esa-sceva/satcom-chunk-collection huggingface dataset:
# HydroLab: A Module for the Investigation of Fertigation Strategies in Hydroponics
## 5 Results and Discussion
### Selected Indicators  
Considering that the objective of the case study was to demonstrate the potential of HydroLab in investigating different fertigation strategies, emphasis was placed on computing economic and morphological indicators and quantifying the utilized resources, i.e., nutrient solution consumption. Whereas, the calculation of physiological indicators will be left to future experiments in which HydroLab will be utilized as a tool to provide technological recommendations.  
In line with the concept of the _performance measurement system_ suggested in maintenance management [32], we opted to compute both leading and lagging indicators. As defined in [33], a leading indicator is a quantifiable factor that undergoes changes before the studied phenomenon starts to exhibit a specific pattern or trend. On the other hand, a lagging indicator is a measurement that only shifts after the phenomenon has already changed. In the context of agriculture, leading indicators can be viewed as metrics that describe the growth process, while lagging indicators assess the outcomes of the cultivation. The indicators selected for quantifying the results of the case study are as follows:  
* _Height_: average height of the plants in each crop row;
* _Weight_: average weight of the plants in each crop row;
* _Nutrient solution consumption_: the amount of nutrient solution utilized for each crop row;
* _Yield_: the ratio between the final weight of the row of lettuce and the occupied space;
* _Nutrient solution efficiency_: the ratio between the final weight of the row of lettuce and the consumed nutrient solution;
* _Cumulative height_: the curve that describes the changes in the crop height over time. This plot is generally defined as the sigmoid curve [34];
* _Cumulative nutrient solution_: the curve that describes the cumulatively delivered nutrient solution over time.  
The selected indicators are presented in Table 4. It can be observed that lagging indicators consist of single values as they indicate results, while leading indicators are represented through curves as they assess the growth process over time. Finally, yield and nutrient solution efficiencies are considered higher-order indicators. Yield is determined through the weight of the harvested crops, while nutrient solution efficiency takes into account both the crop weight and the amount of the consumed nutrient solution. These indicators were chosen because the yield reflects the effectiveness of the implemented fertigation strategy, while the nutrient solution efficiency represents its efficiency in terms of resource utilization. In the context of the experiment, effectiveness refers to the extent to which the fertigation strategy achieves the desired outcome (i.e., lettuce weight), while efficiency pertains to the optimal use of resources (i.e., nutrient solution) to accomplish the goal.
# Lessons for Coastal Applications of IMTA as a Way towards Sustainable Development: A Review
## 5 Economic Value and Financial Viability of Coastal IMTA
### Region- and Country-Specific IMTA
#### 5.2.2 Others Region
When combined with the finfish farming of cobia _Rachycentron canadum_ along India's east coast, the use of IMTA in open sea cage farming resulted in a 50 percent increase in seaweed output, _Kappadhycus alvarezii_[6]. Similarly, an evaluation of the financial viability of a salmon-mussel production system on the Scottish coast was based on information from Scottish mussels and salmon culture farms without seaweed. Researchers evaluated the Net Present Value (NPV) of three different cultivation systems over a period of twenty years using the capital budgeting approach. These cultivation systems were salmon monoculture, mussel monoculture, and integrated salmon-mussel farming. The measured NPV for the USD 2.63M for the integrated salmon-mussel system was higher than the combined NPVs for both the salmon monoculture system (USD 1.7M) and the mussel monoculture system (USD 0.650M), assuming that the mussel production rate for the integrated system was 20% higher. The higher profitability of the integrated system was susceptible to changes in product prices. Even assuming that mussel prices remain constant, a 2% annual decline in salmon priceswould more than offset the benefits of integration and make polyculture an unattractive investment [32]. Related results from a study of 80 farmers on the profitability of shrimp culture with tilapia on the North Central coast of Vietnam indicates that farmers using this integrated polyculture technique realized higher economic gains and lower feed and pond preparation expenses than those using non-integrative practices. Farmers also perceived that the integration of shrimp and tilapia improved their adaptive capacity in the face of weather-related shocks [120]. Available research on the economic value and financial viability at the farm level has, for a considerable period, indicated good wider economic and farm-level potentials for integrated aquaculture, and for IMTA in particular.
