esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results Analysis
### Analysis of Differences in Soil Enzyme Activities  
As can be seen in Figure 3a, there was no significant difference in catalase activity among the four restoration treatments. The catalase activity in the AR treatment was relatively low, which may be related to the enhanced trend of vegetation restoration succession and rhizosphere function (or litter). Compared with AL, the urease activity (Figure 3b) in the AM and AR&AM&MS treatments increased significantly (\(p<0.05\)). However, the urease activity of the AR and MS treatments was significantly lower, and the difference was also significant (\(p<0.05\)). This is consistent with the results of the physicochemical analysis of the soil, and the restoration effect of the mixed forest is better than that of the pure forest treatment, which increases the soil urease activity. A similar analysis can be seen in Figure 3c. Compared to AL, the phosphatase content of these four restoration modes increased, but the increase in the content of AR&AM&MS was greater, and the differences were all significant (\(p<0.05\)). Furthermore, Figure 3d shows that the sucrase content decreased in the AR treatment relative to AL, but the difference was not significant. The sucrase content increased in the AM and MS treatments, but the difference was also not significant. Only the sucrase content in the AM and AR&AM&MS treatments increased with a significant difference (\(p<0.05\)). It can be seen that the restoration mode of mixed forest with a combination of trees and shrubs is superior in the process of vegetation succession [15]. Figure 3e shows that the BG enzyme content of the MS treatment was lower than that of the AL treatment, and the difference was significant (\(p<0.05\)). The AM treatment had an increased enzyme content, but the difference was not significant. Moreover, the AR and AR&AM&MS treatments lead to significantly increased enzyme content (\(p<0.05\)). Figure 3f shows that the NAG enzyme content of AM treatment increased slightly relative to AL; AR, MS, and AR&AM&MS treatments also increased significantly, and the difference was significant (\(p<0.05\)). Specifically, the NAG content of MS was relatively high, which was related to the physiological characteristics of alfalfa and the allelopathic effect of its root system. Therefore, it is clear from the analysis of differences in soil enzyme activity that the mixed forest restoration mode (AR&AM&MS) has more potential, and its various soil enzyme activities are relatively high and the restoration is good.
## 5 Performance Evaluation
### Basic Performance Evaluation
#### 5.1.2 Queuing Delay
In contrast, RBMM demonstrates better scalability in managing large data volumes. The RBMM proposed in this paper demonstrates relatively balanced and lower queuing delays across all packet sizes and scenarios. Particularly in the case of large packets ranging from 512 to 1024 bytes, the delay is significantly lower than that of other strategies. This indicates that RBMM effectively prevents large packets from remaining in the buffer  
Figure 8: Queuing delay of packets of different sizes in the buffer: (**a**) Scenario 1; (**b**) Scenario 2; (**c**) Scenario 3; (**d**) Scenario 4.  
for extended periods, optimizing buffer utilization and providing more equitable service for packets of different sizes, thereby reducing overall queuing delays.
# Theory and Design of Blink Jamming
## 2 Analysis of Formation Jamming  
Monopulse radar systems have been in use for decades, using four or more antennas to measure the angle to a target. This procedure is performed in amplitude comparison monopulse by adding together the received signal from each beam to create a sum channel, and subtracting the relevant beams to create difference channels for azimuth and elevation. In a four antenna configuration, the sum signal is  
\[s=\frac{1}{2}\left(A+B+C+D\right) \tag{1}\]  
where \(A\), \(B\), \(C\), and \(D\) are the signals from the four antennas, shown in Figure 2. Similarly, the azimuth and elevation difference signals are:  
\[d_{az}=\frac{1}{2}\left[\left(C+D\right)-\left(A+B\right)\right] \tag{2}\]  
\[d_{el}=\frac{1}{2}\left[\left(A+C\right)-\left(B+D\right)\right]. \tag{3}\]  
The difference signals are then divided by the sum creating an error signal that is proportional to the target's angle off boresight in each plane:  
\[\varepsilon=\frac{d}{s}=k_{m}\theta \tag{4}\]  
where the constant \(k_{m}\) is a function of the beamwidth and squint angle, and \(d\) is a generic difference signal for either axis [6]. This equation is then used to calculate the indicated angle:  
\[\theta_{i}=\frac{1}{k_{m}}\frac{d}{s}. \tag{5}\]  
However, the error signal is usually the preferred metric, allowing it to be used in a tracking loop to correct for any angle deviation while tracking a target.
