esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature Selection from Lyme Disease Patient Survey Using Machine Learning
## 3 Results
### Comparison to Supervised Feature Extraction  
In the previous sections, we have performed supervised feature _selection_ approaches to identify 30 key features for predicting GROC response labels. A related, but distinct, set of approaches are supervised feature _extraction_ techniques, which seek to identify salient collections of features (topics) which are correlated and contribute significantly to predicting response variables in a predictive model.  
\begin{table}
\begin{tabular}{l c|c c} \hline
**Feature** & **Rank** & **Feature** & **Rank** \\ \hline Abx\_Eff & 1.17 & Abx\_Not\_3 & 24.83 \\ Sx\_Sev\_1 & 1.83 & Abx\_Sfx & 25.00 \\ Sx\_Sev\_4 & 3.17 & Abx\_Not\_6 & 27.00 \\ Sx\_Sev\_10 & 4.17 & Abx\_27 & 27.67 \\ Abx\_Dur & 5.67 & Abx\_20 & 27.83 \\ Sx\_Sev\_5 & 9.00 & Abx\_10 & 28.00 \\ Sx\_Sev\_3 & 10.50 & Abx\_IV\_11 & 29.17 \\ Provider\_5 & 13.00 & Sx\_Sev\_8 & 29.83 \\ Abx\_Not\_9 & 15.83 & Abx\_21 & 30.50 \\ Sx\_Sev\_12 & 16.17 & Abx\_Oral & 30.67 \\ Abx\_Not\_4 & 17.17 & Abx\_23 & 31.17 \\ Abx\_Not\_1 & 18.50 & Abx\_5 & 31.67 \\ Abx\_Not\_5 & 18.50 & Abx\_15 & 32.17 \\ Abx\_IM\_6 & 19.50 & Abx\_13 & 32.50 \\ Sx\_Sev\_9 & 21.00 & Abx\_4 & 32.83 \\ \hline \end{tabular}
\end{table}
Table 2: Top feature ranks.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Model** & **TMLD** & **BMLD** & **MLD** \\ \hline SVM & 0.739 & 0.662 & 0.747 \\ Neural Net & 0.727 & 0.641 & 0.697 \\ Decision Tree & 0.731 & 0.614 & 0.732 \\ KNN & 0.712 & 0.598 & 0.663 \\ \hline Linear Regression & & & \\ \(R^{2}\) & 0.344 & 0.306 & 0.440 \\ \hline \end{tabular}
\end{table}
Table 3: Results for running the classification models and linear regression on the MLD, TMLD, and BLMD datasets. Details for the training and testing procedure are provided in Section 2.5.  
In particular, we present the results of semi-supervised nonnegative matrix factorization (SSNMF) [21] and supervised principal component analysis (SPCA) [14] on our data set of 215 features. In these experiments, we apply these models with three topics to our data and present below the ten features which are most significantly represented in each topic.  
In Tables 4 and 5, we display the results of running SSNMF with three topics and SPCA with three topics, respectively, on MLD. For each topic formed by one of the models, we display the features with the highest presence in that topic. For SPCA, a feature can contribute either positively or negatively to a topic, so we order features based on the magnitude of presence and mark with [+] or [-] whether this feature contributes positively or negatively to the topic. We note that the identified topics (collections of features) bear significant overlap with our identified set of 30 key features. This is unsurprising as both SSNMF and SPCA seek to extract features that not only span the data, but which provide data representations that can be classified into GROC response classes accurately. In addition to this overlap, we also see clear grouping amongst features, with separate grouping of features relating to symptoms and features relating to antibiotics.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 3 TPC-CQF Improved from CQF  
In this section, we analyze the limitations of the CQF mechanism in existing time-sensitive networks and propose a dynamic priority queue scheduling mechanism aimed at providing differentiated forwarding for time-sensitive flows and leveraging delay-redundant time-sensitive flows to enhance overall scheduling capability.
# Remote Sensing Multimodal Image Matching Based on Structure Feature and Learnable Matching Network
## 4 Experiment
### Dataset and Training  
To test the proposed algorithms, 200 remotely sensed multimodal image pairs were used, which were taken from areas such as urban airports, plantations, harbors, and hilly terrain. Some examples of multimodal image pairs are shown in Figure 8. Significant radiometric differences among these remotely sensed multimodal images are observed. In addition, the SAR image contains severe noise distortions and lacks details.  
Figure 7: Transformation parameters estimation based on regression network.  
