esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 LDPC Decoder Architecture Design
Thus, the proposed architecture, inspired by the data-path of the solution proposed in [32], is made of four blocks as shown in the bottom part of Figure 5(a): a processing element (PE) devoted to implement the computation described in Equations (1-6) with the normalized-min-sum approximation; \(S\) and \(R\) memories, where \(S_{j}\) and \(R_{mj}\) metrics are stored; and an address generator. As depicted in the upper part of Figure 5(a) the PE contains: (i) a subtractor to compute \(Q_{mj}\) Equation (1), (ii) a Minimum-Extractor-Unit (MEU), a compare block (CMP) and a multiplication by \(\pm\sigma\) required to compute \(s_{mj}\) Equation (3) and \(R_{mj}^{\rm(new)}\) with the normalized-min-sum approximation Equation (6), (iii) a synchronization FIFO with \(d_{c}^{\max}\) locations, (iv) an adder to compute \(S_{j}^{\rm(new)}\) Equation (5). Figure 4: BER performance of the (\(N=204\), \(R=0.5\)), (\(N=273\), \(R=0.7\)) and (\(N=495\), \(R=0.87\)) LDPC codes. The MEU, detailed in the upper part of Figure 5(b) is made of two parts. The first one computes \(-s_{mj}\) scoring the sign of \(Q_{mn}\) values, _i.e._, the most significant bit (MSB) of \(Q_{mn}\), and saving the result in a D-Flip-Flop (D-FF). The second part computes the absolute value of \(Q_{ty}\). Then, since the \(\min\) function in Equation (6) is on \(\mathcal{N}_{m}\setminus j\), the MEU finds the first two minimum values among the possible \(\mathcal{N}_{m}\) leaving to the CMP block to exclude the \(j\)-th one. The first two minimum values (\(M_{1}\) and \(M_{2}\)) are obtained by the means of two subtractors, three multiplexer and two registers that implement Algorithm 1, where MPV is the Maximum Positive Value. ```
0:\(M_{1}\leftarrow\mathrm{MPV}\) and \(M_{2}\leftarrow\mathrm{MPV}\)
1:for\(t\in\mathcal{N}_{m}\)do
2:if\(|Q_{ty}|<M_{1}\)then
3:\(M_{2}\gets M_{1}\)
4:\(M_{1}\leftarrow|Q_{ty}|\)
5:elseif\(|Q_{ty}|<M_{2}\)then
6:\(M_{2}\leftarrow|Q_{ty}|\)
7:endif
8:endfor
```  
**Algorithm 1** Algorithm to find the first two minimum values  
The CMP block and the multiplication unit are shown in the bottom part of Figure 5(b). The CMP block compares \(|Q_{mj}|\) with \(M_{1}\). If they are equal, \(M_{2}\) is passed to the multiplication unit. The multiplication unit does not contain a real multiplier as \(\sigma=0.875=1-1/8\) requires only a subtractor and a hard-wired three-bit right shift (\(>>3\)). In order to take into account the \(-s_{mj}\) term, two multiplexers, driven by \(-s_{mj}\) are added to obtain \(R_{mj}^{(\mathrm{new})}\) as in Equation (6). Figure 5: Proposed decoder architecture: (**a**) general structure and PE detail; (**b**) MEU, CMP block and multiplication unit block schemes.
## 4 Simulations and Results
### Impact of Periodicity of Packet Generation  
In this section, we also investigate the performance of mitigation methods with non-periodic packet generation. In particular, the packet generation is assumed to follow the CAM generation rules detailed in [35], which is related in our case to the vehicle speed. Since vehicles are moving at different speeds, the generation frequency is different from vehicle to vehicle, and always lower than the periodicity of resource allocation in NR-V2X (which is 10 Hz).  
In the case of IEEE 802.11p and LTE-V2X, which are both shown in [5; 12], a periodic generation with a frequency equal to the resource allocation periodicity of LTE-V2X allows for improving the performance of the No-method test and some of the mitigation methods. This effect is a consequence of the LTE-V2X sensing procedure, which looks at the power measured in the last 1 s to estimate how the resources will be used in the future.  
Different from LTE-V2X, where the estimation of the resources that should not be used is mainly based on the average sensed power, in NR-V2X it is mainly based on the control information associated with the decoded messages (see, e.g., [15; 26] for details). This implies that the periodical transmissions in IEEE 802.11p are ignored by NR-V2X and therefore do not help to reduce the performance worsening due to co-channel coexistence.  
