esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Implication of PGPB on Soil Desalinization  
Soil hosts an enormous number of bacteria (often between \(10^{8}\) and \(10^{9}\) cells per gram of soil) but only about 1% of those bacteria are culturable [21]. Soil microorganisms belong to various groups of fungi, archaea, bacteria, and protozoa. Some of these microorganisms are recognized for their capacity to tolerate and improve plant salinity tolerance [22]. To date, several bacterial genera, like _Streptomyces_, _Azospirillum_, _Clostridium_, _Alcaligenes_, _Bacillus_, _Rhizobium_, _Pseudomonas_, _Thibacillus_, _Serratia_, and _Klebsiella_ are recognized as PGPB working under saline conditions [23]. They are being tested under field trials applied as a one species inoculant or as part of microbial consortia with very encouraging results [24].  
They may alleviate salinity stress by resynchronization stress-stimulated physiological modifications in plants through different mechanisms [25; 26]. Additionally, PGPB promoting impact during stress conditions has also been proposed to be advantageous for crop effectiveness, dry weight of root and shoot and root length, and the plant height [27]. The various ways in which microbes stimulate plant growth and alleviate stress responses include efficient nutrient mobilization in the soil, protection from phytopathogens, and improvement of soil structure and quality by sequestration of toxic heavy metals. Microbes can also aid degradation of various xenobotic compounds [28]. The primary action of PGPM (plant growth promoting microorganisms) is investigated as their role in maintaining osmotic balance, ion homeostasis, and turgor pressure to combat the toxicity of salinity in plants [19]. The salinity-resistant microbial community improves the health of salinity-impacted soil, maintains ecological functions, and sustains and promotes the growth of plants [29]. Hence, the application of beneficial stress-tolerant microbes not only helps in improving the microbial community structure but also in enhancing plant and soil health under salinity. Further research will be required to reveal the hidden mechanisms of stress-tolerant microbial diversity [30]. Moreover, various researchers have demonstrated that the associations between rhizospheric microbes and stressed plants help the plants to adapt to their microenvironment [11].
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Fuzzifier  
The fuzzifier passes each crisp input through an MF to generate a corresponding fuzzy input. Each crisp input variable has a corresponding MF that operates independently of the other MFs. The most commonly used MFs are triangular MF, trapezoidal MF, bell MF, and Gaussian MF. However, the shape of the curve is usually not as important as the number and positions of the curves. Three to seven curves are usually suitable for transferring all possible input values [27]. The Cable Free Team at Wireless Excellence Limited provided a classification of signal strength [28], which is summarized in Table 2. By referring to Table 2, in FLDHDT, we used three triangular functions as the MF to divide the SINR range of [\(-10\) dB, \(30\) dB] into four SINR-value-based intervals, namely bad, bad + mid_P, mid_P+good, and good, as depicted in Figure 6. The degree of the MF of the SINR is expressed as Equation (2), where \(P\) denotes the SINR of the UE. Similarly, three triangular functions are used in the MF to represent the horizontal speed of the UE. By considering scenarios that include pedestrians, bikes, vehicles, and local and express trains, with the exception of ultrafast vehicles such as high-speed trains, the range of horizontal speed was set as \(0\) to \(20\) m/s, and it was divided into four intervals, namely slow, slow+mid_V, mid_V+fast, and fast, as depicted in Figure 7. The degree of the MF of the UE horizontal speed is expressed as Equation (3), where \(V\) represents the horizontal speed of the UE.  
\[(P)=\left\{\begin{array}{ll}1*bad,&P\leq 0\\ \frac{13-P}{13-0}*bad+\frac{P-0}{13-0}*mid\_P,&0<P\leq 13\\ \frac{20-P}{20-13}*mid\_P+\frac{P-13}{20-13}*good,&13<P\leq 20\\ 1*good,&P>20\end{array}\right. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal Strength** & **RSRP (dBm)** & **RSRQ (dB)** & **SINR (dB)** \\ \hline Excellent & \(\geq\)\(-80\) & \(\geq\)\(-10\) & \(\geq\)\(20\) \\ Good & \(-80\)\(-\)\(-\)\(90\) & \(-10\)\(-\)\(-\)\(15\) & \(13\)\(-\)\(20\) \\ Mid Cell & \(-90\)\(-\)\(-\)\(100\) & \(-15\)\(-\)\(-\)\(20\) & \(0\)\(-\)\(13\) \\ Cell Edge & \(\leq\)\(-100\) & \(\leq\)\(-20\) & \(\leq\)\(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal strength indicator.  
