esa-sceva/satcom-chunk-collection huggingface dataset:
# Waste Management in a Sustainable Circular Economy as a Part of Design of Construction
## 2 Theoretical Background
### Life Cycle of Building and Construction Material  
The life cycle of building and construction materials are closely linked to the strategy of the circular economy. The life cycle of building is divided into four main phases: the (i) design phase, (ii) manufacturing and construction phase, (iii) operation and maintenance phase (use), and (iv) end of life (demolition) [40]. The connection between the life cycle of building and the life cycle of construction materials (Figure 1) are interconnected and interactive. Construction design, choice of construction methods, and technologies affect the consumption of given building materials [41] and, consequently, the preference for the location of construction, namely on-site or off-site. We currently know of many construction approaches that allow the construction of off-site, e.g., modern methods of construction, prefabrication, etc. [42]. The renovation or modernization of a building may occur during its use phase. These activities require the supply of building materials but also create CDW. The phase of construction demolition presents the end of a material's life cycle [43]. The end of the life cycle of a construction material is conditioned by waste management. A directive from the European Union [32] determines waste management procedures and waste disposal methods. Considering the application of a CE and a sustainable supply chain, it is necessary to prefer the principles of the 3 Rs (reduce, reuse, and recycle) over landfilling. The demolition of a building does not end the life cycle of a building material, but its life cycle begins through loop-closing [44; 45].  
Waste management decisions need to be supported by information and communication technologies (ICT). Currently, many approaches provide useful drivers for waste management within the circular economy. Drivers consists of: (i) building information modelling (BIM), (ii) internet of things (IoT), (iii) predictive data analytics, (iv) logistics network optimization [40], (v) geographic information system (GIS) [46], (vi) radio frequency identification (RFID) [47] and (vii) material flow analysis [47]. Waste management can be influenced at every phase of the construction life cycle. The initial decision is in the design phase of construction [48]. The design of the building should be sustainable. A sustainable construction design (also called circular design) should also provide the designer with information on waste generation and its 3 Rs options--reduce, reuse, recycle [49]. The decisive knowledge of the generated construction waste is: (i) type, (ii) quantity, and (iii) method for waste disposal. The type of and method for waste disposal are defined by legislation [50]. The crucial point of the decision-making process is the quantifying of construction and demolition waste.
###### Abstract  
Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed \(n\)-level phase shift keying (\(n\)-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the \(n\)-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.  
coherent optical detection; optical fiber communication; carrier phase recovery; feed-back and feed-forward; laser phase noise; equalization enhanced phase noise; \(n\)-level phase shift keying +
Footnote †: journal: _photonics_
# Deep Learning-Based Log Parsing for Monitoring Industrial ICT Systems
## 1 Introduction  
The recent development of information and communication technologies (ICT) has promoted the progression of the industrial revolution from digital to intelligence (i.e., Industry 4.0 [1]). Industrial ICT systems (IICTSs) have been demonstrated to be a critical component of smart manufacturing, as their failure causes catastrophic consequences, such as manufacturing accidents and financial disasters [2]. Drawing increasing interest from both industry and academia, effective monitoring holds great significance in preventing IICTSs from variable and intense physical and cyber threats in practice [3].  
Generally, the IICTS monitor aims to detect anomalies and predict threats by collecting and analyzing operational information about system components [3]. Such monitoring data are usually formed as _logs_, in which data mining methods are commonly applied to knowledge extraction, i.e., log mining. Conventional log mining approaches have three basic steps, i.e., _log parsing, matrix generation_, and _data mining_[4]. As the essential premise of effective mining, log parsing identifies system-specified events according to the corresponding logs. An example of log parsing is shown in Figure 1; the corresponding even labels, such as _super user action, informative report, event report_, etc., are generated for the next data mining tasks.  