Figure 14: Average data age vs. density within 500 m applying different mitigation methods with the unbalanced distribution: (**a**) DA of NR-V2X with More-V\({}_{\rm 11p}\), (**b**) DA of IEEE 802.11p with More-V\({}_{\rm 11p}\), (**c**) DA of NR-V2X with More-V\({}_{\rm NR}\), (**d**) DA of IEEE 802.11p with More-V\({}_{\rm NR}\)These considerations are confirmed by Figure 15, where the TR of both technologies with non-periodic packet generation and balanced technology distribution is shown. Comparing the results in Figure 15 with those of Figure 10, it can be noted that the packet generation pattern has little impact on all mitigation methods. This conclusion is also valid for the other metrics and the unbalanced technology distribution, although results are not shown for the sake of conciseness.
## 3 Proposed Methodology
### DFT-LS-Wiener Channel Estimation  
At the pilot frequencies, channel estimation is performed using the Least Squares (LS,) approach. In this case, DFT-based channel estimation is used. As a result, the channel answers are shortened to the number of channel taps before being approximated using the least square method.  
1. After completing the DFT-based LS estimation, the estimates are fed to the Wiener filter model.
2. The goal of applying the Wiener filter model in channel estimation is to decrease the quantity of noise in a noise-affected signal and to decrease the mean square error between the actual broadcast signal and the received signal.
3. Wiener coefficients are produced based on the order of the Wiener filter; for example, if the order is six, then the Wiener filter model produces six Wiener coefficients.
4. DFT-LS estimates are filtered using these Wiener coefficients.
5. The desired output is a noiseless channel estimate.  
By applying linear time-invariant (L.T.L) approaches to filter a noisy experimental process, a Wiener filter is a signal processing filter that generates an estimate of a desired or target stochastic process. The Wiener filter decreases the mean squared error between the estimated and predicted random processes. By using the pertinent signal as input and filtering the known signal to obtain an estimate as output, a Wiener filter aims to compute a statistical estimate of an unknown signal. For instance, an interesting unknown signal tampered with by additive noise can be present alongside a known signal. It is possible to estimate potentially important signals using Wiener filters to remove noise from distorted signals. A typical deterministic filter's frequency response is designed for the selected carrier frequency. The Wiener filter, however, is created differently. To develop a linear time-invariant filter whose output is as close as possible to the original signal, one must be aware of the spectral properties of both the original signal and noise.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 2 Related Works
### Cost Volume Aggregation Optimization  
The cost volume regularization using 3D convolution requires too much memory, making it impractical to employ MVSNet in high-resolution scenes. Consequently, numerous researchers have proposed various solutions. [PERSON] et al. [21] used the gate recurrent unit (GRU) in a recurrent neural network to iteratively regularize the 2D cost volume along the depth sampling direction. [PERSON] et al. [22] built the RED-Net with a recurrent encoder-decoder structure, which reduces the memory consumption and computational cost. [PERSON] et al. [23] introduced long short-term memory to build the AA-RMVSNet, which includes a view-wise aggregation module that significantly enhances the algorithm's performance on thin and low-texture surfaces. By using edge convolution, [PERSON] et al. [24] proposed the Point-MVSNet algorithm with a coarse-to-fine optimization strategy and the PointFlow module to refine the depth residual map on the rough point cloud model in three-dimensional space, maximizing the target depth map. CVP-MVSNet [14] proposed a cost volume pyramid built via a coarse-to-fine approach to deliver a lighter network. The Cascade-MVSNet, designed by [PERSON] et al. [15], demonstrated that building a feature pyramid optimizes the cost volume aggregation.
# No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
## 1 Introduction
### Contributions  
The main contributions of this study are as follows: (i) decision fusion of deep convolutional architectures for NR-IQA. (ii) The usage of a diverse set of pretrained convolutional architectures to better characterize natural images' authentic distortions. (iii) Extensive evaluation of different deep architectures on large IQA benchmark databases.
