esa-sceva/satcom-chunk-collection huggingface dataset:
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 4 Application to the Investigated Building
### Damping Estimation of the Building
Higher floor dampers contribute more to the damping of the second mode, but the total damping contribution in that mode is negligible, as shown in Table 3.  
It is important to stress once more that the enhanced strain-energy method is built based on the assumptions of harmonic motion and mode-proportional deformation of structure and added dampers, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Longitudinal Direction**} & \multicolumn{2}{c}{**Transversal Direction**} \\  & **Type I** & **Type II** & **Type I** & **Type II** \\ \hline \(c_{d}\) (kN/mm-s\({}^{-1}\)) & 0.696 & 0.685 & 0.876 & 0.516 \\ \(k_{d}\) (kN/mm) & 4.258 & 2.562 & 3.395 & 3.295 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The identified model parameters for monitored oil dampers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Longitudinal Direction**} & \multicolumn{2}{c}{**Transversal Direction**} \\  & **1 st Mode** & **2 nd Mode** & **1 st Mode** & **2 nd Mode** \\ \hline \(\omega_{i}\) (rad/s) & 5.608 & 18.132 & 5.062 & 16.552 \\ \(\xi_{i}\) & 0.031 & 0.061 & 0.036 & 0.053 \\ \(\phi_{i,2}\) & 0.014 & \(-\)0.026 & 0.013 & \(-\)0.026 \\ \(|\phi_{i,6}\) & 0.025 & 0.016 & 0.026 & 0.016 \\ \(p_{i}\) & 47.421 & \(-\)15.392 & 48.952 & \(-\)15.324 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The identified modal parameters for the building. Figure 5: Percentages of the equivalent damping ratios and natural frequencies from oil dampers of same floors: (**a**) Longitudinal direction; (**b**) Transversal direction. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Longitudinal Direction**} & \multicolumn{2}{c}{**Transversal Direction**} \\  & **1 st Mode** & **2 nd Mode** & **1 st Mode** & **2 nd Mode** \\ \hline \(\omega_{s,j}\) (rad/s) & 5.495 & 17.721 & 4.951 & 16.108 \\ \(\omega_{nj,i}\) (rad/s) & 0.113 & 0.411 & 0.111 & 0.444 \\ \(\xi_{s,j}\) & 0.012 & 0.055 & 0.017 & 0.045 \\ \(\xi_{nj,i}\) & 0.019 & 0.006 & 0.019 & 0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The estimated equivalent natural frequencies and equivalent damping ratios provided by oil dampers. The corresponding parameters of the building itself also shown. which violate their real situations during earthquakes. Therefore, it is necessary to verify the method accuracy, which is achieved through model updating and prediction validations.
# Advanced Control and Fault Detection Strategies for District Heating and Cooling Systems--A Review
## 3 Overview of Fault Detection and Diagnosis (FDD) in DHC Systems
### Fault Detection in Substations and Customer Facilities
#### 3.2.3 Detection of Regulation Valves Malfunctioning  
The actuator of the control valves or the valves themselves may wear and tear during operation or after large periods without use. This causes uncontrolled flows in the installation and instability of the flow rate. An analysis of frequency variation and stability of the flow in the primary circuit of the substation was performed by [PERSON] et al. (2020) [121] leading to a simple and easy method to detect this kind of fault.
