esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 4 Prediction of Linear Guide Preload--ANN Modeling
### Preload Variation of Linear Guide  
In this section, the vibration signals assessed from the dynamic motion tests of the stages were used to establish the preload prediction model. The preload conditions of the linear guides and the rigidity of the stage are listed in Table 2. As mentioned above, all the linear guides used in the tests were newly assembled in a factory with preload rates at low, medium, and high amounts.  
1. Case I: Stage assembled with four sliding blocks or carriages preloaded at heavy or high levels. Essentially, the interference at the rolling interface was about 8 \(\upmu\)m for achieving high preload and the initial positioning stage rigidity was 2020 N/\(\upmu\)m.
2. Case II: Stage with four sliding carriages preloaded at the medium level, which was generated by making an interference of 5 \(\upmu\)m in the ball grooves of the linear guide. The stage rigidity was rated as 1696 N/\(\upmu\)m.
3. Case III: Stage with four sliding carriages with low or light preloads. A light preload linear guide could be achieved by reducing the interference at the rolling interface to 1-2 \(\upmu\)m. The structural rigidity of the stage was rated as 1176 N/\(\upmu\)m.  
The dataset for establishing the predictive model was taken from the motion tests associated with the three preloaded conditions, totaling 242 records.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 2 Data  
### Ultrasound Data  
Ultrasound volumes were acquired by an experienced orthopaedic surgeon F.S. using a Philips EpiQ7 US system and a Philips VL13-5 US probe. SonoCT real-time compound imaging technology and XRES image processing were selected on the US system to enhance image quality. Image acquisition settings have been optimized by a US specialist to work best for knee structure visualization: 13 MHz probe frequency, 4 cm penetration depth and far field focus. Given the restriction of movement due to the presence of surgical tools through the medial/lateral patellar portals, the US probe is positioned on the patellar tendon and parallel to the principal axis of tibia, as shown in Figure 1. 4D US sequences were acquired simulating several possible surgical scenarios for the four flexion angles, 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\)[21]. In this paper, we use 4D US static sequences which were acquired when the leg is fixed at one of the reference knee flexion angles and the probe positioned on the patellar tendon, with the scanning plane parallel to the tibial shaft.  
The neutral leg position, where the femoral shaft and tibial tubercle were aligned, was designated as 0\({}^{\circ}\) flexion. The flexion angles were then achieved by bending the leg such that the angle between femur and tibia with the neutral position was half the total angle. For angles other than 0\({}^{\circ}\), the US scans were recorded with the leg placed on the custom made supporting cushion. Eleven 4D US static sequences comprising 61 3D US volumes of the anterior knee portion were collected from six healthy subjects at different flexion angles. Table 1 shows the subject wise angle information. Each 3D volume is of 272 \(\times\) 510 \(\times\) 256 voxels, corresponding approximately to a size of 4 cm \(\times\) 4 cm \(\times\) 5 cm, i.e., each voxel has the dimensions of about 0.14 mm \(\times\) 0.09 mm \(\times\) 0.21 mm. The Queensland University of Technology Ethics Committee (No. 1700001110) granted the ethics approval for the US and MRI data collection. The study objective and measurement procedure were explained to each volunteer and their written informed consent is procured.  
Figure 1: US probe positioning during the knee scan with the corresponding schematic in sagittal view.
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 2 Method
### Multi-Strategy Genetic Algorithm
#### 2.3.1 Chaotic Initialization Strategy  
Random initialization has adverse effects on the population's evolution, such as insufficient diversity, not utilizing the entire solution space, and slowing the GA model's convergence [30]. Chaotic initialization has randomness and regularity. The search space is assessed in a specific range without repetition, improving the solution accuracy and convergence speed. The most common chaos initialization methods are logistic and Chebyshev initializations [31]. Since most of the sample data were close to 0, we used the Singer method to initialize the population as follows:  
\[z_{k+1}=u\left(7.86z_{k}-23.31z_{k}^{2}+28.75z_{k}^{3}-13.302875z_{k}^{4}\right),u\in[0.9,1.08] \tag{7}\]  
where \(u\) is the chaos coefficient. When the parameter \(u\in[0.9,1.08]\) and \(z_{0}\in[0,1]\), the system is in a chaotic state, and the entire space is mapped.  
The population initialization consisted of the following steps. The initial value was randomly generated using Equation (7). An iterative calculation was performed to obtain a numerical sequence, where N is the population size. The chaotic sequence was mapped to the solution value domain, and the entire search space was traversed.
