esa-sceva/satcom-chunk-collection huggingface dataset:
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 2 Problem Formulation
### Double-Sideband Reception Model for BOC Signals
Therefore, the subcarrier ambiguity can be fixed by the code ranging measurement to obtain the unambiguous and high-precision pseudo-range:  
\[\rho=\rho_{sc}+\text{round}(\frac{\rho_{code}-\rho_{sc}}{\lambda_{sc}})\lambda_{sc} \tag{4}\]  
where \(\rho_{sc}\) is the subcarrier ranging measurement, \(\rho_{code}\) is the code ranging measurement, and \(\lambda_{sc}\) is the subcarrier wavelength. In the presence of multipath, code ranging errors may exceed half of the subcarrier wavelength, resulting in an incorrect subcarrier ambiguity solution. Therefore, it is required to reduce the code multipath error to less than half of the subcarrier wavelength. It has been proved that applying the NC technique in the DBT loop can effectively mitigate code multipath errors to meet this requirement [22]. When the subcarrier ambiguity is correctly fixed, the pseudo-range shown in Equation (4) has the same accuracy as the subcarrier ranging measurement. The accuracy of pseudo-range positioning is usually at the meter level, while centimeter-level positioning applications require high-precision carrier ranging measurements. The carrier phase has integer ambiguity, which needs to be fixed with the pseudo-range. However, the pseudo-range error of the BOC signal, i.e., the subcarrier ranging error, may lead to an incorrect resolution of carrier phase ambiguity. Therefore, it is necessary to design carrier and subcarrier multipath mitigation techniques for BOC signals to improve positioning accuracy.
# Research on Magnetic Coupling Flywheel Energy Storage Device for Vehicles
## 3 Core Component Performance Test
### Transmission Characteristics  
As shown in Figure 6, we adjusted the speed of the driving shaft to 350 r/min for testing the transmission relationship of the driving and driven shafts.  
It can be seen from the test data that, with the movement of the magnetic ring, the driving shaft drives the load to start slowly through the driven shaft, and the speed of the two shafts is gradually the same. In this way, the impact and vibration in the starting process is reduced, and the damage of the motor by a large load is avoided.  
The magnitude of the torque transmitted by the magnetic ring at different moving positions is tested, and the results are presented in Figure 7.  
Figure 5: The test platform.  
Figure 6: Diagram of speed and time.  
It can be seen from the test data that the movement distance of the magnetic ring is 60 mm, and the two shafts' contact position of the magnetic ring is set as the origin. When the magnetic ring is at the origin position, the maximum transmission torque is 240 N\(\cdot\)m; when the magnetic ring is at the left end of the shaft, the transmission torque is the smallest. With the movement of the magnetic ring, the transmission torque gradually decreases. The change of the transfer torque in the stage after 30 mm is relatively gentle.
## 2 Materials and Methods
### Prediction Models Based on Artificial Neural Network (ANN)  
The prediction models were developed using the neural net fitting application in MATLAB (R2022a edition). ANN modelling involves simultaneous training and verification. The training utilized the same 160 data points for MRA and ML, whereas the validation employed the same 40 data points. To ensure data separation, the \"divideid\" function was employed to designate indices from 1 to 160 for training and from 161 to 200 for verification.  
