esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Direct Shear Test  
The parameters of shear strength including cohesion (c) and friction angle (\(\phi\)) of untreated and treated samples are demonstrated in Figure 6a,b. Untreated CL showed better shear parameters compared to untreated CH. Comparatively, there was a considerable increase in c of both soils by increasing the POFA-based geopolymer dosage, and the highest values were found for specimens treated with G40 PA.  
The c value of CL-G40 PA was 65 kPa, which is nearly 1.5 times that of untreated CL soil, while c for CH-G40 PA was 53 kPa, which is more than twice the c of untreated CH. This enhancement and increase in the shear strength parameters without curing can be attributed to the cations exchange between the high concentration ions in the geopolymer binder and those of a similar charge found on the soil particles, which leads to their flocculation and subsequent improvement [41].  
Similar behavior for soils was observed by [PERSON] et al. [42] when chemically treated soils with geopolymer were studied to confirm that geopolymer can improve soil cohesion. However, a marginal increase in \(\phi\) was obtained for all treated samples, which is typical in chemically treated clay soils [43; 44]. From the findings, it can be observed that the pattern of strength parameters was almost similar for all the dosages of geopolymer content in both soils.
## 4 Experiments  
Two kinds of typical RC panels which represent the passive and active state were used to verify the effectiveness of the proposed WSN for corrosion monitoring. One RC panel was poured with plain concrete, and the other was formed with concrete containing 3.5% NaCl by weight of mixing water. The mix proportions of C30 concrete is listed in Table 1. The cement is P.O.42.5 Portland cement (Yatai Group Harbin Cement Co., Ltd., Harbin, China). The content of polycarbonate water reducing agent UNF-5 (Kanghua Chemical Co., Ltd., Jilin, China) is 1.0% by the weight of cement. After curing the panels for 2 months with 17 \(\pm\) 3 \(\,\)C and 70% \(\pm\)10% relative humidity (RH) condition as controlled by a SEEK SP-30 AT6 DT system (Jiedi Electronic Equipment Manufacturing Co., Ltd., Nanjing, China), the WSN-based corrosion monitoring tests were performed.  
Figure 5 illustrates the geometry of the RC panels and the distribution of the sensing modes. To recycle the MicaZ motes in the future, the sensing elements including the comb-shaped electrodes and the solid-state reference electrodes (SSREs) prepared by electron-beam vapor deposition are directly embedded in the concrete surrounded by the steel-bar mesh, and these electrodes are wired with the MicaZ motes installed on the surface of the reinforced concrete panels.  
The wireless mote entitled \"SN0\" is mounted on the plain RC panel to monitor the electrochemical emission data as the reinforcing steel is passivated. The other wireless motes entitled \"SN1\", \"SN2\"  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Components** & **Cement** & **Aggregate** & **Sand** & **Water** & **NaCl** & **UNF-5** \\ \hline Passive & 390 & 800 & 1035 & 180 & 0 & 3.9 \\ Active & 390 & 800 & 1035 & 180 & 6.3 & 3.9 \\ \hline \end{tabular}
\end{table}
Table 1: C30 concrete mixture proportions (units: kg/m\({}^{3}\)).  
Figure 5: The schematic plan of the wireless corrosion monitoring system for RC panels.  
and \"SN3\" are connected sequentially to the corresponding sensing elements embedded in the RC panel with 3.5% NaCl to monitor the corrosion status in the active state. It must keep in mind that the sensing elements and the MicaZ motes could be integrated and implanted into the concrete in practical engineering, and the power could be also supplied by the energy scavenged from wind, solar, concrete batteries or the electrochemical reactions happening during the corrosion process. The components of the comb-shaped electrode here include one graphite counter electrode and one Q235 carbon steel working electrode. The solid-state reference used as the baseline to monitor the change of the potential of the working electrode is developed based on NiFe\({}_{2}\)O\({}_{4}\) electrochemical functional materials [30]. Its balance potential is \(-\)238 mV \(\pm\) 5.0 mV (_vs_. Ag/AgCl/KCl\({}_{\text{sat}}\)) in the simulated concrete pore solution (SCPS) at 25 \(\ \ \mathrm{\SIUnitSymbolC}\). The sampling frequency of the electrochemical emission data is set to be 2 Hz.
