esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LNA Characterization
### Measurement Setups  
to  
\(-8\) dBm, and a 10 dB internal attenuation was applied at port 1. RF and DC access to the MMIC was provided using a M150 microwave probe station, from CASCADE MicroTech, along with probes from GGB Industries. The system calibration was carried out using Short-Open-Load-Through (SOLT) calibration substrate (CS5) provided by GGB Industries. The complete setup for this test bench is available on the left side of Figure 13.  
The noise figure was measured using an Agilent E4448A Power Spectrum Analyzer, which was preceded by two low-noise amplifiers (MACOM MAAM-011109 and Hewlett-Packard HP83051A) for preamplification. A standard Y-factor setup was used, incorporating a diode noise source (Noise Com NC346 KA) along with a well-matched 6 dB attenuator. The complete test bench setup is shown on the right side of Figure 13. In both test setups, measurements were automatically collected by a PC running LabVIEW, with communication to the instruments established via a LAN connection.  
Nonlinear measurements were performed with an Agilent E4448A Power Spectrum Analyzer, coupled with an absolute power reference provided by Keysight USB power sensor. The test bench setup schematic for power measurements can be seen in Figure 14, while the real measurement setup from our laboratory is available in Figure 15.  
Figure 14: Test bench setup used for large signal measurements.  
Figure 13: Test bench setups for the small signal and noise figure measurements from 10–20 GHz: (**a**) S-parameters test bench and (**b**) Noise figure test bench.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## Appendix A Derivation of SINR Coverage Probability  
The LOS coverage probability can be written as  
\[P_{c,LOS}(T) =\mathbb{P}\Bigg{[}\frac{|h_{L_{0}}|^{2}G_{0 BS}G_{0 UE}l^{(}t)}{ \sigma^{2}+I_{2}}>T\Bigg{]} \tag{11}\] \[=\mathbb{P}\Bigg{[}|h_{L_{0}}|^{2}>T\bigg{(}\frac{\sigma^{2}+I_{ 2}}{G_{0 BS}G_{0 UE}l(r)}\bigg{)}\Bigg{]}\] (12) \[\approx\mathbb{E}\Bigg{[}e^{\bigg{(}\frac{-T\bigg{(}\sigma^{2}+I_ {2}\bigg{)}}{G_{0 BS}G_{0 UE}l(r)}\bigg{)}}\Bigg{]}\] (13) \[\approx\mathbb{E}\Big{[}e^{\big{(}\mu^{\alpha 2}\big{)}}\Big{]} \mathbb{E}\Big{[}e^{\big{(}\mu_{L}I_{2}\big{)}}\Big{]}\] (14) \[\approx\mathbb{E}\Big{[}e^{\big{(}\mu_{L}\sigma^{2}\big{)}}\Big{]} \mathcal{L}_{I_{2}}(\mu_{L})\] (15) \[P_{c,LOS}(T) =\int\limits_{0}^{\infty}\mathcal{L}_{I_{2}}(\mu_{L})f_{R}(r)dr \tag{16}\]  
Let us denote \(\mu_{L}=\frac{T}{G_{0 BS}G_{0 UE}l^{\sigma^{2}-L}}\) in (14). \(\mathcal{L}_{I_{2}}(\mu_{L})\) in (15) is the LT of LOS interference components and by definition, \(\mathcal{L}_{I}(s)=\mathbb{E}\big{[}e^{-sI}\big{]}\). (16) represents the coverage probability conditioned on the PDF of the distance between typical UE and its nearest serving BS denoted by \(f_{R}(r)\) in (16) defined in (1), and assuming \(\sigma^{2}<<l_{Z}\).  
Similarly, the SINR in the NLOS link between BS and typical UE can be written as,  
\[\text{SINR}_{NLOS}=\frac{|h_{N_{0}}|^{2}G_{0 BS}G_{0 UE}l_{NLOS}(r)}{\sigma^{2} +\sum_{z\in<z}|h_{z}^{2}|G_{z}l(D_{z})} \tag{17}\]  
and the NLOS conditional coverage probability is given as  
\[P_{c,NLOS}(T) =\mathbb{E}\Big{[}e^{\big{(}\mu_{N}\sigma^{2}\big{)}}\Big{]} \mathcal{L}_{I_{2}}(\mu_{N}) \tag{18}\] \[P_{c,NLOS}(T) =\int\limits_{0}^{\infty}\mathcal{L}_{I_{2}}(\mu_{N})f_{R}(r)dr \tag{19}\]  
Here, \(\mu_{N}=\frac{T}{G_{0 BS}G_{0 UE}l^{\sigma^{2}}exp\left(-kr^{\sigma^{2}}\right)}\) and \(\sigma^{2}<<l_{Z}\).
