esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Limitation and Futherwork  
Although the numerical simulation has been performed considering the presence of track irregularity PSD of Chinese high-speed railway, there are many other nonlinear factors influencing the calculated axle-box acceleration responses, such as parameters of primary and secondary suspension systems, the stiffness and viscous damping parameters of the ballastless track model, and others. In this regard, experimental tests are under the plan to carry out on a real track, which allows considering the non-linearity of the vehicle-track coupled system and the environmental influences that cannot be easily predicted in the modeling process.  
Future work will involve measurements in the laboratory and on a running train to prove the effectiveness of the proposed wheel-polygon detection method based on axle-box vibration. This will be followed by the design and development of a real-time system to monitor the development of wheel polygons.  
Figure 10: Peak power amplitude of axle-box acceleration due to 24 th-order polygonal wheels as a function of OOR amplitude.
## 2 Composition, Feature and Utilization of Slag  
### Slag's Chemical Composition and Mineral Composition  
According to method of steelmaking, slag can be divided into converter slag, open health furnace slag and electric furnace slag. In accordance with production stage, it can be divided into steelmaking slag, casting slag and splashing slag [19]. Based on basicity value R = CaO/(SiO\({}_{2}\) + P\({}_{2}\)C\({}_{5}\)), it can be divided into low basicity slag (R < 1.8), medium basicity slag (R = 1.8-2.5) and high basicity slag (R > 2.5) [20]. Due to shape, it can be divided into water quenched granular slag, lump slag and powder slag.  
Slag's chemical compositions mainly include SiO\({}_{2}\), CaO, Fe\({}_{2}\)O\({}_{3}\), FeO, Al\({}_{2}\)O\({}_{3}\), MgO, MnO and so on [21]. Its mineral compositions include C\({}_{3}\)S, Ca\({}_{3}\)MgSi\({}_{2}\)O\({}_{8}\), C\({}_{2}\)S, C\({}_{4}\)AF, a solid solution containing the Fe compound and P compound, free CaO and so on [22]. Slag's chemical compositions and corresponding basicity are the main reasons for the difference of mineral composition.  
[PERSON] et al. [23] believed that the main minerals of low basicity slag included olivine, mernwinite and RO phase (CaO-FeO-MnO-MgO solid solution). Calcium hydrogen silicate and tricalcium silicate were the main minerals of medium alkalinity slag, and calcium silicate was the main mineral of high alkalinity slag.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 2 System Characterization
### Transmitter Characterization  
In this subsection, the \(u\)th UT model with analog precoding is presented. The corresponding block diagram is shown in Figure 1. First, the \(L\)-length data symbol block is obtained \(\left\{s_{u,k}\right\}_{k=1}^{L}\), where \(s_{u,k}\) are symbols selected from a M - quadrature amplitude modulation (\(M\)-QAM) constellation, such that \(\mathbb{E}[\left|s_{u,k}\right|]_{2}=\sigma_{s}^{2}\). Then, it is made a Hermitian symmetric zero-padded data sequence, \(\left\{z_{u,k}\right\}_{k=1}^{N_{c}}\in\mathbb{C}^{N_{c}}\) given by:  
\[\left\{z_{u,k}\right\}_{k=1}^{N_{c}}=\{0,s_{u,1},\ldots,s_{u,L},0,\ldots,0,0, s_{u,L}^{*},\ldots,s_{u,1}^{*}\}, \tag{1}\]  
where the \(N_{2p}\) zeros are used to achieve the effect of oversampling of the time domain sequence, whose oversampling factor is given by \(N_{\infty}=N_{c}/\left(N_{c}-N_{2p}\right)\). An inverse fast Fourier transform (IFFT) of size \(N_{c}=2L+N_{2p}+2\), is applied, and due to the structure of (1), the obtained sequence, denominated by \(\left\{z_{u,t}\right\}_{t=1}^{N_{c}}\) in the following, is only composed by real values, i.e., \(z_{u,t}\in\mathbb{R}\). After that, the phase modulator is applied, whose output, \(c_{u,t}\), is given by:  
\[c_{u,t}=A\exp(j2\pi\hbar z_{u,t}), \tag{2}\]  
where \(A\) is the signal amplitude of \(c_{u,t}\), and \(h\) is the modulation index. To keep the signal power, i.e., \(\mathbb{E}[\left|s_{u,k}\right|_{2}]=\mathbb{E}[\left|c_{u,k}\right|_{2}]= \sigma_{s}^{2}\), we set the signal amplitude as \(A=\sigma_{u}\sqrt{LN_{c}^{-1}}\). The variance of sequence \(\left[z_{u,t}\right]_{t=1}^{N_{c}}\) is normalized to 1, which means that the variance of the phase of \(\left\{c_{u,t}\right\}_{t=1}^{N_{c}}\) is \((2\pi h)^{2}\). To conclude, a cyclic prefix (CP) is added and the analog precoder, \(\mathbf{f}_{a,u}\in\mathbb{C}^{N_{tx}}\), based only on analog phase shifters is applied, with \(\left|\mathbf{f}_{a,u}(n)\right|_{2}=N_{tx}^{-1}\).