## 1 Introduction
Owing to the characteristics of the VSP observation system, seismic data received via the receivers contain a significant number of down-going waves, which are primarily composed of multiples. Therefore, mirror imaging is an effective multiple-wave imaging method for VSP. [PERSON] et al. converted first-order surface multiples into primary reflections above the water surface, thereby enabling VSP multiple-wave imaging [22]. [PERSON] et al. proposed a novel joint RTM method. By placing mirror receivers above the water surface, they converted down-going multiples into up-going waves, enabling the simultaneous imaging of both multiples and primary waves [23]. Interferometric imaging is another effective multiple-wave imaging method. [PERSON] et al. proposed that common propagation paths of seismic signals from the source to different receivers can be canceled under a high-frequency approximation [24]. [PERSON] et al. transformed passive-source data into reflected seismic data using a deconvolution algorithm [25]. [PERSON] et al. achieved the migration of arbitrary-order multiples by using semi-natural Green functions and interferometric imaging [26]. The utilization and imaging of multiples have been challenging. For VSP observation systems, the vertical placement of receivers renders the traditional surface seismic multiple-wave imaging methods unsuitable. Furthermore, the existing research lacks methods capable of simultaneously imaging surface and interbed multiples in asymmetric observation systems. To achieve the joint imaging of primary waves, surface multiples, and interbed multiples in VSP observation systems, a common-receiver-based multiple-wave reverse time migration (CR-MRTM) method is proposed in this study. This approach replaces the commonly used Ricker wavelet with original seismic data containing multiple waves, which are excited at the source location and then reverse-propagated, balancing the complexity of source and receiver wavefields in traditional RTM and enhancing the quality of VSP multiple-wave imaging results. Compared to existing studies, the method proposed in this paper enables the simultaneous imaging of primary reflections and multiple waves without the need for additional signal separation, thereby enhancing the utilization of VSP seismic data. Furthermore, traditional VSP multiple-wave imaging methods typically handle only surface-related multiples, whereas the method presented here performs well for both surface-related and interlayer multiples. Theoretical models and numerical experiments demonstrate that the proposed method can simultaneously image primary and multiple waves, thereby improving the illumination range of VSP imaging. This approach partially addresses the limitation of traditional VSP observation systems, which are restricted to imaging only the vicinity of the observation well, and significantly enhances the overall quality of VSP imaging.
# Numerical and Experimental Investigation of Recycled Brick Coarse Aggregate Concrete
## 3 Results and Discussion
### Calibration of the Numerical Model
#### 3.4.2 Uniaxial Compression Failure Behavior
The initial damage to the RB-100 sample occurred in the interface transition zone with weak mechanical properties. With the increase in loading, the two significant damage zones appeared intensively and continued to expand along the interface between aggregate and mortar. As the damage gradually developed, part of the RBCA was broken, causing the specimen to be damaged. The maximum compression damage value of the model is 0.737. The damage to the RB-50 sample first appeared in the interfacial transition zone and mortar matrix, then a V-shaped central damage zone appeared. Then, the damage began to concentrate as the load displacement increased, forming three prominent damage zones. Finally, the damage zone penetrated through the whole specimen, and the specimen was further damaged as the three main damage zones continued to develop. The maximum compression damage value of the model is 0.737. The initial damage to the RB-0 sample occurred in the interfacial transition zone. It was observed that multiple damage zones expanded along the interface between the aggregate \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Loading Displacement**} \\ \cline{2-4}  & \(-\)**0.0168 mm (Damage Appears)** & \(-\)**0.0280 mm (Peak Strain)** & \(-\)**0.0300 mm (Destroy)** & **Damage Value** \\ \hline \hline \multirow{2}{*}{RB-100} & \multirow{2}{*}{\(-\)**0.0168 mm (Damage Appears)**} & \multirow{2}{*}{\(-\)**0.0270 mm (Peak Strain)**} & \multirow{2}{*}{\(-\)**0.0300 mm (Destroy)**} \\ \cline{2-4}  & & & & \\ \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Loading Displacement**} \\ \cline{2-4}  & \(-\)**0.0168 mm (Damage Appears)** & \(-\)**0.0268 mm (Peak Strain)** & \(-\)**0.0300 mm (Destroy)** & **Damage Value** \\ \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Loading Displacement**} \\ \cline{2-4}  & \(-\)**0.0168 mm (Damage Appears)** & \(-\)**0.0268 mm (Peak Strain)** & \(-\)**0.0300 mm (Destroy)** & **Damage Value** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Destruction process of the model. and mortar with increasing displacement load. Then, the damage was connected and formed a V-shaped damage zone as the load continued to develop. Finally, the cracking became larger and larger and the concrete was damaged under continuously increasing external loading. The maximum compression damage value of the model is 0.737. The internal cracks are discrete when the RBCA substitution rate is low, and there is no apparent continuous crack formation in the section. However, the internal crack width positively correlates with the aggregate substitution rate, and one or two oblique continuous failure cracks are formed in the section. Most of the cracks are concentrated in dense areas of recycled aggregate.