Existing log parsing methods usually depend on conventional data mining techniques, such as clustering and iterative partitioning [4], and a few methods leverage program source code, which is hardly available in practice [5]. However, parsing logs from complex IICTSs have the following requirements, which cannot be satisfied simultaneously by the current solutions:  
* _Generalization_: The parser should be applicable to all logs with different textual properties. Conventional log parsing methods are based generally on the assumption that words with high-presence frequencies in logs imply corresponding types of events,
## 3 Experimental Validation of the Finite Element Method  
In this section, we compare the experimental and numerical results in order to validate the FEM model before extending this numerical approach to different AE sources. In the experimental setup, pencil lead break is carried out on the three specimens, and the signals are detected by means of a laser vibrometer. The pencil lead break is modelled as a displacement source [15]. The simulation intended to validate the use of FEM and the chosen parameters consists of a PLB (Figure 2) on the surface of each sample: EPL3, EPL16 and EPL80. The simulated lead break is applied on the upper surface of the sample (Figure 6). The vertical displacements (\(z\)) of the lower extremities' nodes are blocked. Vertical particle velocity \(v(z)\) on the surface is calculated at 21 mm and at 60 mm from the source (PLB). Simulated signals are then filtered between 1 kHz and 1 MHz by a Butterworth bandpass filter, which is the frequency range of interest in AE. Figure 8 shows a comparison between the measured velocity and the simulated out-of-plane velocity for each case. The green lines indicate the time at which oscillations of simulated signals start, while the black lines indicate the time up to which experimental and simulated signals are similar. The part of the signal before the green line is called Section 1, the part between the green and black line is called Section 2, and the part after the black line is called Section 3. Cross-correlation coefficients (CC) between simulated and measured signals are larger than 0.6 for each geometry, especially if they are computed only on Section 2 (CC = 0.77 and CC = 0.80 in EPL16 at 21 mm and 60 mm). This indicates a good resemblance between signals. In some cases (Figure 8c), oscillations are visible before the green line on the experimental waveforms. The intrinsic noise of the laser vibrometer can cause these oscillations. Despite the signal processing made to discard the noise, some of it could not be avoided. At longer times, there is some discrepancy between the signals in Section 3, due to the reflections on the endings of the samples. In each sample, the arrival time of the reflections corresponds to the beginning of Section 3. The boundary conditions are not exactly the same for the two methods. Moreover, the comparison between the frequency spectrum  
Figure 7: A typical experimental acoustic emission signals and several descriptors in the time (**a**) and frequency (**b**) domains.  
of the numerical and experimental signals shows similar cross-correlation coefficients. Overall, we find a good agreement between the experimental results and the numerical simulations, allowing us to validate the FEM model. Consequently, we can conclude that the numerical approach is able to describe AE signal propagation.
## 2 Generation of Mesostructures and Numerical Models
### Numerical Validation  
To verify the effectiveness of the method for establishing the mesoscopic concrete model with the pore structure proposed in this paper and the accuracy of the established numerical mesoscopic model of concrete, a quasi-static tensile test was carried out and compared with related research [2; 44].  
The boundary conditions of the tensile experiment are that the lower boundary is fixed in the Y direction, the lower-left node is fixed in the X and Y directions, and the upper boundary is loaded with displacement control, and the loading speed is 9 mm/s, as shown in Figure 4. The relevant parameters of the concrete FE model are determined by referring to relevant literature [2; 24; 43; 45; 46; 25] and trial calculations, which are shown in Table 3.  
Figure 5 shows the stress-strain curve under uniaxial tensile loading obtained in this paper, the simulation results obtained by [PERSON] et al. [2], and the experiments results obtained by [PERSON] [44]. It can be seen that the stress-strain curves obtained in this paper are in good agreement with the experimental results [44] and simulation results [2] in the ascending section, softening section, and descending section. It should be noted that the differences between the results in this paper and those in the Ref. [44] and Ref. [2] are due to the different sizes, mesostructures, and material properties of the test specimens. The comparison indicates that the proposed method and the mesoscale concrete numerical models established in this paper are reasonable and can effectively characterize the mechanical behavior of concrete.  
Figure 4: Loads and boundary conditions for uniaxial tensile tests.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bulk Elements**} & \multicolumn{2}{c}{**Cohesive Elements**} \\ \cline{2-5}  & **Aggregate** & **Mortar** & **ITZ** & **Mortar** \\ \hline Density (\(10^{-9}\) t/mm\({}^{3}\)) & 2.5 \({}^{*}\) & 2.2 \({}^{*}\) & 2.2 \({}^{*}\) & 2.2 \({}^{*}\) \\ Elastic modulus (GPa) & 70 \({}^{*}\) & 25 \({}^{*}\) & / & / \\ Poisson’s ratio & 0.2 \({}^{*}\) & 0.2 \({}^{*}\) & / & / \\ Elastic stiffness (MPa/mm) & / & / & 355,000 \({}^{*}\) & 355,000 \({}^{*}\) \\ Tensile strength (MPa) & / & / & 1.14 \({}^{*}\) & 2.28 \({}^{*}\) \\ Fracture energy (N/mm) & / & / & 0.0114 \({}^{*}\) & 0.0228 \({}^{*}\) \\ \hline \hline \end{tabular} Note: \({}^{*}\) denotes that the value is defined according to Ref. [24]; \({}^{*}\) denotes that the value is defined according to Ref. [2]; \({}^{*}\) denotes that the value is defined by trial calculation with reference to Refs. [25; 43; 45; 46].  
