esa-sceva/satcom-chunk-collection huggingface dataset:
# Dependency Analysis based Approach for Virtual Machine Placement in Software-Defined Data Center
## 2 Background and Related Work
### Virtual Machine Placement
In general, most of the existing works attempted to solve the VM placement problem by turning it into a traditional bin packing problem, which is known to be NP-hard and can be solved by heuristic algorithms [34]. Since current data centers usually run thousands of VMs having a large amount of traffic volume, the analysis of traffic information for large scale data centers requires high computational cost and time. Most existing works directly analyzed the traffic information data without reducing its redundancy, and thus their complexity increases exponentially as the number of VMs increases. For solving this issue, the proposed method leverages the PCA and gravity-based clustering algorithms to reduce the complexity of high dimensional data analysis. Therefore, the proposed approach considerably reduces the time complexity for solving the VM placement problem. It reduces the amount of traffic volume and the number of running PMs at the same time. In addition, software-defined technologies are used for obtaining both the computing and networking information in real-time and controlling the VM allocation in the data center.
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 3 Cutting Force Estimation Using LSTM-Based Time-Series Machine Learning
### LSTM Architecture for Cutting Force Estimation  
To estimate the cutting forces (\(\hat{F}_{x}\) and \(\hat{F}_{y}\)), the spindle vibration data (\(a_{x}\), \(a_{y}\) and \(a_{z}\)) collected in the previous section is structured as shown in Figure 10a. The input to the LSTM model consists of these three-axis acceleration signals, while the output is defined as the average peak force values for \(F_{x}\) and \(F_{y}\), as shown in Figure 10b. Each data sample corresponds to one full tool rotation, capturing information from all tool flutes and allowing for real-time monitoring by estimating cutting forces over short time intervals.  
Figure 9: Block diagram of LSTM.  
Since the vibration signals are closely linked to cutting forces, aligning the two datasets enhances feature extraction, enabling accurate estimation of cutting forces. The outputs are designed to estimate the mean peak cutting force for each tool flute in \(F_{x}\) and \(F_{y}\). Given a spindle speed of 19.9 Hz and a data acquisition rate of 1 kHz, each sensor captures 49 data points per axis for every tool rotation. Consequently, the input consists of 147 values (three vibration axes), and the output consists of two values representing the estimated cutting forces.  
This dataset contains 99 data samples per pass, resulting in a total of 124,740 datasets across 1260 passes, as shown in Figure 11. Since LSTM models rely on sequential data, time-dependent variations must be properly structured for effective learning. To achieve this, the training dataset is divided into time-series segments, grouping temporal variations at each cutting position into 99 datasets. From the entire dataset, 80% (80 datasets) is allocated for training, while 20% (19 datasets) is used for validation. The dataset order is randomized during training to improve generalization, but the internal sequence within each dataset is preserved.  
Figure 11: Dataset configuration.  
Figure 10: Configuration of input and output data per tool revolution for LSTM training: (**a**) Input data—spindle vibration signals; (**b**) Output data—mean peak cutting force.  
The proposed learning model, shown in Figure 12, incorporates an LSTM layer followed by a dropout layer (rate = 0.5) to prevent overfitting. The total number of trainable parameters is 288,552, ensuring an appropriate balance between model complexity and computational efficiency.
## 4 Generative Modeling as a Distribution Approximation Process
### Objectives, Tasks, and Architectures  
The objective of generative modeling is to train a neural network \(P_{\theta}\) capable of generating new samples \(y\in\mathcal{X}\) from random seeds (and conditional samples) that closely resemble those in the training data \(\mathcal{S}\). From a frequentist viewpoint, the observations \(x\in\mathcal{S}\) are drawn i.i.d. from an unknown true distribution: \(\mathcal{S}=\{x_{i}\}_{i=1}^{N}\sim P_{X}\). The goal is to learn a generator \(g_{\theta}\) that can produce samples approximating \(P_{X}\) by passing a random variable \(U\) with a fixed distribution, typically \(\mathcal{N}(0,1)\), through the network. Since these models are trained to minimize specific perceptual objectives, the random seed \(U\) can be regarded as private randomness that enhances the perceptual quality of the reconstruction, conditioned on given messages. The decoding process in generative models can be viewed as a sampling procedure. For example, in the context of LLMs, beam search [55] is a common maximum a posteriori (MAP) decoding algorithm. Other sampling algorithms, such as temperature sampling [56], top-\(k\) sampling [57], nucleus sampling [58], and typical sampling [59], have been developed to generate content faithfully according to the learned distribution.  