Figure 5: FL controller of FLDHDT.  
\[f(V)\left\{\begin{array}{c}\frac{6-V}{6-0}*slow,\;\;V\leq 3\\ max\Big{\{}0,\;\frac{6-V}{6-0}\Big{\}}*slow+\frac{V-3}{10-3}*mid\_V,\;\;3<V\leq 10 \\ \frac{16-V}{16-10}*mid\_V+max\Big{\{}0,\frac{V-13}{20-13}\Big{\}}*fast,\;\;10<V \leq 16\\ \frac{V-13}{20-13}*fast,\;\;V>16\end{array}\right. \tag{3}\]
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 2 An Overview on Common Lifecycle Management Models
### Information Lifecycle Management (ILM)  
ILM refers to strategies for administering storage systems on computing devices [36]. Furthermore, ILM is for applying obvious and comprehensive policies to ensure correct information management. Considering the ILM's scope, there are five phases identified: creation and receipt, distribution, use, maintenance, and disposition. ILM is receiving increasing attention due to the rise in information security awareness and in regulations of personal data collection and processing, such as the GDPR (General Data Protection Regulation). ILM is a particular management strategy specifically for information handling; hence, it is usually integrated or supplemented as part of a larger lifecycle management [37; 38]. However, some studies deal with essential information management [39] and data protection questions [40].
## 3 Results and Discussion
### Principal Component Analysis  
The principal component analysis (PCA) was carried out using the 12 variables obtained in this study to assess the differences between the carob samples obtained from wild trees (Figure 2). The explained variance accounted for 45.7% of the total variance, 27.1% accounted for PC1, and PC2 accounted for 18.6%. Figure 2 displayed the weight of each studied variable in this study, showing their contribution to the carob individuals' distribution along PC1 and PC2 (Table 2). The heaviest contribution for PC1 is for TPC, DPPH, and ABTS with an eigenvalue equal to 0.45, 0.49, and 0.48, respectively, while for PC2 are fructose, glucose, and sucrose with an eigenvalue of 0.52, 0.42, and \(-\)0.41, respectively (Figure 2 and Table 2). In addition, PCA analysis showed that three different groups can be extracted depending on the geographical distribution. Lebanese samples were grouped on the right upper side, characterized by the highest amount of moisture and sucrose. On the other hand, Moroccan samples were grouped on the bottom right part of the biplot, characterized by high fructose, glucose, lipid, and ash contents (Figure 2). Finally, the Spanish samples, where the majority were positioned to the left of the PCA biplot (Figure 2), were characterized by high carbohydrates, antioxidant activity (DPPH and ABTS), polyphenol content (TPC), and high sucrose, fructose, and glucose concentrations.  
Figure 2: PCA scatter plot of the wild carob fruits analyzed in this study.  
Nutritionally speaking, glucose and fructose are both monosaccharides that are present naturally in fruits and vegetables, as well as the sucrose disaccharide. Studying these relationships could give insight into the metabolic pathways along carob pulp maturation and help in assessing the sweetness, quality, and potential use of carob in the market.
## 2 Research Topics Represented in This Special Issue
### Power to Synthetic Natural Gas (PtSNG) Plants  
[PERSON] et al. [9] investigated the technology of power to synthetic natural gas (PtSNG) plants as method to deal with the surplus of renewable sources on the electric grid. The aim of their study is to analyze the energy storage potential and the technical feasibility of the PtSNG concept considering different plant sizes (1, 3, and 6 MW). They also analyzed the ratio between the renewable electric energy supplied to the plant and the total electric energy generated by the renewable plant, based on a 12 MW wind farm. The analysis was carried out by a dynamic model and a thermochemical and electrochemical model, allowing one to predict the plant performance in steady state. The resulting annual overall efficiencies are in the range of 42-44%. The plant load factor, i.e., the ratio between the annual chemical energy of the produced SNG and the plant capacity, is equal to 60.0%, 46.5%, and 35.4% for 1, 3, and 6 MW PtSNG sizes, respectively.