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 1 Introduction
In this study, the infrared thermography approach was adopted for impact damage detection of a PVA-ECC beam under low-velocity impact loading. A series of impact tests at constant impact energy were conducted. After each impact, infrared thermography was used for NDE of the PVA-ECC beam. Sequences of images were acquired with starting and ending times set so as to include the entire evolution of thermal phenomenon from both the heating and cooling processes. Experimental results verified the feasibility and effectiveness of infrared thermography for impact damage detection on a PVA-ECC structure.
## 5 Numerical Examples
### Nonstationary Wind Fields
#### 5.2.1 Nonstationary Wind Fields with Time-Invariant Coherence  
Figure 7 presents the comparison of two samples obtained by two phase schemes with the same random phase at Points 1, 21, and 41. It can be observed that the samples at Point 1 are totally consistent and the samples at other simulation points have more or less differences. Therefore, the nonstationary simulation using the classical phase formula possesses an evident error. Furthermore, the error becomes larger and larger as the simulation point number increases.  
Further, 1000 samples are generated to estimate the ensemble auto-/cross-correlation functions based on Equation (30). The target correlation functions are computed by Equation (29). Figure 8 compares the typical correlation functions obtained by the two phase  
Figure 7: Simulated time-invariant coherent nonstationary wind fluctuation samples: (**a**) Point 1; (**b**) Point 21; (**c**) Point 41.  
schemes with the corresponding targets when the time lag \(\tau=0\), 16, and 32 s. It can also be found that the phase has no effect on autocorrelation function, while the influence on the cross-correlation function is rather strong. This shows that the nonstationary simulation using the classical phase formulation is erroneous.
## 2 Numerical Simulation and Experimental Validation
### Numerical Simulation
The side walls formed by the normal stretching are assigned symmetric boundary conditions. By comparing the experimental results [33], the velocity distribution at three different cross-sections of the 180\({}^{\circ}\) U-shaped channel flow is analyzed, as shown in Figure 5. The modified SST k-\(\omega\) model effectively captures the non-uniform velocity distribution caused by the curvature of the streamlines, with a maximum error of only 4.7%, meeting the requirements for engineering applications. Figure 4: Schematic of the 180\({}^{\circ}\) U-shaped pipe computational domain and mesh. Figure 5: Velocity distribution at three different cross-sections of the 180\({}^{\circ}\) U-shaped channel flow. To ensure that the computational grid meets the accuracy requirements, numerical simulations were conducted with grids of different cell counts. Furthermore, the Grid Convergence Index (GCI) method was applied to verify grid independence, using the average inlet flow velocity of the impeller as the error assessment parameter. The average inlet flow velocity of the impeller and the corresponding GCI values for different grid sizes are shown in Figure 6. The final selected computational grid size for the entire domain is 10,064,702 cells, with a GCI value of 0.43%. Table 2 presents the grid sizes for each flow domain. The average y+ value on the impeller surface is 0.12, as shown in Figure 7, which satisfies the accuracy requirements.
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Security Analysis  
The security of the ABE scheme also relies on the security of the transformation technique because we have transformed the SM9 IBE scheme into the ABE scheme following the technique in [34; 35]. Theorem 4.1 in [35] claims that the ABE scheme derived from an IBBE scheme is secure against chosen ciphertext attacks (CCA) if the underlying IBBE scheme is CCA-secure, which is defined, as follows:  
An IBBE scheme is secure against CCA if there exists no probabilistic polynomial-time adversary who can win the following security game in a non-negligible advantage.  
1. Setup. The challenger \(\mathcal{C}\) takes a unary security parameter \(1^{k}\) as input, and returns the master public key \(M_{pk}\) to the adversary \(\mathcal{A}\) and keeps the master secret key \(M_{sk}\) privately.
