esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Single-Cell Oil (SCO)
#### 3.4.4 Biodiesel Properties  
It is well documented that the quality of biodiesel is closely related to the fatty acid composition of the microbial lipids, specifically the type of fatty acids, the chain length, the double bonds number, and position [62; 63]. The fuel properties that have a big impact on biodiesel quality are indicatively the oxidative stability (h), cetane number (CN), kinematic viscosity (mm\({}^{2}\)/s), cold filter plugging point (\({}^{\circ}\)C) (CFPP), together with cloud point (\({}^{\circ}\)C) and pour point (\({}^{\circ}\)C) [12; 64]. The predicted biodiesel properties of the lipids produced by the oleaginous yeasts at the cultivation conditions that yielded the highest lipid concentration are indicated in Table 4.  
A high CN is associated with better ignition and combustion quality of biodiesel [11]. Typical CN values range from 35 to 65. As depicted in Table 4, the SCO derived from _R. toruloides_ has CN values that range from 57.6 to 63.9, while the SCO from _C. curvatus_ ranged from 55.2 to 62.6, all falling within the accepted standard limits. For both yeasts, the oil that resulted in the highest CN values is derived from the cultivation using propionic acid, which is related to the high SFAs amounts that occur in the SCO (Table 3). The lowest CN value for both yeasts was obtained with butyric acid. The kinematic viscosity (\(\upsilon\)) of the produced biodiesel also falls within the narrow range of the standard limits, with the lowest values obtained on propionic acid (3.5 mm\({}^{2}\)/s) and the highest values on acetic acid (4.1 mm\({}^{2}\)/s) for both yeasts. The lower the viscosity is, the more likely that better quality finer fuel droplets will get injected into the combustion chamber, while the higher the viscosity, the more likely that engine performance-related problems will occur [12; 65; 66]. For a better kinematic viscosity (i.e., lower values), a shorter chain length is preferred [8; 67], which is obtained with propionic acid as a substrate (low LCSF values). The cis configuration of the double bonds (such as in C17:1) has also been related to reducing the kinematic viscosity of the produced biodiesel [67; 68]. Oxidative stability (OS) of biodiesel is another important factor and is closely related to the level of unsaturation and the number of double bonds in the fatty acid chains, which are interacting with oxygen when being exposed to air [13]. Specifically, low PUFAs amounts are beneficial for oxidative stability since double bonds together with the adjacent allylic (i.e., CH2 moiety adjacent to a single double bond) and bis-allyi (i.e., CH\({}_{2}\) moiety
## 1 Introduction
### State-Space  
During the execution of the PV GMPPT process, each state depends on the current value of the duty cycle of the DC/DC power converter (Figure 2), the power generated by the PV array and the duty cycle during the previous time-step [28]:  
\[S_{t}=\left\{S\middle|S_{i,j,k}=(D_{i},P_{PV}\;,DO_{k}),\;i\in[1,2,3,...,n],\; j\in[1,2,3,....,m],\;k\in[1,2,3,....,p]\right\} \tag{5}\]where \(n\) is the number of equally quantized steps of the duty cycle (\(D\)) range, \(m\) is the number of equally quantized steps of the PV array output power (\(P_{PV}\)) range and \(k\) is the number of equally quantized steps of the duty cycle range for the previous time-step (\(DO\)). Selecting high values for \(n\), \(m\) and \(k\) will result in a higher accuracy of detecting the MPP, but the learning time and storage requirements are increased. For each value of \(D\), the state is determined jointly by the current and the previous quantization steps that the duty cycle value belongs to, as well as by the quantization step that the PV array output power value belongs to. For example, assume that \(n=10\), \(m=10\), \(k=5\), \(p_{\max}=100\)W, the duty cycle value at the current time-step is equal to 0.64, the PV array output power is equal to 53 W and the duty cycle value at the previous time-step equals 0.5. In this case, the agent's state is equal to (7, 6, 3), because 53 W belongs to the 6 th quantization step of the PV power range, 0.64 belongs to the 7 th quantization step of the current duty cycle range and 0.5 belongs to the 3 rd quantization step of the previous-duty-cycle range.