## 8 Communication Considerations
The former involves a strong (direct) \(b_{\rm B}{\rightarrow}a_{\rm A}\)propagation between the key sites \(a_{\rm A}\) and \(b_{\rm B}\), which determine the overall chemical behavior of reactants, and a weak (intermediate) _double_-bridge communication \(b_{\rm B}{\rightarrow}[a_{\rm B}{\rightarrow}b_{\rm A}]{\rightarrow}a_{\rm A}\), while the latter exhibits only two indirect (_single_-bridge) communications: \(b_{\rm B}{\rightarrow}a_{\rm B}{\rightarrow}a_{\rm A}\) and \(b_{\rm B}{\rightarrow}b_{\rm A}{\rightarrow}a_{\rm A}\). The strongest communications are thus expected for most _covalently_-bonded neighboring fragments \(b_{\rm A}\) and \(b_{\rm B}\) in the HSAB complex, while the weakest propagations are predicted between its two hard (acidic) sites \(a_{\rm A}\) and \(a_{\rm B}\), which bind more _ionically_:  
\[P(b_{\rm B}{\rightarrow}b_{\rm A})>>P(a_{\rm B}{\rightarrow}a_{\rm A}). \tag{108}\]  
In \(\rm R_{\rm HSAB}\), this qualitative analysis thus suggests a _covalent_ character of the chemical bond between the basic sites of both reactants, and the _ionic_ bond between their acidic groups. In \(\rm R_{c}\), one similarly predicts a strong _coordination_ bond between \(a_{\rm A}\) and \(b_{\rm B}\), and a predominantly _covalent_ bond between \(a_{\rm B}\) and \(b_{\rm A}\). The current density \(j(r)\) [see Equation (37)] reflects the flow of electronic probability density \(p(r)\) with an effective velocity measuring the current-per-particle: \(V(r)=j(r)/p(r)\). It implies the associated flux of the system resultant gradient-information, \(J_{\rm I}(r)=I(r)V(r)\). Here, \(I(r)=I_{p}(r)+I_{\phi}(r)\) denotes the density-per-electron of the overall information combining the classical contribution \(I_{p}(r)=[\
abla\mathrm{ln}p(r)]^{2}\) and its nonclassical supplement \(I_{\phi}(r)=[2\
abla\phi(r)]^{2}\) due to the state phase component [see Equation (43)]. The information continuity equation then predicts the vanishing classical contribution to the resultant information source and a finite production of its nonclassical part [62; 63; 64; 67; 65; 66; 673]. The shifts in electron populations on active sites of donor-acceptor systems [Equation (48)] also imply the associated redistributions of the system resultant gradient-information. The concerted flows in \(\rm R_{c}\) redistribute the information density more evenly, compared to a more localized redistribution in \(\rm R_{\rm HSAB}\), where electronic flows net transport the information between the key sites of both reactants: from \(b_{\rm B}\) to \(a_{\rm A}\). The complementary coordination thus corresponds to a lower level of the overall _determinicity_-information [see Equation (53)] compared to that in HSAB-type coordination. Therefore, the former reactive system represents more information uncertainty in comparison to the latter complex, thus exhibiting a higher resultant gradient-entropy (_indeterminicity_-information).