# Research on Quantitative Investment Strategies Based on Deep Learning
## 4 Experimental Simulation
### Data Processing
#### 4.2.1 Calculating Historical Volatility  
So far, both the industry and academia have proposed countless ways to estimate historical volatility. This article selects the Close-To-Close [32] method. As the name suggests, this volatility estimation method uses the closing price to estimate the volatility. Since the standard definition of volatility is the square root of the variance of the variable, only based on the definition of the unbiased estimate of the variance, the formula for calculating the historical volatility is available:  
\[\sigma=\sqrt{\frac{\sum{(x_{i}-\overline{x})^{2}}}{N-1}} \tag{34}\]  
\[\overline{x}=\frac{1}{N}{\sum{x_{i}}} \tag{35}\]  
\[x_{i}=\log(\frac{P_{t}}{P_{t-1}}) \tag{36}\]  
where \(\sigma\) is the historical volatility; \(x_{i}\) is the logarithmic rate of return; \(\overline{x}\) is the mean of the sample's rate of return; \(N\) is the sample size, meaning the number of \(x_{i}\) involved in the calculation; \(P_{t}\) is the closing price at time \(t\). The historical volatility used in this paper refers to the historical volatility of five previous trading days of the day at present.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 3 Results
### Prediction Results' Relationship to Various Factors of the Fabrics  
To understand more details of the model performance, CIELAB color coordinates that contribute to color differences were analyzed. Figure 9 shows the linear fitting of the L*a*b value from the predicted images and that of actual samples from two different fabrics. For the silk twill, the individual L*a*b value predicted by this method has excellent linear fitting performance in terms of lightness L* (R2 = 0.987), chromaticity coordinates a* (R2 = 0.965), and chromaticity coordinates b* (R2 = 0.966). For the silk crepe satin, the individual L*a*b predicted by this method also has excellent linear fitting in lightness (L*) (R2 = 0.984), a* (R2 = 0.984), and b* (R2 = 0.982).  
Figure 8: The representative generated results in two fabrics. \(\Delta\)E\({}_{00}\) is calculated between predicted digital printing image (generated print fabric) and actual printed fabrics (ground truth).  
Figure 9: L*a*b value from the predicted images and that of actual samples of silk twill and silk crepe satin.  
The linear fitting results of predicted colors for two different fabrics in terms of chroma and hue angle were evaluated (Figure 10). For both silk twill and silk crepe satin, the color predicted by this method showed high linear fitting quality in terms of chroma (C\({}^{*}\)ab) (R\({}^{2}\) = 0.960 and R\({}^{2}\) = 0.978, respectively). But the fitting effect was relatively poor in terms of hue angle (h\({}_{\text{ab}}\)) (R\({}^{2}\) = 0.745 and R\({}^{2}\) = 0.816).  
To understand if the current model has a poor performance relating to CIELAB color coordinates, color differences were plotted against L\({}^{*}\) and C\({}^{*}\)ab. Figure 11 shows that the current color prediction method has the largest error for colors with low CIE chroma and high lightness. It seems that the color prediction method performs poorly in the area where the human eye perceives the brightest and most neutral colors, while the predicted color differences' \(\Delta\)E\({}_{00}\) in other areas are less than 5. In the area of low C\({}^{*}\)ab and high lightness, the color prediction performs worse than in the area of high C\({}^{*}\)ab and low lightness. The color prediction in the area with higher chroma values is more accurate and has fewer variations, where individual \(\Delta\)E\({}_{00}\) are below 4.  
Figure 11: Scatter plot of \(\Delta\)E\({}_{00}\) between predicted and actual image versus L\({}^{*}\) and C\({}^{*}\)ab of actual image. Samples with large L\({}^{*}\) value and small C\({}^{*}\)ab value showed higher deviation with large \(\Delta\)E\({}_{00}\).  