A variety of generative model architectures have been proposed, with the most prominent ones including variational auto-encoders (VAEs) [60], generative adversarial networks (GANs) [61], diffusion models [62; 63], and transformers [64]. These models have achieved notable success in generating images, videos, and language content. They are trained by minimizing specific loss functions that quantify the similarity between the model distribution \(P_{\theta}\) and the data distribution \(P_{D}\). For instance, VAEs and diffusion models minimize the negative evidence lower bound (ELBO), which effectively reduces the KL divergence, while GANs optimize objectives ranging from the Jensen-Shannon divergence [61] to the Wasserstein distance [65]. At the core of generative modeling are the divergence measures \(\mathcal{D}(P_{\theta},P_{D})\) between the model and data distributions. Building on these foundations, neural networks for specific tasks, such as lossy compression, can be trained to minimize a combined distortion and perception loss with a weighting factor \(\lambda\):  
\[\mathcal{L}=\Delta(X,\hat{X})+\lambda\mathcal{D}(P_{X},P_{\hat{X}}). \tag{21}\]  
Variants of this loss function have been applied in numerous settings, including those described in [5; 6; 7; 8; 9; 10; 11], to achieve high perceptual quality. These applications underscore the perceptual-distortion trade-off inherent in the training of generative models in practice. As the realism depends heavily on the statistical properties of the training datasets, different dataset statistics may affect the RDP trade-off of generative models.
## 3 Component Selection and Criteria
### Theoretical Calculation  
In this study, standard literature surveys and historical data were consulted, and the initial conditions were determined [12]. Using these related conditions, a UAV was constructed theoretically. Equation (2) was obtained from historical data collected from various previously completed octocopters. The comprehensive relationship between the payload and the overall weights of various octocopters is shown in Figure 3.  
\[\mathrm{W_{PI}}=0.30\ \mathrm{W_{O}} \tag{2}\]  
The weights of the storage device, sprayer, other accessories, and payload were 1000 g, 50 g, 200 g, and 1250 g, respectively. Therefore, the overall weight can be calculated as \(\frac{1250}{0.30}=4166.67\) g.  
Hence, the minimum thrust/lift required by a single propeller of the octocopter was equal to \(\frac{4166.67}{8}=520.83\) g at a normal level. At the maximum level, the thrust-to-weight ratio is assumed to be 1.75, and at a normal level, the ratio is equal to one. With these significant inputs, the primary components were estimated, in which the propeller, motor, battery, and electronic speed controller (ESC) were important. The propeller for this octocopter was designed using the conventional formula in Equation (3). The corresponding calculations were as follows:  
Thrust requirement of the \(\sin\mathrm{gle}\) propeller at maximum level \(=\frac{1.75\ast 4166.67}{8}=911.5\); and  
\[\mathrm{T}=0.5\times\rho\times\mathrm{A}\times\Big{[}\left(\mathrm{V_{UAV}} \right)^{2}-\left(\mathrm{V_{0}}\right)^{2}\Big{]}. \tag{3}\]  
Figure 3: Historical relationship between payload weight and overall weight of octocopter.  
From the field work, the gust aerodynamic velocity [V\({}_{0}\)] was estimated to be 10 m/s, and the maximum speed of the UAV [V\({}_{\text{UAV}}\)] was assumed to be 30 m/s. Accordingly, the dimensions were calculated as follows:  
\[911.5\times 9.81=0.5\times 1.2256\times\text{A}\times\left[(30)^{2} -(10)^{2}\right];\] \[\text{A}=\frac{911.5*9.81}{490.24}=\frac{8.933}{490.3}=0.01822;\] \[\text{and thus the diameter}=6\text{ in}.\]  
After the estimation of the diameter, the pitch estimation plays a major role in the construction of the propeller. In this regard, the historical relationship between the pitch and diameter of relevant propellers was examined and thereby showed the relationship suitable for a six-inch-diameter propeller, seen in Equation (4). Figure 4 shows the variation in the aforementioned design parameters.  
\[\frac{\text{Pitch}}{\text{Diameter}}=0.75\ \Rightarrow\text{Pitch}=0.75* \text{Diameter}\Rightarrow 0.75*6=4.5\text{ inches} \tag{4}\]  
Thus, a \(6\times 4.5\) propeller was estimated for this application, and the total weight of the entire propeller setup was determined to be 120 g (\(8\times 15\) g).