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 3 Optimization of NAV  
Figure 3 shows the packet structure used in the proposed scheme. The _Duration_ field, which is defined in typical CSMA/CA protocols, specifies the period from the instant the packet is transmitted to the instant the handshaking is expected to end. Thus, _Duration_ has been used as the NAV in previous CSMA/CA protocols. The proposed scheme adds some message fields to help the neighbor obtain timing information and adjust the NAV accordingly, so that unnecessarily long silent periods or sleep are prevented. The additional message fields are _TimeStamp_ and _Prop.Delay_. _TimeStamp_ indicates the packet transmission time used by the associated receiver to calculate the propagation delay. _Prop.Delays_ of CTS and DATA specify the propagation delays between the source and destination calculated by the destination and source, respectively. By overhearing the _TimeStamp_ and _Prop.Delay_, a neighbor can acquire all propagation delays of \(\tau(S,D)\), \(\tau(D,N)\) and \(\tau(S,N)\), where \(S\), \(D\), and \(N\) stand for the source, destination, and neighbor, respectively.  
Figure 3: Packet structure of the proposed scheme.  
Figure 2: Illustration of the unfairness problem arising from inefficient NAV determination.  
The _Duration_ is updated for every packet--RTS, CTS, and DATA--with the following values,  
\[D^{RTS}=4\tau_{max}+T_{CTS}+T_{DATA}+T_{ACK} \tag{3}\]  
\[D^{CTS}=2\tau(S,D)+T_{DATA}+T_{ACK} \tag{4}\]  
\[D^{DATA}=2\tau(S,D)+T_{ACK} \tag{5}\]  
where \(D^{X}\) denotes the _Duration_ of packet \(X\). As, at first, the source does not know \(\tau(S,D)\), \(\tau_{max}\) is used in \(D^{RTS}\). Figure 4 shows the duration of each packet. Unlike previous CSMA/CA-based protocols, where _Duration_ itself is used as the NAV, the proposed scheme eliminates the unnecessary parts in _Duration_, based on additional information such as the propagation delay and the node with whom the neighbor is trying to communicate after completion of the current handshaking. The detailed NAV-determination rule in each case is described in the following sections.
## 4 Conclusions  
In this study, in order to investigate the flexural performance of the CFS upright frames strengthened by an innovative reinforcement method, FEM models were developed in ABAQUS and verified by experimental test data. Then, in a parametric study, uprights with different thicknesses and lengths and reinforcement spacing were modeled and analyzed under flexural loading, and the results were obtained. Using ABAQUS, FEM results indicated that with closer reinforcements, the models show a stiffened behavior. According to the literature, FEM is capable of simulating CFS section structural behavior. Additionally, in this study, the MLP-PSO hybrid neural network by combining the evolutionary feature selection technique was used to predict some major characteristics of CFS upright frames, such as \"flexural strength\" and \"deflection\". The feature selection technique was used to avoid trying all possible input modes and wasting time. It provided the best possible input combination that may be overlooked in other methods. The neural network results illustrated noticeable accuracy and a fascinating prediction ability of the MLP algorithm along with the feature selection technique, which has compatibility with the literature.  
According to the FEM results, both in major and minor axis simulations, all models indicated a unique behavior with respect to length/thickness variation. The thicker models showed higher flexural capacity, especially those with shorter lengths. Moreover, taller uprights faced capacity loss due to buckling. This deficiency was somehow addressed by the proposed reinforcements. The proposed fasteners played a noticeable role as reinforcement not only in longer uprights, but also in thinner ones. By reducing the fastener spacing, the ultimate load of the models was increased.  
According to the analytical study, the FEM results were verified and proved to be authenticated through the training phase of the analytical analysis. The predictions were performed for both the deflection value and ultimate flexural load, and the accuracy of the prediction was evaluated. Employing a hybrid neural network based on the feature-selection technique successfully predicted the normalized ultimate load and the deflection.  