## 3 Results
### Heat Transfer Coefficient in the Tropical Island Region
#### 3.3.2 Monthly Average Heat Transfer Coefficient  
Figure 13 shows the relationship between heat transfer coefficients, wind speeds, and rain hours for every month throughout the year in the tropical island region. It can be observed that THTC-Y in July is the largest (up to 46.3 W/(m\({}^{2}\cdot\)K)), since the average wind speed and total rainfall hours in July are all large among the twelve months. Moreover, less rain hours and the highest monthly average wind speed induce THTC-Y in June to rank second only to that in July (up to 39.0 W/(m\({}^{2}\cdot\)K)). Furthermore, because the average wind speeds and total rain hours are generally small in March, April, and May, accordingly, the values of THTC-Y in these months are all small (around 26 W/(m\({}^{2}\cdot\)K)). Except for the months described above, the THTC-Y values of the remaining months have little differences, and are approximately 32 W/(m\({}^{2}\cdot\)K).  
Figure 11: Daily variation relationship between heat transfer coefficients, wind speeds, and rain hours in the tropical island region.  
The difference between THTC-Y and the total heat transfer coefficient without considering evaporation heat transfer (THTC-N) is mainly reflected in EHTC. From Figure 13, it can be observed that the difference can be divided into three categories according to average wind speeds and total rain hours of every month:  
Figure 12: Frequency distribution of daily average heat transfer coefficient.  
Figure 13: Monthly variation relationship between heat transfer coefficients, wind speeds, and rain hours in the tropical island region.  
1. The differences between THTC-Y and THTC-N are large in January, July, September, and October with climate features of high wind speeds and strong rainfall, or relatively low wind speeds and especially frequent rainfall. For July particularly, EHTC reaches up to 12.5 W/(m\({}^{2}\cdot\)K) under the combined action of high wind speeds and strong rainfall.
2. The differences between THTC-Y and THTC-N are small from February to May, as these months possess relatively low or medium wind speeds and very few total rain hours. The EHTCs in these several months are all less than 2.5 W/(m\({}^{2}\cdot\)K) (only 0.8 W/(m\({}^{2}\cdot\)K) for April).
3. The differences between THTC-Y and THTC-N are in the range of 4-6 W/(m\({}^{2}\cdot\)K) in August, November, and December, when the outdoor mean wind speeds and total rain hours are medium.  
It should be noted that the difference between THTC-Y and THTC-N in June does not belong to any of the categories above, and the characteristic of high wind speed and little rainfall is particularly similar with that of winter in northern China. As a result, the EHTC in June is 4.6 W/(m\({}^{2}\cdot\)K).
## 5 Numerical Results  
This section presents the implementation of the proposed ATS-GRU algorithm, followed by its comparison with the traditional fixed time-stepping algorithm, FTS-NLMS. To ensure a just comparison of performance, we implement the original fixed time-stepping algorithm, FTS-NLMS, with the GRU technique, referred to as FTS-GRU. In addition, to further explain and illustrate the importance and advantages of the ATS method in traffic load prediction, we also compared the prediction results of three different step sizes (FSS, VSS, and ATS) based on NLMS.
## 4 Results and Discussion
### Effect of Fin Design Parameters
#### 4.3.3 Fin Height  
The effect of the fin height on both the hot (solid curves) and cold side (dash curves) are illustrated in Figure 9. For fin height on the hot side, as fin height rises, pressure drop descends continuously. Heat transfer area increased linearly when fin height varied from 2 mm to 4 mm, and it experienced a sharp decrease when fin height increased to nearly 8 mm, finally increasing up to when fin height was up to 20 mm. For the fin height on the cold side, it seems fin height has no relation to pressure drop on the hot side, but it does influence heat transfer area. When fin height changed in the same range, the heat transfer area experienced a decelerated decrease stage firstly, finally rising when fin height varied from around 10 mm to 20 mm. According to the above analysis, it seems that 20.00 mm and 13.02 mm are reasonable choices for fin height on the hot and cold side, respectively.
