\mu\zeta_{p}+1.1325 \zeta_{p}{}^{2} \tag{7}\]  
\[\zeta_{opt}=0.607\sqrt{\mu}+0.120\mu+0.129\zeta_{p}-3.423\mu^{2}-0.485\zeta_{p} {}^{2}+2.312\mu\zeta_{p} \tag{8}\]  
To verify the accuracy of the fitting Formulas (9) and (10), the fitting error between the formulas and Table 1 are shown in Figure 3.  
It is presented in Figure 3a that the fitting error of optimum frequency ratio are all smaller than 0.3%. It is presented in Figure 3b that the fitting error of optimum damping ratio are all smaller than 1.0%. Therefore, the fitting formulas agree well with Table 1. Optimum TMD parameters can be obtained from Table 1 or calculated from Formulas (7) and (8).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mu\)} & \multicolumn{3}{c}{\(\xi_{p}=0.01\)} & \multicolumn{3}{c}{\(\xi_{p}=0.02\)} & \multicolumn{3}{c}{\(\xi_{p}=0.03\)} & \mul  
Figure 3: Comparison of fitting error of fitting formulas. (**a**) Comparison of optimum frequency ratio; (**b**) Comparison of optimum damping ratio.  
Figure 2: Comparison of the optimum parameters of TMD systems based on artificial fish swarm algorithm (AFSA) for different mass ratios and damping ratios of the primary structure under external excitations. (**a**) Comparison of optimum frequency ratio; (**b**) Comparison of optimum damping ratio; (**c**) Comparison of optimum response.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Numerical Simulation Methods
The numerical simulation method is generally recognized internationally because it can realize the dynamic damage simulation by considering the damage mechanism affecting the formation and the damage characteristics of each factor and thus became an international research hotspot and frontier research direction. The means of numerical simulation are also changing day by day, including lattice Boltzmann simulation, molecular simulation, continuous time stochastic simulation, pore full-size simulation, etc., which are used to better describe the formation damage mechanism from the pore scale. The methods of simulation have also gradually evolved from continuous media mechanics (hydrodynamic equations) to more complex numerical systems of probabilistic processes ([PERSON], 2013; [PERSON], 2014), which have enriched the means of pore-scale (small-scale) studies. These numerical simulation techniques are mainly focused on the fluid flow behavior and fluid-rock surface interaction in porous media. Computational simulation studies are carried out through modeling, which is highly theoretical but poor practically. However, in a recent study, [PERSON] et al. (2015, 2016, 2017) established a quantitative simulation technique for the spatial and temporal evolution of damage in a variety of common formations. The simulation results of this numerical simulation technique are listed in Table 3. The model correlation coefficients are above 0.9 for all datasets (Figure 6), with an average accuracy of 85.3% compared to the measured data (Figure 7). This technique not only enables the simulation of the spatial and temporal evolution of each damage type and the total damage degree but also provides the sensitivity of each damage type to the total damage degree. However, one should be aware of the risk of nonlinearity of the interaction between factors (pressure, temperature, chemical reactions), which can significantly impact the simulation results. As a result, it can provide quantitative information on the percentage of each damage type in the total damage level, which provides the core technology for precise control and release of formation damage and the restoration of well production and water injection capacity of water wells ([PERSON], 2013). This study has led to the development of formation damage diagnosis technology and is at an internationally advanced level.
## 2 Data Collection and Analysis
### Sound Parameters  
There were 29 sound parameters extracted from one particular dog bark sound, including energy, loudness, energy difference, density difference, pitch maximum and minimum, number of voice cycles, and measurements of tonality. These considered parameters described the quality of sound obtained, which changed considerably over time. For example, voice cycles are the unit movements of the vocal folds, which detail the air being exhaled during the production of the sound. Tonality and harmonics-to-noise ratio give the tonal frequency components over the noise caused by the irregular movements of the vocal folds [42; 43; 44]. There were variations in the dog sounds based on different contexts. Figure 1 shows the variations in the sounds produced by the dogs through spectrogram analysis. In a similar manner, other dog sounds were considered for analysis. From the sounds recorded, 5200 files of individual sounds were analyzed, and the sounds were classified into different scenarios.