2. Learn 1. \(\mathcal{A}\) submits a series of queries \(q_{1},\ldots,q_{n}\), where \(q_{i}(i\in 1,\cdots,n)\) is either a private key query or a decryption query. Note that in a private key query, \(\mathcal{A}\) sends an identity to \(\mathcal{C}\), and then \(\mathcal{C}\) returns the corresponding private key to \(\mathcal{A}\); in a decryption query, \(\mathcal{A}\) sends a ciphertext and an identity to \(\mathcal{C}\), and then \(\mathcal{C}\) decrypts the ciphertext for \(\mathcal{A}\).
3. Challenge. \(\mathcal{A}\) submits two equal-length messages \(M_{0},M_{1}\) and a challenge set of identities \(\left\{ID^{*}_{1},\ldots,ID^{*}_{n}\right\}\) to \(\mathcal{C}\). Afterwards, \(\mathcal{C}\) uniformly randomly selects \(b^{\prime}\in\{0,1\}\), encrypts \(M_{b^{\prime}}\) under \(\left\{ID^{*}_{1},\ldots,ID^{*}_{n}\right\}\), and finally sends the ciphertext \(\mathcal{C}^{*}\) to \(\mathcal{A}\).
4. Learn 2. \(\mathcal{A}\) repeats the steps in the _Learn 1_ phase except for querying the identities and the ciphertext involved in the challenge.
5. Guess. \(\mathcal{A}\) outputs a guess \(b^{\prime\prime}\in\{0,1\}\) of \(b^{\prime}\) and wins the game if and only if \(b^{\prime\prime}=b^{\prime}\).  
\(\mathcal{A}\)'s advantage in the above game is defined as \(\left|\Pr[b^{\prime\prime}=b^{\prime}]-\frac{1}{2}\right|\).  
Figure 9: ABE algorithms’ energy consumption on Raspberry Pi 3 and OnePlus A6000 smartphone.  
Obviously, the proposed ABE scheme is derived from an underlying IBBE scheme that straightforwardly repeats the SM9 IBE for a limited number of times, and thus the IBBE scheme's security can be reduced to the security of SM9 IBE. As a cryptographic standard, we assume that the SM9 IBE is secure against CCA.
## 2 Materials and Methods
### 5 TS Test Analysis
#### 2.5.3 Calculations  
* Time: the total duration of the SSTS Test was computed as the difference between the time coordinates of STARTC1 and STOPE5. The duration of the single concentric phases was calculated as the difference between the time coordinates of STARTC and STOPC (STS 1-5). Thereafter, the duration of the five single concentric phases was averaged and expressed as % of the total test duration. The durations of the eccentric phases were calculated as the difference between the time coordinates of STOPC and STOPE (STS 1-5).
* Displacement: the time coordinates of the above-defined significant events were also used for the processing of the position signal. (Figure 3, middle panel). The displacement of the concentric phases was calculated as the difference between the space coordinates of STARTC and STOPC (STS 1-5). The displacement of the eccentric phases was calculated as the difference between the space coordinates of STOPC and STOPE (STS 1-5).
* Velocity: for each STS repetition, the mean concentric velocity was computed as the ratio between displacement and duration of the concentric phase.