## 5 Conclusions  
This paper mainly studied the resource allocation to maximize the throughput by jointly optimizing the bandwidth assignment and power allocation subject to the QoS constraint for the multi-cell multi-user uplink system. According to the variable attributes of the joint resource allocation problem, we proposed a JPRL method to decouple the optimization problem into two sub-problems, where the MADDQN was used to allocate bandwidth, and the P-MADDPG assigned uplink power with the given importance of transition. In order to compare the loss value and learning performance of the different networks with various learning rates, we set the appropriate parameters for the proposed JPRL method and analyzed the impact of the different learning rates. Furthermore, we evaluated the reward value and throughput of the proposed JPRL method against other existing methods. the simulation results showed that our approach can (1) obtain a better performance and be more applicable to the complex environments than other alternative methods (e.g., the average throughput was approximately 10.4-15.5% better than the average throughput of the benchmarks.) and (2) be universally applicable to other large-scale scenarios.  
It is worth noting that, for simplicity, the single antenna system was used in this work. As for multi-antenna systems such as MIMO, the impact of more complex channel matrices caused by multiple antennas on user interference needs to be considered. In future work, the multiple antennas, the users' trajectory, and cloud computing will be taken into consideration in multi-cell systems to facilitate communication-computing integration. By considering the interference corresponding to the complex channel matrix, the optimization is relevant to the compromised performance of the computing delay and energy consumption, which is based on the resource allocation and task offloading under various constraints, such as QoS constraints and offloading decisions. Moreover, multi-dimensional and deep analysis will be researched to validate the system tradeoff.  
Conceptualization, C.Z. and T.L.; methodology, P.H.; software, Z.L.; validation, C.Z., T.L., and P.H.; formal analysis, J.Z.; investigation, Y.R.; resources, T.L.; data curation, C.Z.; writing--original draft preparation, C.Z.; writing--review and editing, T.L.; visualization, C.Z.; supervision, T.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant number 62271068 and 61827801, the Beijing Natural Science Foundation under Grant number L22046, and the Basic Scientific Research Project under Grant NS2022046.  
Not applicable.  
The authors declare no conflict of interest.
# Modeling and Parameter Calibration of Morchella Seed Based on Discrete Element Method
## 2 Materials and Methods
### Experimental Materials  
The Morchella seeds used in this experiment were sourced from the Morchella Cultivation Laboratory at Tarim University. The physical parameters tested included both physical properties (such as triaxial dimensions, moisture content, and density) and mechanical properties (such as Poisson's ratio, elastic modulus, and shear modulus). A random sample of 50 seeds was selected and placed in a three-dimensional coordinate system, where length, width, and thickness were defined along the respective axes. The triaxial dimensions were measured using a digital caliper with a precision of 0.01 mm. Seed density was determined using an electronic balance with 0.001 g precision and a graduated cylinder with 0.1 mL accuracy [25]. The moisture content was measured following the standard procedure outlined in the \"Testing Rules for Crop Seeds--Moisture Determination (GB/T 3543.6-1995)\" [26], using a low-temperature drying method at 103 \({}^{\circ}\)C for 8 h [27]. The moisture content was calculated using a thermostatic drying oven and beakers. Each parameter was measured 10 times, and the average values were recorded. The detailed results are presented in Table 1 and Figure 1.