# High Repetition Rate, TEM\({}_{00}\) Mode, Compact Sub-Nanosecond 532 nm Laser
## 3 Experimental Results
### SHG Characteristics  
Due to the limitations in the volume and thermal control, we set the operating point of the amplifier at a pump power of 7 W, as the seed-injected power was 317.8 mW. The output average power of the fundamental laser was 1.26 W. Furthermore, we focused the fundamental beam on a 10 mm-long Type I LBO (\(\theta=90^{\circ}\), \(\beta=11.4^{\circ}\)) crystal for the SHG. The beam of 532 nm had an average power of 0.6 W with an RMS of 0.66% (recorded by the 4\(\pi\) power meter from Laser Point) at 16 kHz. The SHG efficiency of this laser was about 47.6% from 1064 nm to 532 nm. The pulse energy was 37.5 uJ, and the peak power was about 48.7 kW. The pulse width and repetition rate were recorded using a Tektronix DPO70804C 8 GHz Digital Phosphor Oscilloscope, and they were 769.6 ps and 16 kHz, respectively, as shown in Figure 7. The average value of the repetition rates was 16.22 kHz with a Std of 141.8 Hz, and the maximum value and minimum value were 16.57 kHz and 15.87 kHz, respectively.  
Quality factor \(M^{2}\) of the 532 nm at 16 kHz was recorded by M\({}^{2}\)-200 S-FW from Ophiri-Spiricon with a 300 mm focal length lens. The values of \(M_{x}^{2}\) and \(M_{y}^{2}\) were 1.112 and 1.164, respectively, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**The Pump Power** & **The Output Power** & **Circularity of the** & **Circularity of the** \\
**of the Seed (W)** & **of the Seed (mW)** & **Seed Beam** & **Amplified Beam** \\ \hline
2.4 & 37.8 & 0.876 & 0.661 \\
3.3 & 206.4 & 0829 & 0.747 \\
3.9 & 317.8 & 0.779 & 0.927 \\ \hline \end{tabular}
\end{table}
Table 1: The circularity of the amplified beam with a pumped power of 7 W versus different pump powers of the seed.  
Figure 7: The pulse width and repetition rate of 532 nm. (a) Single-pulse profile and pulse width. (b) Pulse train profile and the pulse repetition rate.
# Magnesium Oxychloride Cement: Development, Opportunities and Challenges
## 10 Conclusions  
The review primarily emphasizes recent progress in MOC, specifically highlighting advancements such as the development of water-resistant MOC and ductile FRMOC.  
It also discusses various curing methods and explores the impact of different additives and the reactivity of MgO on MOC behavior. Furthermore, the review delves into the potential applications of MOC in fire-resistant coatings, solid waste management, and its incorporation into wood-MOC-modified composite building materials. Based on the present study, the recent advancements in MOC can be summarized into the following key points:  
* Different mineral admixtures and chemical reagents such as acids and soluble phosphates were added to the MOC over the past few decades, and it was found that they improved the water resistance performance up to a certain level. Moreover, recently, a water-resistant MOC mix (molar ratios of MgO/MgCl\({}_{2}\)-9 and H\({}_{2}\)O/MgCl\({}_{2}\)-13, 30% FA, 0.5% PA, and 0.5% MFP) was discovered with a strength retention co-efficient of 1.08 and 0.9, respectively, at normal (25 \({}^{\circ}\)C) and warm water (60 \({}^{\circ}\)C) immersion. This discovery potentially broadens the scope of MOC to outdoor applications.
* Controlled curing conditions such as a temperature of 24 \(\pm\) 1 \({}^{\circ}\)C and relative humidity of 60 \(\pm\) 5% were identified as suitable for curing MOC composites. Additionally, accelerated CO\({}_{2}\) curing demonstrated significant enhancements in the strength and water-resistant characteristics of MOC. This improvement was attributed to the formation of an insoluble amorphous gel and the densification of the microstructure.
* The addition of 2% PE fiber was effective in improving MOC composites' ductility with tensile strength ranging from 5 to 10.95 MPa and tensile strain capacity within the range of 4.41 to 8% without compromising the overall performance of MOC. This demonstrates the potential of FRMOC for utilization in structural applications.
