esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
#### 3.1.2 Mass Loss Rate and Corrosion Area Rate of Reinforcing Steel  
Figure 9a illustrates the mass loss of reinforcing steel in chloride-containing concrete under the influence of _Xanthin sibiricum_. As the corrosion inhibitor was added, the proportion of mass loss in the reinforcing steel gradually decreased, directly reflecting a reduction in the level of steel corrosion. In the control group D-2, where steel mass loss was most severe, the loss rate reached 12.4%. In contrast, in the Sa-3 group, which showed the best corrosion inhibition effect, the mass loss ratio of the reinforcing steel was only 1.9%.  
Figure 9b shows the changes in the corrosion area ratio of reinforcing steel in chloride-containing concrete under the influence of _Xanthin sibiricum_. It is evident that the control group without the inhibitor experienced the most severe corrosion, with corrosion covering almost the entire surface of the reinforcing steel, reaching a corrosion area ratio of 93.7%. As the amount of _Xanthin sibiricum_ corrosion inhibitor increased, the extent of reinforcing  
Figure 8: Changes in natural potential of steel bars when inhibitor is added: (**a**) specimens with a chloride ion content of 0.5%; (**b**) specimens with a chloride ion content of 1%.  
steel corrosion significantly decreased. When the chloride ion content was 0.5% and the inhibitor addition ratio was 1.5%, the corrosion area ratio of the reinforcing steel was as low as 3.78%. Conversely, when the mass ratio of _Xanthinium sibircium_ to chloride ions was 1:1, the corrosion area ratio of the reinforcing steel increased significantly, exceeding 30%.  
Comparing Figure 9a,b, it is evident that when the mass ratio of chloride ions to _Xanthin sibircium_ remains constant, the corrosion of steel bars is more severe in a 1% chloride ion concentration environment. This reflects the weakening effect of increased chloride content in concrete on the corrosion inhibition efficacy of the _Xanthinium sibircium_ inhibitor.
## 2 Results
### Thermal and pH Stability Assays  
Factors such as temperature and pH largely influence phage viability, which may be a determinant for phage application. Thermal stability assays revealed that XaC1 showed no changes on infectivity between 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C but a significant reduction in titer of about \(<\)1-log unit at 45 \({}^{\circ}\)C (\(p\)\(>\) 0.05) compared with the initial titer (10\({}^{6}\) PFU/mL); in contrast, phage XbC2 was stable at temperatures from 25 to 45 \({}^{\circ}\)C. At higher temperatures, XaC1 exhibited a reduction of 2.5-log units and XbC2 a reduction of 2-log units when exposed at 55 \({}^{\circ}\)C, showing XbC2 was significantly more stable than XaC1. However, both phages had a 4-log reduced viability at 65 \({}^{\circ}\)C (Figure 8a).  
Figure 8: Stability assays for _Xanthomonas_ phages: (**a**) Temperature, and (**b**) pH. Different letters indicate statistically significant differences (\(p\)\(<\) 0.05). Error bar represents standard deviation.  
Figure 7: Intergenomic similarities between related phages of XaC1 and XbC2 generated by VIRIDIC. The right half of the image represents a visualization of phage genome clustering based on the similarity between intergenomic regions. Phages with darker colors are more closely related. The left half shows the three indication values of each pair of genomes. These values are presented in order: aligned partial genome 1 (row), genome length ratio (between genomes), and aligned partial genome 2 (column).  
Stability at different pH values showed that all phages lost viability at pH 2, resulting that no lytic plaques were obtained. The phage XaC1 was stable at the pH range from 5 to 9, but viability showed a significant reduction of >1-log at pH 11 and 12 after exposure for 24 h. The phage XbC2 was relatively stable at a pH range from 5 to 9, with less than a 1-log decrease in titer, similar to XaC1, but exhibited about a 2-log and 1-log reduction at pH of 11 and 12, respectively (Figure 8b). These findings clearly suggest that each phage displayed a different pH stability, and was more stable at a pH range from 5 to 9.