## 3 Pasteurization Targets and Regulations  
Foodborne outbreaks continue to be reported around the world, and more than 250 different foodborne diseases have been described (CDC 2022) [30]. In the US, 841 foodborne disease outbreaks were reported in 2017, resulting in 14,481 illnesses, 827 hospitalizations, 20 deaths, and 14 food product recalls (CDC, 2019) [31]. Foodborne bacterial infections from raw and heated foods include _Salmonella enteritidis_ (poultry and eggs), _Escherichia coli_ serotype O157:H7 (beef, cooked hamburgers, raw fruit juice, lettuce, game meat, cheese curd), _Listeria monocytogenes_ (milk, soft cheese, ice cream, cold-smoked fish, chilled processed meat products, such as cooked poultry), _Vibrio parahemolyticus_ (improperly cooked, or cooked, re-contaminated fish and shellfish), _Vibrio cholerae_ (water, ice, raw, or underprocessed seafood), and foodborne trematodes from fish and seafood produced by aquaculture (WHO, 2018; [PERSON] et al., 2014; EFSA and ECDC, 2018) [2, 32, 33]. Pasteurized milk and dairy products may also be contaminated with _Brucella_, thermophilic _Streptococcus_ spp., and _Mycobacterium avium_ subsp. partuberculosiss (MAP) ([PERSON], 1978; [PERSON] et al., 1996; [PERSON], 2003) [34-36], which can be infectious at low cell numbers, although they cannot grow at chill temperatures. _Coxiella burnetii_, the causative agent of 'Q-fever,' can also be a problem in milk ([PERSON] and [PERSON], 2006; [PERSON] et al., 2014) [2, 37].  
Pathogenic bacteria are the main cause of foodborne outbreaks, although viruses and parasites can also be associated with problems. It is known that the acidity of high-acid foods (pH < 4.6) inhibits the growth of vegetative pathogens and the germination and growth of microbial spores ([PERSON] et al., 2014) [2]. However, bacterial pathogens are a major food safety concern in low-acidic foods (pH > 4.6). Therefore, low-acid pasteurized foods should be stored and distributed under refrigeration below 7 \({}^{\circ}\)C ([PERSON] and [PERSON], 2009) [1]. Among vegetative pathogens, _L. monocytogenes, Yersinia enterocolitica, Salmonella, V. parahaemolyticus_, and _Aeromonas hydrophilla_ are of great concern, because they are able to grow at refrigeration temperatures ([PERSON], 1991; [PERSON] et al., 1990) [38, 39] and can thus be a problem in HPP products, which are generally distributed and stored under refrigeration.  
Vegetative pathogens actively grow, metabolize, and divide their cells in foods, and consequently, they are a focus of concern for the food industry, food regulators, and food control agencies. One of the main objectives of food pasteurization technologies, such as HPP, is to ensure a reduction in the numbers of these pathogenic microorganisms in foods to a safe level for consumption ([PERSON] and [PERSON], 2009) [1]. As mentioned previously, foodsin which HPP is applied as a pathogen reduction step include vegetables and fruits (sauces, juices, dressings), meat (ready-to-eat meats and poultry), and seafood (shellfish and fish products) (EFSA, 2022) [5].
