esa-sceva/satcom-chunk-collection huggingface dataset:
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
###### Abstract
The proposed scheme reduces the latency and complexity and improves the error probability to meet the fundamental requirements of 5G and the applications shown above. Furthermore, our proposed approach is not restricted to the regular LT codes but can adapt to any desired degree distribution. To test the validity of the proposed approach, we derive the decoding success probability (DSP) for the Y-networks. Furthermore, we use a novel technique of  
Figure 1: The Y-network topology. optimizing the codes' parameters that maximize the derived DSP. Finally, we compare our proposed scheme's DSP and error probability with different works presented in the literature using the same settings. This paper is organized as follows. In Section 2, we provide a brief review of the encoding and decoding processes of LT codes and review the related works. Next, the proposed algorithm of using the concept of the MBLT in the Y-network is presented in Section 3. Then, the performance analyses of regular LT codes and the proposed scheme are provided in Section 4. Subsequently, the numerical results of optimizing the LT codes and the proposed schemes are shown in Section 5. Finally, we conclude the paper in Section 6.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 2: Self-Constructed Bearing Experimental Dataset
#### 4.3.3 Model-Comparison Experiments and Analysis Under Noise Interference  
Consistent with the noise-interference experiment in Case 1, GWN with different SNRs was added to the test set signals. The experimental results are shown in Figure 16.  
After noise was added, the proposed model still maintained the highest average accuracy, surpassing the other six models by 25.71%, 21.25%, 5.5%, 3.55%, 2.45%, and 1.3%, respectively. As the level of noise interference increased, the accuracies of all models decreased significantly. At 4 dB, SqueezeNet and MobileNetV3 exhibited the largest accuracy gaps with the proposed model, at 38.14% and 31.58%, respectively. Comparatively, ResNet-18, MPNet, and RepViT exhibited slightly better noise resistance than SqueezeNet and MobileNetV3, with deficits of 10.48%, 8.43%, and 6.34% relative to the proposed model, respectively. Although the accuracy of the proposed model was suboptimal at 2 dB, 1.76% lower than the accuracy of EfficientNetV2-S, it showed a notable improvement at 4 dB, surpassing that of EfficientNetV2-S by 4.2%. In summary, these findings suggest that models with larger parameter counts are more resilient to severe noise interference but that an optimal design can also improve the model's robustness to noise.
## 3 Highlights and Key Contributions of Published Articles
### Seismic and Geothermal Anomalies  
In addition to mining-induced seismicity, the impact of natural earthquakes should not be overlooked. [PERSON] et al. (contribution 2) conducted a quantitative analysis of thermal infrared anomalies before and after earthquakes based on temperature changes induced by stress variations. Initially, they derived the temperature change in rocks during elastic deformation from thermodynamic theory. Subsequently, they validated the temperature changes caused by stress variations through laboratory experiments using an infrared camera in the 8-12 micron spectral range. Finally, they developed a mechanical model for thrust faults to assessoseismic temperature drops induced by such faults. The model indicates that the coseismic temperature drop in rocks should be in the order of 0.18 K. This change in rock temperature could cause a thermal change equivalent to a shallow atmospheric temperature variation of 3.0-6.0 K, consistent with temperature anomalies observed by satellite thermal infrared remote sensing. Additionally, the temperature changes caused by crustal stress variations may involve large spatial scales, covering the entire epicentral area, and exhibit regional distribution characteristics, which are favorable for satellite observation. Thus, temperature changes induced by crustal stress variations can provide a quantitative explanation for satellite-observed thermal infrared seismic anomalies.
# Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis
## 3 Results
### EFNA4 Overexpression Promotes the Proliferation and Migration of Lung Tumor Cells  
To explore the relationship between high expression of _EFNA4_ and occurrence and development of lung cancer, lentivirusirus-mediated _EFNA4_-overexpressed lung tumor cells (H1299, A549 and PC9) were constructed (Figure 2A). Interestingly, it was found that _EFNA4_ overexpression enhanced the ability of colony formation (Figure 2B), cell proliferation (Figure 2C), migration (Figure 2D) and cell adhesion (Figure S2A) in the lung cancer cell lines. To further investigate the function of _EFNA4_ in vivo, we examined the tumorigenic ability of _EFNA4_-overexpression cells in immunodeficient mice. The stable _EFNA4_-overexpression A549 cell line and wild type cells were subcutaneously injected into the nude mice, and the corresponding transplanted tumor was formed after continuous feeding for 30 days, and the size and weight of each transplanted tumor were measured. In sharp contrast to control cells stable _EFNA4_ overexpression A549 cells developed significantly bigger tumors (Figure 2E). RT-qPCR analyses of these tumor xenografts validated the over-expression of _EFNA4_ (Figure S3). Together, these results indicate that _EFNA4_ can promote the proliferation and migration of lung tumor cells.  
Figure 2: _EFNA4_ overexpression can promote the proliferation and migration of lung tumor cells. (**A**) Western blot analysis for EFNA4 in H1299, A549 and PC9 control cells and _EFNA4_-overexpression (OE) cells that were transfected with _EFNA4_ expression virus vector; (**B**) representative images of colony formation of H1299, A549 and PC9 _EFNA4_-overexpressing cells and control cells. Colonies were stained with methylene blue; (**C**) MTT cell proliferation assay. H1299, A549 and PC9 control cells and _EFNA4_-overexpression cells proliferation was assayed by MTT method. Cell survival rates were analyzed at 1, 2, 3, 4 and 5 days. Data are expressed as mean \(\pm\) SD of three independent experiments; (**D**) representative images of scratch wound healing assay of control and _EFNA4_-overexpressing lung tumor cells; (**E**) volume and wet weight of xenograft tumors. _EFNA4_-overexpressing and control A549 cells were subcutaneously implanted in nude mice (\(n=3\)). Data are expressed as mean \(\pm\) SD of three independent experiments. OE: overexpression. Significant \(p\)-values are represented as * \(p<0.05\), **\(p<0.01\) and *** \(p<0.001\).
## 2 Background
### Minimum Spanning Tree  
A minimum spanning tree (MST) of an undirected graph is a graph that spans all the nodes as vertices and contains no cycles [11]. It can also be presented as a sub-graph that contains all the vertices where the sum of the weights of edges is no larger than the total weight of any other spanning tree from the graph [12]. The concepts of the spanning tree may be applied to construct the topology. Thus, several algorithms for routing and topology control based on MST have been developed. These algorithms use a few methods that allow the construction of the MST-based topology, such as the \"relax\" algorithm [13], the \"adjust weight\" algorithm, the \"adjust tree\" algorithm [14], the \"STPtree-based\" algorithm [15] and others. In general, spanning tree algorithms have shown energy efficiency for different extreme sensor network conditions. The Kruskal algorithm [16] starts by computing the cost between all nodes and continues until all the possible pairs have calculated there costs. Then, it sorts all edges in increasing order and considers all of these edges for inclusion in the tree. The tree initially has all vertices, but no edges; an edge is included in the tree if and only if its addition does not create a cycle in the already constructed tree.
# Prediction and Control of Broadband Noise Associated with Advanced Air Mobility--A Review
## 2 Sources/Types of BBN
### Blude Self-Noise  
Self-noise is caused by the interaction between an airfoil blade and the turbulence produced in its own boundary layer and wake space. If an airfoil encounters smooth inflow, self-noise is the only kind of noise generated. Figure 5 shows five self-noise mechanisms for subsonic flow: turbulent boundary layer trailing-edge noise (TBL-TE), trailing edge separation/stall noise (angle dependence for TBL-TE), bluntness vortex shedding noise (BVS), tip vortex formation noise, and laminar boundary layer vortex shedding noise (LBL-VS). Turbulent boundary layer trailing edge (TBL-TE) noise arises when the turbulent boundary layer convects past the trailing edge of the airfoil, generating a scattering of sound waves. Trailing edge separation/stall noise occurs when the boundary layer separates from the airfoil surface, often at high angles of attack, causing large-scale unsteady flows that produce significant noise. Bluntness vortex shedding (BVS) noise is generated by the formation and shedding of vortices from a blunt trailing edge, resulting in periodic sound waves. Tip vortex formation noise is caused by the tip vortices that form at the blade tips and interact with the surrounding flow, producing noise. Lastly, laminar boundary layer vortex shedding (LBL-VS) noise occurs when vortices shed from the laminar boundary layer interact with the trailing edge, generating discrete tonal noise.  