## 4 Simulation and Experimental Validation
### Comparison and Verification of Simulation Results
The excitation inductance \(L_{\mathrm{m}}\) of the transformer jumps by 8.62%, which is the output voltage response waveform when it jumps from 580 \(\upmu\)F to 530 \(\upmu\)F. When the excitation inductance of the transformer jumps, the overshoot of the system output voltage fluctuation under second-order LADRC is 3.25%, and the overshoot under PID control is 3.83%. It can be seen that the system has more advantages in resisting internal parameter disturbances under second-order LADRC. The simulation results show that using LADRC not only achieves good and fast output without overshoot but also has better adaptability and robustness to input voltage, load jump, and self-parameter jump under second-order LADRC compared to PID control.
## 7 Future Research Directions
### Autonomous Navigation  
Currently, robots that can successfully perform regular autonomous navigation through the unstructured and dynamically changing environments of construction sites do not exist. Therefore, future research should develop control and autonomy algorithms to this end. Additionally, robots are efficient data collection agents because of their speed and consistency, ensuring that the data available to project stakeholders are up-to-date and consistent [145]. Studies on autonomous navigation, including [30], have considered predefined scan locations from where the robot would perform scans for as-built modeling. A good quality laser scan may take several minutes at each point to complete [263]. Therefore, it is important to minimize the number of scan locations to enable the robot to cover the whole structure on a single charge. Human reasoning is still required for selecting the optimum locations from where the robot would collect data through autonomous navigation. The next step towards improving autonomy should be making the robot able to select scan locations itself. The selection of target scan locations requires experience and domain knowledge in addition to the knowledge about the structural geometry. Future studies can investigate the process followed by humans and the visual cues used by them in selecting right locations for capturing site images or performing laser scans. This knowledge can help robots to independently identify the navigation goals that meets human requirements.
## 2 Materials and Methods
### Extraction Procedure and Analysis  
To detect residues of chloramphenicol classes, MG, LMG, and quinolone classes, we extracted and cleaned each shrimp sample by using a modification of the veterinary drug residue analysis technique reported by [PERSON] et al. [5] and [PERSON] et al. [8] for aquatic products. Briefly, 2 g of sample was weighed in a propylene centrifuge tube (volume 50 mL) and transferred to a homogenizer containing 100 \(\upmu\)L of internal standards (100 ng/mL), 50 \(\upmu\)L of TMPD, and 10 mL of ACN. Then, we added 5 mL of n-hexane saturated with CAN to the homogenate, which was shaken in a vortex mixer for 5 min, followed by centrifugation at 4500 rpm for 10 min. We aspirated and subsequently discarded the hexane layer. The ACN extraction layer was collected and dried at 40 \({}^{\circ}\)C in a nitrogen evaporator. We re-extracted the remaining tissue pellets using 10 mL of ACN and 5 mL of ACN-saturated n-hexane and then centrifuged them. The first extract was combined with the ACN layer. Subsequently, we evaporated the combined extracts to dryness at 0.5 mL. An additional 0.5 mL of ultrapure water was added, after which the vortex was mixed and then sonicated for 1 min. The reconstituted extracts underwent centrifugation at 4500 rpm for 5 min. Finally, a 0.2 \(\upmu\)m polyvinylidene fluoride filter (Whatman, Maidstone, UK) was employed to filter the supernatant layer, and the filtrate was transferred to an autosampler vial prior to being injected into the chromatographic system.  