# A Miniaturized Design for a Terahertz Tri-Mirror CATR with High QZ Characteristics
## 1 Introduction
To address the above challenges, this paper presents a miniaturized terahertz tri-mirror CATR design methodology with low cross-polarization, operating in the 100-500 GHz band. Based on the large-aperture CATR design in [24; 25], this design proposes a method using the beam mode expansion technique, regulating the expected output field QZ radius and the edge taper of the feed, and incorporating rolled-edge structures on the primary mirror to achieve a high-performance CATR design with a small aperture, low edge diffraction, and low cross-polarization. Meanwhile, during experimental measurements, this work references content from [26; 27; 28] on planar wave area error analysis, mirror surface roughness effects, and the application of microwave-absorbing materials on the test prototype surface for a sub-THz CATR, thereby reducing measurement inaccuracies in the system prototype. Specifically, the methodology first determines the geometric configuration of the tri-mirror CATR system under low cross-polarization constraints by using cross-polarization cancelation conditions based on beam mode analysis theory [29]. Then, building on dynamic ray tracing based on GO and the dual-paraboloid expansion method [30, 31], the two shaped mirrors are shaped by adjusting the expected output near-field QZ width and feed-edge taper. Finally, a rolled-edge treatment is applied to the paraboloid primary mirror to suppress edge diffraction effects [32], achieving high QZ characteristics. Following this approach, a miniaturized tri-mirror CATR system with low cross-polarization is designed, featuring a small main structure size of 0.61 m \(\times\) 0.2 m \(\times\) 0.66 m. The results of the numerical simulations and experimental testing demonstrate excellent agreement, validating the correctness and effectiveness of the proposed methodology. This design approach provides a theoretical foundation and technical support for designing and constructing flexible, portable, and high-performance miniaturized CATR systems in spatially constrained measurement environments.
## 2 MHT for Data Authentication
### The Inefficiency of the MHT
#### 2.2.2 Impact Analysis of MHT Inefficiency  
To describe the impact of the MHT's iterative process on service performance, Figure 3 shows the evaluation results of a digital signature-based segment authentication scheme and an MHT-based segment authentication scheme regarding computation time. Additionally, it shows the comparison of the computation time for verifying data with the download time for retrieving the data.  
The analysis uses flooding-based name routing and CCN implementation [6]. It takes the network size of 100 nodes as in [26]. It uses 1024 (\(=2^{10}\)) segments, and each  
Figure 3: The performance evaluation of data authentication schemes.  
Figure 2: The inefficiency of the MHT-based authentication scheme caused by duplicated processing.  
segment packet size is 4 K bytes. In this case, the average end-to-end delay measurement for 100 segments is 8 ms.  
Figure 3 demonstrates the measurement of the end-user service performance of a device (AMD Opteron 8354 2.2 GHz processor under Linux) 10 times in the following three cases.  
1. Case 1 ([retrieve]): The device measures the response time to retrieve all segments without verifying the segments. It shows 0.083 s on average.
2. Case 2 ([sign]): It verifies the digital signature (RSA 2048) for each segment after receiving the segment. Then, it measures the total signature verification time.
3. Case 3 ([mht]): It uses MHT consisting of 1024 leaf nodes and SHA 512. Measuring the performance of computing hash values does not verify the digital signature of the root node value of the MHT.  
As shown in Figure 3, computing the hash values of the MHT is the main cause of service delay under the NDN environment. Especially, it shows that the latency in computing hash values takes longer than verifying the digital signature and receiving data segments. When considering the number of segments (\(2^{n}\)), if \(n>4\), the latency in verifying the digital signature of each segment is shorter than that of computing the node values of the MHT. Generally, it is known that the performance of a hash function is very efficient. However, severe inefficiency of MHT occurs when the total number of calls to a hash function is large, as in the NDN environment.
# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 5 Simulation Run-Time Resources Deployment Algorithm
### The LGASRD Algorithm
#### 5.1.1 Constraint Handling Mechanism  
As shown in Equation (1), this encoding method makes it easy to ensure that some constraints are satisfied by reasonable crossover and mutation operators, which mainly restrict the properties of variables and the relationships between variables, as formulated in Equations (15)-(21). However, it is difficult to satisfy the server resource occupancy constraints, e.g., Equation (14), by intuition. In some cases where server resources are limited, we must adopt strategies to ensure that the solution obtained is feasible.  
To address this issue and preserve the diversity of the population as much as possible, we have divided the algorithm into two stages. In the first stage, we transfer the two objectives into one by weighting them using \(\omega_{1}\) and \(\omega_{2}\) and apply a penalty function to penalize solutions that violate the resource capacity constraint. To enhance the effectiveness of this mechanism, the penalty coefficient \(\mathbb{M}\) increases exponentially with the iteration of the algorithm. Therefore, the objective function in the first stage is  
\[f(DS)=f_{1}(DS)\omega_{1}+f_{2}(DS)\omega_{2}-max\{0,f_{2}(DS)-1\}\mathbb{M} \tag{22}\]  
In the second stage, we consider both \(f_{1}(DS)\) and \(f_{2}(DS)\). To enhance the algorithm's exploratory ability, we apply a parameter \(\epsilon\) to relax the resource occupancy rate constraint and make \(\epsilon\) rapidly approach 0 with each iteration of the algorithm. This ensures the feasibility of the solution obtained.  
