esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Correlation in the Performance of Massive MIMO Systems
## 1 Introduction
In this physical model, the correlation between the antenna elements is considered based on realistic base station settings in terms of angle of departure, angular spread, and antenna distance. * This study considers the regularized zero-forcing (RZF) precoder technique in the downlink, which is designed based on the uplink channel state information (CSI) estimation. This is because the RZF precoder has the ability to suppress the interference when the SNR values are increased. * This study develops an analytical closed-form expression for the MSE, which is valid for any channel model. * Unlike state-of-the-art research works where the system performance has been investigated by using the MSE only, this study focuses on investigating the m-MIMO system performance considering both the MSE and SE with different antenna configurations. We found that when a URA topology is used, the level of correlation is increased and the MSE is minimized. This is because the power is concentrated in a few directions under a highly correlated scenario. However, the results demonstrate that the SE is significantly degraded using the URA topology even when the RZF precoder is used. This is because the level of interference is significantly increased in the highly correlated scenario. The results show that a substantial improvement in the SE performance of the m-MIMO system can be achieved with a ULA topology in comparison to the performance obtained using a URA topology. This can be justified as follows. When a ULA topology is used, a noticeable improvement in the spatial resolution can be achieved by the array gains and the degrees of freedom in the channels, which allows the interference to be considerably reduced. However, the interference is increased when a URA topology is used because the antenna elements are closely spaced in a limited physical direction. Overall, this paper deliverssome essential insights about the m-MIMO system design, which are useful for industries and academic researchers. Finally, this paper also provides some recommendations for future work, which open up new research directions. Paper organization: The organization of this paper is presented as follows. In Section 2, the system model is introduced. In Section 3, the effective received signal and the SE based on RZF precoding are presented. The imperfect channel estimation is discussed in Section 4. In Section 5, the physical channel correlation model is developed. To characterize the m-MIMO system performance, the numerical results are presented in Section 6. Finally, the conclusion of this research is provided in Section 7. Notation: A bold uppercase symbol is assigned to a matrix while a bold lowercase symbol is assigned to a vector. The operation corresponds to a Frobenius norm and denotes the trace of a matrix **A**. The operations,, and correspond to the transpose, Hermitian, and the inverse of matrix **A**, respectively. The Kronecker product of **A** and **B** is denoted by. Finally, denotes the Gaussian distribution with mean and covariance **G**.
## 3 Double-Beam System Traversed by a Moving Force
### Verification of the Analytical Solutions in Section 3.1
#### 3.2.1 Fixed-Fixed Boundary Conditions  
The Newmark-Beta integration method is adopted to obtain numerical solutions of Equation (52) and compared with its analytical solutions. It is found in computations that using only the first basic mode (\(m=1\)) is good enough to reach satisfied accuracies for the fixed-fixed double-beam system. For comparison, only the first basic mode is used in the following analyses for other kinds of boundary conditions. Figure 12 shows the comparisons between the results by the two methods for the dimensionless speed (\(\alpha\)) of 0.5 and contact stiffness ratios (\(s_{\rm e}\)) of 10 and 100. It can be seen from the figure that the two kinds of results have very good agreements. In addition, it can be found that the maximum displacement of the secondary beam (\(\overline{w}_{2,\rm max}\)) is smaller than that of the primary beam (\(\overline{w}_{1,\max}\)) and the instants for \(\overline{w}_{2,\max}\) lag behind those for \(\overline{w}_{1,\max}\). When \(s_{\rm e}\) becomes larger, the difference between \(\overline{w}_{1,\max}\) and \(\overline{w}_{2,\max}\) is smaller.
## 3 Key Technologies Achieved by the System
### Design of Acquisition Data Reliable Transmission Based on a Loss Recovery Strategy  
To raise the reliability of acquisition data transmission, a data transport protocol based on loss recovery is designed for loss detection and loss retransmission so as to lower packet loss rate. The acquisition nodes are responsible for loss detection and regularly detect sending flag of the equipment by timer interrupt. When data frame is missing during transmission, it is resent by the acquisition nodes. To avoid affecting data acquisition in the next cycle, maximum number of retransmission is set up. As there are multiple acquisition nodes, data collision must be taken into account. The distributed MAC protocol is adopted in design. During system initialization, the gateway nodes regularly send out a data acquisition command. After receiving an address-matching acquisition command, the acquisition nodes start timer interrupt to regularly collect environmental parameters and send them to the gateway nodes. At the same time, wireless module is set up and carrier sensing is enabled. And AUX status is tested before data packets are sent. Low level indicates that there are carrier signals in the air. To avoid interfering in the signals in the air, a random time delay of 100 ms-500 ms is loaded. After the delay, radio-frequency channel in the air is monitored again and data packets will not be sent until the channel is idle. If AUX is of high level, it means the channel is idle and data packets are sent immediately. The carrier sensing  
Figure 2: Absolute error curves before and after compensation for the forward path.  
