esa-sceva/satcom-chunk-collection huggingface dataset:
# Antioxidant Production in _Dunaliella_
## 4 Tuning Antioxidant Enzyme Activity in _Dunaliella_
### Salinity  
Salinity induces osmotic stress, which has been found to affect antioxidant enzyme activity in _Dunaliella_ species. Osmotic stress is divided into two types based on the salinity of the medium: hyposolaine (salinity \(<\) 0.5 M) and hypersaline (salinity \(>\) 0.5 M).  
CAT and APX activities were found to be highest when the cells were grown at a low salinity level (\(>\)0.5 M), with the activity decreasing with increased salinity. In contrast, SOD activity increased twofold at 1 M and threefold at 2 M compared to the activity at 0.5 M NaCl [122]. However, above 2 M NaCl, a decrease in the SOD activity was observed [122]. Above 2 M NaCl, accumulation of glycerol acts as an osmolyte for balancing high salinity-induced osmotic stress [123], which may be the reason for reduced enzymatic antioxidant activity. Similarly, CAT, APX, and GPX activity were found to decrease in _D. tertiolecta_ concurrently with an increase in salt concentrations [124], perhaps due to the participation of glutathione (GSH) and ascorbate in the scavenging of H\({}_{2}\)O\({}_{2}\)[124; 125]. A higher GR activity in _Dunaliella_ species (_salina and tertiolecta_) was observed at a low salt concentration (0.1 M NaCl) [124] which would increase the ratio of NADP +/NADPH, thereby ensuring the availability of NADP + to accept electrons resulting in a reduced flow of electrons to O\({}_{2}\) for generation of ROS [126].  
On the contrary, a study using _D. tertiolecta_ demonstrated that CAT, SOD, DHAR, and GTX activity were unaltered under a wide range (0.05-3.00 M NaCl) of salinities, whilst APX activity increased by 200%, and MDHAR activity by 300% relative to the optimal growth salinity (0.5 M NaCl) [125]. Increased level of enzymatic activity in hypersaline condition may be due to the increase in the formation of ROS (peroxides) or increases in ATP synthesis via the Mehler-peroxidase reaction (which generates ATP by substituting dioxygen for carbon as the electron acceptor) [127]. Also, _Dunaliella_ can retain a higher level of Mehler-peroxidase reaction at higher saline conditions [125].  
The data above indicates that high salinity-induced osmotic stress is not an effective way to activate all antioxidant enzymes in _Dunaliella_ strains. The above investigations also suggest that different _Dunaliella_ strains, when exposed to external salinity, show different antioxidant enzyme activity due to differences in resistance and response. Hence, consideration of strain selection must be given if this approach is chosen.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 4 Results and Discussion
Once the health status of the lettuce crop was identified as 'Yellow Foliage--Leaf Chlorosis', the potential causes were automatically extracted from the ontology model and displayed on the application panel. Figures 11-14 in Appendix A show an example of the working of the proposed method and application for a lettuce crop when its foliage was detected to be yellow. The primary causes of lettuce foliage chlorosis could be inadequate environmental conditions (humidity, air temperature), poor water quality (inadequate pH or EC), nutrient deficiency, etc. By analyzing sensor data and the possible causes of leaf chlorosis, it is possible to reach the specific cause of the problem. For instance, if sensor data show that all the parameters are within their optimal ranges, then \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **N (Truth)** & **N (Classified)** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