Figure 4: Illustration of a blade encountering a turbulent field generated by the other blades of the rotor [8].  
Figure 5: Blade self-noise sources [10].
## 4 Results and Discussion  
### AE Properties  
The AE characteristics generated by the gas leak were investigated as a functions of gas pressure (p\({}_{0}\)), axial distance (\(\eta\)), and angle (\(\theta\)) to the LS position (for convenience, the experimental test is presented as DaPbnc@d, where a, b, c and d denote the diameter of LS (mm), internal pressure (bar), axial distance from the PS (cm), and the tangential angle (degree), respectively). The cylinder internal pressure was kept at a given b bar. The AE signals were measured as waveforms. From observed signals, we evaluated several useful AE parameters such as count, maximum amplitude, duration, signal RMS, rise time, signal energy, peak frequency, etc. Figure 3a,c show two typical AE signal waveforms captured at D0.2P2\(\eta\)1\(\times\)0 and D1.2P4\(\eta\)1\(\times\)0, respectively. To reveal the resonant frequency of the AE signal transmitted through the surface of the cylinder, the time-domain spectra were converted to the frequency-domain spectra by applying FFT. After applying the Savitzky-Golay smoothing method, the amplitude FFT spectra were resolved by fitting with a Gaussian function. As shown in Figure 3b, the FFT spectrum of the AE signal of D0.2P2\(\eta\)100 consists of a strong peak at 173.4 kHz with several weak peaks in the 30-340 kHz region. Among them, the shortest and the longest frequencies appeared at 70.2 kHz and 284.5 kHz, respectively. The 173.4 kHz and the 145.6 kHz bands have weights of 45% and 18%, respectively, while the remaining bands have weights of less than 10%. In contrast, for the AE of D1.2P4\(\eta\)100, the main band at 108.1 kHz appeared with a weight of 43%, and the 171.2 kHz band appeared as the second peak with 19 wt.% (Figure 3d). It is important to note that the 108.1 kHz band appeared as an additional band for the AE signal from D0.2P2\(\eta\)1\(\times\)0.  
Figure 3: Typical AE signals (a,c) and their resolved FFT spectra (c,d) observed by D0.2P2\(\eta\)180 (a,b) and D1.2P4\(\eta\)1\(\times\)0 (c,d), respectively.  
The distribution of the mean frequency for the AE signals from D0.2P2\(\eta\)1\(\omega\)0 and D1.2P4\(\eta\)1\(\omega\)0 is illustrated in Figure 4. Except for the signals from D1.2P4\(\eta\)1\(\omega\)0, all the acquired AE signals show the peak frequencies in the range of 170-175 kHz. For D1.2 with \(\theta\
eq 0\), the peak frequencies in the range of 60-70 kHz were seen with weights less than 40%.
