esa-sceva/satcom-chunk-collection huggingface dataset:
However, with a substantial number of satellites now capable of transmitting triple-frequency data, it is necessary to examine the performance of medium baseline RTK using triple-frequency-only satellite data, which is very important for high-frequency on-embedded terminals, such as 10 Hz, as they have limited computing power and significant latency. Up to now, there are approximately 16 GPS satellites capable of transmitting triple-frequency signals (L1/L2/L5), about 24 Galileo satellites capable of transmitting triple-frequency signals (E1/E5a/E5b), and about 26 BDS satellites capable of transmitting triple-frequency signals (B1I/B2a/B3I). Therefore, in this research, we mainly present the medium baseline RTK positioning performance based on triple-frequency-only satellite data of BDS/Galileo/GPS. The algorithm for medium baseline triple-frequency RTK is presented in detail. Then, based on the analysis of DD ionosphere delay and troposphere delay using real baseline data, the positioning performance of dual-frequency, dual-frequency/triple-frequency, and triple-frequency-only RTK is provided, including positioning accuracy, fixing rate, convergence time, and computational cost time. Finally, the results are summarized in the conclusion.
## 1 Introduction  
The accumulation of lipids within and around kidney tissues has been linked to different renal pathophysiologies [1; 2; 3; 4]. Recently, it was suggested that fatty kidney disease deserves designation as a specific clinical entity similar to fatty liver disease [5]. With this increase in interest in renal lipids, a non-invasive in vivo method to investigate their accumulation levels and locations is needed. Proton magnetic resonance imaging (MRI) and spectroscopy (MRS) methods have shown their potential to assess lipid-related kidney diseases, such as diabetic nephropathy, using fat fraction (FF) quantification [6; 7; 8; 9].  
Different MRI techniques for detecting abdominal lipid have been introduced [10; 11; 12; 13; 14]. However, these imaging methods provide summed fat fraction information, as they cannot differentiate lipid components (triglyceride fatty acids). Alternatively, the indirect detection of intracellular lipid (since it is considered a biomarker of renal cell carcinoma (RCC)) has been previously tried by an MRI method based on the signal drop in out-of-phase T1-weighted images [15; 16; 17]. This limitation of MRI can be addressed by implementing MRS, which can directly identify specific fatty acids and other metabolites, such as choline and lactate that can help in differentiating and grading RCC [18; 19; 20; 21; 22; 23]. Due to the alteration in lipid content within the tumor cells of RCC, differentiation from other histological subtypes could be feasible by detecting intracellular lipid contents. For example, the elevation of the amount of lipid peak resonating at 1.3 ppm was used to discriminate clear cell RCC from non-clear cell RCC subtypes, which have less amount of this fatty acid [19]. This suggests that MRS could be useful for RCC characterization and tumor grading. However, renal MRS remains technically challenging. Although single-voxel MRS can differentiate lipid peaks in the kidney, it does not provide their signal distributions within large heterogeneous tumors. Conventional magnetic resonance spectroscopic imaging (MRSI) addresses this limitation by delivering spatially resolved spectra over many voxels but requiring a long acquisition time [24; 25; 26].  
We recently demonstrated a high-resolution, density-weighted concentric ring trajectory (DW-CRT) metabolite cycling (MC) free induction decay (FID) MRSI acquisition technique to provide the spatially resolved musculoskeletal water and lipid spectra simultaneously [27]. In this work, our major goal was to investigate the feasibility of this accelerated MRSI acquisition to acquire reliable quantitative renal data in healthy volunteers with the intent to establish the spectral signature of the lipid composition of healthy renal tissues, which will be used as a future tool for the non-invasive characterization of renal diseases.