A shallow neural network (SNN) with one hidden layer, and an ANN with multiple hidden layers were constructed. The input layer comprised 10 nodes, representing 10 input parameters, while the output layer consisted of 1 node. In the SNN structure, only one hidden layer was used; however, the number of nodes in the hidden layer varied from  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**No.**} & \multirow{3}{*}{
\begin{tabular}{c} **Descriptive** \\ **Statistics** \\ \end{tabular} } & \multicolumn{10}{c}{**Si**} & \multicolumn{1}{c}{**Mn**} & \multicolumn{1}{c}{**P**} & \multicolumn{1}{c}{**S**} & \multicolumn{1}{c}{**Ni**} & \multicolumn{1}{c}{**Cr**} & \multicolumn{1}{c}{**Mo**} & \multicolumn{1}{c}{**Cu**} & \multicolumn{1}{c}{**Heat Input**} & \multicolumn{1}{c}{**Yield Strength**} \\  & & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(kJ/cm)** & **(MPa)** \\ \hline
1 & Min. & 0.02 & 0.3 & 0.61 & 0.011 & 0.001 & 7.85 & 14.99 & 0.02 & 0 & 7 & 327 \\ \hline
2 & Max. & 0.19 & 0.96 & 5.75 & 0.043 & 0.032 & 18.04 & 21.06 & 3.41 & 0.29 & 28 & 510 \\ \hline
3 & Mean & 0.07 & 0.62 & 2.52 & 0.020 & 0.01 & 10.74 & 17.55 & 1.48 & 0.1 & 15.1 & 423 \\ \hline
4 & Std. Dev & 0.05 & 0.16 & 1.72 & 0.009 & 0.007 & 2.6 & 1.75 & 1.15 & 0.07 & 5.38 & 40.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the dataset for the verification test.  
2 to 20. In the ANN structure, the hidden layer varies in the node count from 2 to 20, as depicted in Figure 1.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment
### Discussions  
The experimental results demonstrate that the multi-view combined detection method has a significant advantage in improving the diagnostic accuracy of apple core browning. This finding aligns with recent research trends advocating for multi-source data fusion, such as the work of [44, 45], who also highlight the benefits of integrating different types of sensor data for enhanced prediction capabilities. The enhancement in the detection performance of our method can be attributed to its ability to capture subtle biological and physical changes associated with apple core rot disease. Specifically, the integration of hyperspectral and dielectric data provided a more comprehensive view of the fruit's condition, reflecting visual characteristics such as color and texture, as well as subsurface electrical properties like conductivity and dielectric constant, which often indicate internal decay. This comprehensive approach is supported by studies like [46], where the fusion of different data types led to improved detection of internal defects in fruits.  
The application of canonical correlation analysis (CCA) for feature dimension reduction plays an important role in enhancing the performance of the optoelectronic fusion detection technique, as suggested by [47] in their work on feature extraction methods for hyperspectral data. By simplifying the feature set to the most representative hyperspectral and dielectric attributes, it minimizes the interference of noise and irrelevant information, thereby highlighting the features that predict browning, which is particularly effective in high-dimensional data environments. This aligns with the findings of [48], who demonstrated that CCA can effectively reduce dimensionality while preserving the most informative features for classification tasks.  
The SVM (radial basis function) classifier chosen in our study exhibited superior performance compared with other classifiers we evaluated. Its proficiency in handling complex data patterns made it a contributing factor to its robust classification performance in our dataset, as it effectively navigated decision boundaries in the high-dimensional feature space combined from optical and dielectric data. This is consistent with the observations by [49, 50], who reported that a SVM with RBF kernels is effective in classification problems involving complex patterns and high-dimensional data. Furthermore, ref. [51] demonstrated that careful parameter tuning of the SVM can lead to further improvements in classification accuracy.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Cooperative Spectrum Sensing
#### 2.3.3 Relay-Assisted Cooperative Spectrum Detection  
Relay cooperative spectrum sensing is a technique that concerns collaboration among multiple relay nodes for dynamic spectrum allocation in radio communications, primarily for channel fading and shadowing problems. Since in real communication situations, CR users encounter both superior and inferior sensing and reporting channels in detection,  
Figure 7: Distributed cooperative spectrum detection model.  
CRs interact with the fusion center through relay nodes for sensing information so that they can cooperate and complement each other.  
The basic principle is that the relay node monitors and collects perceptual information from the surrounding local nodes by monitoring and collecting metrics such as spectrum utilization, signal strength, and interference level. This information can be transmitted via wireless links or alternative communication methods to the fusion center, which performs an information fusion analysis to determine the available free spectrum and then sends the scheme to each relay node to guide each CR to make policy adjustments. The relay-assisted cooperative spectrum-sensing detection model is shown in Figure 8.  
Due to its network topology and management complexity, namely, in large-scale networks, the management and maintenance of such systems can be complex and challenging, including network topology design, communication protocols, and resource allocation. Relay-assisted cooperative spectrum sensing still has great potential for research and development.