Conceptualization, E.T. and B.S.; methodology, E.T. and B.S.; software, E.T.; validation, E.T.; formal analysis, E.T.; investigation, E.T.; resources, E.T., S.E.F, Y.Z., B.S.; data curation, E.T.; writing--original draft preparation, E.T.; writing--review and editing, E.T., S.E.F, Y.Z., B.S.; visualization, E.T.; supervision, E.T., B.S.; project administration, E.T., B.S.; funding acquisition, E.T., B.S. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the R&D contract budget of Western Sydney University. The materials for laboratory testing were provided by Dexion Australia.
## 4 Experiments and Analysis
### Residual Analysis  
The posteriori residuals of the observations refer to the discrepancies between the actual observations and the theoretical predictions, which is an effective means to assess the level of the data processing in orbit determination. Therefore, before analyzing the performance of the combined ISL/SLR orbit determination, it is essential to firstly analyze the observation residuals. Taking the results of DOY 053 with broadcast ephemeris as the a priori orbit as an example, Figures 5 and 6 present the residual of the ISL and SLR observations, respectively. In Figure 5, the different colors express the residuals of different inter-satellite links, whereas Figure 6 denotes the ground station-satellite SLR observations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **POD Mode** & **The a Priori Orbit** \\ \hline Experiment 1 & ISL & Broadcast ephemeris \\ Experiment 2 & ISL & Precise ephemeris \\ Experiment 3 & ISL/SLR & Broadcast ephemeris \\ Experiment 4 & ISL/SLR & Precise ephemeris \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of the designed experiment.  
Figure 5: Residuals of ISL observations.  
Figure 6: Residuals of SLR observations.  
From Figure 5, it is evident that the residuals of the ISL observations are almost distributed within \(\pm\)0.20 m. Overall, the comprehensive statistical analysis reveals that the root mean square (RMS) of the residuals of ISL observations for all satellites is around 0.05 m, which aligns with the findings from existing studies [37]. Meanwhile, as depicted in Figure 6, the residuals of the SLR observations are generally within \(\pm\)0.05 m, with very few outliers reaching 0.08 m. Overall, its RMS value is better than 0.03 m. Considering the insufficient number of SLR observations during the processing period, the statistical results are easily affected by the large residuals. Therefore, it is expected that an increase in the number of SLRs in the future may lower the RMS value. In addition, as mentioned above, the biases in the SLR observation ignored in this study may also affect the RMS of the residual. Despite all this, the current results of the residual statistics fully demonstrate that the data processing strategy used in this study is correct and effective.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
## 3 Experimental Results
### Image Matching and Outlier Removal in an Overlap Area  
We generated a grid of 50 and 100 pixels along line and sample directions in the overlap area, respectively. Then, we carried out _NCC_ image matching between the reference and the adjacent projected images for the grid points. As matching parameters, we used \(77\times 77\) pixels for the matching window size, search range 60 pixels. We selected the matching parameters considering the geolocation accuracy of the sensor modeling for KOMPSAT-3, which has 48.5 m (CE90, Circular Error 90% confidence range).  
The matching pairs are showing _NCC_ larger than 0.7 were selected as matching candidates in this study. Then, the image coordinates differences were computed between the matching pairs and plotted in Figure 8. Figure 8a,b shows the line and sample coordinates differences between Strips #1 and #2. Figure 8c,d shows the line and sample coordinates differences between Strips #2 and #3. The blue dots show all the coordinates differences for the matching candidates.  
We applied the RANSAC algorithm with second polynomial models for each line and sample coordinate differences to suppress the matching outliers. The polynomial model was applied to each scene in an image strip. The red dots show the results after the outlier removal.  
Figure 7: Projected images onto the reference image space.