###### Abstract  
This study evaluated the adsorbent capacity of the Ecuadorian avocado (_Persea americana_ Hass.) seed and peel wastes as an alternative method for cadmium (Cd), mercury (Hg), lead (Pb), and nickel (Ni) ion removal from aqueous solutions. The laboratory microscale process was performed using fixed-bed columns containing 1 g of 600 \(\upmu\)m particles of biomaterial pretreated with ethanol and ethylene glycol. Subsequently, metal solutions of different concentrations were eluted and measured by flame atomic absorption spectroscopy. Results showed that fixed-bed columns allow efficient adsorption of Pb (2.6 mg/g) with ethanol pretreatment. Lower adsorption capacity was achieved for Cd, Hg, and Ni ions. Favorable adsorption with high retention capacity was found for Pb\({}^{+2}\) for the ethanol pretreated bio-adsorbent at higher concentrations (120 mg/L). Lower removal percentages were found for Cd\({}^{+2}\), Hg\({}^{+2}\), and Ni\({}^{+2}\); Ni showed the lowest adsorption capacities and negative R\({}_{\rm L}\) values, suggesting inefficient adsorbent development. Regeneration of Cd, Hg, and Pb ions from avocado peel and seed showed the highest recovery when 1 mol/L HCl solution was used. Regarding the adsorption isotherms, the Langmuir model was the one that best fit our data, demonstrating that adsorption takes place in a uniform monolayer and that each contaminant ion occupies a single site.  
biosorption; bio-adsorbent; cadmium; desorption; mercury; micro-scale process; nickel; lead Article
## 2 Methods for Determination of Lipophilicity
### Indirect Experimental Determination of Lipophilicity
#### 2.3.1 Reversed-Phase Thin-Layer Chromatography  
Reversed-phase thin-layer chromatography (RP-TLC) is the easiest LC method that can be used for the indirect determination of isotropic lipophilicity [58]. In RP-TLC [59] and reversed-phase-high performance thin layer chromatography (RP-HPTLC) [60], the stationary phase is a commercially available C8 or C18 silica gel plate (Figure 3). Alternatively, it is possible to prepare a non-polar stationary phase from the commonly available silica gel plates after its pulverization with \(n\)-octanol 5% (_v/v_) in diethyl ether. The mobile phases consist of binary mixtures of water (or buffer) and an organic solvent (modifier), usually methanol, which is the preferred solvent due to its compatibility with water, or alternatively tetrahydrofuran or acetonitrile [27].  
The retention in reversed-phase systems is mainly governed by partitioning between the stationary and mobile phases [61; 62]. Therefore, the lipophilicity index measured in RP-TLC can be derived from the RM value [61; 62]:  
\[\text{RM}=\log\left[\left(\frac{1}{\text{Rf}}\right)-1\right] \tag{4}\]  
where Rf is the retention factor which is obtained by dividing the distance covered by the sample by the distance covered by the mobile phase.  
RM values are determined in the presence of different organic solvent concentrations in the mobile phase. The linear relationship between the RM values and different mobile phase proportions is established, and the partition coefficient is calculated by extrapolating to a pure water mobile phase [63].
# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 2 Related Works
### Semantic 3D Reconstruction Based on an Eye-in-Hand Camera  
Since the position of the object in the 3D space is necessary for robotic manipulators to operate objects, the eye-in-hand camera is usually applied to get this information and make 3D scene reconstruction. [PERSON] et al. [18] used Time of Flight (ToF) cameras to acquire images and optimize the images through the Iterative Closest Point (ICP) algorithm. [PERSON] et al. [19] used the LSD-SLAM method to create sparse scene maps, using object edge information to identify objects. [PERSON] et al. [20] used a monocular eye-in-hand camera and a laser radar to obtain the point cloud of the scene and combined it with the Computer Closer Point (CCP) and ICP methods to improve the matching accuracy. The above methods can only build 3D maps without semantic information, causing them have to use all the point clouds to perform ICP matching. That induced a low calculation speed and low matching precision due to the background interference. Moreover, since there is no semantic segmentation of the scene, the object-level 3D reconstruction cannot be achieved.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 5 Simulation Results
### Performance Analysis
It can be seen that as the number of targets increases, the time step of FND occurrence for all the algorithms decreases because the sensors undertake more sensing and data processing tasks. When there are six targets in the surveillance area, the average time step for FND is 374 for DPT, 686 for DIRL, 601 for TOTD, 875 for DCBC, and 945 for MTT-SS. MTT-SS gives a higher lifetime than DPT, DIRL, TOTD, and DCBC, which are approximately 153%, 38%, 57%, and 8%, respectively. In DPT, although only sensors near the target need to be activated, there is a lack of scheduling strategies for sensor resources. In order to meet the tracking quality requirements, a large number of sensors need to remain active in DPT, thus causing excessive energy consumption. Both DIRL and TOTD are sensor-scheduling methods based on reinforcement learning. DIRL focuses on the amount of interactive information and energy consumption between sensors when designing the reward function. Additionally, TOTD focuses on the trade-off between network-tracking quality and energy efficiency. Compared with TOTD, DIRL takes more consideration into the agents' contribution to energy efficiency, so it achieves lower energy consumption and a longer network lifetime. The tracking chain structure of DCBC can improve energy efficiency and balance network energy consumption through dynamic increase and decrease in nodes and the local data fusion mechanism. However, the lack of consideration of resource allocation balance in multi-objective scenarios makes the FND index of DCBC inferior to the proposed MTT-SS. In contrast, the proposed MTT-SS not only considers the requirement for tracking accuracy but also encourages the sensor to sleep when the tracking requirements are met in the reward function. In addition, the consideration of the balance of sensor resource allocation in the exploration-exploitation policy also helps to balance the sensor load. Therefore, MTT-SS has the best performance in terms of network lifetime as defined by FND. Figure 8: Energy consumption. In simulation experiments, we comprehensively evaluate the performance of scheduling algorithms in terms of tracking accuracy and energy efficiency. The results reveal that the proposed sensor-scheduling algorithm can achieve high energy efficiency while ensuring high tracking quality.
## 2 Materials and Methods  
### Collection of Positive Controls  
For the denitrification microorganisms, six species of bacteria were selected as candidate positive controls: _Achromobacter denitrificans_ KACC 12986, _Aquitalea denitrificans_ KACC 12729, _Comamonas denitrificans_ KACC 13430, _Paracoccus denitrificans_ KACC 12251, _Pseudomonas fluorescens_ KACC 10068, and _Pseudomonas stutzeri_ KCTC 1066. The bacteria were collected and cultured using the standard method specified for each particular culture collection. After harvesting the cultured cells, chromosomal DNA was extracted using the FastDNA Spin Kit for Soil (MP Biomedicals, Solon, OH, USA). The candidate positive templates that can amplify the genes of other N-cycle microorganisms were obtained from a total of 25 surface soil samples. Four surface soil types (field, forest, bare land, and grassland) were collected for this study from sites near the Han River in Gyeoggi and Chungcheongbuk Provinces, South Korea, from April to December 2021. We selected two grassland sites and one site for each of the other three soil types (Table 1). A total of 25 samples