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 3 Results and Discussion
### Growth Kinetics and Biochemical Characterization  
#### 3.2.1 Growth Kinetics  
Figure 1 illustrates the growth curve of _Dunaliella_ sp. over a 24-day period. Initially, on day 1, the biomass dry weight was approximately 0.74 g/L. From days 7 to 19, there was a steady increase in dry weight concentration, indicating an active biomass production phase. This period likely corresponds to the exponential growth phase, where the conditions are optimal for _Dunaliella_ biomass production. After day 19, the results showed that the concentration of microalgal dry weight levels was around 2.5 g/L, indicating a plateau. This plateau stage suggests that the system reached a stationary state, possibly because of the depletion of essential nutrients or the accumulation of inhibitory byproducts that limit further biomass production [61]. [PERSON] et al. [62] reported that the highest biomass production of _Dunaliella salina_ (BMAK 116) was achieved in Conway medium, yielding 0.25 g/L, followed by 0.19 g/L in F/2 medium and 0.16 g/L in Johnson medium. Higher biomass concentrations have been reported for _Dunaliella tertioleta_ and _Dunaliella salina_, which were attributed to the addition of growth hormones and pentoses in the culture medium. This led to biomass productions of 800 mg/L and 1.25 g/L, respectively [63; 64]. [PERSON] et al. [65] compared the growth of _Dunaliella salina_ under varying salt stress conditions (0.5-2 M) and found that the optimal biomass dry weight pattern was observed at a salt concentration of 1 M (approximately 7.2 g/L, day 15). In contrast, [PERSON] et al. [66] identified 2 M NaCl as the optimum concentration for the growth of _Dunaliella salina_ and 1 M NaCl for _Dunaliella viridis_ under a photoperiod of 16 h of light and 8 h of darkness with a shaker set to 80 rpm.  
Figure 1: The growth curve of _Dunaliella salina_ in terms of dry weight.
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access
### Advanced Channel Coding and Modulation
#### 2.3.1 Channel Coding  
A binary channel can be binary erase, symmetric, or additive white Gaussian noise. The single-link channels' Shannon limit serves as the performance benchmark. As multiple access techniques are primarily orthogonal, they have been important in mobile communications since the 2G era, in situations where multiple users serving from same base station or spatial domains use separate frequency and time resources. NOMA, however, can be utilised in addition to OMA to expand system capacity and service additional connections. This opens up a completely new field called \"multi-user oriented channel coding\", which is essentially an improved form of \"interleave-division multiple access\" (IDMA). It is important since multi-user channel channel capacity is not yet fully understood or established, particularly for uplink with the near-far effect and independent channel fading. Theoretically, any channel code might be taken into consideration in order to boost the capacity of multi-user channels, provided that the code itself can allow for optimization, such as going from a single user to multiple users. Multi-user low-density parity-check codes (LDPC) have recently been suggested for non-orthogonal uplink transmission [52].  
Non-binary (sometimes referred to as multi-variant) codes can be taken into consideration to improve the channel codes' robustness in fading channels and under high SNR conditions. Multi-variant codes currently come in two main categories. The multi-variant LDPCs, as discussed in [53] are classified in the Galois field (q). Lattice codes are the second class of these codes. The low-density lattice code [54], which is also represented by the Tanner graph and parity check matrix, is a very promising variation of the parent code.
## 3 Unsaturated Soil
### Soil Water Characteristics of Unsaturated Soil  
Matrix suction is the main reason why unsaturated soil is different from saturated soil, and it is also a key factor to reflect the mechanical properties of unsaturated soil [83]. The SWCC is a conceptual and interpretative tool to describe the soil water holding capacity when water content changes with suction [84]. SWCC defines the relationship between suction (matrix suction), volume water content (\(\theta\)) or saturation (\(Sr\)) of unsaturated soil. A typical SWCC is shown in Figure 4 (determined by the change in water content). The suction state (abscissa of the SWCC) can be expressed by the total suction of the soil water potential. In the high humidity state, the matrix suction (\(u_{a}-u_{w}\)) is used, and in the low humidity state, the total suction is used. Since there is little difference between the total suction value and the matrix suction in the low humidity state, the entire SWCC is often expressed by the matrix suction.  