Figure 10: C\({}^{*}\)ab and h\({}_{\text{ab}}\) values from the predicted images and those of actual samples of silk twill and silk crepe satin.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 3 Strategies
### Doping and Compositing  
In addition to tuning the morphology, doping and compositing are considered potential approaches to improve the piezoelectric performance. PENGs based on materials such as ZnO, PZT, lead-free perovskite materials, and polymers exhibit excellent piezoelectric outputs; however, the generated outputs are insufficient for conventional applications. The significant advantages of doping and compositing are that they increase the piezoelectric coefficient and the dielectric constant; additionally, compositing with a polymer can provide good flexibility and stretchability, which ultimately improves the output performance and durability. In the previous section, we provided an overview of the effect of morphology on the piezoelectric performance of materials, and in this section we summarize the effect of different dopants and composite materials on piezoelectric performance. (I) ZnO  
As discussed above, ZnO is the most extensively used piezoelectric material in PENG applications and it provides excellent output performance. The piezoelectric performance of ZnO-based PENGs can be further improved using appropriate dopants; \(p\)-type and \(n\)-type dopants are used. Doping ZnO with \(p\)-type dopants has been considered the simplest approach for enhancing the piezoelectric output. \(p\)-type doping can enhance electron transport by reducing the shielding effect on the electrons generated in the ZnO nanowires, whereas \(n\)-type doping induces lattice strain along the polar axis. The ionic radius of the dopant is an important factor to consider while doping the piezoelectric host material. If the dopant radius is too large or the concentration is too high, more lattice defects are generated, which affect the charge transport in the external circuits and decrease the output voltage. Some examples of dopants used for ZnO are halogen group elements, namely, iodine (I\({}^{-}\)), bromine (Br\({}^{-}\)), chlorine (Cl\({}^{-}\)), and fluorine (F\({}^{-}\)). Metal dopants including copper (Cu\({}^{2+}\)), cerium (Ce\({}^{3+}\)), europium (Eu\({}^{3+}\)), gadolinium (Gd\({}^{3+}\)), magnesium (Mg\({}^{2+}\)), manganese (Mn\({}^{2+}\)), lanthanum (La\({}^{3+}\)), chromium (Cr\({}^{3+}\)), vanadium (V\({}^{5+}\)), yttrium (Y\({}^{3+}\)), terbium (Tb), tin (Sn), and silver (Ag) [76; 77; 78; 79], as well as nonmetallic dopants such as sulfur (S) [5], have also been used to explore the effect of doping on output voltage, as shown in Figure 6.  
F\({}^{-}\) is the least used among the halide groups because its radius is smaller than that of oxygen ions, which hinders the output performance by inducing compressive strain within the crystal structure. However, the other halide dopants have a radius larger than the oxygen ion, and the generation of expansion strain along the c-axis of the ZnO lattice
# A Review on Observer Assistance Systems for Harvested and Protected Fish Species
## 3 Proposed Electronic Monitoring System
### An Overview of Hardware System  
The hardware structure is shown in Figure 1.  
In Figure 1, the learning server, prediction server, network, and reporting device are operated on land, and the image collector client is installed on the ship. Images are recorded in the image collector client and transmitted to the land in units of 1000 frames. A real-time video is produced, but limitations in the satellite communication environment cannot be avoided. Therefore, transmission is limited to 5 to 10 FPS. The learning server extracts images from collected images and uses them as data for learning. The prediction server identifies illegal catches from the collected images, estimates and calculates the total number of catches, and creates and provides a report for each ship.