function enables data collision avoidance to some extent, effectively decreases data transmission collisions of nodes, and raises data transmission reliability.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 2 Materials and Methods
### Experiment Set-Up  
Figure 3 showcases an experimental set-up for studying the novel strut, consisting of air and fuel supply systems and two preheating systems with combustors. The strut, made from 304 stainless steel, was selected due to its excellent resistance to corrosion, high temperatures, and mechanical stress [27]. Injection pressure was regulated to maintain consistent fuel mass flow rates into the vitiated hot gas through stabilizing valves. The burner's inlet conditions varied widely, including temperatures between 450 and 1200 K and velocities of 50-200 m/s, controlled by adjusting the mass flow rate, monitored by vortex flowmeters. Details on the test facility have been elaborated upon in our prior studies [28].  
Figure 3: Structure of experimental system.  
The measurement system collected and recorded data in real-time using an acquisition card. Fuel mass flow rates were accurately measured using LZD-15 type intelligent metal tube flowmeters, with a precision error under 1.5% and a range of 1-200 g/s. Air mass flow rates were measured by LUGB-24 type vortex flowmeters, offering a range of 0.2-2.4 kg/s and accuracy of \(\pm\)1.0%. K-type thermocouples measured inlet air temperatures, while B-type thermocouples determined flame temperatures. Species profiles were analyzed using the NOVAPlus multifunctional flue gas analyzer by MRU, with maximum permissible errors documented in our previous works [29; 30].  
The experimental image acquisition set-up, as illustrated in Figure 4, consists of a high-speed camera and computer. The imaging system uses a FASTCAM NOVA S9 camera manufactured by Photron (Tokyo, Japan), featuring a maximum adjustable resolution of 1024 \(\times\) 1024 pixels, with a pixel size of 20 \(\upmu\)m. The camera offers a maximum frame rate of 9000 fps at full resolution and has a memory capacity of 32 GB. The minimum exposure time is 1.05 us, making it well suited for capturing combustion events. For imaging, a Nikon 50 mm fixed-focus lens is employed.
The study in [11] investigated the influence of energy storage location and capacity on the overall economy of the grid and wind energy utilization, where the premise is a third party owns the energy storage. However, this did not consider the improvement of energy quality caused by energy storage. In [12, 13], according to the data collected by the power quality detection device, an appropriate evaluation method to calculate the power quality level of the access point after the energy storage system was connected. However, it did not combine the planning of the energy storage system with a power quality assessment. In summary, considering the power quality and the impact of load characteristics, there are still few works concerning solving the location and capacity selection problem for ETSHE (not basic electric energy storage). Based on the above discussions, this paper proposed a method for selecting the location and capacity of the ETSHE connected to the distribution network, which avoids the disadvantages of high costs and the existence of pollution. The adaptive inertial weight improved particle swarm optimization (IMOPSO) method was proposed, which is suitable for distribution network systems with larger nodes. The influence of load characteristics on the planning was considered, thus the research results were closer to the actual situation. The model uses the power quality as the optimization target, which effectively solves the power quality problem caused by the new energy generation with high permeability. The main contributions are introduced from four aspects, as follows:  
1. First, the minimum intra-class distance is used as the objective function to perform fuzzy clustering on the heat load. Second, on the basis of conventional forecasts, the influence weight of the meteorological factors on the outdoor temperature of the day is calculated to correct the outdoor temperature, improve the linear fitting goodness with heating heat load, and provide initial classified heat load data for the optimal configuration of ETSHE. 2. The sensitivity coefficient is calculated based on the sensitivity radar chart and sensitivity calculation formula. The sensitivity discrimination of each node in the distribution network can effectively eliminate the nodes that do not meet the sensitivity and prevents the solution from falling into the wrong node, which can improve the efficiency and accuracy of the solution. 3. Establish a location and capacity planning model for ETSHE and multi-objective functions including harmonic distortion, voltage deviation, voltage fluctuation, and energy storage equipment costs. 4. In the planning stage, data envelopment analysis (DEA) is used to evaluate the power quality of access schemes with multiple indicators and single indicators, and an effective and unified method for distribution network planning and power quality assessment is proposed.