## 2 Materials and Methods
### Color Assessment  
The specimen color or shade was assessed using a spectrophotometer (Vita Easyshade@V Compact Vita, Zahnfabrik, Bad Sackingen, Germany) to evaluate the color matching ability and color stability (staining resistance) of the single-shade OM resin in comparison to a conventional resin composite. A positioning jig was used to place the specimens in a reproducible manner. The photometer was calibrated in accordance with the National Institute of Standards and Technology (NIST) tiles. The setting at which the tests were run included a 10 nm wavelength interval, 360 to 750 nm spectral range, and 45\({}^{\circ}\) reflectance angle. The background was black and an average of three scans were performed for each specimen. The CIELAB color system was employed, comprising L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) axes identification. Here, \"L \" ranged from 0 to 100 (brightness to darkness), the \"a \" axis represented red to green colors (90 to 70 range value), and the \"b\" axis represented yellow to blue colors (coordinate value range from: 80 to 100). Values for L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) were compared from baseline (tooth color) to post-restoration for the color matching assessment. For the color stability assessment (stain resistance), L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\) differences (\(\Delta\)) were calculated before and after stain solution immersion. The color change \(\Delta\)E was calculated using the following equation:  
\[\Delta\mathrm{E}=(\Delta\mathrm{L}^{2}+\Delta\mathrm{a}^{2}+\Delta\mathrm{b}^{ 2})^{1/2} \tag{1}\]
## 4 Methodology
### Baseline Methods for Comparison  
To benchmark performance, we compare Edge-FLGuard+ against  
* Centralized LSTM Autoencoder: A full data-centralized model trained on all available data.
* Local-only Model: Each client trains an independent GRU autoencoder with no FL.
* FedAvg + CNN Encoder: A variation using lightweight 1D CNN instead of GRU.
* Unsupervised Isolation Forest (centralized and federated): To benchmark traditional ML-based anomaly detectors [5].  
All methods are evaluated on accuracy, F1-score, false-positive rate, and communication overhead.
## 5 Conclusions  
_Streptomyces_ sp. Y1-14 with a strong antifungal activity against Foc TR4 was isolated from the rhizosphere soil of banana. Its extracts effectively inhibited the mycelian growth and spore germination of Foc TR4. _Streptomyces_ sp. Y1-14 also showed a broad-spectrum antifungal activity against 12 pathogenic fungi. Twenty-three compounds were identified by GC-MS, and nerolidol was tested for antifungal activity. After treatment with _Streptomyces_ sp. Y1-14 extracts, the ultrastructure of Foc TR4 cells exhibited a deformation feature and the organelles gradually disappeared. The energy metabolism pathway of Foc TR4 was seriously disrupted.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/jof8121291/s1](https://www.mdpi.com/article/10.3390/jof8121291/s1)]([https://www.mdpi.com/article/10.3390/jof8121291/s1](https://www.mdpi.com/article/10.3390/jof8121291/s1)), Table S1. Growth characteristics of strain Y1-14 on different culture media; Table S2. Physiological and biochemical characteristics of _Streptomyces_ sp. Y1-14; Table S3. Carbon and nitrogen source utilization of _Streptomyces_ sp. Y1-14; Figure S1. Determination and analysis of VOCs produced by _Streptomyces_ sp. Y1-14. (A) Total ion current chromatograms of VOCs. (B) Antifungal activity against Foc TR4 of different concentrations of Nerolidol; Figure S2. Effects of _Streptomyces_ sp. Y1-14 Extracts on Foc TR4 Metabolites. (A) PCA-3D diagram of all samples. (B,C) Heatmap based on the metabolome data of Foc TR4 treated with extracts of _Streptomyces_ sp. Y1-14 at 6 h and 12 h. (D,E) HMDB classification diagram of different metabolites in each group.  
[PERSON], Q.C., [PERSON], and [PERSON] developed the ideas and designed the research plans. [PERSON] and [PERSON] supervised the research work and provided funding support. [PERSON], Q.C., Y.W., D.Q., Y.C., X.L., and W.L. were performed experiments. [PERSON], Q.C., B.C., Y.L., L.Y., and Q.L. analyzed the data. [PERSON], [PERSON], and [PERSON] prepared the manuscript. All authors contributed to the article. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Central Public-interest Scientific Institution Basal Research Fund (1630052022006), the Natural Science Foundation of Hainan (320 CXTD441, 322 MS126, 321 MS084) and the China Agriculture Research System (CARS-31).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.  
We thank [PERSON] and [PERSON] for their help in this work.  
The authors declare no conflict of interest.
## 4 Model Validation
### Experimental Results Validation  
In order to further verify the accuracy of the BO-LSTM model prediction, it is compared with the far-near field monitoring and early warning system for rock bursts in the Gengcun Coal Mine. The far-near field monitoring and early warning system of rock burst is shown as Figure 10.  