Nitrofuran metabolite extraction from samples was performed through the execution of a TFDA-procedure-based method [9]. Briefly, in a centrifuge tube measuring 50 mL in volume, we fortified 2 g of a sample with 100 \(\upmu\)L of internal standards (100 ng/mL), followed by sequentially adding 0.125 M HCl (9 mL) and 50 mM 2-NBA in MeOH (400 \(\upmu\)L). Samples were vortex mixed (1 min), followed by overnight incubation (16 h, 37 \({}^{\circ}\)C) with gentle shaking in a water bath. In order to neutralize the samples, we added 0.8 M NaOH (1 mL) and 0.1 M dipotassium phosphate buffer (1 mL), and we adjusted the reaction mixture to pH 7.1-7.5. The mixture underwent 1 min of vigorous vortex mixing and was then centrifuged at 3500 rpm for 5 min. After the collection of the supernatant, the remaining tissue pellet was re-extracted using ultrapure water (3 mL), as described earlier in the text, and centrifuged again. The combined extracts were re-extracted using 0.5 g of NaCl and 12 mL of EtOAc with vortex shaking of the samples for 1 min. The reconstituted extracts were again centrifuged for 5 min at 3500 rpm. The solvent was evaporated at 40 \({}^{\circ}\)C in a nitrogen evaporator. We reconstituted the resultant dry extract in 1 mL of 50% MeOH, after which it was vortex mixed for 1 min. Subsequently, 1 mL of n-hexane was added to the extracts, which then underwent centrifugation again, as describedearlier. The lower layer was collected and filtered (0.2 \(\upmu\)m filter membrane). The filtrate was placed in an autosampler vial prior to analysis.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 2 Methods
### Improvement on Mask R-CNN  
CT images, as grayscale images, contain only a single intensity value per pixel, which reflects the density variations of different tissues within the object. In contrast, RGB images consist of three color channels: red, green, and blue. Therefore, when designing detection algorithms, the unique characteristics of CT images must be taken into account. Targeted image processing and deep learning techniques are essential to achieve accurate recognition. This paper proposes an improved model, Defect R-CNN, based on the classical Mask R-CNN algorithm, to address the industrial need for CT image defect detection with a focus on both accuracy and speed. The structure of Defect R-CNN, shown in Figure 2, consists of three main components: the backbone network, the RPN, and three output branches. The backbone network replaces the original ResNet50 with Edge-Prior Net (EP-Net), which integrates convolutional components (EPCB) to extract multi-scale feature maps from the input image. These feature maps are then passed to the subsequent modules for detection and segmentation. The RPN, based on Faster R-CNN, is responsible for generating candidate target regions on the feature map. After ROI Align refinement, these candidate regions are more accurately localized. Finally, the fully convolutional network outputs the target category, bounding box location, and segmentation mask for each candidate region, while simultaneously performing defect detection, classification, and instance segmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Params (M)** & \begin{tabular}{c} **Parameters** \\ **Proportion** \\ \end{tabular} & \
\begin{tabular}{c} **FLOPs** \\ **(GFLOPs)** \\ \end{tabular} &
\begin{tabular}{c} **Computation** \\ **Proportion** \\ \end{tabular} \\ \hline ResNet50 & 16.88 & 50.38\% & 265.34 & 38.51\% \\ FPN & 1.51 & 4.50\% & 195.76 & 28.41\% \\ RPN & 0.60 & 1.79\% & 139.66 & 20.27\% \\ ROI Head & 14.52 & 43.33\% & 88.21 & 12.81\% \\ Total & 33.51 & 100.00\% & 688.97 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation of parameters and FLOPs for the Mask R-CNN model.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 3 Fuzzy Rule Generation Method According to Input Space Partitioning Method  
In the ANFIS model, the fuzzy inference process consists of a divide-and-conquer method that divides the \(n\)-dimensional input space composed of input variables into specific regions and computes the result of inference in each specific region. In other words, the conditional part of the fuzzy rule divides the input space into specific areas, and the conclusion part of the fuzzy rule has the inference result in each specific area. Thus, creating a fuzzy rule is closely related to how the input space is divided. In the ANFIS model, the grid partitioning method and scatter partitioning method are used to divide \(n\)-dimensional space composed of input variables. Figure 3 shows the grid partitioning method of the input space division method. Next, ANFIS using grid partitioning (ANFIS1), ANFIS using SC (ANFIS2), ANFIS using FCM clustering (ANFIS3), and ANFIS using CFCM clustering (ANFIS4) will be described.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 4 Performance Tradeoffs Analysis  
Practically, it is impossible to maximize all performance gains at the same time in massive MIMO system due to the effect of pilot contamination and also these gains are maximized by different transmit power distributions. In this section, two types of performance tradeoffs will be discussed among these gains under imperfect CSI.
## 2 Background  
### V2X Use Cases and Literature Review  
Multiple surveys focus on the achievable use cases in this domain, as summarized in [1]. In [2], the authors present 5G-V2X use cases and enabling technologies. The authors of [3] collect cooperative automated driving use cases. The authors of [4] introduces an implementation of infrastructure-supported cooperative maneuvers. In [5], the authors survey the scope of V2X testing, while [6; 7] analyze the service requirements in this domain.  