were collected (five samplings at each site per season, including the rainy season in summer). The sampling method followed the Ministry of Environment National Institute of Environmental Research Notification No. 2017-22 soil pollution process test standard, and the sampling distances were adjusted according to the field conditions [17]; five locations per site were selected to ensure the representativeness of the samples. After removing a foreign substance layer that consisted of weeds and other organic matter, a soil sampler was used to collect the samples from a depth of 10-15 cm. The soil samples collected from the five locations were mixed well and packed into polyethylene bags and 50-mL conical tubes, which were placed in an ice box and transported to the laboratory. Total DNA was extracted from the surface soil samples using a FastDNA Spin Kit for Soil (MP Biomedicals). For each sample, nucleic acids were extracted in triplicate and combined. Chromosomal DNA from the bacterial cells and total DNA from the surface soils were quantified using a DS-11 spectrophotometer (DeNovix, Wilmington, VA, USA). ANAMMOX, anaerobic ammonium (NH\({}_{4}\)+) oxidation; AOA, ammonia (NH\({}_{3}\))-oxidizing archaea; AOB, NH\({}_{3}\)-oxidizing bacteria; DNRA, dissimilatory nitrite (NO\({}_{2}\)-) reduction to NH\({}_{4}\)+; _amo_, ammonia monooxygenase; _norB_, nitric oxide reductase; _hao_, hydroxylamine oxidoreductase; _hdh_, hydrazine dehydrogenase; _hzo_, hydrazine oxidase; _hzs_, hydrazine synthase complex; _hmp_, flavohemoglobin; _narG_, nitrate (NO\({}_{3}\)-) reductase; _napA_, periplasmic NO\({}_{3}\)- reductase; _niffH_, N fixation and nitrogenase; _nirB_, NO\({}_{2}\)- reductase large subunit; _nirK_, NO\({}_{2}\)- reductase K; _nirS_, NO\({}_{2}\)- reductase S; _nosZ_, nitrous oxide reductase; _nrf_, NO\({}_{2}\)- reductase; _nxr_, NO\({}_{2}\)- oxidoreductase.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 5 Energy-Latency-Reliability Trade-Off  
In WSNs, finding the energy-latency-reliability trade-off is very important, since it captures the interdependence of key parameters from a QoS point of view. The goal of this section is to determine the successful reception probability of a packet when using different data transfer schemes and when at most R transmissions are allowed for each packet.  
Figure 13: The measured path-loss versus the free-space and estimated ones in the (**a**) urban and (**b**) suburban areas.
## 3 Conclusions  
Visualization tests of the kaolinite-PACI flocculation process (from initial flocculation to breakup and rearrangement) were conducted using the Couette device of the horizontal  
Figure 6: The relationship between \(D_{90}\) and the \(G\) value in the breakup/rearrangement stage is shown. To match the rpm conditions, image data were extracted at the timing when the \(GT\) value reached 13,000. For ALT ratios of 0.03 and 0.1, breakup was found to occur relative to the \(G\) value; for an ALT ratio of 0.05, breakup remains unclear, and the floc size was found to be maintained and densified.  
outer cylinder rotation type. In a constant laminar shear flow, the change of floc structures was observed by varying the ALT ratio and \(G\) value, resulting in the following findings:  
* After the floc structure develops and becomes bulky, the floc becomes fine through breakup and rearrangement;
* The growth rate and breakup/rearrangement rate have a power-law relationship with respect to the \(GT\) value. Additionally, growth and breakup/rearrangement stages are clearly distinguishable around a certain \(GT\) value;
* Aggregation rate \(\alpha\) during the growth stage increases with the ALT ratio. In an ALT ratio of 0.10, the effect of the \(G\) value is less significant;
* In the breakup/rearrangement stage, at ALT ratios of 0.30 and 0.50, \(\beta\) depends more on \(G\) values than on ALT ratios; at the ALT ratio of 0.10, \(\beta\) depends more on the ALT ratio than on \(G\) values;
* In the breakup/rearrangement stage, the floc diameter is maintained with respect to the \(G\) value under near-charge-neutralization conditions (an ALT ratio 0.05). Separate breakup is followed by erosive breakup, and the flocs become denser by incorporating the small fragments generated. Erosion breakup predominates and the floc granulates spherically. The granulation of flocs is related to the gelation of aluminum hydroxide gel.  
In summary, we were able to clearly detect changes in the floc structure by conducting the test under a constant laminar flow. We are one step closer to elucidating the flocculation conditions under which flocs become denser and more compact. In the future, we plan to conduct tests at lower \(G\) values to investigate the phenomenon in more detail.