###### Abstract  
During storage, raw meat is exposed to many external factors, which cause visible changes on the surface of the meat and which affect its water-holding capacity. This study aimed to determine the effect of oxygen content in modified atmosphere packaging (MAP) used for storing fresh pork on the colour, pH, value and water-holding capacity during refrigerated storage. The study also analysed the dynamics of changes in colour using the colour difference (\(\Delta\)E) coefficient and sensory quality. In the study, slices of pork loin were packed in MAP using the following gas compositions: 55% O\({}_{2}\)/40% CO\({}_{2}\)/5% N\({}_{2}\) and 75% O\({}_{2}\)/20%, CO\({}_{2}\)/5% N\({}_{2}\); they were then stored for 15 days at a temperature of 4 \({}^{\circ}\)C. The colour of pork stored in MAP was significantly affected by time, but not by the proportion of oxygen. During storage, the meat's lightness (\(L^{*}\)), yellowness (\(b^{*}\)), chroma, and hue angle increased, whereas its redness index (\(a^{*}\)/\(b^{*}\)) decreased. Significant differences in colour between freshly packed and stored samples were noted after days 7 and 9 in MAP containing 55% and 75% oxygen, respectively. The values of pH, free water, and purge and cooking loss were not affected by gas concentration but changed over time. Lowering the oxygen content from 75% to 55% in MAP opens the possibility of reducing the oxygen demand from the meat industry without compromising the quality of the meat.  
colour; meat; modified atmosphere; quality; storage Article
## 6 Result and Discussion  
### Performance of GPR Model  
The GPR model's efficiency were assessed using coefficient of correlation (\(r\)), mean absolute error (\(MAE\)), root mean square error (\(RMSE\)), ratio of root mean square error (\(RSR\)) and Nash-Sutcliffe coefficient (\(NSE\)). The trend line for GPR in training and testing phases has been drawn by comparing the observed regression in Figure 2 scatter plot, and the GPR findings have the maximum inclination to the line \(y=x\) (i.e., \(R^{2}=0.9402\) in training and \(R^{2}=0.894\) in testing phases). Table 5 shows clearly that for the training model, \(r=0.9697\), \(MAE=0.3403\), \(RMSE=0.5597\), \(RSR=0.248\) and \(NSE=0.938\). Whereas for the testing model \(r=0.9455\), \(MAE=0.5443\), \(RMSE=0.8438\), \(RSR=0.387\) and \(NSE=0.851\). The trend line for GPR in training and testing phases has been drawn by comparing the observed regression in Figure 2 scatter plot, and the GPR findings have the maximum inclination to the line \(y=x\) (i.e., \(R^{2}=0.9402\) in training and \(R^{2}=0.894\) in testing phases).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance** & \(RSR\) & \(NSE\) \\ \hline Very Good & \(0\ \leq RSR\ \leq\ 0.5\) & \(0.75<NSE\ \leq\ 1\) \\ Good & \(0.5<RSR\ \leq\ 0.6\) & \(0.65<NSE\ \leq\ 0.75\) \\ Adequate & \(0.6<RSR\ \leq\ 0.7\) & \(0.5<NSE\ \leq\ 0.65\) \\ Inadequate & \(RSR>0.7\) & \(NSE\ \leq\ 0.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical indicators for model performance evaluation.  
Figure 2: Scatter plot presenting the measured \(D_{H}\) values versus the predicted \(D_{H}\) (**a**) training and (**b**) testing model.  
The performance of the developed GPR model was compared to the evolutionary polynomial regression (EPR), MLR and ANN models in literature based on the \(R^{2}\), \(MAE\), and \(RMSE\) criteria and the results are summarized in Table 5. Furthermore, in terms of the \(MAE\) and \(RMSE\) statistical measures in training, the lowest value was found for GPR (\(MAE=0.3403\) m, \(RMSE=0.5597\) m) compared to EPR (\(MAE=0.537\) m, \(RMSE=1.003\) m), ANN (\(MAE=0.702\) m, \(RMSE=1.074\) m), and MLR (\(MAE=0.81\) m, \(RMSE=1.24\) m). Whereas the prediction results in the testing, the MAE and RMSE values was found less for GPR (\(MAE=0.544\) m, \(RMSE=0.8438\) m) compared to EPR, ANN, and MLR except the MAEs values of EPR (=0.291 m) and MLR (=0.43 m) models. The superiority may be owing to the fact that the GPR model excellently captures the nonlinear relationships between lateral displacement and its influencing factors. It can therefore be concluded that, based on statistical indices, the GPR model had the best results. However, due to the use of different number of datasets, a comparison between these results is unwarranted. A project that uses different datasets is needed to gives generalized model to geotechnical earthquake engineering.