There are multiple articles covering C-Roads C-ITS deployments in the literature. However, no available documents provide realistic channel utilization simulation results [8; 9; 10; 11]. Currently, the most relevant use cases from the deployment perspective are the so-called Day1 and Day2 use cases. Various aspects of these use cases are examined in the available papers. A list of the most important use cases is provided in Table 1 below.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 1 Introduction
### Phased Array  
A phased array is defined as a multiple-antenna system that electronically alters or directs the transmission or reception of an electromagnetic (EM) beam [1; 2; 3]. These systems can be realized by introducing a time variation or phase delay in every antenna's signal path to compensate for the path differences in free space. Figure 1 shows an illustration of a phased-array receiver with N-channels.  
A uniform antenna spacing \(d\) was kept between consecutive antenna elements, and signals from various paths were combined using a variable-delay block on each signal's path [2].  
Numerous designs and structures for low-cost mm-wave electronic scanning antennas have been assessed. They contain active or passive-array structures, printed planar arrays, reflect arrays, or lens arrays. Each design may consist of different radiating elements with various properties, such as narrowband or wideband, linear or circularly polarized, digital or analog phase shifters, as well as various kinds of array feeding structures. Two fundamental phased-array structures are illustrated in Figure 2[1].  
From a radio frequency (RF) perspective, passive phased arrays differ from typical radiating arrays owing to the addition of phase shifters, which can introduce a phase delay in the array, and hence steer the beam of the antenna. Passive phased arrays do not contain active components and include a transmitter and receiver. Despite this, active phased-array structures are distributed, and contain active circuit components to produce and amplify the power. Active phased-array designs possess many benefits compared to passive-array designs, including reduced power losses and noise figures, flexible designs, and multi-function characteristics [1].
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 1 Introduction
LoRaWAN remains adopted when the EDs operate on the unlicensed spectrum, while LoRaCog extends the access for LoRa transmissions to the licensed frequencies. The main contribution of the paper can be listed as follows:  
* A new LPWAN protocol that provides access to the unutilized or booked spectrum without impacting the primary user's performance. * LoRaWAN protocol modification to adapt the cognitive radio requirements maintaining the same battery consumption of LoRaWAN EDs by allocating spectrum sensing to GWs. * Rearrangement of the ED's receive window giving the chance to collect the broadcasted unused channels by GWs. * NS new role to receive all sensing from GWs and allocate the channels that suit each ED by correlating the best RSSI. * Extensive simulations are performed to verify the efficiency of the proposed protocol simulating real scenarios from different distribution of EDs and GWs to the appearance of primary users. The rest of the paper is organized as follows: Section 2 presents recent works on the performance of LoRaWAN and its cognitive implementation. Section 3 shows a brief explanation and highlights the CR concept, which is followed by Section 4, where the current LoRaWAN standard communication protocol is presented. The suggested new protocol and modifications on the current LoRaWAN protocol are detailed in Section 5. Performance evaluation and simulation environment are presented in Section 6. Then, results are discussed in Section 7. Finally, Section 8 summarizes and concludes our work.
## 3 Error Modeling and Analysis
### Geometrical Error of CCR
According to Equations (5)-(8), we can derive the OPD of six sectors induced by the dihedral angle error. If the surface has a planeness error, its shape is a sphere with a large curvature; therefore, the different position has a different normal. Suppose the additional small quantity caused by the tangential deviation from the ideal reflection point due to the planeness error is not considered. In that case, the composition OPD caused by planeness error is the summation of the OPD caused by three reflection points on three surfaces, which can be described as  
\[dt=\sum_{j}\Delta(x,y)\cos\theta_{i}\ldots j=1,2,3\;dt=\sum_{j}\Delta(x,y)\cos \theta_{i}\ldots j=1,2,3. \tag{9}\]  
In Equation (9), \(j=1\), \(2\), \(3\), which represents different reflective surfaces, \((x,y)\) is the coordinate of the point, and \(\theta_{i}\) represents the ray incoming angle of every point. Suppose that the curvature of a surface is \(R_{i}\) and the vertex of the aperture is in the center of the sector. The expression of the surface with the shape error can then be written as  
\[(x-x_{0})^{2}+(y-y_{0})^{2}+(z+R_{i})^{2}=R_{i}{}^{2}. \tag{10}\]  
Generally, the Newtonian rings (N1, N2, N3...) are used to evaluate the planeness of a CCR surface, and the conversion relationship can be found in [17]. According to Equations (1)-(10), the OPD of the CCR with dihedral angle error and planeness error can be deduced. Based on the above analysis, the wavefront simulation is performed in MATLAB, and the results can be seen in Figure 3a-i. The results show that if only the dihedral angle error exists, the wavefront is a regular pattern composed of fan-shaped surfaces of different sizes. If only the surface error exists, the wavefront shape is a regular shape surrounded by six petal-shaped surfaces. If both errors exist, its shape combines the two wavefront shapes. Because the CE is determined according to the beam spot centroid, the beam energy distribution simulation of the reflected laser by the CCR must be performed. In Figure 4a-c, the simulation results are shown, including the reflected beam energy distribution of the CCR without error, only dihedral angle error, only planeness error, and both errors. From the results, we can find that different kinds of CCR geometrical errors have different influences on the spot energy distribution. When both errors exist, the spot energy distribution becomes very messy.