The ARAMIS microseismic monitoring system was installed in the Gengcun Coal Mine. The system is capable of monitoring vibration events with a vibration capacity exceeding 100 J, a frequency range spanning 0 to 150 Hz, and a sound pressure level of less than 110 dB. In accordance with the varying monitoring ranges, the system is capable of selecting distinct frequency ranges for the sensors. The data transmission system enables the transmission of three-dimensional vibration rate change signals (X, Y, Z) over long-distance transmission cables. The system employs a 24-bit \(\sigma\)-\(\delta\) converter to convert and record vibration signals in real time. This enables the system to monitor vibrations continuously and in real time, thereby facilitating the collection of data for subsequent analysis. The monitoring substation is capable of transmitting the monitored data to the ground SQL Server database. Figure 11 depicts the current configuration of ARAMIS microseismic monitoring points in the Gengcun Coal Mine.  
A 90-day monitoring period of microseismic data yielded 105 instances of large energy releases, four of which coincided with an early warning index indicating a medium impact risk. These observations align with the model's predicted outcomes, substantiating the practical application value of the BO-LSTM combination model.  
Figure 10: The far–near field monitoring and early warning system for rock bursts.
## 3 Results and Discussions
### Convergence Time Comparison  
Figure 11 shows that the proposed EMBLR routing approach provides a significantly lower convergence time in all device speed scenarios as compared with the MP-OLSRv2 and MEQSA-OLSRv2 routing approaches. The EMBLR routing approach prohibits data flows in the congested and longer paths that minimize the data packets transmission time between the source and destination devices, which leads to low convergence time. Moreover, the proposed EMBLR routing approach exploits the  
Figure 10: Energy cost vs. various devices speed.  
Figure 9: Energy consumption vs. various devices speed.  
HETX metric value with the use of the information contained in the probe packets in order to reduce the chance of link failure among the devices. Therefore, the EMBLR routing approach instantaneously updates the routing table information with any changes occurring in the network that minimizes the network convergence time. Overall, Figure 11 shows that the proposed EMBLR routing approach achieves around 20.72% and 12.43% lower convergence time when compared with the MP-OLSRv2 and MEQSA-OLSRv2 routing approaches respectively, at device speed 30 m/s.
## 2 Project Overview  
The case history of the foundation pit and the underneath shield tunnel in operation are essentially orthogonal to each other in plane, and their location relationship is displayed in Figure 1. The twin-tube tunnels are constructed by the shield-driven tunnelling method; the outer diameters (\(D_{t}\)) are 6.0 m and the inner diameters (\(d_{t}\)) are 5.4 m. The tunnel linings are C50 precast concrete segments with a thickness of 0.3 m. The initial cover depth (\(C_{t}\)) of the tunnels before pit excavation is 17 m, and the clearance (\(L_{t}\)) between the twin tubes is 13.0 m. The foundation pit is about 35 m in length (\(L_{p}\)) and 20 m in width (\(W_{p}\)), with a final excavation depth (\(H_{p}\)) of 8 m. It is noted that the foundation pit's side along the tunnel transversal direction is defined as the length (\(L_{p}\)), while the other side along the tunnel longitudinal direction is defined as the width (\(W_{p}\)). The clearance (\(H\)) between the crown of tunnels and the bottom of the foundation pit is 9 m. The foundation pit was supported by a diaphragm wall combined with transverse steel support. The final excavation depth (\(H_{w}\)) of the diaphragm wall is 16 m. The C25 concrete is utilized for diaphragm wall construction, with a thickness of 0.8 m. To reduce the deformation of the enclosure structure and enhance the reliability of the foundation pit, the support scheme of setting three-level steel supports along the excavation direction in the foundation pit was adopted. The depths of three-level struts are 1 m, 3 m and 5 m from ground surface.  
The worksite consists of four soil layers within 60 m depth [33], which are backfilling, silt, silty clay and sand, with thicknesses of 1.5 m, 17 m, 20 m and 30 m, respectively. The soil stratigraphic profile in the worksite is shown in Figure 2. It can be seen from the physical and mechanical parameters of the site that the soil weight (\(\gamma\)) was in the range of 17.8-21 kN/m\({}^{3}\), the elastic modulus (\(E\)) was 25-40 MPa, Poisson's ratio (\(\mu\)) was 0.3-0.31, the cohesion (\(c\)) was 19.6-22 kPa, and the internal friction angle (\(\varphi\)) was 24-35.6\({}^{\circ}\).  