## 4 Materials and Methods
### Determination of Okaramine B Production and Mycelial Wet Weight  
During fermentation, 2 mL of liquid or semi-solid broth were taken out from each flask daily to detect Okaramine B. Five times methanol, vortexed and fully mixed, was added to each culture broth sample, then supernatant was filtered with a 0.22 \(\upmu\)m syringe filter, and two microliters were injected into a C18 reverse-phase column on a Waters Acquity Ultra Performance Liquid Chromatography (UPLC) system (Waters Acquity UPLC BEH C18.17 \(\upmu\)m, 2.1 \(\times\) 100 mm, 0.45 mL/min). The mobile phases were water (0.2% acetic acid,A) and acetonitrile (0.2% acetic acid, B). The linear elution gradient was: 0-0.2 min, 95%A; 0.2-4.2 min, 95%A-100%B; 4.2-5.2 min, 100%B; 5.2-5.70 min, 100%B-95%A; 5.70-7.00, 95%A for 10 min, at a flow rate of 0.45 mL/min. The detection parameters of Okaramine B using the multiple reaction monitoring (MRM) mode on electrospray ionization mass spectrometry (ESI-MS, Waters XEVO TQD, Milford, MA, USA) were as follows: parent ion, 567; daughter ion, 531, 172; cone, 40 V; collision, 12 V. The spectra and calibration curve are shown in Figure S1. The remaining liquid broth was filtered through a 100-mesh nylon cloth, and the remaining mycelia in the semi-solid broth were scraped and moved to a 100-mesh nylon cloth. The mycelia on the cloth were quickly washed three times with pre-chilled sterile water, screwed to dry, and moved into a centrifuge tube for centrifugation at 8000 rpm for 5 min at 4 \({}^{\circ}\)C. After removing residual water, the mycelia were immediately weighed and stored in liquid nitrogen for sequencing. Because the peak area of Okaramine B measured during the fermentation process of most strains is significantly less than the calibration equation's lower limit, particularly in the first three to five days, the converted mass concentration is insufficiently precise. In this work, the Okaramine B yield was defined as the peak area per gram wet weight.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Zinc Selenide Films  
Zinc acetate and sodium selenosulfate were used to prepare n-type zinc selenide films [101]. EDX and XRD results showed the films were selenium deficient and amorphous phases, respectively. The band gap and electrical resistivity value were 2.8 eV and 10\({}^{7}\)\(\Omega\cdot\)cm, respectively. The influence of the number of cycles on the properties of films was studied by [PERSON] and co-workers [102]. Generally, the hexagonal wurtzite phase--the well-defined nano-grain structure--could be seen in all samples, as well as high transmission (more than 80%) in visible and infrared regions. They observed that the film thickness, band gap, and grain size increased, but the microstrain and dislocation density reduced when the deposition cycle increased from 40, 50, to 60 cycles. [PERSON] and co-workers [103] reported on the synthesis of thin film on silicon substrates for 45 deposition cycles. Experimental findings showed good rectifying properties at room temperature in Zinc/ZnSe/n-Silicon/gold-antimony sandwich structures.
## 4 Results and Discussion
### On the Initial Conditions  
GAN is trained using a gradient based method that is sensitive to initial conditions (weights). Figure 17 illustrates the impact of the initial conditions on the fault classification results. As shown in that figure, RF-GAN was able to produce an acceptable accuracy for any of the initial set of weights used. However, and as expected for local optimization methods, in 30 independent runs (initializations), it was possible to identify a particular set of initial random weights that outperformed all the others.  
Figure 16: The effect of the distribution used for sampling the input \(z\) of the GAN generator: (a) standard normal distribution and (b) normalized uniform distribution.  
Figure 17: The effect of initial weights (generated from different random generator seeds) on the classification accuracy of RF-GAN.
## 3 Methodology
### Data Standardization
#### 3.3.3 Feature Selection
Apart from the custom wrapper method that was implemented to measure the MDA and MDI metrics, another wrapper method that measures the P-value and the F-score for each feature, was used for an automatic selection of the k-best features. Figure 7 projects the values of these metrics for each feature cluster. Thus, a second subset was created using an automatic feature selection method which selects features according to the \(k\) highest scores by computing the \(chi2\) statistic. This subset is identified next as the automatically selected features. In Table 8, the feature subsets for each feature selection strategy is provided.
## 3 Results
### Introduce Uncertainty of Input Market Data in the Surplus Value Model  
The input data for the three markets displayed in Table 1 are based on nominal values and assumptions. These parameters are highly uncertain and impact the choice of customers and stakeholders. The sources of uncertainty in future megaconstellation applications are represented by:  
* The future prices of xenon and krypton. These parameters impact the cost of the propellant required to enter the operational orbit.
* The revenue that the operators will generate from megaconstellation businesses. This parameter impacts the benefit of higher thrust, as the operator can enter in operation earlier and thus start to generate revenues earlier. At the same time, this parameter also impacts the benefit of higher reliability, since there is less probability of losing the mission during operation.
* The cost of launch per kilogram. This parameter impacts the overall launch cost for the satellite constellations, which is a decisive factor to determine the impact of the wet mass of the EP.  
To represent the uncertainty of these parameters in the surplus value model, a Wiener model was adopted. The Wierner model assumes that a parameter varies over time following a Brownian motion. This assumption is commonly used in the financial domain to model the price of a stock [25].  