###### Abstract  
In this paper, we adopted a simple and efficient strategy to prepare a \(\beta\)-cyclodextrintrin (\(\beta\)-CD)-modified hyper-crosslinked polymer (CDM-HCP). The structures and physicochemical properties of the as-synthesized polymer were also evaluated. It was applied to the removal of anlines from aqueous solutions. The introduction of \(\beta\)-CD into the hyper-crosslinked polymer significantly enhanced adsorption properties for the removal of various amines. The adsorption kinetics agreed with the pseudo-second-order mode very well. The adsorption isotherm data of \(p\)-methylaniline (\(p\)-MA) and \(p\)-aminobenzoic acid (\(p\)-ABC) were in agreement with the Langmuir isotherm, whereas aniline and \(p\)-chloroaniline (\(p\)-CA) were fitted best with the Freundlich model. The maximum adsorption capacities (\(q_{\rm max}\)) determined by adsorption isotherms were 148.97 mg/g for aniline, 198.45 mg/g for \(p\)-MA, 293.71 mg/g for \(p\)-CA, and 622.91 mg/g for \(p\)-ABC, respectively. It had higher adsorption capacities than those of some commercial polymeric resins, such as XAD-4, PA66, and AB-8. The interaction mechanism was investigated by FTIR, XPS, and the ONiOM2 method. A CDM-HCP can be regenerated efficiently and used repeatedly, indicating its potential technological applications in removing organic pollutants from actual industrial effluents.  
CDM-HCP; adsorption; amines; hyper-crosslinked polymer; interaction mechanism Article
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
## 2 System Model
### Codebook  
First, the mother codebook for each user group is designed, divided into diversity groups and assigned to users shared on each resource block. Each user maps their bit stream to its own codebook and transmits the mapped symbols to the base station.  
The SCMA encoder maps \(\log_{2}(M)\) bits to a \(K\times M\) codebook, \(\mathbb{B}^{\log_{2}(M)}\to X\), \(X=[x^{(1)},x^{(2)},\cdots,x^{(J)}]\). In this model, each user occupies \(d_{k}(d_{k}<K)\) resource blocks and each resource block accommodates \(d_{j}(d_{j}<J)\) users.  
\[d_{j}=\frac{J\times d_{k}}{K} \tag{1}\]  
\[J=\binom{K}{d_{k}}=\frac{K\times(K-1)\times\cdots\times(K-d_{k}+1)}{d_{k}\times (d_{k}-1)\times\cdots\times 1} \tag{2}\]  
Two mother codebooks, \(\mathcal{C}_{g_{1}}\) and \(\mathcal{C}_{g_{2}}\), are constructed with \(\mathcal{C}_{g_{1}}\) assigned to near users and \(\mathcal{C}_{g_{2}}\) assigned to far users. The radius of \(\mathcal{C}_{g_{1}}\) is bigger than \(\mathcal{C}_{g_{2}}\) and there is a rotated angle between them for distinction. In the uplink system, near users have better channel states, which are easier to be decoded. Thus, \(P_{g_{1}}\) is set bigger than \(P_{g_{2}}\) to obtain a more accurate result because of the joint detection. The mother codebooks are divided into \(d_{j}\) diversities, respectively, to distinct users shared on the same resource block. Figure 3 shows the codeword distribution on the same resource block and users in these two groups both adopt QPSK modulation. The power of signals is normalized and the radius means the transmitting power of users in two groups. Constellation points in each group are \(M\times d_{j}(d_{j}=2)\) in Figure 3.