* Force: The mean concentric force was calculated as the average of the ground reaction force signal within each concentric phase (i.e., within the time coordinates of STARTC and STOPC 1-5) (Figure 3, bottom panel).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables (Unit of Measurements)** & **Formula** & **Notes** \\ \hline Vertical displacement (m) & \((50\%\times\text{stature})-\text{box\,height}\) & \begin{tabular}{c} \(\text{Stature in meters;}\) \\ box height = 0.49 m \\ \end{tabular} \\ \hline \begin{tabular}{c} Concentric time (s) \\ \end{tabular} & \(10\%\times\text{Total trial duration}\) & \
\begin{tabular}{c} Total trial duration in s \\ \end{tabular} \\ \hline STS mean velocity (m/s) & Vertical displacement/Concentric time & \\ \hline STS Force (N) & \(90\%\times\text{Body Mass}\times\text{g}\) &
\begin{tabular}{c} Body Mass in kg; \\ g = Gravity acceleration 9.81 m/s\({}^{2}\) \\ \end{tabular} \\ \hline STS mean Power (W) & STS Force \(\times\) STS mean velocity & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables of the [PERSON]’s calculation of sit-to-stand (STS) mean power.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 3 Materials and Methods
### Optimization of the Feedback Loop  
Initialization of separation matrix: Since the separation matrix in the feedback loop comes from the output result of the last blind source separation, in the initialization phase, the configuration of the separation matrix is as follows:  
\[W_{N\times M}=\begin{bmatrix}E_{M\times M}\\ O_{(N-M)\times M}\end{bmatrix} \tag{10}\]  
Figure 4: Processing flow of interference monitoring based on a deep complex network.  
where is \(E\) the identity matrix and \(O\) is the zero matrix.  
The monitoring frequency should be significantly less than the sum of the running time of the interference-monitoring module and the blind source separation module, and the feedback loop oscillation caused by high frequency and low threshold should be avoided. There are a variety of wireless communication systems in coal mines, which correspond to different application scenarios, such as main road, fully mechanized mining face, and driving face. They have different signal-to-noise ratios, sensitivities, delays, power consumption, etc. The performance requirements of different systems can be adapted to different scenarios by adjusting the monitoring frequency and judgment threshold.
# _Legionella pneumophila_--Virulence Factors and the Possibility of Infection in Dental Practice
## 3 Virulence Factors
### Legionella and Protozoan Interactions  
Certain species of protozoa are crucial for the growth of _Legionella_ in natural and artificial environments [90]. Accordingly, the presence of _Legionella_ in these environments depends on the spectrum of protozoa present in the host [91]. _Acanthamoeba_, _Hartmannella_, and _Naegleria_ are most commonly isolated from _Legionella_-contaminated water systems [92]. Other protozoa associated with _Legionella_ are _Saccamoeba_, _Vexillifera_, and _Platyamoeba_[93].  
Propagation within the amoeba _L. pneumophila_ increases the ability to produce polysaccharides, which increases its ability to form a biofilm (Figure 2) [74]. Protozoa provide nutrients for intracellular _Legionella_ and protect them from adverse environmental influences. Bacteria survive high temperatures, disinfection procedures, and drying inside the _Acanthamoeba_ cyst [19]. _L. pneumophila_ can use protozoa to colonize new habitats, so inhaled protozoa are an effective mode of transmission to humans [94]. Thus, the symbiosis of _Legionella_ and protozoa contributes to the infection process itself [22]. After intracellular replication within the protozoan, _L. pneumophila_ shows more excellent resistance to stress [30; 95]. During coevolution with protozoan cells, _L. pneumophila_ acquires highly sophisticated and diverse strategies for taking over the host cell process [96; 97]. It secretes hundreds of effectors into the host cell, controlling host signaling pathways and key cellular processes [57; 98; 99]. _L. pneumophila_ can also alter host transcription and translation processes and utilize epigenetic mechanisms in the cells in which it is found to counteract host responses [100].  
Upon internalization, intracellular bacteria reprogram the endosomal-lysosomal pathway of host degradation [101]. The multiplication of _Legionella_ bacteria within a maturation-blocked vacuole, which fails to acidify and fuse with lysosomes, shows many similarities to human phagocytic cell infection [102]. It includes the recruitment of the rough endoplasmic reticulum surrounding the membrane-bound vacuole. Interaction with protozoa is thought to be the driving force in the evolution of _Legionella_ pathogenicity. In recent years, tremendous progress has been made in unraveling the mechanisms by which intracellular pathogens attack host cells and establish intracellular infections [103].