When the soil changes from a saturated state to a dry state, the distribution of solid, liquid, and gas phases in the soil will also change with the change in stress state. The relationship between these phases has different forms and affects the engineering properties of unsaturated soil. In some cases, the properties may be mainly related to the volume of the separated phase (such as water content), or to the continuity and curvature of the liquid phase (such as permeability coefficient, molecular diffusion), or to the gas phase (such as evaporation coefficient or diffusion coefficient). In other cases, the properties of interphase contact areas that control stress transmission (such as shear strength and volume change) or interphase mass transfer (such as chemical adsorption and volatilization) determine soil properties. The relationship between these phases can be derived from the data of the SWCC, which can then be used to estimate the parameters of unsaturated soil [85].  
In the SWCC measure, the saturated water content A can be obtained by tests, while the residual water content B is often difficult to determine, influenced by the instrument precision and soil properties. Therefore, some SWCC models have been proposed to  
Figure 4: Typical water retention curve.  
predict the result of the SWCC. The widely used expressions of SWCCs are displayed in Equations (11)-(13).  
\[\ln\psi=a+b\ln\theta \tag{11}\]  
\[\theta=\left[\frac{1}{1+q\psi^{n}}\right] \tag{12}\]  
\[\theta=\left[\frac{1}{1+\left(q\psi\right)^{n}}\right]^{m} \tag{13}\]  
where \(\psi\) is suction; \(\theta\) is the volume water content; and \(a\), \(b\), \(q\), \(n\), and \(m\) are fitting parameters.
# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 4 Discussion
### Causes of NDVI Change
Therefore, we present three reasons for the positive impact of NTL on NDVI in HRB: (1) the development of modern agriculture was enhanced; (2) people and governments were willing to devote more resources to improve the environment for better living conditions, and more vegetation and a favorable climate would attract more human settlement; (3) urbanization development would be in favor of vegetation growth when the NTL value was lower than 15 or higher than 55. Nevertheless, numerous studies did not focus on the role of CO\({}_{2}\)[57]. As a primary substrate for photosynthesis, the increase in atmospheric CO\({}_{2}\) concentration is expected to enhance photosynthesis and improve productivity through CO\({}_{2}\) fertilization [14]. In semi-arid areas, plants strategically close their stomata to preserve water and increase water use efficiency to improve photosynthesis [58]. Therefore, the effects of CO\({}_{2}\) on plants are complex and cannot be overlooked. Recently, the importance of CO\({}_{2}\) in vegetation change has been a central concern, although the dominant effect of CO\({}_{2}\) fertilization on vegetation growth has been debatable [59; 60; 61]. Factorial simulations with multiple ecosystem models implied that CO\({}_{2}\) fertilization effects explained 70% of the satellite-observed leaf area index (LAI) trend globally [7; 15]. Changes in the vegetation structure and CO\({}_{2}\) fertilization offset the decrease in gross primary productivity (GPP) caused by reduced solar radiation [62]. Rising air temperature and CO\({}_{2}\) promoted the GPP, but the contribution of CO\({}_{2}\) was greater, more than 50% over the Tibetan Plateau [63]. [PERSON] et al. [64] used model experiments based on a machine learning method suggested that the CO\({}_{2}\) was the most important factor to the increasing trend of NDVI across the temperate semi-arid grassland of China from 1982-2015. The relative contribution can be up to 87.2%. Compared with these studies, we especially focused on the influence of CO\({}_{2}\) and afforestation on NDVI, which has not been done in the HRB before. CO\({}_{2}\) increases have a strong positive impact on the whole region. Very few negative impacts exist. The reason for the larger positive impact in the south-eastern area is that the carbon sequestration capacity of croplands located in the south-east was stronger than that of other land-use types. The results of our work were reasonable and indicated that CO\({}_{2}\) dominated the NDVI trend in more than 50% of counties, and HUM drove over 30% of the 334 counties (Figure 7d). Therefore, our study provides new insights into the attribution of vegetation greening in the HRB.