To ensure the availability of feasible solutions, our basic idea is to accept some unfeasible solutions in the early stages of the algorithm to increase the diversity of individuals in the population, while focusing more on the feasibility of solutions as the algorithm iterates.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Autonomous Boat Development
#### 2.1.4 Herbicide Dispersal System  
For design of the herbicide dispersal system, _Hydrilla_ was chosen as the targeted aquatic weed and Aquathol-K, a liquid-based chemical effective in the treatment of _Hydrilla_ and other aquatic weeds, was selected as the treatment herbicide. A downstream chemical dilution system, with a drop hose outlet and an RC-controlled (via electronic speed controller, ESC) variable-speed pump, was utilized for dispersal. With this design, herbicide concentrate is stored within an onboard tank, diluted with water drawn from the lake, and dispersed through a hose submerged below the water surface. Dilution occurs at an injector downstream the pump, which passively combines the concentrate with lake water via the Venturi effect. Fluid analysis of the dispersal system and a series of tests were conducted for pump selection (8000 series SHURflow industrial pump, 100 psi rating) and to characterize chemical dispersal rates as a function of RC input (Figure 17b). These relationships allowed optimal RC pump control inputs to be determined as a function of transect spacing, boat velocity, desired application rate, and lake depth. Dispersal system design and analysis is further detailed in [23].  
Design optimization methodologies, incorporating [PERSON]'s law of fluid statics, were used to optimize the placement of onboard components (batteries, electronics bin, chemical tank, propulsion system, dispersal pump, etc.). The objective of the algorithm, implemented with Excel Solver, was to minimize the pitch angle of the boat throughout all levels of tank payload (empty to full); a key constraint was avoidance of forward pitching (bow pitched downward). The methods and results, as detailed in [23], were adapted for later prototype generations as component dimensions, weights, and placement options (e.g., component storage within the pontoons), were altered.
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## 3 Numerical Results
### Example 4  
In the last example, we set the function \(g(x)\) in order to obtain the exponential solution \(y(x)=e^{x}\). The parameters are set to be \(\rho_{10}=\zeta_{10}=1\) with \(c_{1}=1+e\) for \(0<\gamma<1\), while for \(1<\gamma<2\), we set \(\rho_{10}=\zeta_{20}=c_{1}=1\), \(c_{2}=e\) and \(\rho_{20}=\zeta_{10}=0\).  
The behavior of the error for different values of \(\gamma\) and \(h\) is shown in Table 5. The numerical results show that the error increases slightly when \(\gamma\) increases, while it decreases fast with \(h\).  
Figure 5: Example 3. The rescaled left (panel (a)) and right (panel (b)) Caputo derivatives and the Riesz–Caputo derivative (panel (c)) of the function \(y(x)=\sin(\omega x)\), for different values. of \(\gamma\) (panel (d)) with \(\omega=5\pi\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\gamma\) & **0.25** & **0.75** & **1.25** & **1.75** \\ \hline \(h=\frac{1}{3}\) & \(2.45\times 10^{-5}\) & \(1.45\times 10^{-5}\) & \(1.11\times 10^{-4}\) & \(1.31\times 10^{-4}\) \\ \(h=\frac{5}{3}\) & \(3.20\times 10^{-6}\) & \(1.15\times 10^{-6}\) & \(1.07\times 10^{-5}\) & \(2.53\times 10^{-5}\) \\ \(h=\frac{1}{16}\) & \(2.83\times 10^{-7}\) & \(7.89\times 10^{-8}\) & \(1.12\times 10^{-6}\) & \(4.91\times 10^{-6}\) \\ \(h=\frac{1}{32}\) & \(1.92\times 10^{-8}\) & \(5.13\times 10^{-9}\) & \(1.17\times 10^{-7}\) & \(9.72\times 10^{-7}\) \\ \(h=\frac{1}{64}\) & \(1.31\times 10^{-9}\) & \(3.33\times 10^{-10}\) & \(1.25\times 10^{-8}\) & \(1.96\times 10^{-7}\) \\ \(h=\frac{1}{128}\) & \(5.80\times 10^{-10}\) & \(4.66\times 10^{-10}\) & \(1.82\times 10^{-9}\) & \(4.02\times 10^{-8}\) \\ \hline \end{tabular}
\end{table}
Table 5: Example 4: The infinity norm of the error \(\|e_{h}\|\) for different values of \(\gamma\) and \(h\).