## 4 Discussion
### Module Key Hub Genes and Their Protein Functions
#### 4.2.3 Metabolic Reprogramming in ccRCC  
The constructed gene network suggests that it may be associated with the metabolic reprogramming of ccRCC supported by the PI3K/Akt and HIF-1 pathways. The yellow module (Table 10) highly represented the role of metabolic pathways. Several upregulated genes like _INS_, _PRKACG_, and _CALML3_ found in the brown and red modules were involved in these pathways. Metabolic reprogramming has been associated with tumor development and progression in ccRCC. The Warburg effect involves conditions of normal oxygen, resulting in the generation of lactate, along with an acidic microenvironment that boosts the migratory capabilities of the cancer cells. Glutamine metabolism is elevated in ccRCC cells. As in most cases of ccRCC, mutation of the VHL gene disrupts the degradation of HIFs, initiating metabolic changes [119; 120].  
NMR studies have identified metabolites like lactate and glutamate in RCC [121]. In ccRCC, glutamate influences the PI3K/AKT pathway, with glutamate derived from glutamine via glutaminase [121; 122]. The downregulation of GLUD1, which converts glutamate to \(\alpha\)-ketoglutarate, is linked to increased malignancy and poor prognosis by enhancing PI3K/Akt/mTOR. Thus, GLUD1 is a potential target in ccRCC [122; 123; 124; 125]. Another study also identified increased levels of creatine, alanine, lactate, and pyruvate using 1 HNMR. Metabolites findings with transcriptomic data from ccRCC tumor stem cells showed changes with HIF-\(\alpha\). Pyruvate, betaine, and creatinine were also found in HIF-1 in ccRCC. Pyruvate is an intermediate in glycolysis and thereby reflects increased glycolytic activity by HIF-1\(\alpha\) through the upregulation of genes related to glycolysis. Betaine intersects with HIF-1\(\alpha\) involved in methionine and choline metabolism. Creatinine, known for renal function and dysfunction, is indirectly related to HIF-1\(\alpha\) activity [126]. Moreover, lactate stabilizes HIF-1\(\alpha\), making it a potential indicator of the HIF-1\(\alpha\) pathway in ccRCC [34; 126].
###### Abstract  
The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image preprocessing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbbot index cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail.  
Keywords:rail internal defect; ultrasonic detection; defect identification and localization; YOLOv8 network +
Footnote †: journal: Applied Sciences  
Article
## 4 Optimization of Balanced Partition Routing Algorithm
### Algorithm Efficiency Analysis  
The inter-star model we selected is composed of 30 star nodes. The data in the Topo table represent the direct distance between stars and satisfy the distribution of \(\left(\mu,\ \sigma^{2}\right)\). We explore the actual performance of two balanced partition routing algorithms under different variances. We first simulate the average routing path delay calculated by the two optimization algorithms under different variances and the \(\mu\) is 5. The simulated data are shown in Table 3.  
It can be seen from the above table that we carried out 10 simulation experiments. According to the average value of data obtained from the 10 simulations, the routing path delay calculated by algorithm 1 is the best, algorithm 3 is the second, and algorithm 2 is the worst. In order to eliminate chance, we performed another 20 simulations and drew a simulation graph of the average routing path delay.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
\begin{tabular}{c} **Conditions** \\ **Sequence** \\ \end{tabular} & \multicolumn{2}{c}{\(\mu=5,\ \sigma^{2}=6\)} & \multicolumn{2}{c}{\(\mu=5,\ \sigma^{2}=10\)} & \multicolumn{2}{c}{\(\mu=5,\ \sigma^{2}=15\)} \\ \cline{2-10}  & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** & **Algorithm 1** & **Algorithm 2** & **Algorithm 3** \\ \hline
1 & 6.9727 & 7.5865 & 7.7721 & 8.98671 & 11.7425 & 11.1119 & 13.3474 & 19.5487 &  
As can be seen from the Tables 3 and 4, as the variance of data in the Topo table increases, so does the variance of the average path length calculated by each algorithm. Internally, under the same conditions, the variance of the average path length calculated by algorithm 2 is the smallest, indicating that the path length calculated by algorithm 2 for each routing path is relatively close, and the efficiency of the algorithm is the most stable. Figures 9-11 also reflect the same results. In practical applications, such algorithms are more resistant to destruction. If the lengths of different paths calculated differ greatly, some star nodes will be overloaded while others are idle, which wastes resources and reduces the efficiency of inter-star communication [24].