* MOC exhibits promising potential in non-structural applications such as cladding or coating considering its superior ability to withstand high temperatures without compromising the integrity of the underlying substrate. However, existing literature revealed a complete loss in mechanical properties for MOC exposed to temperatures between 600-800 \({}^{\circ}\)C, highlighting the need for further research to enhance its fire resistance capabilities, especially for structural applications.
* MOC also exhibits strong bonding potential along with rapid hardening and a significant early strength gain with various industrial byproducts such as sewage sludge ash, pulverized fuel ash, phosphogypsum, and flue gas desulfurization gypsum. Hence, MOC may serve as an environmentally friendly alternative, reducing waste accumulation and contributing to a more sustainable environment.
* MOC also demonstrates good compatibility with wood as opposed to OPC, and this further establishes MOC as a green and sustainable material for the development of lightweight wood-based composite building materials.
## 1 Introduction
### Major Contributions  
This section shows that multimedia and transmission range demands are significant issues in today's world. Secondly, High bandwidth capacity and long distance CNs include the issues like NLIs. The full duplex WDM-RoF CN also produces PAPRIs by reason of O/E and E/O conversions. The main objectives of this system are to manage the NLIs and PAPRIs, that ROF could transmit data up to a long range, including maximum capacity information. The primary goals of the proposed framework are listed as follows.  
1. The techniques like balancing phase error (PE), dispersion compensation fiber (DCF), optical and electrical filters and full duplex RoF CN are applied for investigating and compensating the NLIs and PAPRIs.
2. The mathematical estimations are conducted for the above mentioned techniques aiming to enhance the accuracy of the proposed RoF system.  
3. The simulation model is examined using the analytical approach in terms of laser power, received power, RF frequency, fibre length, and filters.
4. Two measuring parameters, bit error rate (BER) and normalized signal to noise ratio, are used to estimate the conduction of the proposed RoF model against PAPRIs, NLIs and PEs.  
Following the introduction section, the rest of the paper includes the layout of the proposed model in Section 2, analytical modelling in Section 3, results and discussion of offered CN in Section 4 and conclusion of the proposed system in Section 5.
## 3 Research Data and Methods
### Research Methodology
#### 3.2.1 RRIM Terrain Visualization Map  
A RRIM terrain visualization map is calculated and converted from three elements: ground slope, topographic positive openness and topographic negative openness. The literature [26] proposes that topographic positive openness is the mean value of eight maximum zenith angles, expressed by \(O_{P}\), and topographic negative openness is the mean value of eight maximum bottom angles, expressed by \(O_{N}\). The larger the \(O_{P}\), the more  
Figure 2: Research Flow Chart.  
convex the terrain, and the larger the \(O_{N}\), the more concave the terrain. An RRIM terrain visualization map can be obtained by overlaying the slope map after red transformation with the ridge and valley index I value map calculated by using the terrain positive openness and the terrain negative openness. The calculation formula of I is as follows [27]:  
\[\text{I}= \frac{(O_{P}-O_{N})}{2} \tag{1}\]  
where \(O_{P}\) is topographic positive openness, \(O_{N}\) is topographic negative openness, and the I value is the newly generated elemental ridge and valley index.  
The value range of ridge and valley index I is generally [1; 2; 3; 1]. When I \(>\) 0, a larger I represents a more convex terrain, and the area shown in the figure is brighter. When ridge I \(<\) 0, the smaller the I value, the more concave the terrain and the darker the area shown in the figure [34]. In the RRIM map generated by the superposition of the I value map and the red slope map, the ground slope is shown in bright red, the top of the ridge is bright white, the valley at the bottom is shown in dark gray, and the flat surface (such as landslide steps) is visible in obvious gray.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Complete Pipeline of the Proposed Methodology
#### 2.1.3 Multilabel Binarizer  
In multi-class arrangement, the accurate label usually corresponds to a single integer. However, the input can be associated with multiple classes in a multilabel arrangement. For instance, a movie poster can have multiple genres, where labels are usually one-hot encoded for multi-class arrangement problems. By one-hot encoding, binary vectors are represented as categorical variables. Values in categories are first mapped to integer values. Then, each integer value is represented as a binary vector where all values are zero, excluding the integer index, marked with a 1.  