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 5 Heuristic Search Algorithm Based on a Symmetric Recurrent Neural Network
### Optimization Algorithm and Loss Function  
We use the cross-entropy cost function (see Formula (14)) and the Adam optimization algorithm [37] to train the schedulable probability calculation model based on the recurrent neural network.  
\[L=\sum_{X\in D}-\sum_{y=1}^{K}p(y,X)logP(y|X) \tag{14}\]  
where \(p(y,X)\) is the gold one-hot distribution of the training sample \(X\), and \(P(y\big{|}X)\) is the probability distribution function of each type.
# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 3 Design of AUV Motion Control Algorithm
### Optimization and Analysis of Controller Parameters  
In the design of the network, we consider several parameters such as the number of layers of the network, the number of neurons in each layer, the activation function, the initial value, and the learning rate. The following are its selection principles:  
1. The number of layers in the network It has been proved in theory that a network with deviation and at least one S-shaped hidden layer plus one linear output layer can approach any rational function. Increasing the number of layers can further reduce the error and improve the network accuracy, but at the same time, it also complicates the network. In this study, we used a four-layer neural network.
2. Number of hidden layer neurons The improvement in the training precision of the network can be obtained by using a hidden layer and by increasing the number of neurons in it. This is much simpler in terms of structural implementation than increasing the number of layers in the network. We use the accuracy and time required to train the network for measuring the quality of a neural network design: 1. When the number of neurons is too small, the network cannot learn efficiently, the number of training iterations is relatively large, and the training accuracy is low. 2. When the number of neurons is too large, the more powerful the network function, the higher is the network accuracy, and the number of training iterations is also large, which may result in overfitting. Therefore, depending on the accuracy of the AUV heading error, we divide them into seven levels, so the number of neurons in the hidden layer is seven.
3. Selection of the initial weights In general, the initial weight is a random number between \((-1,1)\). The initial weight of a neural network design is assigned random decimal numbers that are unidentical to those generated by a random generator.
4. Learning rate Generally, the learning rate is between 0.01 and 0.8. A large learning rate might lead to instability of the system, whereas a small learning rate might lead to slow convergence and would require a longer training time. For highly complex networks, different learning rates might be required at different locations of the error surface. In order to reduce the training times and the time required to search for the learning rates, an efficient method would be to adopt the adaptive learning rate of variation to set different learning rates at different stages. On the basis of debugging, we designed the adaptive transformation of the learning efficiency according to the heading error. When the heading error is greater than \(10^{\circ}\), the learning rate is 0.6; when the heading error is greater than \(5^{\circ}\), the learning rate is 0.3; and when the heading error is less than \(5^{\circ}\), the learning rate is 0.1. Experiments show that adaptive adjustment of the learning rate can accelerate convergence and avoid overshooting.
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
## 3 Results and Discussion
### Adsorption Uptake  
The adsorption uptake represents the ability of the bed to efficiently adsorb or desorb the desirable gas. A greater uptake shows that the bed capacity is high due to good cooling processes, and low uptake during the regeneration process indicates an efficient heating process. Of the three examined beds, the triangular one shows the greatest capacity during the adsorption process (Figure 6a) and the lowest capacity for an adequate desorption process time (Figure 6a). The square cross-sectional bed also has good uptake values. The difference between the three beds is not high (e.g., at t = 10.8 min, adsorption uptake is 6.04 mmol/g for the circular bed, 6.13 mmol/g for the square bed, and 6.17 mmol/g for the triangular bed); however, the square bed is best for large and industrial scales. In addition, for large scales, the triangular cross-sectional beds can be arranged compactly, compared to circular ones, which have cavities between beds. Figure 6 exhibits, in general, an increase in the adsorption uptake with time until the bed is saturated during the adsorption process, and the opposite occurs during the desorption process.  
Figure 5: Carbon dioxide molar fraction at bed exit during the adsorption process (y\({}_{\text{in}}\) = 0.15).