**Q = 1** & 60 & 61 & 0.95 & 0.95 & 0.97 & 0.96 \\
**Q = 0** & 60 & 59 & 0.95 & 0.97 & 0.95 & 0.96 \\
**Average** & - & - & 0.95 & 0.96 & 0.96 & 0.96 \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the performance metrics. the problem could be related to nutrient delivery or the design of the system. A reasonable treatment can be suggested after problem identification. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Techniques and Parameters Used**} & **Average** & **Average** & **Average** & **Average** \\  & & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{[PERSON] et al. [18]} & SVM (support vector machine) and a* (CIELAB color space), G (green from RGB color space), and H (hue from HSV color space) & 0.91 & 0.92 & 0.93 & 0.925 \\ \hline [PERSON] et al. [19] & [PERSON]’s method and k-means clustering technique & 0.92 & 0.93 & 0.93 & 0.93 \\ \hline [PERSON] et al. [20] & HSV (hue, saturation, and value) color space and decision tree method & 0.89 & 0.91 & 0.90 & 0.905 \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & [PERSON]’s method, SVM, k-NN (k-nearest neighbor) and MLP (multi-layer perceptron) & 0.90 & 0.91 & 0.91 & 0.91 \\ \hline [PERSON] et al. [25] & L*a*b* color histogram, k-NN, and random forest & 0.94 & 0.95 & 0.94 & 0.945 \\ \hline - & Proposed model & 0.95 & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of existing methods. Figure 9: Example of correctly classified cases. The proposed model was developed using open-source frameworks, and, hence, it can easily be expanded or adjusted as per the requirement by adjusting the threshold values. The significance of the model is that it is fully automated and offers a non-destructive, low-cost and reliable approach to identifying leaf chlorosis and determining the quality of lettuce plants along with the possible causes. In contrast to the computer vision and machine learning-based models, the proposed methodology requires less data.
## 2 Materials and Methods
### Gait Analysis  
Patients walked barefoot at a self-selected speed in the gait laboratory. Kinematic data (8 MX T10 cameras, VICON Motion Systems, Oxford, UK) were collected synchronously to kinetic data (two OR-6-7-2000 force plates, Advanced Mechanical Technology, Inc., Watertown, MA, USA). Markers were placed on anatomical landmarks according to a modified version of the Plug-in-Gait model to improve the reliability and accuracy of the gait data in the frontal plane [27]. Of all the obtained trials, those in which one foot was completely on the force plate and no markers were missing were classified as good. Five good trials for each subject were selected for further processing. Control subjects walked at their own speed and at a slow walking speed (\(\approx\)1.0 m/s) which was similar to patients' walking speed shortly before surgery [14]. Since no significant differences were detected between the left and right sides in the control subjects, the left side was chosen for further analysis and for comparison with the involved and non-involved sides of the patient group.  
Kinematic and kinetic gait variables were calculated using the inverse dynamics equations [28] in Vicon Nexus (version 2.5, VICON Motion Systems, Oxford, UK). In these equations, the hip joint center was obtained according to a geometrical prediction method [28] and the external joint moments are calculated from the force plate data and the mathematically derived joint centers. Kinematic and kinetic data were exported to Matlab (version R2018b, The Mathworks Inc., Natick, MA, USA) to normalize the data over the gait cycle. The peak external knee and hip adduction moment during the first (KAM1 and HAM1) and second (KAM2 and HAM2) half of the stance phase were computed for each trial (normalized to body weight and expressed in newton meters per kilogram) and averaged over the trials. As has been published previously [26], kinematic parameters during the stance phase of gait were also extracted (a) for the hip: the maximum adduction angle in the frontal plane, the maximum flexion and extension angle, and the hip RoM in the sagittal plane; (b) for the knee: the maximum flexion and extension angle, as well as the RoM in the sagittal plane (the difference between the maximum flexion in the first half and the maximum extension during the second half of the stance phase); (c) for the trunk: maximum sideward displacement (LTD--lateral displacement of the trunk relative to the supporting limb); (d) for the foot: mean progression angle in the transverse plane (FPA--the angle of the long axis of the foot segment relative to the direction of walking). Furthermore, the pelvic drop of the contralateral leg during the swing phase was determined. The gait speed was defined as the average value over the included trials.