## 3 Results
### Identification and Quantitative Analysis of Volatile Compounds  
A wide variety of VOCs were identified from the three selected cucumber varieties using HS-SPME/GC-MS analysis. Six replicate samples were taken from the flesh of three cucumber varieties. Details of the types and amounts of VOCs detected are provided in the Supplementary Table S1. A total of 163 VOCs were detected in the 18 cucumber flesh samples, including 7 amines, 14 alcohols, 6 aromatic compounds, 5 phenolics, 2 sulfur compounds, 8 halogenated hydrocarbons, 23 aldehydes, 1 acid, 8 terpenoid substances, 25 hydrocarbons, 12 ketones, 32 heterocyclic compounds, and 20 esters (Table S1). These included (_E_,_Z_)-2,6-nonnadien, (_E_)-2-nonenal, 2,4-heptdiagn  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\#.} & \multirow{2}{*}{**Cultivar**} & \multicolumn{2}{c}{**Fresh Cucumber-**} & \multirow{2}{*}{**Sweetness**} & \multirow{2}{*}{**Astringency**} & **Total Soluble** \\  & & & & & **Solids (\%)** \\ \hline
1 & YX & 5.25 \(\pm\) 0.83 & 3.4 \(\pm\) 0.54 & 0.96 \(\pm\) 0.36 & 5.2 \(\pm\) 0.25 \\
2 & YN & 5.13 \(\pm\) 0.51 & 3.2 \(\pm\) 1.76 & 1.05 \(\pm\) 0.32 & 5.1 \(\pm\) 0.47 \\
3 & C17 & 4.85 \(\pm\) 1.16 & 3.15 \(\pm\) 0.57 & 1.15 \(\pm\) 1.13 & 5.1 \(\pm\) 0.26 \\
4 & JM83 & 5.1 \(\pm\) 2.54 & 3.25 \(\pm\) 0.86 & 1.32 \(\pm\) 0.88 & 5 \(\pm\) 0.36 \\
5 & Baixu & 5.15 \(\pm\) 1.56 & 3.2 \(\pm\) 0.47 & 1.12 \(\pm\) 0.48 & 5.08 \(\pm\) 0.58 \\
6 & C7 & 4.78 \(\pm\) 1.7 & 2.95 \(\pm\) 1.12 & 1.4 \(\pm\) 0.67 & 4.9 \(\pm\) 0.48 \\
7 & Haiyang & 5.15 \(\pm\) 1.23 & 3.1 \(\pm\) 1.04 & 1.25 \(\pm\) 2.21 & 5 \(\pm\) 0.95 \\
8 & Jin5-508 & 4.32 \(\pm\) 0.78 & 3.11 \(\pm\) 1.37 & 1.6 \(\pm\) 1.36 & 4.37 \(\pm\) 0.67 \\
9 & Deruitel & 4.98 \(\pm\) 1.28 & 3.1 \(\  
caryophyllene, among others. Of these, (\(E\),\(Z\))-2,6-nonadienal is the substance found to be responsible for conferring cucumbers their characteristic aroma [16].  
YX had the highest total VOC content, followed by KX, while GX had the lowest. Moreover, the contents of these volatile compounds mainly related to fruit flavor were found to be higher in YX (Table S1). Figure 2 shows the GC-MS total-ion current chromatograms of the VOCs in the flesh of the three cucumber cultivars.
## 4 Experiments and Analysis
### Evaluation Metrics  
To assess the efficacy of our eggplant disease detection methodology, several benchmark performance indicators commonly utilized in deep learning were adopted to validate our model's performance. These indicators encompass precision (P), recall (R), mean average precision (mAP), giga floating-point operations per second (GFLOPs), and parameter count. Precision calculates the ratio of true positives to all positive predictions made by our model. Recall, on the other side, determines the share of actual positives correctly classified by the model. Average precision (\(AP\)) is derived from the relationship between P and R. It represents the area beneath the Precision-Recall curve. \(mAP\), the average of AP across disease classes, serves as the primary metric for assessing the overall performance of our object detection model, with a higher mAP indicating superior performance. Notably, [EMAIL_ADDRESS] refers to the \(mAP\) computed using an IoU threshold of 0.5, and [EMAIL_ADDRESS] averages mAP across IoU thresholds ranging from 0.5 to 0.95. Additionally, the F1 score delivers a thorough examination of the model's classification strength. Regarding computational efficiency, GFLOPs is employed as a metric, wherein lower GFLOPs indicate a more efficient model. The equations for these matrixes are as follows:  
\[P= \frac{TP}{TP+FP} \tag{20}\] \[R= \frac{TP}{TP+FN}\] (21) \[AP= \int_{0}^{1}P(R)dR\] (22) \[mAP= \frac{1}{C}\sum_{i=1}^{C}AP_{i}\] (23) \[F1= \frac{2\times Precision\times Recall}{Precision+Recall} \tag{24}\]
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture  
### Architecture  
This section explains the proposed architecture for the system. Figure 2 shows the general traffic detection system. The proposed system includes the use of vehicular communication and big data clusters to detect traffic congestion using V2V, V2I and Infrastructure to Infrastructure (I2I) communications. The system uses the LORA-CBF algorithm because it has been previously validated in urban scenarios. LORA-CBF was selected because it is a dynamic, hierarchical (gateway, cluster heads and member roles), multi-hop algorithm, which avoids the flooding of control packets and controls unnecessary retransmissions [70; 71; 72]. To achieve V2V and V2I communication, the system uses On-Board Units (OBU) which obtain vehicle position and speed at one-second intervals and forwards their data to cluster head cars, which are responsible for performing the data aggregation, an approach reported in [73]. To forward the aggregated data, cluster head cars send the information to the Road Side Unit (RSU) directly or through cluster/gateway nodes. The RSUs are then responsible for forwarding the information packets to the big data cluster for storage and further processing to determine if significant traffic congestion is present in specific locations. All the vehicular information is encapsulated in DATA packets by the LORA-CBF algorithm which permits the network nodes to both transmit and receive them. When DATA packets are received by an RSU, they are un-encapsulated and re-encapsulated by Hypertext Transfer Protocol (HTTP) for their transmission in a TCP packet to the big data cluster.