In multilabel arrangement problems, any number of classes can be associated with a single instance. It is hypothesized with confidence that the labels are mutually exclusive. Thus, instead of one-hot encoding, multilabel binarization is performed. Here the label (which can have multiple classes) is transformed into a binary vector. All values are zero except the indexes associated for each class in that label, marked with a 1. In this dataset, there is a class imbalance or multilabel arrangement problem. So, to remove this problem, we use the built-in function of python, arrangement problem-label-binarize, which will generate a vector of length 21 and assign 1 to the concepts associated with the image; otherwise, it will place a 0. It has been observed that the maximum length of concepts for imagery is 11, and the minimum length is 1. Figure 4 shows the transformation process applied to the original CUI table into a binary array.  
The input table contains multiple CUI codes associated with each image, while the output binary array represents the presence or absence of each concept in a format suitable for multilabel classification. This binarization step is crucial to enable the model to handle the multilabel nature of the medical annotation task.  
Figure 4: Class imbalance and multilabel encoding analysis.
# Design of a Technique for Accelerating the WSN Convergence Process
## 3 Theoretical Principles Used in Research
### Hybrid Routing  
Hybrid routing protocols have the advantages of both proactive and reactive routing protocols. They are trying to balance the latency and the complexity of the overhead  
Figure 5: The principle of creating a backup node IBR-AODV.  
Figure 6: Redirection in IBR-AODV.  
in the network. The proactive part of hybrid routing oversees routing in an area where nodes are known and available. The reactive part takes care of sending requests to areas outside known nodes. The entire operation of hybrid routing is based on areas. All nodes are partitioned into areas that use reactive and proactive features for route maintenance and discovery. In general, hybrid routing reduces overhead and performs better under dynamic changes in the network. An example of a hybrid protocol is the Zone Routing Protocol (ZRP).  
With the ZRP, most of the communication occurs between nodes located close to each other. In principle, however, each node has its own zone. The size of the zone is assigned by the so-called Joan radius. This number indicates how many hops from the given node belong to its area. Each node has many overlapping regions, and each region can be of different sizes. However, there are the following three components of ZRP [69; 70]:  
* IntraZone Routing Protocol (IARP): It is used for communication in an area with known nodes (proactive component). A route to a destination in the local area can be created from IARP routing tables, which are stored in the memory of individual nodes.
* InterZone Routing Protocol (IERP): It is used for communication outside the area of known nodes (reactive component) [71].
* Bordercast Resolution Protocol (BRP): It is used to increase the efficiency of communication outside the area of known nodes [72].
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 4 Experimental Verification  
### Experimental Test 1  
In order to verify the role of the HSDAE method in fault diagnosis of rotating machinery components, the rolling bearing public data set (CWRU) of Case Western Reserve University in the United States was selected for verification. The test platform is shown in Figure 7. In this study, the driving end signal with a sampling frequency of 48 kHz was selected as the test data.  
The data under four load conditions of HP0, HP1, HP2 and HP3 are used in the CWRU data set. There are 10 types of faults in each load condition, including normal conditions. The experiment uses data augmentation to increase the number of samples, which means overlapping sampling with a smaller sample length. There are 600 groups of each type of failure sample, and each group has a sample length of 600. Half of the data are randomly selected as the testing set, and the remaining half is divided into the training set. The detailed division of the data set is shown in Table 1. The parameters of the PSO algorithm are consistent in this experiment; the optimization boundary is [0.0050, 0.4000], [\(\omega_{0}\), \(\omega_{end}\), \(c_{1}\), \(c_{2}\)] are set to [0.1, 0.01, 1.5, 1.5], respectively, the total number of particles is 20 and the number of iteration steps \(N\) is 100. For hierarchical sparse parameters [\(\rho_{1}\), \(\rho_{2}\), \(\rho_{3}\), \(\beta_{1}\), \(\beta_{2}\), \(\beta_{3}\)], the optimized dimension is 6, and for non-hierarchical sparse parameters [\(\rho\), \(\beta\)], the optimized dimension is 2. The parameter settings of HDSAE are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Data Set** & **Load (HP)** & **Type of Failure** & **Depth of Failure (mm)** & **Number of Samples** & **Label** \\ \hline \multirow{8}{*}{A/B/C/D} & \multirow{8}{*}{0/1/2/3} & IF1 & 0.5334 & 600/600/600/600 & 0 \\  & & IF2 & 0.3556 & 600/600/600/600 & 1 \\  & & IF3 & 0.1778 & 600/600/600/600 & 2 \\  & & OF1 &