## 2 Model and Review of Classical Control Methods
### Review of Control Method Using DQ Transformation  
For the simplification of surface-mounded PMSM model (2), DQ transformation [22] is applied as follows:  
\[\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix} =\begin{bmatrix}\cos(p\theta)&\sin(p\theta)\\ -\sin(p\theta)&\cos(p\theta)\end{bmatrix}\begin{bmatrix}i_{a}\\ i_{\beta}\end{bmatrix}, \tag{3}\] \[\begin{bmatrix}v_{d}\\ v_{q}\end{bmatrix} =\begin{bmatrix}\cos(p\theta)&\sin(p\theta)\\ -\sin(p\theta)&\cos(p\theta)\end{bmatrix}\begin{bmatrix}v_{\alpha}\\ v_{\beta}\end{bmatrix}\]  
where \(i_{d}\) and \(i_{q}\) are the direct and quadrature currents, and \(v_{d}\) and \(v_{q}\) are the direct and quadrature voltages, respectively. The DQ transformation provides a DQ frame model of the surface mounded PMSM, which is as follows:  
\[\dot{\theta}= \omega \tag{4}\] \[\dot{\omega}= \frac{3}{2}\frac{p\Phi}{\gamma}i_{q}-\frac{B}{\gamma}\omega- \frac{\tau_{L}}{\gamma}\] \[\dot{i}_{d}= -\frac{R}{L}i_{d}+p\omega i_{q}+\frac{1}{L}v_{d}\] \[\dot{i}_{q}= -\frac{R}{L}i_{q}-p\omega i_{d}-\frac{p\Phi}{L}\omega+\frac{1}{L }v_{q}.\]  
A block diagram of the general control method using the DQ transformation is shown in Figure 1. In Figure 1, \(\theta_{r}\) denotes the reference position. As shown in Figure 1, the DQ transformation also involves an inverse DQ transformation. The structure of the system will be simpler if the controller is designed without a DQ transformation. Generally, PL controllers are used for both mechanical and electrical dynamics.
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 3 Energy of Acceleration Signals
### Hilbert Amplitude  
The amplitude of the signals is extracted while using the Hilbert transform. This represents the energy of the vertical acceleration signal by extracting its instantaneous amplitude. The Hilbert transform of a\({}_{2}\)(t) is [33]:  
\[\alpha(t)=\frac{1}{\pi}P\int\frac{a_{z}(\tau)}{t-\tau}d\tau \tag{1}\]  
where P presents the Cauchy principal value of the singular integral. The analytic signal of a\({}_{z}(t)\), which is a complex signal is defined by [33]:  
\[\beta(t)=a_{z}(t)+j\alpha(t) \tag{2}\]  
where j is \(\sqrt{-1}\). The polar form of \(\beta(t)\) is:  
\[\beta(t)=\text{amp}_{inst}(t)e^{in(t)} \tag{3}\]  
The instantaneous amplitude \(\text{amp}_{inst}(t)\) is calculated as:  
\[\text{amp}_{inst}(t)=\sqrt{a_{z}{}^{2}(t)+\alpha^{2}(t)} \tag{4}\]  
The Hilbert amplitude of a vertical acceleration signal, a\({}_{z}\), as measured over a distance of 300 m from a sample of the data set, is shown in Figure 4. It shows how the Hilbert transform extracts the instantaneous amplitude \(\text{amp}_{inst}\) of the signal, which represents the signal energy.  
Figure 4: Hilbert amplitude of a sample acceleration signal, (**a**) 6500 to 6600 m, and (**b**) 6500 to 6510 m.
## 3 On-Boarding Process for Distributed Analysis
### On-Boarding Workflow  
After briefly presenting our reference architecture and its relevant components, within the next sections, we explain our on-boarding process, which has been built upon this infrastructure. Note, for the reason of simplicity, in our scenario, each institution has exactly installed one station (client). However, our approach still enables the installationof multiple stations per institution. A top-level view of the concept has been depicted in Figure 2.  