## 3 Results and Discussion
### General Comparison and Impact on Applications  
Overall, the comparison between TOM and DEM profiles across all three performance metrics--throughput, PDR, and delay--demonstrates the clear advantages of using TOM-optimized protocols in dynamic environments such as VCNs. TOM's focus on specific performance metrics (throughput and PDR) ensures high data transmission efficiency and reliability, making it highly suitable for applications where these parameters are critical, such as real-time video streaming, safety alerts, and autonomous vehicle coordination. DEM, on the other hand, while improving over the baseline protocols, provides a balanced approach by considering multiple performance aspects, making it suitable for applications where a trade-off between delay, throughput, and PDR is acceptable. Comparing these findings with the latest research, the optimization methods used in TOM profiles align with recent advancements in hybrid routing protocols for VCNs, which focus on improving specific performance metrics for specialized applications. The performance improvements seen with TOM and DEM profiles echo the results found in studies such as those by Refs. [31; 32], where hybrid routing techniques are employed to optimize VANET communications. In conclusion, TOM profiles offer superior performance in terms of throughput, PDR, and delay, making them ideal for critical real-time applications, while DEM profiles provide a well-rounded solution for general-purpose VANET applications where multiple performance metrics need to be considered simultaneously [23; 26]. We employed the BAHG simulation to evaluate the protocol's performance under increasing node density and traffic volumes to assess its robustness in high-density VANET scenarios [24; 25]. The simulation of BAHG on a realistic urban map demonstrated its effectiveness  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Traffic Generation** & **QOS-** & **QOS-** & **QOS-** & & & \\
**(Interval)** & **AODV** & **AODV-** & **AODV-** & **AODV-** & **GPSR** & **GPSR-** & **GPSR-** \\
0.11 s & 0.022 & 0.020 & 0.023 & 0.030 & 0.010 & 0.025 \\
0.21 s & 0.024 & 0.021 & 0.024 & 0.032 & 0.011 & 0.027 \\
0.31 s & 0.025 & 0.022 & 0.025 & 0.035 & 0.012 & 0.029 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Average delay comparison (seconds).  
compared to existing intersection-based protocols such as GPCR and GyTAR [27, 33]. The results showed that BAHG significantly reduced end-to-end delay by up to 85% compared to GPCR and 70% compared to GyTAR [12]. In terms of packet delivery ratio (PDR), BAHG achieved improvements of 50% over GPCR and 30% over GyTAR, highlighting its superior reliability and throughput [34, 35]. These performance gains can be attributed to BAHG's intelligent backbone selection mechanism, which ensures more stable and direct routing paths even in high-mobility and intersection-heavy environments.
## 3 Image Processing Approaches Used for EAT and PAT Segmentation
### Traditional Image Processing Techniques
#### 3.1.3 Active Contours  
Active contours or \"snakes\" are an image segmentation method that uses curves to segment part of an image [30]. A wide and rough curve is first initialized around the target object. Then, during each iteration, the active contours algorithm tightens the curve around the object until the curve follows the object's shape. The algorithm does this by minimizing a cost function, usually called the energy function. The energy function assigns a numerical value to each curve. The smoother the curve and the closer an edge, the lower its energy. By minimizing this function, the algorithm finds a curve that smoothly follows the edges of an object. An example of using active contours is shown in Figure 5.  
Active contours often require user interaction to initialize the curve or prior knowledge about the shape they are segmenting. They can track dynamic objects and follow contours that change from image to image. However, they often ignore very fine features of the contour and are sensitive to local minima that might draw the contour away from the target object.  
Figure 4: An example of using region growing to segment the inside and outside regions of the heart on a CT slice of the heart, thresholded to the adipose tissue range. The image on the (**left**) is the input image, while the image on the (**right**) shows the segmentation results of pixels outside the heart in red and inside the heart in blue. The green points on the image are two seed points manually selected to start the region growing process.  
Figure 5: An example of using active contours to fill missing portions of a pericardium line obtained by thresholding a CT scan, shown in white. The contour is shown in blue, initially a perfect circle around the image. In each iteration, the contour is deformed to better fit the image.
# A Review on Distribution System State Estimation Algorithms
## 4 Auxiliary Algorithms
### Observability
The authors also compare the results provided by the correlation approach to the respective ones provided by the GMM. When it comes to learning-based approaches, the popularity of DNN seems to be increased. For example, the authors of [74] have developed a DNN that produces pseudo-measurements, which are used as input in a typical WLS DSSE. It is reported that the computation time for the training of the DNN is approximately 10 min, using 17,520 sets of real power flow measurements and injections. Furthermore, the authors of [75] have developed a Parallel Distribution Processing network (PDP) for the generation of pseudo-measurements related to the load time-series of MV distribution networks. In a more complex manner, the authors of [76] propose the implementation of a nonlinear autoregressive exogenous (NARX) model for the efficient forecasting/estimation of loads.