For each remotely sensed image, 100 templates of different sizes were randomly selected and matched to the base image. Since the dataset was small, 60% of the samples were employed for training and 20% of the samples were employed for validation and 20% were employed for testing. The final result was generated with the testing set. Data augmentation techniques such as grayscale variation, noise injection, and random erasing were adapted to the training set. Since our dataset was small, DHE used the pre-trained model provided by [25] and fine-tuned it with our dataset. The regression network of the LMN was totally trained with our dataset.  
To evaluate the algorithms, 16 tests were performed. Table 1 summarizes the test information. Before the testing, the sensed image was manually corrected to the same coordinates as the base image. After the correction, the true position of the template in the based image and the position of the template selected from the sensed image were the  
Figure 8: Examples of multimodal image pairs.  
same. In addition, Gaussian noise with different variances was added to test the noise adaptiveness of the algorithms.
## 8 Discussion  
This study evaluated the comparative efficacy and safety of the More Young and 7D HIFU systems, offering insight into their clinical performance across multiple skin parameters. The findings demonstrate that the More Young system produced superior results in key areas such as wrinkle reduction, skin tightening, and texture enhancement. These improvements were not only statistically significant but also supported by high patient satisfaction ratings and objective measurements via VISIA analysis.  
The superior performance of the More Young device may be attributed to its innovative engineering features, such as dynamic Treatment Control Points (TCPs), high-resolution transducers, and advanced cooling and safety systems. These features likely contributed to the more uniform energy delivery and deeper penetration capabilities compared to the 7D system. Previous studies have similarly highlighted the role of precise focal energy delivery in optimizing collagen remodeling and patient outcomes.  
When compared with prior research on conventional and 7D HIFU devices, our findings are consistent in terms of efficacy for wrinkle reduction and lifting. However, the degree of improvement observed with the More Young device appears more pronounced,  
\begin{table}
\begin{tabular}{c c c} \hline
**Satisfaction Criteria** & **More Young (Mean \(\pm\) SD)** & **7D (Mean \(\pm\) SD)** \\ \hline Overall Satisfaction & 4.3 \(\pm\) 0.5 & 3.8 \(\pm\) 0.6 \\ \hline Wrinkle Reduction & 4.4 \(\pm\) 0.4 & 3.9 \(\pm\) 0.5 \\ \hline Skin Tightening & 4.5 \(\pm\) 0.4 & 3.7 \(\pm\) 0.5 \\ \hline Texture Improvement & 4.2 \(\pm\) 0.5 & 3.6 \(\pm\) 0.6 \\ \hline Even Skin Tone & 4.3 \(\pm\) 0.4 & 3.7 \(\pm\) 0.5 \\ \hline Pore Size Reduction & 4.1 \(\pm\) 0.5 & 3.5 \(\pm\) 0.6 \\ \hline \end{tabular}
\end{table}
Table 5: Patient satisfaction survey results at six months post-treatment for More Young and 7D devices.  
especially in areas involving pore size and elasticity, indicating a technological advancement in HIFU design and delivery.  
Despite these promising results, the study does have several limitations. First, while the sample size of 100 participants provided sufficient power for primary outcomes, subgroup analyses based on skin type, age, or lifestyle were not performed. These factors can significantly influence treatment response. Second, the follow-up period of six months, although adequate to assess early efficacy and safety, may not capture long-term maintenance of results. Notably, the absence of a control or placebo group restricts the ability to account for nonspecific effects or natural improvement over time. Including a sham group in future studies could help isolate true device-related outcomes.  
A longer follow-up period is needed to understand the durability of the treatment effects. Lastly, the trial was conducted in a single center, which may limit generalizability to broader population.
###### Abstract  
This article presents a wideband reconfigurable integrated low-pass filter (LPF) for 5G NR compatible software-defined radio (SDR) solutions. The filter uses Active-RC topology to achieve high linearity performance. Its bandwidth can be tuned from 2.5 MHz to 200 MHz, which corresponds to a tuning ratio of 92.8. The order of the filter can be changed between the 2 nd, 4 th, or 6 th order; it has built-in process, voltage, and temperature (PVT) compensation with a tuning range of \(\pm\)42%; and power management features for optimization of the filter performance across its entire range of bandwidth tuning. Across its entire order, bandwidth, and power configuration range, the filter achieves in-band input-referred third-order intercept point (IIIP3) between 32.7 dBm and 45.8 dBm, spurious free dynamic range (SFDR) between 63.6 dB and 79.5 dB, 1 dB compression point (P1 dB) between 9.9 dBm and 14.1 dBm, total harmonic distortion (THD) between \(-\)85.6 dB and \(-\)64.5 dB, noise figure (NF) between 25.9 dB and 31.8 dB and power dissipation between 1.19 mW and 73.4 mW. The LPF was designed and verified using 65 nm CMOS process; it occupies a 0.429 mm\({}^{2}\) area of silicon and uses a 1.2 V supply.  