Figure 2: Overview of the on-boarding service and its interaction with the DA infrastructure. First, the responsible person (station admin) for the station has to provide (meta-) information about the prospective station. After the registration, the station registry triggers the on-boarding procedure in the CS. The output of this procedure is an encrypted file containing connection credentials for the station to communicate with the CS. For the encryption, the procedure uses the OTP priorly provided by the station admin. This file is sent via an email service. After receipt, the file can be decrypted using the OTP and on-boarding wizard for the station software. Using the wizard, the admin can create key pairs for future use in the DA infrastructure. The keys are send to the CS followed by a connection test (ping).
# Formation Kinetics and Morphology Characteristics of Natural Gas Hydrates in Sandstone Fractures
## 3 Results and Discussion
### Hydrate Reformation  
As shown in Figure 18, comparing hydrate induction times during hydrate first formation and reformation stages reveals that the induction time for the hydrate first formation is  
Figure 16: A morphology diagram of the residual hydrate in fractures during the dissociation process.  
Figure 17: Temperature variations during hydrate decomposition under different pressures.  
significantly longer than that for hydrate reformation under all conditions. This is consistent with previous research findings [48]. This difference may be attributed to the persistence of crystal nuclei structures at their original locations following hydrate dissociation. During the hydrate reformation stage, these residual nuclei are reactivated, promoting the rapid formation of hydrates. Figure 19 also demonstrates that hydrates preferentially form in the original hydrate region, illustrating the \"memory effect\" described in earlier studies [54]. Furthermore, hydrate induction times for CH\({}_{4}\) under varying fracture widths continue to follow the trend observed during the initial formation stage. Specifically, the induction time is the longest at a fracture width of 3 mm, followed by 10 mm, and the shortest at 6 mm, further confirming the consistency of earlier findings. This suggests that hydrate formation is most favorable at a fracture width of 6 mm.  
Simultaneously, an observation of the gas consumption curves in Figure 20 reveals that the hydrate formation rate during the reformation process is faster than during the first formation stage under all conditions. This phenomenon can be attributed to two primary factors. On the one hand, it could be attributed to the previously mentioned \"memory effect.\" On the other hand, this may be due to a greater quantity of dissolved gas remaining in the liquid phase following depressurization and dissociation. When the temperature is  
Figure 19: Nucleation area distribution of hydrate formation in rough fractures.  
Figure 18: Hydrate induction time for hydrate first formation and reformation.  
increased to reformation, the hydrate, gas, and liquid molecules combine more thoroughly, leading to a faster formation rate. Additionally, the dissociation of the hydrate may alter the surface tension and free energy of the liquid in the system, thereby facilitating the formation of stable hydrate structures through their combination with gas molecules. A further comparison of the gas consumption curves for hydrate first formation and reformation reveals that the final quantity of hydrate formed during the first formation, across different fracture widths, is higher than that formed during the reformation under identical conditions. This may be attributed to the inherent limitation of CH\({}_{4}\) hydrate solubility. Consequently, at lower pressures, it cannot efficiently combine with the liquid phase. Moreover, the faster formation rate during reformation could cause clogging in the gas mass transfer process, thereby affecting the overall hydrate formation.
###### Abstract  
This paper presents a flexible heating, ventilation and air conditioning (HVAC) modeling framework developed for building digital twin implementation. The framework is showcased for the modeling and simulation of four ventilation systems in a 8500 m\({}^{2}\) university building. The developed model includes multiple objective model predictive control (MPC) with three objectives: electricity cost, indoor air quality and CO\({}_{2}\) emission attributed to electricity consumption. A control strategy comparison is conducted between several MPC solutions with different objective weightings and a rule-based control strategy, which emulates the current system control. A novel approach for air quality evaluation is proposed and used for the MPC modeling and strategy comparison in this study. In this comparison, a \"balanced\" MPC strategy reduces energy costs by 18% compared to rule-based control while also providing significantly better air quality. An economic strategy achieves 24% savings with some air quality reduction, while an air-quality-focused strategy provides nearly \"perfect\" air quality with 11% savings. Finally, an environmental strategy shows the potential for prioritizing CO\({}_{2}\) emissions over electricity costs. In this way, the strategy comparison illustrates the potential of MPC for the efficient operation and flexible objective prioritization according to stakeholder interests.  