## 3 Control Algorithm  
The literature reveals that various controllers have been used for the purpose of capacitor voltage balancing. Examples include a fuzzy controller [28], model predictive controller [29] and proportional-resonant controller [30]. In this work, a PI controller is selected owing to its simple structure and robust performance that can be achieved in a wide range of operating conditions [31; 32; 33]. Another advantage is that the steady-state error can be reduced or eliminated altogether. Figure 4 presents the block diagram of the PI controllers. Four DC-link capacitors are connected between the converter and the inverter. The voltages across each of the capacitors are denoted by V\({}_{1}\), V\({}_{2}\), V\({}_{3}\) and V\({}_{4}\). In order to achieve voltage balance, each capacitor is set to have a reference voltage of V\({}_{\rm in}/4\). Since there are two separate parts in the converter circuit, two controllers are applied. Each PI controller is used to minimize the error between two capacitor voltages, which then leads to the derivation of the controller output. Here, e\({}_{1}\)(t) is the error obtained by subtracting V\({}_{2}\) from V\({}_{1}\). Meanwhile, e\({}_{2}\)(  
The power switches are switched ON or OFF based on the applied switching signals. Here, \(y_{\text{U}}(t)\) and \(y_{\text{L}}(t)\) are compared with the carrier signal in order to generate the switching signals or the gate signals as shown in Figure 4. The carrier waveform is generated according to certain frequency and amplitude so that the power switches can be properly switched ON or OFF. This is to ensure that the capacitor voltages are always directed to their respective references. The switching pattern depends on the need of either discharging or charging the capacitors. However, it is important to note that until there is an attainment of full charging of the capacitors, the discharging and charging processes would be continuously repeated.
## 2 Related Work
### Path Planning  
Early algorithms firstly investigated the problem to ensure the robot moved to the goal in very short time. \(A^{*}\) search [8] is a well-known best-first graph search algorithm, which is based on a static environment. \(D^{*}\) Search and focused \(D^{*}\)[9] can be used for dynamic path planning. They conduct path replanning when dynamic events happens. Field \(D^{*}\)[13] is the extension of \(D^{*}\) and \(D^{*}\) Lite, which uses linear interpolation in order to generate paths with low-cost. [PERSON] et al. [14] proposed the lane-based optimal routing protocol for delay-tolerant maritime networks, which plans the ship path based on predicted meeting times.  
Later path planning algorithms focused on mobile robots' navigation in complex environments. The path-planning algorithm using corridor maps [15] was developed to enhance the smooth trajectory of the robot moving from the start point to the target point. It uses a corridor map and one force to restrict the motion to collision-free paths. The Bugalgorithm [16] has three versions, i.e., Bug-1 algorithm, Bug-2 algorithm, and Distance-Bug algorithm. They all contain a move-to-goal mode and a collision avoidance mode. In collision avoidance mode, the robot moves along the side of the obstacle while planning a new move-to-goal path. The Follow the Gap Method (FGM) [17] finds the widest gap among the obstacles and navigates the robot to move through the center of the obstacles. Path planning using particle swarm optimization [18] tries to speed up the path planning decision. Recent work used the Convolutional Neural Network (CNN) [19] to tackle the path planning problem for visual-based robots. The path planning problem is converted into an environment classification problem based on visual information.