## 5 Case Study
### Results and Discussion
Comparing Figure 10 with Figures 11-13, it can be found that when the tunnel under-passes the box culvert at a short distance (\(d\leq 2.0\) m), the blasting damage of surrounding rock between the box culvert and the tunnel is highly related to \(d\); especially when \(d\leq 2.0\) m, the box culvert structure has a significant \"amplification effect\" on the surrounding rock blasting damage, and the amplification effect attenuates with the increase of \(d\); when \(d\geq 4.0\) m, it can be considered that the box culvert structure has very little influence on the surrounding rock blasting damage of the tunnel. The \"amplification effect\" phenomenon on surrounding rock blasting damage may be caused by the facts as follows: the inner space of the box culvert may create an additional free face for detonation holes if the spacing between the box culvert and the tunnel is too small (\(d\leq 2.0\) m). Furthermore, the compressive wave generated by blasting reaches the box culvert and is reflected to form the tensile wave, which acts together with the subsequent stress wave to accelerate the damage development speed of surrounding rock in this zone. From Figure 14, it can be noted that when the distance between the box culvert and the tunnel is 3-4 m, the blasting damage of surrounding rock beneath the box culvert does not exceed \(D_{\text{lim}}\). Thence, when \(d>3.0\) m, the surrounding rock around the box culvert are essentially not damaged by blasting. From Figure 15, it can be noted that when the blasting damage of surrounding rock beneath the box culvert shows an upwardly concave parabolic change with \(d\). From Figures 10-15, it can be suggested that the safe distance between the tunnel and the existing box culvert in blasting construction should be not less than 4.0 m. In addition, it can be suggested that the pre-reinforcement of surrounding rock overlying the tunnel should be implemented before tunnel blasting when \(d<2.0\) m. The sewage box culvert was successfully under-passed using the pre-reinforcement of pipe sheds before tunnel blasting, together with laying out one empty hole without explosives between every two blasting holes with explosives for the peripheral holes of tunnel. Figure 13: Blasting damage diagram of surrounding rock (\(d=4\) m). (**a**) three-dimensional blasting damage diagram. (**b**) cross-section blasting damage diagram.
## Materials and Methods
### Chemical and Sensory Analysis  
An Agilent (7890B and 5977A) GC-MS was used for analysis, fitted with two columns in series. The first column was non-polar (BPX-2, 83 m \(\times\) 530 \(\upmu\)m \(\times\) 0.5 \(\upmu\)m, SGE-Trajan Scientific, Pflugerville, TX, USA) and pressure balanced at the midpoint with a second polar column (DB-WAXETR, 30 m \(\times\) 530 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). Effluent from the second column was split 1:3 by restrictor columns to the single quadrupole mass spectrometer and olfractometry sniff port, respectively (1 part to MS and 2 parts to O-port). The GC temperature profile was initially 40 \({}^{\circ}\)C, held for 3 min, 7 \({}^{\circ}\)C/min ramp to 220 \({}^{\circ}\)C, held for 11.29 min. Data acquisition was collected in full scan mode, the mass range was m/z 33 to 450, and the electron ionization energy was 70 eV. The instrument was tuned daily prior to analysis. MassHunter (v. B.07.00.1413, Agilent, Santa Clara, CA, USA) was used for mass spectral data acquisition and analysis. AromaTrax (v. 10.1, MOCON, Round Rock, TX, USA) was used for sensory data acquisition (i.e., the aromagram). Multitrax Multidimensional Control  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cultivar** & **Harvest Date** & **Berry \({}^{\circ}\)Brix** & **Berry pH** \\ \hline Frontenac gris & 24 September 2015 & 19.5 & 3.00 \\ Frontenac gris & 1 October 2015 & 23.1 & 3.06 \\ Frontenac gris & 9 October 2015 & 23.6 & 3.18 \\ Brianna & 4 September 2015 & 15.4 & 3.09 \\ Brianna & 11 September 2015 & 17.6 & 3.19 \\ Brianna & 18 September 2015 & 18.6 & 3.29 \\ Brianna & 25 September 2015 & 19.6 & 3.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Brianna and Frontenac gris grapes’ characteristics at harvest.  