\end{table}
Table 3: Material properties of the concrete mesoscopic FE model.
# Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
## 2 Materials and Methods
### Methodology
#### 2.2.3 Validation Methods and Scores
CTH bias:  
\[\Delta\text{CTH}=\text{CTH}_{SEVIRI}-\text{CTH}_{ref} \tag{9}\]  
\[\text{bias}=\frac{1}{N}\sum^{N-1}\Delta\text{CTH}. \tag{10}\]  
Standard deviation:  
\[\text{SD}=\sqrt{\frac{1}{N-1}\sum^{N-1}\left[\Delta\text{CTH}-\overline{ \Delta\text{CTH}}\right]^{2}} \tag{11}\]  
Owing to different characteristics of satellite observations, such as differences in viewing geometry and observation time, unwanted large CTH differences can be found, particularly over non-homogenous cloud areas. Thus, two filters were used in this study for evaluating and characterising the SEVIRI CTH products more strictly. The first is a spatial uniformity filter, which excludes the data when the CTH standard deviation of the surrounding 5 \(\times\) 5 pixels is larger than 1 km. The second is that any data with large CTH variances from the SEVIRI output (\(\geq\)1 km\({}^{2}\)) are filtered out because they do not converge sufficiently for generating accurate CTH values in the cloud retrieval algorithm.
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Slurry Plane Diffusion Law
#### 4.1.2 Slurry Diffusion Law under Different Grouting Rates  
Typically, in engineering applications, the rate at which grouting is performed ranges from 0.2 m/s to 0.3 m/s. To study the changes in the shape of the stone body under different grouting rates, simulations were conducted at rates of 0.1 m/s, 0.2 m/s, and 0.3 m/s. The results are presented in Figure 3, where \(V_{\mathrm{g}}\) represents the grouting rate. It is evident that when the grouting rate gradually increases, both the spreading distance of the slurry and the morphology of the stone body also increase. Using a grouting time of 60 s as the reference point, the slurry diffusion distance is 0.62 m when the grouting rate is 0.1 m per second. Similarly, the slurry diffusion distance is 0.87 m at a grouting rate of 0.2 m per second and 1.1 m at a grouting rate of 0.3 m per second.  
Figure 2: Computational model for porous media.  
The slurry pressure and rate were simulated under different grouting rates of 0.1 m/s, 0.2 m/s, and 0.3 m/s. The findings are displayed in Figure 4. It is evident that the maximum slurry pressure increases as the grouting rate increases. There is a direct relationship between the slurry flow rate and the grouting rate, meaning that when the grouting rate increases, the slurry flow rate also increases. At a consistent grouting rate, the slurry flow rate decreases as the distance from the grouting hole increases. Furthermore, after passing through the ore-rock contact zone, the flow rate decreases dramatically compared to its prior value.
## 2 Dataset, Models and Method
### Dataset
#### 2.1.4 Satellite-Derived Soil Moisture Products  
JAXA's soil moisture products are based on AMSR-E data by applying the 10-36 GHz algorithm for simultaneous retrieval of the soil moisture and vegetation water content from two indices: the polarization index and index of soil wetness [13]. These global moisture products are available twice daily at 0.1\({}^{\circ}\) spatial resolution.  
National Aeronautics and Space Administration (NASA) soil moisture products were derived from the AMSR-E data and Land Parameter Retrieval Model (LPRM). The LPRM uses dual polarized channels (6.925 or 10.65 GHz) for the retrieval of both surface soil moisture and vegetation water content [14]. These global moisture products are also available twice daily at 0.25\({}^{\circ}\) spatial resolution.
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 6 Discussion  
According to the Figures 2-7, the RMSE and MAE initially decrease as the number of iterations increases. However, once over-fitting occurs, the RMSE and MAE will rise as the number of iterations rises. In this experiment, the lowest point of the RMSE and MAE before over-fitting is chosen for comparison. From these results, we have following findings:  
1. By comparing the 4 models in Figures 2-7, it can be seen that the biased PSL model requires fewer iterations to achieve the best recommendation result;
2. By comparing the 4 models in Figures 2-7, it can be seen that the final RMSE and MAE of the biased PSL model are smaller, which means it has better recommendation effect;
3. By comparing Figures 2, 4 and 6, as well as Figures 3, 5 and 7 we can conclude that the size of the dataset has little impact on the number of iterations needed to achieve the best recommendation effect of the biased PSL model.  
The biased PSL model proposed in this paper can combine the advantages of the biased SVD model and the PSL model.  