## 1 Introduction  
Chemical constituents released from gas discharges in active and quiescent volcanic complexes and geothermal systems can be related to: 1) primary (magma degassing) and 2) secondary (gas-water-rock interactions occurring at relatively shallow depth) sources. The ratio between magmatic _vs._ hydrothermal contributions is generally indicative of the state of activity of a volcanic system and is a basic parameter in terms of volcanic surveillance [1; 2; 3; 4]. Magma degassing produces highly acidic and corrosive gas compounds that may affect the geothermal potential of a hydrothermal reservoir. Hydrothermal fluid composition is constituted by water vapor and CO\({}_{2}\) and show significant concentrations of reduced gas species, such as H\({}_{2}\)S, H\({}_{2}\) CO and CH\({}_{4}\)[5; 6]. Magmatic-related fluid contributions, although mainly consisting of the same gases dominating hydrothermal fluids, _i.e.,_ water vapor and CO\({}_{2}\), can unequivocally be recognized in thermal discharges by the presence of highly acidic compounds, especially SO\({}_{2}\)[7; 8; 9]. Secondary interactions, such as gas scrubbing processes within shallow aquifers [10; 11], are able to strongly affect this highly soluble and reactive gas compounds, frequently masking any clue of magmatic-related fluid contribution at surface. The behaviour of hydrocarbons in natural fluid discharges has recently been considered as a potential tool to investigate the thermodynamic conditions controlling fluid reservoirs feeding fumarolic exhalations in volcanic and geothermal systems [12; 13; 14; 15]. These investigations have demonstrated that light hydrocarbons, especially the C\({}_{2}\)-C\({}_{4}\) alkenes-alkanes pairs, play an important role in both geochemical surveillance of volcanic systems and geothermal prospection. On the contrary, little attention was devoted to heavier organic compounds for similar purposes.  
In the present work, 139 gas emissions from active volcanoes and geothermal systems set in different geodynamical environments were analysed for the determination of non-methane VOC (Volatile Organic Compound) composition, especially that of heteroaromatic and aromatic compounds. On the basis of this dataset, the main goals were to: 1) assess the origin of thiophenes and furans in naturally discharged fluids and 2) evaluate the possible use of these compounds as geochemical tracers to discriminate different fluid source regions in volcanic-hydrothermal environment.
# A Comparative Analysis of Big Data Frameworks:
## 4 Big Data Processing Frameworks  
Parallel and distributed computing has emerged as a prime solution for the processing of very large datasets. Yet, their complexity and some of their features may prevent its maximum productive utilization to common users. Data partitioning and distribution, scalability, load balancing, fault tolerance, and high availability are among the major concerns. Various frameworks have been released to abstract these functions and provide users with high-level solutions. These frameworks are typically classified according to their data processing approach, i.e., batch processing and stream processing (as shown in Figure 4). MapReduce has emerged as one of the earliest programming models for batch processing. Today, it is recognized as a pioneer system in big data analytics. In 2004, under the influence of an ever-increasing amount of data on the web, Google developed Google File System [26] and MapReduce [27]. In a MapReduce program, the user specifies the computation in the form of two functions map and reduce, which can be executed in parallel on multiple computing nodes and easily scaled to large clusters. The Map function takes in a key/value pair and generates a collection of intermediate key/value pairs. The Reduce function combines all the intermediate values assigned to the same intermediate key. The runtime system is responsible for data partitioning, distribution of data and computation across the cluster, handling machine failures, and managing necessary communications among computing nodes.  
Efforts have been made to convert these technologies into open-source software, which resulted in the development of Apache Hadoop [28] and Hadoop file system [29], that laid the foundation for other big companies like Yahoo!, IBM, Twitter, LinkedIn, Oracle and HP to invest in building solutions for large scale data processing. MapReduce and its related distributions had established them as a sound solution to batch workloads. However, a well-defined processing model for distributed stream processing was still lacking. Consequently, a number of reliable and popular open-source frameworks were developed, such as Sparks, Storm, Samza and Flink. This section sheds light on the five most prominent and widely adopted big data processing frameworks.