## 6 Implementation and Evaluation
### Performance Evaluation
#### 6.3.2 Congestion Control Optimizations
In addition to this delay there are other delays that impact the communication. The nodes and the EM need some time to process the CoAP packets they send and receive (e.g., time needed by nodes to prepare the CoAP reply and the delay needed by the EM to combine all replies into one reply to the client). We call the sum of all such delays the Processing Delay \(D_{p}\). Finally we call the sum of the average signal propagation time and the average time need to send and receive a packet over any transmission link the Transmission delay \(D_{t}\). We have experimentally evaluated \(D_{p}\) and \(D_{t}\) by averaging the values for 100 transmissions for our network topology when communicating with the nodes that are only 1-hop, 2-hops or 3-hops away. Table 2 provides a summary of the delays in our experimental topology. \begin{table}
\begin{tabular}{l l} \hline
**Entity Delay between Requests to Group Members** & \(\mathbf{D_{e}=50\ ms}\) \\ \hline Processing delay & \(D_{p}=120\ ms\) \\ Transmission delay & \(D_{t}=23\ ms\) \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of communication delays in used topology. Figure 17: The average response time of an entity can be improved by inserting small delays between the requests for individual entity members. The best delay depends on the topology and the used MAC protocol.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Fruits and Vegetables
#### 5.2.3 Nutrients  
Nutrients such as phosphate ions have been successfully recovered from aqueous solutions using an adsorbent derived from rice husk and lemon juice residues [49]. It displayed a removal efficiency of 95.8% at 298K and pH 6; the initial adsorbate concentration, adsorbent dose (5 and 3 g/L respectively), and contact time (three hours) were important to achieve the highest efficiency in the process. Banana peels were used as an environmentally friendly option for uptake of fluoride from groundwater, reaching a removal efficiency of 86.5% (q\({}_{\text{m}}\) = 8.15 mg/g) under optimal conditions for the sorbent [68]. Anthocyanin-based bioadsorbents were subsequently implemented to remove ions of radioactive iodine (I\({}^{-}\)) from an aqueous environment. Mangosteen pericarps were the most efficient adsorbent, showing total removal of iodine (100%), followed by red onion peels (92%), passion fruit rinds (87.5%), and red dragon fruit peels (68.4%) after a contact period of 192 h [79]. The Freundlich, Langmuir, and pseudo-first and second order were the isothermal and kinetic models employed for the analysis of nutrients adsorption data (Table 4).
## 3 Background
### Test Case Prioritization and Sampling  
[PERSON] et al. [8] defined the Test Case Prioritization Problem in the following way:  
Given: \(T\), a test suite; \(PT\), the set of permutations of \(T\); \(f\), a function from \(PT\) to the real numbers. Problem: Find \(T^{\prime}\in PT\) such that \((\forall T^{\prime\prime})\)\((T^{\prime\prime}\in PT)\)\((T^{\prime\prime}\
eq T^{\prime})\)\([f(T^{\prime})\geq f(T^{\prime\prime})]\).  
Here, \(PT\) represents the set of all possible permutations (in terms of orderings) of \(T\), a specified test suite; \(f\) is a transition function that can evaluate an awarded value for any ordering of \(PT\). There are many techniques available for test prioritization, such as search-based, coverage-based, risk-based, fault-based, and so on [17]. Here in this paper, we use fault-based prioritization. [PERSON] et al. [22]  
Figure 1: Automatic Program Repair (APR): Generate and Validate Approach.  
implemented random sampling that chooses test cases randomly in a uniform way and time-aware test-suite reduction (introduced by [PERSON] et al.) that uses a genetic algorithm to reorder the test suite based on testing time constraints. Both have a high chance of including test cases that are n ot related to the modification point (program variant) or the test cases that are least likely to fail. The base work to our modification point-aware prioritization is that of [PERSON] et al. [18; 19] who proposed the FRTP (Fault-Recorded Testing Prioritization) technique in the GenProg environment and RSRepair. These are the only available papers on prioritization in APR (which we have searched using keywords and confirmed). The reason we chose this technique as the base work is that the Fault-Recorded Prioritization technique does not require any previous execution of test cases. Rather, it iteratively extracts test case execution information during the repair process. Then, their proposed method ranks the test cases based on the failing test cases, which helps in early incorrect patch detection. Every time the same test suite is updated, prioritized, and validated against all the program variants generated by GenProg [10], RSRepair [19]. In other words, regardless of the modification point chosen, it follows the prioritized order of previous execution. Therefore, to overcome this problem, we have proposed a modification point-aware prioritization where a different prioritized test suite is maintained for every program variant based on modification point information.