## 2 Background
The comparative simulations reported that the proposed scheme improved the signal-to-noise ratio (SNR) by more than 4.6 dB, reduced the channel error by 3.5 times or more, and reduced the peak-to-average power ratio (PAPR) by more than 6 dB, excluding time division multiple access (TDMA). According with the introduction and considering the background of the existing research on the topic, the work presented here focuses the research on VLP systems based on PSDs. This work is based on the work shown in [47]. In that work, several proposals were made for LPS based on optical signals from PSDs as a function of the number of emitters within the field of view of the emitter. Experimental tests were performed in a real environment. Total stations (TS) were used to obtain accurate ground truth. The accuracy and precision of the each proposal were evaluated. To obtain these results, a formal geometric calibration had to be performed beforehand, as shown in [49]. This calibration requires taking different images of a very precise template, at different positions and angles. With the information retrieved from several images (usually more than 10), the parameters of the PSD + lens system, such as focal length, optical center, and radial and tangential distortion parameters, can be obtained. It is a long and laborious process. Besides, any small movement of the lens, for example, moving it from the calibration bench to its position on the mobile agent, modifies the parameters, and it is necessary to do the process again. This paper proposes a weak calibration procedure to obtain some parameters of the PSD + lens system that allow one to obtain the positioning in an accurate way. A comparison between the positioning results obtained with the proposed model and formal calibration is presented. An experimental evaluation in a real environment is provided to show the benefits of the proposed method. The main contributions of this work are:  
* The calibration proposal is a quick method of calibration compared to formal calibration. System parameters can be obtained with only six measurements. * Calibration is performed in the actual environment, so no complex infrastructure is required for receiver calibration. * The absolute positioning error obtained using the proposed calibration is below 35 mm, so the accuracy achieved is higher than what is achievable for most other existing IPS alternatives. * Highly qualified experts are not needed to calibrate the modules and put them into operation almost immediately.
## 1 Introduction
In this paper, we show how a reliable artificial neural network may be built, and what its setup should be, in order to approximate the behavior of reinforced concrete slabs through the numerical homogenization technique. The ANN built computes the effective properties of the representative volume element (RVE) of reinforced hollow core (HC) slab. Reinforced HC structures were selected for the study because they have periodic geometry and can be computationally expensive due to their geometric complexity. RVE of reinforced HC slab was selected as a periodic hollow core unit forced by the hole spacing, and a square RVE base was assumed. First, the numerical model of RVE of reinforced hollow core slab was assumed with six parameters, which are related with HC slab geometry and area of steel reinforcement. For more details, see Section 2.1. Second, according to the optimal Latin Hypercube Sampling (LHS) the spread of the parameters was defined. For more details regarding sampling method, please see Section 2.2. Thus, the number of RVEs, built through the sets of particular parameters, represented various reinforced concrete HC slabs. Then, the effective mechanical properties of selected HC slabs were computed according to [PERSON]'s numerical homogenization method [10], see Section 2.3. In a later section, we show how the numerical framework was built to train and validate the ANN for computing the effective properties without using the homogenization method; see Section 2.4. In Section 3, first, the verification of the used homogenization method was shown in comparison to the data presented in the literature; see Section 3.1. Additionally, the selected ANN setups in reference to their accuracy were demonstrated; see Sections 3.2 and 3.3. Finally, we show an example of the influence of changing one of the design parameters of HC slab on its effective properties. The overall structure of the study is presented in the Figure 1. The particular elements of the study will be described in the forthcoming sections.
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 3 Case Study  
### Study Area  
The study working panel is 22101 in Taiyuan, Shanxi Province, China, as shown in Figure 10.  
Figure 10: (**a**) Location of Shanxi province in China; (**b**) Location of Taiyuan city in Shanxi province; (**c**) Location of the working panel 22101.  
Figure 9: Dynamic prediction model.  
The overlying rocks in the working panel 22101 are the Shihezi Group and Shanxi Group strata, mainly composed of fine sandstone, sandy mudstone and sand shale interbedded, medium-hard lithology, with a thickness of about 140 m. The Quaternary loess has a thickness of about 70 m. The soil has a large sand content, is not strongly bonded and has vertical joints; thus, the tensile and shear strength is low.  