MPC; HVAC; ventilation; building; simulation; system control; multi-objective optimization; component-based modelling Article
## 1 Introduction  
Polyethylene (PE) is considered one of the most important thermoplastics used today [1], mainly due to its low price, high durability, chemical inertia, and easy conversion into various forms and sizes of plastic products. This semi-crystalline polyolefin is widely used to manufacture plastic films [2], bags, and other agricultural products [3], produced via blown film extrusion. Both HDPE (high-density polyethylene) and LLDPE (linear low-density polyethylene) are largely used today and exhibit great mechanical performance, even with low gauge [4]. Due to their growing popularity, many studies have been made to improve their manufacturing process, particularly in the extrusion operation, with the goal of enhancing their mechanical properties [5]. However, there is still the need for a better understanding of the effects that the process conditions have on the molecular orientation and therefore on the film's mechanical properties. Because polyethylene is considered to be the dominant packaging material among polymers, it is very important to consider in detail which parameters and properties need to be studied and correlated in order to improve its use [6].  
There are few studies that have analyzed the effect of process conditions such as the neck height (NH), blow-up ratio (BUR), take-up ratio (TUR), and thickness reduction (TR), on the mechanical properties of HDPE films. [PERSON] et al. [7] reported how the gauge reduction is related to the dart impact strength, and found that for HDPE with a high molecular weight, the dart impact strength increases, while for HDPE with a lower molecular weight, it decreases. [PERSON] et al. [8] studied the effect of the BUR on elongation mechanical properties for different LLDPE resins and found that, within the range from 1.7 to 2.8, the machine-direction tensile strength decreased and the transverse-direction tensile strength increased. Furthermore, [PERSON] and [PERSON] [9] presented a range of NH values to obtain optimal dart impact strength. These studies presented results that relate some process parameters with mechanical properties in HDPE films or in LLDPE films. However, there are no studies that analyze all of the above-mentioned process parameters to establish correlations with mechanical properties on an industrial scale using an HDPE and LLDPE blend.  
The purpose of this study is to analyze how different variations of the main process parameters of blown film extrusion (NH, BUR, TUR, and TR) affect the mechanical properties of the final product, such as the dart impact strength and tensile elongation, in the machine direction (MD) and transverse direction (TD) of HDPE and LLDPE blend films on an industrial scale. The correlations found between the process parameters and the mechanical properties are presented.
# Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model
## 2 Materials and Methods  
### Patients, Samples and Gene Expression Data  
Gene expression profiles of human tissues and cancer tissues were collected from GEO [21]. The datasets were downloaded with the R software [22] using the GEOquery package [23]. A total of 16 datasets were collected according to the criteria: (i) studies involving cancer/normal tissues, (ii) mRNA expression profiling, (iii) different cancer types.  
Table 1 reports the detailed description of considered samples.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Dataset** & **GEO ID** & **\# of Cancer Samples** & **\# of Normal Samples** \\ \hline Bladder Urothelial Carcinoma & GSE13507 & 165 & 10 \\ \hline Breast invasive carcinoma cancer & GSE39004 & 61 & 47 \\ \hline Colon adenocarcinoma & GSE41657 & 25 & 12 \\ \hline Esophageal carcinoma & GSE20347 & 17 & 17 \\ \hline Head and Neck squamous cell carcinoma & GSE6631 & 22 & 22 \\ \hline Kidney Chromophobe & GSE15641 & 6 & 23 \\ \hline Kidney renal clear cell carcinoma & GSE15641 & 32 & 23 \\ \hline Kidney renal papillary cell carcinoma & GSE15641 & 11 & 23 \\ \hline Liver hepatocellular carcinoma & GSE45267 & 48 & 39 \\ \hline \end{tabular}
\end{table}
Table 1: Cancer type, Gene Expression Omnibus (GEO) ID, size of cancer and normal cases for each dataset. #: number.