Figure 1: Spatial relationship between the foundation and twin-tube shield tunnels.  
Figure 2: Soil properties profile of worksite [33].
## 3 Results and Discussion  
### Optimization of the HS-SPME Method  
#### 3.1.1 Sample Weight  
To study the effects of different sample quantities on the total concentration of rice VOCs, we chose sample weights of 1.0, 2.0, 3.0, 4.0, and 5.0 g for extraction of rice odorant active compounds. The relationship between the sample weight and the total concentration of the VOCs is shown in Figure 1A. When the sample weight was 1.0-3.0 g, the VOCs content of rice significantly increased from 39.56% to 48.57%. In general, the amount of analyte adsorbed is directly proportional to the quality of the sample [15]. Therefore, higher extraction efficiency can be obtained by increasing the sample quality. However, when the sample weight is 3.0-5.0 g, the VOCs content of rice has no significant difference. It might be because fiber overload may adversely affect the sample's relative response to volatile compounds [15]. The sample weight of 3.0 g was chosen for further study.
# SbMBR Tree--A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining
## 4 Experimentation and Results
### Comparison of Peak Signal-to-Noise Ratio (PSNR) under the Same Compression Ratio  
The peak signal-to-noise ratio (_PSNR_) measures the difference between the original and compressed versions of an image or video in terms of the peak signal power and the amount of noise introduced during the compression process. The _PSNR_-compression ratio  
Figure 8: Comparison of nodes number under the same data. (**a**) Nodes number comparison of different methods under the same sparse data. (**b**) Nodes number comparison of different methods under the same dense data.  
curve serves as a visual tool to understand the trade-offs between compression and quality in a compression algorithm and to evaluate its performance compared to other algorithms.  
_PSNR_ can be defined as follows:  
\[PSNR=10\cdot\log_{10}\left(\frac{(m-n)^{2}}{e}\right) \tag{14}\]  
where \(m\) means the max value in the data set, \(n\) means the min value in the dataset, and \(e\) means the mean-square error between the compressed data and the origin data.  
Figure 9 shows the comparison results between methods based on the SbMBR tree, quadtree, and brute R-tree. The meanings of Test 1, Test 2, Test 3, and Test 4 are described in detail in Section 4.1.  
The data used in Test 1 and Test 3 are dense, while the data used in Test 2 and Test 4 are sparse. In sparse data, the method based on the SbMBR tree is generally superior to the method based on quadtree and brute R-tree. And in dense data, the method based on the SbMBR tree is generally superior to the method based on quadtree, but inferior to the method based on brute R-tree.
## 5 Analyses on Dynamic Behavior of Test FPSE
### Root Locus Analysis  
Figure 7a show the root locus trajectory with increasing charge pressures of 0-50 bar for case 3. The fourth-order characteristic equation has four roots with two pairs of complex conjugates, so that only two upper roots are considered. The roots are plotted in the complex plane by denoting the real part along the horizontal axis and the imaginary part along the vertical axis.  
With increasing charge pressures, the right upper root moves right, becomes positive at 11 bar, and has a maximum value at 27 bar, then moves left; while the left one moves in the opposite way with negative real values. The right upper root moves upward slightly, implying the increases in the operating frequency.  
Note that from the stability theory in mechanical vibrations, FPSEs are considered to start to operate when one of the real parts is greater than zero with diverging amplitudes [6; 13]. In reality, the engine has limited piston amplitudes due to energy extractions by damping devices, internal and external. On the other hand, roots with negative real parts have a converging transient response and disappears eventually.  
Figure 7: (**a**) Root locus trajectory with charge pressure increasing from 0 to 50 bar for case 3 and (**b**) real and imaginary values of upper poles (roots 1 and 3).  
Interestingly, although the roots seem to move continuously, a switch between the root loci occurs at a certain pressure. Figure 7b shows the real and imaginary values of upper poles (roots 1 and 3). The real parts of roots 1 and 3 clearly show a switch with each other at a charge pressure of 14 bar, and form continuous curves. Overall, the real part of root 3 remains positive and maximizes at 27 bar with relatively constant imaginary parts around 220 rad/s, corresponding to an operating frequency of 35 Hz.