5G; active-RC; reconfigurable; software defined; low-pass; wideband Article
## 3 Simulation Setup  
To investigate the performance of the proposed PDM-DSB FASCD and the improved algorithm ISEA, we use a 28 GBaud PDM-OFDM 16-QAM simulation system with VPI 8.7 and MATLAB 2023b. The system structure is shown in Figure 1, and the specific DSP flow and spectral diagram are shown in Figure 3. The transmitter- and receiver-side DSP are performed in MATLAB 2023b, and the fiber, optical, and electrical devices are modelled in VPI. At the transmitter, the generated pseudo-random bit sequence is mapped to 16-QAM symbols, followed by a serial-to-parallel conversion. The resulting parallel symbols are then transformed into the time domain via an IFFT. Here, the IFFT length is set to 128, with 112 subcarriers used to carry data. The remaining subcarriers are set to zero, serving as a guard band to mitigate inter-symbol interference and improve signal robustness. The signal-to-carrier spacing is 2 GHz. Afterward, eight cyclic prefix samples are added to mitigate inter-symbol interference caused by channel impairments. The signal is then upsampled and shaped using a root raised cosine (RRC) filter with a roll-off factor of 0.01. We use an AWG and a linear EA to drive the DP-IQ modulator, which is biased at the null point to modulate the light from the laser. A reference carrier is added to align its polarization state with the x-polarization signal. This combined signal is then injected into a 320 km SMF. The parameters of the SMF are loss of 0.2 dB/km, chromatic dispersion coefficient of 17 ps/nm/km, and PMD coefficient of 0.1 ps/km\({}^{1/2}\).  
At the receiver, the optical carrier is extracted using an OBPF, and its power is amplified by an EDFA to enhance the signal detection performance. The carrier is then evenly split into two parts: one part is used to receive the x-polarization signal, while the other part is passed through an FRM to construct its orthogonal polarization state, which serves as the carrier for detecting the y-polarization signal. The optical signal is converted into electrical signals using the receiver.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 5 Simulation
### Data Set Map Experiment
From the dataset experiments, it can be seen that the total path turning angle of the AP-JPS algorithm is much smaller than that of the A* algorithm and JPS algorithm, and the total path turning angle of the AP-JPS algorithm is reduced by 43.90% to 89.51% compared with JPS. The path length of the AP-JPS algorithm is 0.57% to 5.36% shorter than the JPS algorithm and 0.51% to 5.36% shorter than the A* algorithm. The AP-JPS algorithm has better path smoothing and better paths compared to the path smoothing. The shortest path expressiveness in an approximate real environment is closer to the actual situation.
## 3 Results
### Coatings' Characterization
#### 3.1.3 Scanning Electron Microscopy  
In order to further investigate the morphology at the micro- and nano-scale, SEM images in the top view were acquired at different magnifications (see Figure 5).  
Figure 4: Optical microscopy images acquired at 140\(\times\) of CR (**a**), C1 (**b**), C2 (**c**), SR (**g**), S1 (**h**), and S2 (**i**) samples’ surfaces, and the corresponding roughness profiles for CR (**d**), C1 (**e**), C2 (**f**), SR (**j**), S1 (**k**), and S2 (**l**).  
By comparing the SEM images of C1 and C2 and S1 and S2 samples with CR and SR, respectively, it highlighted that in both cases, the coatings were able to completely cover the substrate morphology, since neither the sponge-like morphology of the commercial paint, characterized by the presence of micropores, nor the scratches of the SR surface were visible once the coatings were applied. Generally speaking, it seemed that the same coating, applied on different substrates generated the same morphology, with the presence of micropores. The same morphology was observed in the same manner as the coating.  