## 1 Introduction  
Mining-related failures can be catastrophic and result in loss of life, environmental consequences, and financial losses for the company [1]. High Reliability Organisations have the ability to learn from failures and disasters through the use of various tools and methods to prevent the occurrence or repeats of the same failures. Organisations, designers, and engineers can implement measures that can prevent the reoccurrences of failures or disasters by learning from these failures [2]. Various studies [2, 3] have demonstrated how several reliability analysis techniques can be applied as framework models for learning from failures.  
In this paper, a hybrid model that consists of a number of reliability analysis techniques such as Fault Tree Analysis (FTA), Reliability Block Diagrams (RBDs), and Failure Mode Effect and Criticality Analysis (FMECA) are applied to a case study of a High Potential Incident (HPI) fire incident at an underground hard rock mine in Southern Africa, which will be referred in this paper as the PGM mine, as PGM is the name of the case study organisation, which for reasons of anonymity cannot be revealed. Studies have indicated that mine fires are the highest contributing factors to mass deaths in mines [4, 5]. Anotherstudy [5] further expressed concerns over the threats these fires pose to underground mine workers.  
Therefore, the main research questions of this work are: (1) How can a hybrid model that combines various useful features be employed? (2) Can this approach be demonstrated through a case study? To explain the usefulness of the hybrid model through the case study, the analysis will also be carried out with the perspective of the High Reliability Organisation principles.  
The next section will detail how the various tools mentioned above have been used by other researchers to learn from failures. A brief summary of the HPI to be used for analysis is also provided, with the next sections outlining the methodology applied, analysis performed, and results obtained. The last section will give the conclusions of this study, outlining the strengths and weaknesses of the approach and future work.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 3 Quantitative Analysis
### Keyword Co-Occurrence Network Analysis  
To elucidate the principal research trajectories and thematic concentrations concerning the applications of digital technologies in industrialized construction, we employed VOSviewer software 1.6.19 to construct a comprehensive keyword co-occurrence network. Within the corpus of the 173 selected articles, we utilized both \"author keywords\" and \"indexed keywords\" derived from article titles and abstracts sourced from the Scopus database. To enhance semantic clarity and analytical precision, lexically synonymous keywords were harmonized, as delineated in Table 1. For instance, terms such as \"additive manufacturing\" and \"concrete printing\" were amalgamated under the consolidated rubric of \"3D printing\". Moreover, generalist keywords such as \"concrete\", \"building\", and \"facades\" were judiciously excluded. While these terms are undoubtedly relevant to the broader context of construction, they do not directly contribute to the understanding of how digital technologies are being applied and developed in industrialized construction. Conversely, keywords such as \"industrialized construction\", \"off-site construction\", and \"precast elements\" were retained due to their direct alignment with the research objectives. Through this meticulous curation process, a total of 47 distinct keywords were curated for analysis. Figure 3 illustrates the keyword network, wherein each node corresponds to a  
Figure 2: The publication year of the selected articles. unique keyword, with the magnitude of the node size denoting its frequency of occurrence across the ensemble of selected articles. The interconnecting lines between nodes delineate co-occurrence relationships among keywords, with the thickness of each line indicative of the robustness of the respective association, while the spatial separation between nodes signifies their semantic correlation. Detailed quantitative insights are presented in Table 2, where the total link strength metric indicates the extent to which a keyword is connected to others in the network, revealing the centrality and influence of certain technologies or concepts within the research landscape. For instance, the high total link strength of \"BIM\" and \"industrial robots\" suggests these technologies are strongly associated with various aspects of industrialized construction and are likely key enablers of digital transformation in this field. The occurrences metric highlights the popularity and prevalence of specific research topics, with higher occurrences indicating greater scholarly attention and potential impact. The top five most frequently occurring keywords are \"BIM\", \"precast elements\", \"life cycle
## 1 Introduction
In 2018, [PERSON] et al. theoretically explained the principle of WMPAS technology and experimentally demonstrated it through the detection of water vapor [29]. WMPAS is suitable for use in the gas sensing area due to its advantages of good selectivity, high-sensitivity, and no-destructiveness. In this paper, we elaborated a WMPAS system for gaseous chloroform detection for the first time in the near infrared range by utilizing a DFB laser emitting at 1683 nm with a light power of 24.5 mW. A home-made PA resonator with a resonance frequency of 6390 Hz was employed, and a multi-microphones approach was used to improve the PA signal. To make the system compact, we used the data acquisition (DAQ) card to generate analog signals for modulation, and replaced the huge desk type lock-in amplifier with a board-type one. Finally, all of the devices were integrated into a 39 cm \(\times\) 40 cm \(\times\) 20 cm chassis for convenience and practical use. In view of the complex absorption spectrum of chloroform in this spectral range, our laser scanning spectrum covered its two adjacent absorption peaks to observe and analyze the influence. Different concentrations of chloroform gas were measured at the room temperature and atmosphere pressure, and the results show the excellent performance of the system, which indicates great practical value for chloroform gas detection.