The working panel adopted an all-collapse method. The mining height is \(M\) = 3.18 m; the lengths of the working panel are 440 m (\(D_{1}\)) and 140 m (\(D_{3}\)), respectively; the dip angle is \(\alpha\) = 1\(\sim\)3\({}^{\circ}\), which can be considered horizontal seam mining; the average mining depth is \(H_{0}\) = 220 m; the average thickness of loose layers \(h\) = 80 m; the average advance velocity is \(v\) = 2.2 m/d. According to Equation (23), the parameters of MMF time function which include \(\ln\)\(a\) and \(b\), are 22.38 and 5.80, respectively.  
Two observation lines were laid and there were 45 monitoring points, as shown in Figure 11. The traverse measurement and the trigonometric elevation measurement were performed to gain the plane position (\(x\), \(y\)) and the elevation (\(h\)) of each point. A Leica T402 total station was used for measurement, the angular nominal accuracy was 2\({}^{\prime\prime}\) and the distance accuracy was 3 mm + 2 ppm.
## 2 Methodology  
### Data Collection  
This section details the datasets employed in predicting diabetes, which involves utilizing data collected from multiple sensors. In this research, wearable sensor-based health training datasets are utilized and obtained from open-source platforms available on the Internet [7; 21]. These datasets were selected based on their relevance and quality to ensure the robustness and accuracy of our analysis. The diabetes type 1 non-invasive activity monitoring (D1 NAMO) multi-sensor dataset used in this article is described in detail in [7; 21]. This dataset contains four kinds of sensor information: ECG sensor, glucose measurements, breathing sensor data, and accelerometer (ACC) signals. It also includes pictures annotated with food information, but we are only focusing on the data from the two sensors, the ECG sensor and glucose measurements, for this study because ECG and glucose levels are critical parameters in the diagnosis and management of various health conditions such as diabetes and were collected using standardized protocols, ensuring consistency and reliability. Glucose levels are a direct measure of glycemic control, while ECG data can provide insights into cardiovascular health, which is often affected by diabetes. By analyzing both, we aimed to develop a more comprehensive predictive model for diabetes. Moreover, integrating cardiac and metabolic health data allows for a more holistic approach to diabetes prediction. Since cardiovascular health is closely linked to diabetes, combining ECG and glucose data can improve the accuracy of the predictive model. The data come from patients in non-clinical settings, gathered using three Zephyr BioHarness wearable devices [7; 21]. The multimodal sensor dataset involves information from 29 patients, with 20 of them being healthy individuals and 9 diagnosed with diabetes. However, the data collected from public sources for all real-world applications are not sufficient for training the model and predicting chronic diseases such as diabetes. Therefore, to address this issue and enable artificial intelligence in scenarios with limited data, a meta-learning method is proposed.  
Signal Processing  
The process involves cleaning and transforming raw signal data to remove noise and artifacts, thereby making the signals more suitable for analysis. First, the raw ECG signals are cleaned using DWT denoising techniques to eliminate noise and unwanted signals. Next, important features such as heart rate variability (HRV), QT interval, and other ECG morphology metrics are extracted based on their relevance. Simultaneously, glucose level signal data are processed and smoothed using averaging methods. Finally, a meta-learning technique is employed to predict diabetes and glucose levels. Below, the details of the proposed meta-learning model and DWT are discussed.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 2 Materials and Methods
### Research Methodology
#### 2.3.4 Analysis of Factors Influencing the Stability of Cultivated Land Productivity  
Multiple linear regression is a statistical method used to study the relationship between dependent and multiple independent variables. The method assumes that the dependent variable (usually expressed as \(Y\)) and the independent variable (usually expressed as \(X1\), \(X2\),...) have a linear relationship, where the dependent variable can be expressed as a linear combination of the independent variable plus the constant and error terms [39]. Mean precipitation (\(MAP\)), mean air temperature (\(MAT\)), field road distance, average patch size (_AREA-MN_), distance between the field and the road (_field-road distance_), distance between the field and the river (_field-river distance_), nearest neighbour distance (_ENN_), and fractal dimension (_FRACT_) were considered as factors that may influence cultivated land productivity. Using multiple linear regression, the relationship between cultivated land area and influencing factors was established as follows:  