Figure 5: Top View of SEM images at 250\(\times\) (**a**), 750\(\times\) (**b**), 2500\(\times\) (**c**), and 7500\(\times\) (**d**) of CR, SR, C1, S1, C2, and S2 samples.  
of micro-sized aggregates which emerged from the matrix. However, some little differences could be detected. In fact, by comparing C1 and S1, and C2 and S2 samples, it seemed that for both coatings, the aggregation of nanoparticles emerged in the C samples more than it occurred for the S samples. This result could be ascribed to the different surface nature and/or morphology of the substrates. The same results were observed by [PERSON] et al. [33] by applying the same layers number of the same coating to different polymeric substrates. In fact, they found different nanoparticles aggregation at the surface depending on the substrate nature (i.e., TPSU, PMMA, PC, and TPU), which could probably be responsible for the slight differences in CA. In Figure 5, it should also be noticed that at the highest magnification, small microcracks and fractures appear, most of all on C1 and C2 surfaces. As reported in [19], during the IWT tests, these fractures on the samples' surfaces could become ice nucleation sites.
## 4 Results and Discussion
### Ablation Study
#### 4.3.2 DSConv Analysis  
Table 4 shows the results of the analysis of the DSConv used in the EAR-Net in terms of the number of parameters and MIoU. It can be seen that DSConv makes a significant contribution to reducing the number of parameters. We prove that DSConv can reduce the number of parameters by about \(\frac{2}{3}\). Furthermore, when the DSConv is used, MIoU is improved by about 1.7%. In general, using DSConv results in the same or slightly lower accuracy than when using traditional convolution. However, the EAR-Net shows higher accuracy. It is assumed that the reason is that the hyperparameters that have a large influence on the learning are not suitable for the model that uses traditional convolution, so the learning is not performed smoothly.
###### Abstract  
The problem of ground target detection with passive radars is considered. The design of an antenna array based on commercial elements is presented, based on a non-uniform linear array optimized according to sidelobe level requirements. Array processing techniques are applied in the cross-ambiguity function domain to exploit integration gain, system resolution and the sparsity of targets in this domain. A modified two-stage detection scheme is described, which is based on a previously-published one by other authors. All of these contributions are validated in a real semiurban scenario, proving the capabilities of detection, the direction of arrival estimation and the tracking of ground targets in the presence of big buildings that generate strong clutter returns. Detection performance is validated through the probability of false alarm and the probability of detection estimation with specified estimation errors.  
passive radar; array; beamforming; non-ULA; DOA; detection 1
## Materials and Methods
### Data Processing  
A series of preprocessing steps including radiometric calibration, polarimetric matrix generation, speckle filtering and geocoding, were conducted for each RADARSAT-2 image used. A boxcar filter with a 9 by 9 window was employed to suppress the speckle noise. A 30 m digital elevation model product (PDEM) of Ontario, Canada, was applied to geocode the polarimetric matrix in the Universal Transverse Mercator (UTM) geographic reference with an output cell resolution of 10 m by 10 m. Afterwards, for each acquisition date, 27 feature images of polarimetric parameters, listed in Table 3, were extracted. These images had the same geometry and pixel spacing of 10 m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Polarimetric Observable** & **Description** & **Abbreviation** \\ \hline
1 & HH (C11), HV (C22), VV (C33) &
\begin{tabular}{c} Backscattering coefficients in the linear \\ polarization channels \\ \end{tabular} & LP \\
2 & HH + VV (T11), HH-VV (T22) & Backscattering coefficients in the Pauli polarization & Pauli \\
3 & Span & Total backscattering power & Span \\
4 & HH/VV, HV/HH, HV/VV & Backscattering ratios & Ra \\
5 & \(\rho_{HHVV},\rho_{HVV},\rho_{HHHV},\rho_{HH+VV,HH-VV}\) & Correlation between polarimetric channels & Ro \\
6 & \(\phi_{HHVV},\phi_{HHVV},\phi_{HHHV},\phi_{HH+VV,HH-VV}\) & Phase difference between polarimetric channels & Pha \\ \hline \hline \end{tabular}
\end{table}
Table 3: The list of 27 polarimetric observables in 10 groups selected for temporal evolutions and classification in this study.
# Riboregulation in Nitrogen-Fixing Endosymbiotic Bacteria
## 4 Assigning Functions to Rhizbial _Trans_-sRNAs
### Cell Cycle
The two EcpR1 confirmed mRNA targets (_dnaA_ and _gcrA_) base pair to the same GC-rich conserved SL in the sRNA, whereas GspR represses expression of _ctrA_ and _cspA5_ using different domains. Furthermore, considering the targeted mRNA region, _dnaA_ and _cspA5_ are canonically regulated by sRNA binding to the vicinity of the RBS and the start codon. However, _gcrA_ and _ctrA_ are silenced by a still unknown mechanism that involves base pairing to nucleotides located far upstream of the translation initiation region within the long 5'-UTR of the mRNAs. CspA5 homologs usually act as RNA chaperones facilitating translation, which is probably the observed effect of CspA5 on the long 5'-UTR of the _ctrA_ mRNA. Therefore, GspR and CspA5 are arranged in a coherent feed-forward loop that adds a novel post-transcriptional layer to the intricate CtrA regulation [89]. These results anticipate that RNA-mediated post-transcriptional control of cell cycle genes may be a universal mechanism linking cell growth and stress in bacteria. From a biotechnological perspective, these cell-cycle related sRNAs might be exploited as drug targets in antibacterial therapies.