# Design of a Power Converter for Solar Energy Storage System
## 2 Circuit Structure and Operation Model
### R2G Mode: Where Renewable Energy Is Supplied to a DC Grid  
Figure 8 shows the circuit diagram for a mode in which renewable energy is supplied to the DC grid. The converter circuit is a combination of an interleaved boost-type converter, a transformer, and a full-bridge rectifier. The theoretical waveform is illustrated in Figure 9. The phase-shift angle between the left arm (\(S_{1}\), \(S_{2}\)) and right arm (\(S_{3}\), \(S_{4}\)) is set to 133.2\({}^{\circ}\). When the renewable energy source transfers energy to the DC grid, the battery storage unit must be ready to compensate for the intermittent nature of the renewable energy unit. Therefore, the battery unit is connected in advance.  
The voltage of the external battery is assumed to be \(V_{bat,C}\), which is identical to the voltage of the battery port. According to Equations (9) and (10), the average output power is 0, despite current flowing inside the battery unit. Therefore, the renewable energy source supplies energy to the DC grid. Depending on the relationship between the renewable energy unit and the floating voltage of the battery, the duty cycle \(D\) of this mode is either greater than or less than 50%. The gating signals responsible for changing the phase angle control the output energy transferred from the renewable energy unit to the DC grid.  
\[V_{bat,C}=\frac{V_{reg}}{(1-D)}=V_{bat} \tag{9}\]  
\[\Delta V_{bat}=V_{bat,C}-V_{bat}=0 \tag{10}\]  
Figure 7: Theoretical waveforms for R2B mode with \(D>50\%\).
## 3 Results and Discussion
### Regression Models
On the one hand, it minimizes the differences between the maximum and minimum absorbances of different samples by reducing scattering effects and increasing the resolution of the peaks of the spectra [61]. On the other hand, its application on the spectra reveals different peaks that would be related to fat (962-968 nm) and that would improve the prediction of fatty acids [52].
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Energy Refocusing  
A very interesting application of TR that could be seen as a structural active control procedure is that of energy refocusing [34]. This application switches the usual implementation of TR where the forward step is assumed to be the one realized physically as the data acquisition procedure and the backward one to be the one to be held numerically. As an energy refocusing example, the issue could be stated as: find the excitation to be imposed on a limited number of specific locations of the structure, such that the energy to be focused on some particular location of the structure in a controlled manner and in a well-approximated time. In order to succeed, the forward problem is numerically solved on a digital twin of the structure, by exciting some source location and the response on a limited number of spatial points and selected DOFs for a time duration \(T\) is collected. Afterwards, the structure is stimulated by exciting the selected DOFs on the specific locations by imposing loads as the time reversed collected response data and expected the energy to be refocused on the original source location after the passage of time equal to the original forward procedure duration \(T\). The intelligent usage of this concept, for instance, with the help of remote sensors and actuators, will enhance the performance of structural health monitoring systems.