# Optimization of DLTS Hinges for the Assembly of the Solar Arrays of a Communication CubeSat
## 2 Methodology
### Design of Double-Layer Tape Spring (DLTS) Hinge (Coupling Mechanism)
#### 2.2.2 Double-Layer Tape Spring Hinge/DLTS  
The DLTS hinge is composed of two tape springs which are positioned tightly together and are mounted to hinge brackets with the aid of bolts and spacers. The geometric configuration of a DLTS consists of the overall longitudinal length (\(L\)), the clamp end's length (\(b\)), the layer thickness (\(t\)), the curvature's radius (\(R\)), and the subtended angle (\(\theta\)) [16]. The \(L\) and \(b\) parameters were determined to be 2.45 mm and 2 mm, respectively. The \(L\) parameter was precisely calculated to acquire the desirable creased area of the DLTS by the following equation:  
\[L=\sqrt{\frac{t}{2}} \tag{1}\]  
where \(L\) = overall longitudinal length and \(t\) = thickness of each individual solar panel (3 mm).  
The \(t\), \(R\), and \(\theta\) are not fixed variables as they are defined as critical design variables; hence, they were optimized in the finite element analysis (FEA) until the appropriate  
Figure 2: Membrane technique—CAD model.  
numerical value was obtained. The geometric model and analyzed parameters are shown in Figure 3.  
The structural complexity of the DLTS was derived from its non-linear mechanical behavior. It was established to be a key feature because it enhances robustness and restricts failure during the stowed and deployed state [16]. The non-linear mechanical behavior of the DLTS was delineated to be 2D, and two types of folds were recognized, equal sense and opposite sense bending. In equal sense bending, the DLTS exhibited low stiffness, and subsequently, when moment was applied, torsional lateral bucking appeared and numerous sharp edge kinks were generated. The sharp edge kinks were integrated into a solitary elastic fold where uniform transverse and longitudinal curvatures were developed with a positive bending moment and angle (Figure 4a). In opposite sense bending, the DLTS was displayed to procure high stiffness initially, and eventually, a snap-through formation was created by acquiring constant moment behavior. Figure 4b illustrates the opposite sense bending where transverse and longitudinal curvatures are formed with a positive bending moment and angle. A hardened steel alloy (Aermet 340) was used for the DLTS hinge because of its exceptional toughness, strength, and long-term durability.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Multiplex PCR  
In Multiplex PCR, several pairs of primers are used to target simultaneously different DNA sequences. This technique takes advantage of the high copy number of rRNA genes,length, and sequence variability of the _ITS_ regions of _Candida_ spp. A comparison study between phenotypic methods and multiplex PCR portrayed this last one as a high-accuracy diagnostic tool [73]. Some authors used multiplex PCR to distinguish clinically important _Candida_ species from oral samples [74; 75; 76; 77] and blood [78]. A diagnostic strategy was created targeting approximately twenty clinically relevant yeast species, _Candida_ included. The results were 100% consistent with the MALDI-TOF MS data [79].  
Table S1 contains detailed information on primers' sequence and annealing temperature for _Candida_ spp. Multiplex PCR provides rapid and effective results. In oral samples, the elimination of the DNA extraction step saves sample preparation time avoiding hazardous or expensive chemicals [80]. Although this method is used in some clinical laboratories, it requires proficiency in primer design and protocol optimization [81].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom
### Scheduling and Resource Allocation
#### 6.3.3 Cognitive Radio  
Cognitive radio efficiently utilizes the wireless channel, for the best allocation of resources. [PERSON] et al. presented an overview of a cognitive radio network powered by RF energy [147]. This work addressedthe problem of relay selection and resource allocation, in cognitive radio sensor networks, to harvest power wirelessly [148]. The advantages of combined WPCN and cooperative cognitive radio have been explored [143]. The authors further presented a sum throughput optimal  
Figure 33: Data scheduling for backscatter WSN.  
resource allocation (STORA) scheme. STORA provides a trade-off between fairness and overall throughput while considering this combined scheme [143; 149]. Similarly, a technique for the improvement of data rate was proposed in the secondary system of an RF-powered backscatter cognitive radio network [150; 151]. The maximum data rate of the secondary system was obtained by optimization of the allocated time for energy harvesting, transmission, and backscattering, respectively.  