Software (v. 10.1, MOCON, Round Rock, TX, USA) was used for pressure balance programming. A single trained human panelist was used to assign aroma descriptors and intensity to each compound. This initial research on the popular two cold-hardy varieties was a \"screening\"-type work aiming to find odor-active compounds. At this (screening) stage, using one panelist is sufficient to achieve the stated aims, i.e., to preliminarily characterize odor-active compounds. This information should be used for follow-up studies as a starting point for proper experimental design. Since ethanol was expected to be present in each sample, the intensity of ethanol was assigned as 50 on a 1 to 100 intensity scale. This process has been described in detail elsewhere [22; 23].
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Feature Extraction  
Feature extraction plays a crucial role in fault diagnosis by reducing data dimensionality and extracting relevant information that covers the essence of the original data. In the literature, researchers have previously proposed various feature extraction methods tailored to different domains of fault diagnosis. For fault diagnosis in rotating machinery, researchers often prefer wavelet transform due to its effectiveness in analyzing non-stationary signals and capturing transient features that indicate faults. In chemical systems and other fields, Principal Component Analysis (PCA) is commonly used for feature extraction. It identifies patterns in data and reduces the dimensionality while retaining most of the variation present in the data. Feature learning has emerged as an effective alternative to traditional feature extraction methods. It involves training models to automatically learn features that are most relevant to the target problem, thus overcoming the limitations of relying on domain-specific knowledge for manual feature extraction. With the continuous development of deep learning, there has been significant attention on feature extraction methods based on this technology. A CNN-based deep learning model for state detection has demonstrated its capability by automatically extracting features from data to identify different states or conditions [11]. Adaptive learning rate CNN models are specifically designed to enhance feature extraction capabilities [12]. These models adjust learning rates dynamically during training to optimize feature extraction performance. These advancements in deep learning-based feature extraction methods have significantly improved the efficiency and accuracy of fault diagnosis. They enable the extraction of complex, hierarchical features directly from raw data, contributing to more robust diagnostic models across various application domains.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Performance with Different Code Rates  
When simulated with a code length of 1024 and code rate of 0.25, 0.5, and 0.75, the BER performance is shown in Figure 13. The BER curve of KSNNAD with code rate 0.25 is marked as KSNNAD \(K\) = 0.25, while LSNNAD with code rate 0.25 marked as LSNNAD \(K\) = 0.25, RSC decoding scheme with code rate 0.25 marked as FSCD \(K\) = 0.25, and so on.  
As shown in Figure 14, both of the KSNNAD and LSNNAD have bit error performance improvement compared with SC and the lower the code rate, the better the improvement. When the code rate is 0.25, compared to SC decoder, LSNNAD needs 0.049 dB less to reach BER \(10^{-4}\), while KSNNAD needs about 0.078 dB less.  
Figure 13: Decoding results of different \(N_{S}\).
# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
## 3 Data Acquisition and Discussion
### The Identification of Parameters
#### 3.2.1 Quantitative Evaluation Method for Identification Accuracy  
Since the real values of \(X\) are unknown, the accuracy of the identification algorithm cannot be evaluated by comparing the identification values with the real values. The original intention and application of exoskeleton parameter identification is to calculate joint torques. Therefore, using the error between the identified torque and the measured torque to evaluate the identification accuracy is consistent with the original intention and application of exoskeleton parameter identification. The method of evaluating identification accuracy by the error between identified torque and measured torque has been verified and applied [21; 32]. [PERSON] et al. evaluated the identification accuracy by the agreement between the identified torque and measured torque [21]. [PERSON] et al. used the comparison of identified torque and measured torque for evaluating the identification accuracy and comparing between PSO and LS. In summary, when the parameters are unknown, it is feasible and effective to evaluate identification accuracy by the errors of identified torque and measured torque in the exoskeleton parameter identification whose original intention and application is the calculation of joint torques. Therefore, this method was also adopted in this paper to evaluate identification accuracy as shown in Figure 7. The method can quantitatively evaluate the accuracy of the identification algorithm. Take the experiment in Figure 6a as an example. The identification of \(X\) was implemented based on the data measured from trajectory 1. \(H_{2}\) was acquired in trajectory 2. \(T_{2}^{\prime}\) was calculated from Equation (4). The difference between \(T_{2}^{\prime}\) and \(T_{2}\) was the torque identification error \(T_{error}\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Performance Index** & **Hip Angle** & **Knee Angle** \\ \hline Tracking accuracy (\({}^{\circ}\)) & 0.0217 & 0.0358 \\ Response time (s) & 0.6806 & 0.9725 \\ \hline \end{tabular}
\end{table}
Table 2: Mean of tracking accuracy and response time of APAL.  
Figure 7: Quantitative evaluation method for identification accuracy.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 6 Experimentation and Results
### Energy Consumption Analysis and Verification
Last, the functions obtained through data fitting, as shown in Figure 11, showed approximately linear relationships in all three cases. They may be used as predictive models to guide the planning of battery use and replacement in practice. It is admitted that caution is needed since this test was based on laboratory testing that assumed idealized data sensing and collection conditions. More field testing with real data collection scenarios is needed if an improved battery management strategy is desired.
## 3 Results and Discussion
### DEGs involved in the Terpenoid Backbone Biosynthetic Pathway  
The induction of the monoterpene biosynthetic pathway by cluster thinning in berries is shown in Figure 9a. Terpenoid backbone biosynthesis and monoterenoid biosynthesis are related to monoterpene synthesis [28]. There were 34 DEGs among the treatments (Figure 9a) including five 1-deoxy-D-xylulose-5-phosphate synthases (DXSs; _VIT_09s0002g02050, VIT_05s0020g02130, VIT_11s0052g01730, VIT_00s0218g00110_ and _VIT_04s0008g04970), five acetacetyl-CoA thiolases (_VIT_18s0089g00570, VIT_00s0531g00050, VIT_18s0089g00560, VIT_12s0057g01200 and VIT_18s0089g00590), five geranyl pyrophosphate synthesis (GPPSs; _VIT_04s0023g01210, VIT_15s0024g00850, VIT_05s0020g01240, VIT_18s0001g12000 and VIT_19s009s00530), six monoterpense synthase (TPSs; _VIT_00s0372g00020, VIT_00s0271g00010, VIT_00s0266g00070, VIT_00s0385g00020, VIT_00s0385g00010_ and _VIT_00s0266g00020_), two 3-hydroxy-3-methylglutaryl-CoA reductases (_VIT_18s0122g00610_ and _VIT_03s0038g04100_), two 3-hydroxy-3-methylglutaryl synthases (_VIT_14s0036g00810_ and _VIT_02s0025g04580_), and two 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductases (_VIT_00s0194g00290_ and _VIT_03s0063g02030_). In conclusion, cluster thinning influenced the expressions of genes in the terpenoid synthetic pathway, regulating the synthesis and accumulation of monoterpenoids.  
Figure 7: KEGG enrichment analysis of the differentially expressed genes between L, M, and H at the E-L 35, E-L 36, and E-L 38 stages, respectively (**a**–**c**).