###### Abstract  
Smartphones with the Android operating system can acquire Global Navigation Satellite System (GNSS) raw pseudorange and carrier phase observations, which can provide a new way for the general public to obtain precise position information. However, only postprocessing precise orbit and clock offset products in some older smart devices are applied in current studies. The performances of precise point positioning (PPP) with the smartphone using real-time products and newly smartphones are still unrevealed, which is more valuable for real-time applications. This study investigates the observation data quality and multi-GNSS real-time PPP performance using recent smartphones. Firstly, the observed carrier-to-noise density ratio (C/N0), number of satellites and position dilution of precision (PDOP) of GNSS observations are evaluated. The results demonstrate that the C/N0 received by Huawei Mate0 is better than that of the Huawei P40 for GPS, BDS, QZSS and Galileo systems, while the GLDNASS is poorer, and the PDOP of the Huawei P40 is slightly better than that of Mate04. Additionally, a comprehensive analysis of real-time precise orbit and clock offset products performance is conducted. The experiment result expresses that the orbit and clock offset performance of GPS and Galileo is better than that of BDS-3 and GLDNASS, and BDS-2 is the worst. Finally, single- and dual-frequency multi-GNSS combined PPP experiments using observations received from smartphones and real-time products are conducted; the results indicate that the real-time static PPP using a smartphone can achieve decimeter-level positioning accuracy, and kinematic PPP can achieve meter-level positioning accuracy after convergence.  
real-time streaming; smartphone; PPP; single- and dual-frequency; BDS/GPS/GALILEO [PERSON]  
15 May 2023  
15 June 2023  
1
###### Abstract
Previous studies have shown that adding a single type of fiber is insufficient to improve the performance of concrete, but adding two types of fiber with different characteristics can significantly improve mechanical properties [18; 19; 20; 21]. [PERSON] et al. showed that compared to single fiber use, hybrid fiber use can significantly improve the mechanical properties and reduce the free shrinkage value of lightweight concrete [22]. Soon, they studied the strengthening effects of different mixed forms of steel and polypropylene fibers on the properties of oil palm shell concrete, and found that the single-fiber mixing of steel could significantly increase the bending deflection of the specimens and that the mixing of steel and polypropylene fibers could improve the mechanical properties of the same [23]. [PERSON] compared the mechanical strength and flexural properties of three composite concrete materials--steel polypropylene, polypropylene carbon, and carbon steel fiber--and showed that the mechanical properties of the three hybrid fibers together were improved in comparison with those of a single fiber [24]. In recent years, in order to characterize the distribution characteristics and evolution laws of concrete cracks, domestic and foreign authors have conducted a series of studies with high-speed photography and digital image correlation methods [25; 26; 27; 28; 29]. [PERSON] et al. [30] conducted impact splitting tests on concrete test blocks with a SHPB rod device, scanned and recorded the cracking situation of test blocks during the impact process with an ultra-high speed digital camera, and drew conclusions regarding the relationship between the cracking of test blocks and time. [PERSON] et al. [31; 32] used high-speed photography to reproduce two moments in the specimen deformation process when studying the dynamic compression properties of SFRC. [PERSON] et al. [33] used three-dimensional digital image correlation (DIC) technology to obtain and process images of the surface changes of samples under different loads. The test results showed that DIC can be used to effectively study the real-time location of concrete microscopic damage. [PERSON] et al. [34] used DIC technology and the Mazars damage model to study the average strain variation of concrete, and they obtained the distribution characteristics and evolution law of the plastic shrinkage microcracks based on the research results. However, there has still been insufficient research on the dynamic mechanical properties of high performance mixed-fiber-reinforced concrete used as shaft lining. In this paper, hybrid fiber concrete was prepared by adding steel and polypropylene fibers to the C60 concrete to improve its dynamic bearing capacity. Our research results can provide reference for the selection of shaft lining materials.