Figure 6: Tradespace between lifecycle costs (LCC) and SV for the different options, considering Market 1. The data regarding SV and LCC have been normalized to preserve company sensitive information.  
The Wiener model assumes that the price of a stock is depending on the price at the previous time interval and a parameter which represents the volatility of the stock price. The volatility parameter \"scales\" the uncertainty in future prices. The Wiener model involves random variables, in which the volatility is varied randomly over time. A common method to run a Wiener model is based on Monte Carlo simulation, in which a very high number of price variations scenarios are considered. Figure 7 shows the Monte Carlo simulation of 1000 scenarios, in which the four critical parameters (xenon price; krypton price; revenue per year; cost launch per kg) are varied. The data regarding the Revenue per year (Figure 7c) is normalized to preserve company sensitive information. The value of each revenue per year has been normalized based on the highest value (i.e., the highest value corresponds to 1 in the normalized scale). However, the normalized data are still within \"assumable\" ranges for a practitioner working in the electric propulsion business. Based on the partners' assumptions, the volatility of the krypton price has been considered lower than the fluctuation of the xenon price (due to the reasons given in Section 3.1). How the variation of these parameters impacts the way of looking at the 4 alternatives (Figure 4) is presented in the next section.
# An Enhanced Adaptive Block Truncation Coding with Edge Quantization Scheme +
## 2 Previous Works
### [PERSON]'s ABTC-EQ  
In previous BTC and its variants, e.g., AMBTC and MBTC, the quantization approaches are all the same. They all use bi-clustering approaches (two quantization levels) in all blocks. ABTC-EQ is an edge-based block truncation scheme. Its quantization is based on the edge information. In ABTC-EQ, we find the edged image from the given input image by Canny edge detector [15], where the process of the detection algorithm is composed of 4 different steps: (1) smooth the image with Gaussian filter to remove the noise. (2) find the intensity gradients of the image using finite-difference approximations for the partial derivatives. (3) apply non-maximum suppression to the gradient magnitude. (4) use the double thresholding algorithm to determine potential edges.  
For a block of the edge map (bitmap) \(E=[e_{i}]\) (sized \((k\times k)\)) obtained from the given input image based on the process, we classify the block into an edge block or a non-edge block using the criteria. That is if all pixel values in the block \(E\) are '0', it classifies to a non-edge block, otherwise, it classifies to an edge block. Notes: The image created after applying the edge detector algorithm is a bitmap and includes edges representing the shape of objects. By applying this feature, the set of blocks may be classified into edge blocks or non-edge blocks.  
For these two types of blocks, we use various quantization approaches. In case of non-edge blocks, we use MBTC. Therefore, a non-edge image block can be represented as a trio \((\mu_{0},\mu_{1},M_{n})\), where we intentionally use \(M_{n}\) notation to represent a bit map for the non-edge block. On the other hand, for edge blocks, we use tri-clustering approach. The pixels in a block are classified into three clusters \((c_{0},c_{1},c_{2})\), which similar pixels are grouped into the same cluster, by k-means clustering algorithm [16]. A bitmap \(M_{e}=[b_{i}]\) of the edge block is generated by Equation (5).  
\[b_{i}=\begin{cases}00,\ \ \text{if}\ x_{i}\in c_{0},\\ 01,\ \ \text{if}\ x_{i}\in c_{1},\\ 10,\ \ \text{if}\ x_{i}\in c_{2}.\end{cases} \tag{5}\]  
The mean value \(\mu^{\prime}_{i}\) of each cluster \(c_{i}\), \(0\leq i\leq 2\), is calculated with Equation (6), where \(\mu^{\prime}_{0}\leq\mu^{\prime}_{1}\leq\mu^{\prime}_{2}\). Thus, an edge image block is represented as \((\mu^{\prime}_{0},\mu^{\prime}_{1},\mu^{\prime}_{2},M_{e})\).  
\[\mu^{\prime}_{i}=\left\lfloor\frac{1}{|c_{i}|}\sum_{x_{i}\in c_{i}}x_{i}\right \rfloor,0\leq i\leq 2. \tag{6}\]  
Bi-clustering and tri-clustering approaches are performed for non-edge blocks and edge blocks, respectively. To discriminate edge blocks from non-edge blocks, an identifier flag \(f\) should be defined and assigned with the value 0 (respectively, 1) for the edge block (respectively, non-edge block). Finally, \(k^{2}\) pixels may be represented as \((f=1,\mu_{0},\mu_{1},M_{n})\) or \((f=0,\mu^{\prime}_{0},\mu^{\prime}_{1},\mu^{\prime}_{2},M_{e})\). Therefore, the CR of ABTC-EQ is dynamic not static like previous BTC schemes.