# Low Temperature Combustion Modeling and Predictive Control of Marine Engines
###### Abstract  
The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NO\({}_{x}\)) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermokinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines.  
low temperature combustion; reactivity controlled compression ignition; RCCI; model predictive control; MPC; physics-based modeling; phenomenological modeling; marine engines +
Footnote †: journal: Applied
## 2 Theoretical and Methodological Considerations
### Defining Cognitive Units in the Translation Process  
To capture the way translators allocate cognitive resources, the concepts of translation unit (TUs) and attention units (AUs) have been proposed to quantify continuous cognitive processing over time. Originally defined in linguistics as the smallest meaningful segment in a translation [27; 28; 29; 30; 31; 32; 33; 34; 35; 36], the translation unit (TU) has evolved within cognitive translation studies to represent cognitive processing effort. Specifically, it denotes the focus of attention during translation [28], encapsulating the segment that is actively processed [12] and serving as a key unit of cognitive activity [29] (p. 953). This expanded definition integrates real-time keystroke and eye-tracking data, enabling a detailed analysis of cognitive resource allocation during translation [28].  
[PERSON] and [PERSON] [34] describe AUs as segments of text that attract a translator's attention at any given moment. Their research suggests that AU boundaries shift depending on cognitive demands and translator experience. [PERSON] [12] formalized these observations by identifying three AU types: ST (comprehension), TT (production), and parallel (combined). This study adopts [PERSON]'s AU framework to measure real-time cognitive effort across ST, TT, and parallel processing [12] (p. 116). These units provide a precise means of evaluating cognitive allocation patterns, enhancing models of translation as a cognitively regulated process. To achieve this, we categorize our data into four groups\(-\)Group A, Group B, Group C and Group D\(-\)based on the distinct macro- and micro-AU classifications of [PERSON] [12], as presented in Table 1.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**AUs Classification** & **Macro AUs** & **Micro AUs** & **Details** \\ \hline Group A & ST AU (Source Text Attention Unit) & ST Gaze & Corresponds to ST comprehension; includes eye gaze on ST and typing without gaze. \\ \hline Group B & TT AU (Target Text Attention Unit) & Gaze Off + Typing No Gaze + Typing TT Gaze + Typing TT Gaze & Corresponds to TT production; includes gaze on TT and typing activity. \\ \hline Group C & Parallel AU & ST Gaze + Typing processing & Reflects simultaneous ST and TT processing. \\ \hline Group D & No data & Gaze Off & Inactive moments, pauses, or unclassified activities. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Macro- and micro-AU classifications used in this study.
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
###### Abstract  
Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.  
channel estimation; orthogonal frequency division multiplexing (OFDM); statistical frame; confidence level; symbol error rate (SER) +
Footnote †: journal: Applied Sciences
# A Novel Generalized Group-Sparse Mixture Adaptive Filtering Algorithm
## 4 Results Analysis
### Performance Comparisons of Four GGS-MAF Algorithms  
From the Equation (15), it is clear to see that there are four forms of GGS-MAF algorithms. An experiment is created to discuss the performance of these GGS-MAF algorithms with colored noise (CN) input. One group system is used for the first 7000 iterations, and we use the two group system for the next 7000 iterations. The experiment is similar to those in [9; 14; 19]. The CN is realized by the WGN filtering via a first-order autoregressive process at a pole of 0.8. Herein, \(B=4\) and \(\beta=20\) are selected for all the GGS-MAF algorithms, and the simulation results are demonstrated in Figure 2. We can see from the results that the GGS-MAF-1 algorithm behaves the best since it only uses the \(l_{2,0}\)-norm constraints where the \(l_{0}\)-norm can well exploit the sparsity properties in the systems. Thus, we only choose GGS-MAF-1 to give a comparison with previous AF algorithms with different group sizes.
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 3 Case Study  
### Experimental Platform  
Experimental data was provided by the bearing data center of Western Reserve University [35]. The experiment platform was mainly composed of a motor, torque sensor, power meter and electronic control equipment, as shown in Figure 6. The type of rolling bearing was SKF6205. The single-point faults in the inner raceway, the outer raceway and ball were introduced to the test bearings using electro-discharge machining with fault diameters of 0.007 inches, 0.014 inches and 0.021 inches. Vibration data was collected by using accelerometers, which were installed at the 12 o'clock position at the fan end of the motor housing.  
According to the health state, different fault location and different fault degree, the data of 100 segments of length 2048 were randomly selected, generating 10 rolling bearing fault diagnosis sample data sets, as shown in Table 1. Each data set contained 30 training samples and 70 test samples. The sampling frequency was 12 kHz, the motor speed was 1797 rpm and the load was 0.  
Figure 6: Experimental platform of rolling bearing faults.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 5 Transport Management with SDN
### Traffic Priority  
Increasing heavy traffic needs to be organized using efficient traffic management. Some services, such as medical services, depend on traffic prioritization, which is the organization of the traffic according to priorities. Although many traffic management algorithms are available, the SDN-based MA and its functional requirements allow 5G users and service providers to improve traffic priorities.