\[Y=a0+a1X1+a2X2+a3X3+a4X4+a5X5+a6X6 \tag{4}\]
## 3 Architecture
### IPCRSA Overview  
Figure 1 illustrates the overall design of IPCRSA. Its network topology is divided into three levels: L\({}_{3}\), L\({}_{2}\), and L\({}_{1}\). The main entities include the Scheduling Node (SN); Gateway Node (GN); Computing Resource Node (CRN); and User Client (Client), User, and Resolution Node (RN). There is a one-to-one connection relationship between SNs and GNs, and GNs establish either a one-to-one or one-to-many connection relationship with CRNs. The L\({}_{3}\), L\({}_{2}\), and L\({}_{1}\) level network topologies and various entities within their coverage areas collectively constitute the scheduling domains in IPCRSA at levels L\({}_{3}\), L\({}_{2}\), and L\({}_{1}\). The specific functions of each entity will be explained in the following sections.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 5 New Research Directions and Challenges
### Using End-to-End Security and Reducing Trust on Third Party  
The best, easiest, and most profitable solution is to install end-to-end security or security at the application layer. Most mobile communications security vulnerabilities (except SIM cloning and DoS attacks) do not aim at ordinary people. Their targets are usually restricted to special groups so it is reasonable and economical that such groups make their communications secure by using the end-to-end security. Since the encryption and security establishment is performed at the end-entities, changes to the mobile architecture will not be required. In this way, even if the conversation is eavesdropped by the police or legal organizations, they cannot decrypt the transmitted data without having the true CK, provided that a secure enough cryptographic algorithm is deployed. Therefore, in order to avoid illegal activities, it should be transparent to both network operators and service providers. It may also be necessary to find solutions for a legal interception or a key screw scheme. The end-to-end security establishment has complete flexibility with the deployed algorithms so the appropriate upgrades can be easily undertaken when necessary [67].
## 1 Introduction
proposed a theoretical modeling method to accurately determine the maximum power transfer condition of a UTMWPT system when driving a resistive load [19; 20]. The simulation results showed a good consistency in trend with the experimental measurements. They also presented a spider clamp attachment method to bond the piezoelectric transducers to the metal wall in a UTMWPT system [21]. A prototype system built with this new attachment method was shown, which could, at best, transmit 1.0 W of power through a 1-mm-thick steel plate with an efficiency of 38%. To the best of the authors' knowledge, no UTMWPT system has been capable of generating a medium-power regulated DC output with a high overall efficiency. Besides, the ability of UTMWPT systems to drive medium-power electronic appliances has not been demonstrated. Therefore, we developed a novel and efficient UTMWPT system. Based on the achievements of [PERSON] et al. we for the first time adopted an RF AC-DC converter with input impedance matching in a UTMWPT system. This RF AC-DC converter made our UTMWPT system the first one capable of efficiently converting the transmitted RF signal to a regulated DC voltage. We also designed a computer-controlled measurement method to accurately test the effectiveness of the full system. In addition, we successfully demonstrated the system using real electronic appliances-ARM\({}^{\text{\textregistered}}\) Cortex\({}^{\text{TM}}\)-A8 evaluation boards with liquid-crystal display (LCD) panels. The paper is organized as follows: Section 2 describes the methods used to design the system. Section 3 describes the computer-controlled measurement method, discusses the measurement results and presents the demonstration experiment. Section 4 draws the conclusions.
## 5 Results and Discussion
### Year-Long Results
#### 5.1.3 Battery Cycles and SOH  
Concerning the battery cycles, the first observation is the small number of cycles (most of the time below one per day). Ultimately, this means that it is improbable that the limit of 6000 cycles will be reached before the ten-year calendar aging of storage devices.  
Furthermore, due to the low number of cycles, the 2-TOU with pre-charge control strategy adds to profitability without significantly compromising the storage device's longevity. This can be observed from the very similar SOH after one year in both control strategies.