# Calculation Schemes for Determining Contact Stresses in Railway Rails
## 1 Introduction
are non-proportional) and their cyclic nature, deformation accumulation (ratcheting) occurs, leading to the exhaustion of the plastic properties of the material and the formation of cracks, which subsequently develop through fatigue mechanisms. The initiation sites of cracks are influenced by material homogeneity, purity, anisotropy of mechanical properties, the distribution of residual technological stresses, and many other factors. As such, predicting the service life of rails and rolling stock wheels (as they undergo similar defect formation processes) remains a highly challenging engineering problem. These predictions are based on calculating the stress-strain state of rails under combinations of external mechanical and thermal influences. Recently, there has been a growing interest in mathematical modeling of the processes of defect initiation and development under conditions of contact interaction between rails and wheels [11; 12; 13; 14; 15]. The life cycle of a rail consists of several stages, the duration of which varies significantly [2]. Considering only the evolution of rail defects, we can distinguish three main stages: defect initiation, its gradual growth, and transition into a critical state, i.e., its growth to dangerous sizes, at which the rail requires repair or removal from the track. Modeling each stage has its own specifics. Most studies aim to predict the moment of initiation of an isolated defect in the form of a crack, using both analytical and numerical methods to calculate the stress-strain state of rails in the zones of contact interaction with the rolling stock wheels, addressing relevant boundary value problems in the mechanics of solids. A critical review of known approaches to such calculations is the aim of this work. For modeling crack growth processes, including their interaction and determination of critical defect sizes, the mathematical framework of fracture mechanics is utilized. Such models, however, are beyond the scope of this study.
## Appendix A An Example of Unstable Case that Satisfy the Conventional Stability Conditions  
When we set \(\alpha=0.5\), \(\beta=0.3\), \(\gamma=0.2\), and \(\delta=0.8\) for the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter, these gains satisfy the conventional stability conditions (11)-(14). We substitute these gains in the characteristic polynomial as:  
\[f_{\alpha\beta\gamma\delta}(z)=z^{4}-\frac{89}{30}z^{3}+\frac{133}{30}z^{2}- \frac{13}{6}z+0.5. \tag{1}\]  
Thus, the poles of this polynomial are:  
\[p_{1},p_{2}=-0.314\pm 0.285,\quad p_{3},p_{4}=-1.170\pm 1.190. \tag{2}\]  
Thus, we have \(|p_{3}|=|p_{4}|=1.67>1\), which indicates that the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter with the above gain setting is unstable. With the above setting, \(x_{\text{p},k}\) of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter actually diverged in a numerical simulation assuming a target moving with constant velocity and \(B_{\text{x}}=0\). We have confirmed that the conventional stability conditions are satisfied in most cases. However, this example indicates that there  
Figure 6: Simulation results for high-maneuvering target. (**a**) Low gain case; (**b**) High gain case.  
are some cases for which the filter becomes unstable even when the conventional stability conditions are met.
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 3 Materials and Methods
### Principles of the Enhanced Feature Matching Algorithm  
The core problem of feature matching is establishing the corresponding relationships on a two-dimensional plane for images of the same target at different times, under different lighting conditions, and in different positions at the pixel level. Correspondence building between identical points in multi-view images is the foundation of multi-view image reconstruction. Consequently, this paper employs the SIFT algorithm for feature detection, complemented by KD-Tree for feature matching. The high discriminative descriptors of the SIFT algorithm effectively reduce false matches, while the efficient search capabilities of KD-Tree enable the processing of a greater number of feature points. The combination of these two methods not only ensures accuracy but also expands the pool of candidate matches. Furthermore, the enhanced RANSAC algorithm, with its adaptive threshold adjustment, retains a larger number of correct match pairs, thereby increasing the overall count of matching point pairs and providing foundational conditions for generating three-dimensional models with a higher quantity of point clouds.