###### Abstract  
Background: A transverse discrepancy refers to a dimensional difference in the transverse component of the upper and lower dental arches with functional and esthetic implications. The main purpose of this study is to compare the effectiveness of transverse expansion movement of dentoalveolar nature performed by two innovative orthodontic appliances: elastodontic devices and clear aligners. Specifically, it is intended to determine which of the two methods is more effective in terms of dentoalveolar expansion in a sample of children with mixed dentition. **Methods:** In total, 29 patients aged 6 to 13 years with mixed dentition were included in the present study and divided into two groups according to a case-control scheme. Of the subjects, 15 (8 females and 7 males) represented the test group and were treated with Eptamed elastodontic devices, while the control group, consisting of 14 patients (12 females and 2 males), received a Spark aligner. The efficacy of the devices was evaluated by comparing cross-sectional measurements of the arch scans at time T0 (before the start of treatment), T1 (6 months after the start of treatment) and T2 (one year after the start of treatment) by measurement in mm of the distance between the palatal cusps of the first upper premolar (if missing, of the corresponding deciduous molar). Statistical analysis was conducted using a nonparametric approach with the Wilcoxon signed-rank test. Statistical significance was set at \(p<0.05\). The data resulting from the measurements were expressed as the mean (standard deviation). **Results:** No statistically significant difference was observed between the two groups at either T0 (\(p=0.3\)), T1 (\(p=0.78\)), or T2 (\(p=0.66\)), thus allowing the conclusion that both treatment modalities are comparable. **Conclusions:** Both elastodontics and clear aligners proved to be effective devices in the treatment of transverse discrepancies of a dentoalveolar nature. Elastodontics also plays a crucial role in the rebalancing of stomatognathic and extrastomatognathic functions.  
aligners; elastodontic devices; dentoalveolar transverse discrepancies; growing patients 2025155263
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 6 Conclusions  
In this paper, we delve into the application of deep learning in the field of environmental identification for intelligent vehicles, especially under the unpredictable conditions of rainy weather. Our work not only demonstrates the potential of Channel State Information (CSI) in wireless communication but also proves the practicality of an efficient Convolutional Neural Network (CNN) model in environmental perception. This model, utilizing CSI values extracted from vehicle-to-vehicle (V2V) communication, successfully identified various rainy environments, providing precise parameter adjustment references for autonomous driving systems.  
Furthermore, our research findings are significant for the autonomous behavior adjustment of intelligent vehicles under adverse weather conditions. By analyzing CSI data in real-time, intelligent vehicles can automatically adjust driving strategies, such as speed, significantly enhancing driving safety and reliability. This breakthrough not only optimizes the response mechanisms of autonomous driving systems but also provides a solid technical foundation for the future deployment of intelligent vehicles under a broader range of climatic conditions.  
In summary, the contribution of this paper lies in proposing an innovative framework for rainy environment identification, combining wireless channel characteristics with advanced deep learning technologies. Our CNN model, by accurately capturing the subtle changes in wireless channels, provides reliable navigational support for intelligent vehicles under the most challenging weather conditions. This achievement not only enhances the adaptive capabilities of intelligent vehicles but also offers new perspectives and possibilities for the future direction of autonomous driving technology.  
**Author Contributions:** Conceptualization, J.F. and X.L.; Methodology, X.L.; Formal analysis, H.F.; Investigation, H.F.; Data curation, X.L.; Writing--original draft, J.F. and X.L.; Writing--review & editing, J.F.; Supervision, J.F.; Project administration, J.F. and H.F.; Funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The processed data required to reproduce these findings cannot be shared as the data also form part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 MAC Layer in WMSN
### Error Control  
Due to the unreliability of the wireless medium in WMSNs, the transmitted data such as multimedia content is exposed to losses or errors mostly caused by multi-path fading, co-channel interference, jamming... _etc._ Therefore, in order to improve the perceptual quality of the received multimediacontent, techniques for error correction and loss recovery should be employed in WMSNs to combat the unreliability of the wireless channel at the physical and MAC layer. Forward Error Correction (FEC) and Automatic Repeat Request (ARQ) are examples of these techniques. The usefulness of ARQ in sensor network applications is limited by the additional re-transmission cost and overhead. On the other hand, decoding complexity is greater in FEC, as error correction capabilities need to be built-in. Considering this, simple error control codes with low-complexity encoding and decoding might present the best solution for sensor networks. In the design of such a scheme, it is important to have good knowledge of the channel characteristics and implementation techniques.  