As shown in Figure 34, the concept of integration of an RF-powered cognitive radio network with Amb-BackCom was proposed to improve the performance of secondary systems [152]. Figure 34a delineates the case where a primary transmitter sends signals (i.e., a primary channel is busy), and the secondary transmitter transmits data to a secondary receiver using BackCom. In other cases, the secondary receiver can harvest energy as demonstrated in Figure 34b. The symbol \(\beta\) represents the normalized channel idle time period, and \((1-\beta)\) denotes the normalized channel busy time period. In the case of the busy channel, \(\alpha\) stands for the time ratio for energy harvesting, and \((1-\alpha)\) denotes the time ratio for BackCom. Notation \(\mu\) shows that the data transmission period [152]. Moreover, the trade-off between HTT and the BackCom network protocols was analyzed. The results show that the proposed scheme outperforms the individual backscattering mode and HTT protocols [152]. A hybrid HTT and BackCom mode was suggested for a secondary communication system in cognitive wirelessly powered radio networks [153]. Subsequently, [PERSON] et al. proposed a network architecture based on cognitive radio. This architecture enables secondary users to harvest energy while primary users operate on the same frequency spectrum [154]. [PERSON] et al. investigated the cognitive BackCom with multiple RF sources and showed high throughput with multiple antennas at a single tag [155].
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 4 Model Construction and Result Analysis
### Loss Function and Evaluation Metrics  
In this study, mean squared error (MSE) is utilized as the model's loss function, and it is defined as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(\hat{y}_{i}-y_{i})^{2} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grid Search Parameters** & **Interval** & **Best Parameters** \\ \hline N\_estimators & [100, 200, 300, 500, 800, 1000] & 500 \\ Max\_depth & [10, 20, 30, 50] & 50 \\ Min\_samples\_split & [2, 5, 10] & 2 \\ Min\_samples\_leaf & [1, 2, 4] & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RF Model Parameter Settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Range** \\ \hline Number of hidden layers & 10 \\ Number of nodes in hidden layers 1–10 & 64, 64, 512, 512, 1024, 1024, 1024, 2048, 2048, 2048 \\ Learning rate & 0.0001 \\ Momentum parameter & 0.01 \\ L2 parameter & 0.01 \\ Loss function & MSE \\ Activation function & relu/linear \\ Optimizer & Rmsprop \\ Epochs & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Stress field model structure and hyperparameter settings.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Portfolio Number** & **Epochs** & **Learning Rate** & **Momentum Parameter** & **MSE** & **MAE** & **MAPE** & **R2** \\ \hline
1 & & 0.0005 & 0.01 & 3.056 & 0.524 & 0.053 & 0.982 \\
2 & 90 & 0.0001 & 0.1 & 1.916 & 0.653 & 0.059 & 0.988 \\
3 & & 0.0001 & 0.01 & 1.213 & 0.586 & 0.081 & 0.992 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Learning rate and momentum parameter trial scores.  
where \(\hat{y}_{i}\) represents the predicted value from the MLP model, \(y_{i}\) denotes the computed value from the FEM, and \(n\) is the total number of samples.  
To evaluate the model's performance and explanatory power, three metrics are employed: mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination (R\({}^{2}\)). These metrics are defined as follows:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}{\left|{{{\hat{y}}_{i}}-{y_{i}}}\right|} \tag{4}\]  
\[MAPE=\frac{100\%}{n}{\sum\limits_{i=1}^{n}}{\left|{\frac{{{\hat{y}}_{i}}-{y_{ i}}}{{y_{i}}}}\right|} \tag{5}\]  
\[R^{2}(y,\hat{y})=1-\frac{{\sum\limits_{i=1}^{n}}{\left({{{\hat{y}}_{i}}-{y_{i} }}\right)^{2}}}{{\sum\limits_{i=1}^{n}}{\left({{{\overline{y}}_{i}}-{y_{i}}} \right)^{2}}} \tag{6}\]  
where \(\overline{y}_{i}\) denotes the average of the FEM-calculated values. MAE visualizes the deviation between predicted results and calculated results; MAPE quantifies the extent of deviation between the predicted and calculated results; and R\({}^{2}\) measures the proportion of variance in the dependent variable explained by the independent variable, thereby assessing the explanatory power of the statistical model. The integration of these three evaluation metrics provides a comprehensive assessment of the model's performance.