According to Tables 2-4, the biased PSL model outperforms the SVD model, the biased SVD model, and the PSL model in terms of the recommendation result in each dataset with different number of latent factors. Among the three datasets, the smallest dataset (100 K) can best reflect the advantages of the biased PSL model. As shown in Table 2, the RMSE of the biased PSL model decreased by 0.0227, about 1%, compared with the traditional SVD model, the model with the worst recommendation effect. Even if the difference is not extraordinarily obvious, its result is still superior to the biased SVD model and PSL model. From the data in Tables 2-4, it can be proven that the combination of the biased SVD model and the PSL model can result in improved performance.
## 4 Discussion  
A review of the main commercial H-UAS and hybrid generators has been presented together with an analysis of their advantages and main drawbacks. The conclusion of this analysis is that the thrust vs. weight ratio is too low in most cases when considering the physical limit of a stable flight for the MTOW specified by manufacturers. Moreover, the hybrid generators analysed are not capable of producing powers beyond 3 kW, and except in one case, all of them operate at constant throttle.  
A non-variable throttle forces the ICU to work at the RPM at which maximum power is delivered, and this high rotating speed is maintained whether or not the propulsion system needs maximum power.  
This principle of operation leads to increased heating, ICU wear and fuel consumption. Moreover, most of the studied products do not include refrigeration. Although the validity of these products is undeniable for low- to medium-weight payloads, a different approach is needed to increase aircraft performance, reduce fuel consumption and improve the lifespan of the hybrid generator.  
A prototype that aimed to solve, or at least reduce, these issues was designed, and its preliminary results have been presented. Its differences and improvements have already been shown and are reviewed and compared to the other systems in Section 5. The prototype is still far from becoming an industrial product; however, it is the authors' opinion that both the introduction and review, as well as the novel architecture, can be useful for both researchers and professionals. Our review not only compares the different products available on the market but also presents independent tests that demonstrate how even the most prestigious manufacturers display their product specifications with a high level of inaccuracy.  
The structure of the prototype introduces new emerging technologies such as SCs and some rarely found solutions, such as the start-up system, that can certainly be useful for other researchers.
## 4 Bromelain Use as an Exfoliant Agent in Cosmetic Applications
### Chemical Properties and Structure of Bromelain
#### 4.3.2 Stability and Activity Under Different pH and Temperature Conditions  
Bromelain's stability and proteolytic activity are significantly influenced by both pH and temperature conditions. The optimal pH values for bromelain's activity are generally between 3 and 7, with some sources citing a slightly narrower range of 5.5-8.0, or even 6.5-7.5. Beyond this range, its activity declines, although both acidic and basic forms preserve substantial enzymatic activity, down to pH 4.0 [35; 36; 46]. Stem bromelain operates within a pH range of 4-10, although its activity decreases above pH 10 (optimal values of 4-8), whereas fruit bromelain functions properly at pH values of 3-8 [36]. Its stability is also pH-dependent; it remains stable at pH 5.0 over extended time periods and across all temperatures, likely due to the proximity to the pH of fresh pineapple fruit. However, its activity and specific activity continue to rise up to pH 7.0 [35; 36; 46].  
Temperature is a major destabilizing factor for bromelain, with higher temperatures leading to enzyme degradation and decreased activity, while lower temperatures are believed to improve its stability. Bromelain is inactivated at pasteurization temperatures and its thermal denaturation is irreversible. However, it does exhibit some heat stability, maintaining proteolytic activity between 40 and 60 \({}^{\circ}\)C, with an optimal temperature of around 50 \({}^{\circ}\)C [35; 36; 38]. Nonetheless, the specific values may vary considerably among different clinical trials. Stem bromelain, for instance, functions properly at an optimum temperature of 40-60 \({}^{\circ}\)C, in contrast to fruit-derived bromelain's optimal temperature range of 37-70 \({}^{\circ}\)C [36]. The glycosylated bromelain form reaches maximum activity at 30 \({}^{\circ}\)C and pH 7.0, with its activity decreasing by 17% when the temperature increases to 40-60 \({}^{\circ}\)C. Bromelain retains more than half of its original action after 30 min at 60 \({}^{\circ}\)C, which is completely lost when heated to 100 \({}^{\circ}\)C for 10 min. Regarding its storage, it is mainly stable at 4 \({}^{\circ}\)C (lowest activity loss) and for extended periods at \(-20\)\({}^{\circ}\)C. Bromelain auto-degrades at 37 \({}^{\circ}\)C (optimal), where it exhibits peak proteolytic activity. The impact of temperature on bromelain activity is, therefore, pH-dependent [44].