A comparison between two techniques of error compensation of transmission distortion in multipath WMSN is conducted in [31]. First technique is the Error Concealment (EC) algorithm that reconstructs the distorted multimedia data as closely as the original one by utilizing the use of modified discrete wavelet transform for embedding downsized replicas of original image into itself. EC does not need increasing in bandwidth demand as well as retransmissions and consequent delay, however EC algorithm needs more processing power at the source and sink nodes comparing to the other technique. The second technique is the Forward Error Correction (FEC) based on Reed-Solomon coding that compensates and corrects the wireless link errors by utilizing the use of redundant data. FEC technique can mitigate the wireless link impairments but it cannot solve the errors from instant node failure problem and require considerable increase in the transmission bandwidth. It is shown in [31] that EC technique with multipath routing is more promising than FEC based RS coding to compensate for error and losses in WMSN.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 1 Introduction
### State of the Art
The interest in medical imaging is the transition from macroscopic to microscopic analysis by using image sub-pixelation algorithms so that the exact tumor contours in medical images can be determined. The algorithm proposed by us achieves a good ratio between noise reduction and conservation of contours in medical images, taking into account the conservation of Hounsfield coefficients (radiodensity) used in medical imaging. The results obtained with this algorithm will be used in the future to develop a method of sub-pixelation of tumor regions to develop a new algorithm called re-segmentation using neural networks. Applying the described algorithm on some medical images obtained by CT and magnetic resonance, it is found that the noise was reduced by up to 80%, and the maintenance of the details in the images was at least 80%. The proposed filter is superior to the Wiener filter proposed in [53] and [54], due to the sensitivity to contours and other important features in the images, retaining some high frequency data in the image. The direct spatial correlation of the undecided wavelet transform on several adjacent scales obviously improves the important contours in the image, while the noise and non-essential (small) features in the image are suppressed. The compromise that this filter makes is between suppressing noise and retaining smaller details in the image. Those characteristics that induce coefficients higher than the noise are easily retained, as they are not degraded. Characteristics that induce coefficients of the same magnitude as noise are suppressed because they are not distinct from noise.
## 3 Application
### Mixing  
As an important unit operation to ensure the quality of mixed materials, mixing uses mechanical or hydrodynamic methods to disperse two or more materials to a certain uniform state. Mixing equipment includes various forms of mixers, stirred tanks and reactors. The most commonly used method in the mixing process is stirring; that is, the liquid, gas or solid powder particles are evenly dispersed in the liquid. The simulation of the mixing process can not only obtain the multiphase movement and distribution in the container but also have positive significance for the subsequent biochemical behavior.  
Due to the complexity of multiphase hybrid simulation, the conventional CFD-DPM may not be realized. [PERSON] et al. combined the population balance equation (PBE) with the CFD model to evaluate the number density function of droplets in the static mixer by solving the PBE and further calculate the droplet breakage and coalescence rate [80] (Figure 6). [PERSON] et al. presented an Euler-Euler-Lagrange mixture model, which combines Lagrange DPM and Euler-Euler TFM to simulate liquid-liquid emulsion mixing behavior in a stirred tank [81]. [PERSON] et al. used the lattice Boltzmann method (LBM) to model the three-dimensional computational fluid dynamics in a continuous stirred tank reactor (CSTR). Through the image analysis technology, the solid-liquid mixing efficiency was calculated from the numerical results of the DPM. It was found that the fluid velocity and fluid motion direction played a major role in the mixing characteristics [82]. VOF is also applied to simulate the liquid surface behavior in two or more immiscible fluids [83; 84].  
Figure 6: Benzene droplet trajectories in Kenics static mixer [80].  
In addition, in the study of multiphase flow in the mixing process, CFD-DPM pays more attention to the movement of the dispersed phase, such as the behavior of bubbles, particles and droplets in the container. [PERSON] et al. obtained the local gas holdup and gas residence time distribution in the stirred tank by CFD-DPM and the tracing bubble method, and they explored the dispersion and back-mixing effect of bubbles [85; 86]. [PERSON] et al. used the shear stress transfer (SST) model to simulate turbulent motion and mainly focused on the droplet behavior of immiscible liquid in a static mixer, mainly including droplet size distribution (DSD) and residence time distribution (RTD) [80]. [PERSON] et al. described the size distribution of light particles in a stirred tank through the mixing characteristic curve, including fluid characteristics, impeller geometric parameters and particle dispersion characteristics, and the accuracy was verified [83].  
For different production processes, the mixing scale has various requirements. For example, the rapid reaction between two fluids requires not only macroscopic uniformity but also microscopic rapid mixing. CFD-DPM and its related methods can be effectively applied to the mixing process and track the distribution of discrete phases, but this is only for the macro scale.
## 5 Evaluation and Results
### Access Outcome Analysis and Performance Metrics
#### 5.2.3 Evaluation Accuracy  
Manual cross-checks revealed zero mismatches in access decisions, confirming full decision consistency as verified during earlier validation.