## 2 Fundamentals of Pricing Schemes: Uniform, Zonal, and Nodal Pricing
### Market-Clearing Models
#### 2.1.1 Power Balance Constraint  
The power balance constraint, a fundamental principle in electricity markets, ensures that the supply satisfies the demand at all times. At the node level, this constraint can be formulated mathematically as shown in Equation (1), where the index \(i\) represents the individual nodes. This formulation adapts to different market aggregation levels: at the zone level, \(i\) represents the zones; at the national level, it is simplified to a single entity representing the entire electricity market.  
\[\sum_{g\in G_{i}}p_{g,t}+\sum_{k\in\delta_{i}}f_{k,t}=D_{i,t},\ \ \ \ \forall i\in I,t\in T \tag{1}\]  
where \(p_{g,t}\) indicates the generation from generator \(g\) at time \(t\), \(f_{k,t}\) is the net power flow on branch \(k\) at time \(t\), and \(D_{i,t}\) is the demand at bus \(i\) at time \(t\). The set \(G_{i}\) includes all generators at bus \(i\), and \(\delta_{i}\) includes all branches connected to bus \(i\).  
The level at which this constraint is implemented--national, zonal, or nodal--is intrinsically linked to the choice of the pricing scheme. The national-level balance corresponds closely to uniform pricing, whereas the zone-level balance aligns with zonal pricing. Finally, the node-level balance is fundamental to nodal pricing. Each of these implementation levels reflects a different approach to managing power balance and pricing in electricity markets. The concept is detailed in [32], using the Electric Reliability Council of Texas (ERCOT) as an example to compare zone- and node-level representations.
## 3 Results
### Rock-Physics-Based Inversion for Model with Intergranular Material  
An example of such an inversion was only demonstrated for Sample 1 and Dataset 1, i.e., the porosity was obtained with the porosimeter, and the aspect ratio of isometric voids was 0.50. This sample had the greatest porosity among others. The formulas of the lower Hashin-Shtrikman bound [28,36] were applied to model the effective elastic moduli of the host matrix represented by calcite crystals surrounded by intergranular material. As mentioned above, this material was introduced to take into account the effect of grain boundaries and the possible imperfection of the crystalline structure in their vicinity. The SCA method was used to calculate the effective elastic properties of the host  
Figure 12: Surfaces of characteristic _Discrepancy measure_ (\(\%\)) versus microcrack porosity and their aspect ratio for Sample 1, Sample 2, and Sample 3. Only acceptable solutions are shown. For each sample, solutions obtained with the use of three sets of input data (Dataset 1, Dataset 2, and Dataset 3) are shown.  
matrix with quasi-isometric pores. Figure 15 demonstrates the inverse problem solutions for the effective elastic moduli of the host matrix. As seen, the domain of solutions for the host matrix moduli was quite wide. Among the solutions, we selected only those that, at the next step of inversion, allowed us to obtain the result satisfying the following conditions: the Poisson's ratio of the intergranular material should be positive and smaller than 0.5; the elastic moduli of the intergranular material should be smaller than those of the calcite polycrystal; the bulk modulus of the host material should be greater than that of water; and the total volume concentration of the intergranular material should not exceed 2%.  
Commonly, these solutions exhibit a bulk modulus varying from 2.2 to 9 GPa and quite a small shear modulus of around 0.16-0.7 GPa. If we increased the possible volume  
Figure 14: The _Discrepancy measure_ versus density of microcracks for three sets of input data for Samples 1–3.  
Figure 13: Examples of solutions for conditional minimum for Sample 3. The value of conditional minimum is shown in the plot’s caption.  
content of intergranular material up to 5%, the range became wider for the bulk modulus, 8-19 GPa, and narrower for the shear modulus, 0.3-0.4 GPa. In both cases, the Poisson's ratio was quite high, varying from 0.43 to 0.48. The following questions arise: Is this material realistic or not? Or is it only a formal result of inversion?