## 4 Experiment Results and Analysis
### Experimental Results
The same 10 image categories as those in Fashion-MNIST are considered from the CIFAR-10 dataset. However, the CIFAR-10 dataset contains colored images. The accuracy of this study was higher than that of the other two algorithms, and as the characteristics of the respective datasets tended to be more personalized, the accuracy gradually increased to 92%. The FedProx algorithm mainly emphasizes improving the local loss function and adjusting the epochs of each client without extracting the feature layer of each client. Consequently, the accuracy rate was less than 10% for the multicategory non-IID dataset. In the CIFAR-100 dataset, the accuracy of this study surpassed that of the FedProx and FedPer algorithms. This is because of the simulation environment of 10 clients, although the maximum convergence accuracy is limited to approximately 57%. Based on accuracy results, it was determined that this algorithm could prevent the decline in the overall model accuracy caused by client drift in a non-IID dataset by truncating the client characteristics and category classification.
## 3 Results and Discussion
### St-Co-DVB Polymerization in the Presence of Flocculated SNPs
Although SNPs do not participate in the polymerization process, as they poorly interact with oligomers of negatively moieties, they indirectly interfere in the nucleation and coagulation processes, due to the formation of a three-dimensional network in the aqueous phase. Indeed, the nanoparticles' aggregates allow reducing considerably the coagulation process, acting as a steric barrier against oligomers' collapse. From these statements, a limited homogeneous coagulative nucleation mechanism is then  
Figure 2: Microsphere size distributions prepared in Pickering emulsion polymerization at pH = 9.  
postulated [57]. A nucleation step is promoted by the presence of VBS as co-aid occurs, but the collapsing of the growing oligomers is limited, as an SNp's steric barrier impedes direct collapsing, allowing the formation of submicronic polymer particles. Finally, due to the highly flocculated SNps and their poor interactions with the polymer, the latex particles are thus partially covered by the SNps [26; 35]. For a better description of the limited homogeneous coagulative nucleation process, a schematic representation of the polymerization mechanisms is presented in Figure 3. In this figure, the flocculated SNps are schematically represented in blue, forming a three-dimensional network, in which monomer droplets are dispersed due to mixing by the propeller. As soon as the initiator is added to the reactor, oligomers (green) form, as a result of the reaction of VBS (dark dash), DVB, and St monomers (red), which will further precipitate to produce primary copolymer particles, which will then grow by monomer swelling, provided by the droplet reservoirs (yellow), to finally give rise to the formation of polydisperse polymer particles with an irregular shape, the result of the difference in reactivities between St and DVB. Herein, an alternative effortless route of poly(St-co-DVB) microsphere preparation is presented at a relatively high polymer particle content at different DVB crosslinking degrees. To obtain monodisperse and homogeneous latex particles, optimizing the copolymerization conditions is required by modifying, for instance, DVB feeding, initiator nature, and concentration and/or by functionalizing the SNps.
# Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
## 3 Averaging a Set of GPS Time Series
### Averaging and Outliers Removal  
The averaging is obtained using either medoid or centroid approaches, which corresponds to Steps 2 and 4 in Figure 1. When iTEKA centroid (Algorithm 1) is selected, the medoid according to the \(\mathcal{K}_{\text{rdtW}}\) measure is used to initiate the reference time series \(C_{0}\).  
Once the centroid \(C\) of set \(\mathbf{X}\) is obtained, the mean \(\mu_{C}\mathbf{x}\), and the variance, \(\sigma_{C}\mathbf{x}\), of \(Log(\mathcal{K}_{\text{rdtw}}(C,\mathbf{x}))\) measure are evaluated, as \(x\) samples the elements of \(X\).  
For iTEKA approach, when \(\sigma_{C}\mathbf{x}>\tau\), the time series \(\mathbf{x}\) such that  
\[Z_{score}(x)=\frac{Log(\mathcal{K}_{\text{rdtw}}(C,\mathbf{x}))-\mu_{C} \mathbf{x}}{\sigma_{C}\mathbf{x}}\geq 0 \tag{12}\]  
are removed from the set \(\mathbf{X}\), as far as \(|\mathbf{X}|\geq 3\). Here, \(\tau\) is a threshold that we empirically set to 5. Basically, all trajectories that are \"log-distant\" of at least one standard deviation are removed if \(\sigma_{C,\mathbf{X}}>5\), and are kept otherwise.  
For all other methods, the \(Log(\mathcal{K}_{\text{rdtw}})\) is replaced by the similarity measure that is used instead. This corresponds to Step 3 of the procedure depicted in Figure 1.  
Once the outliers have been removed from set \(\mathbf{X}\), if any, a second averaging procedure is then carried out on the new set \(\mathbf{X}\) initialized with the previous centroid estimation, \(C_{0}=C\) (Step 4 in Figure 1). The final centroid estimation \(C\) associated to the initial set \(\mathbf{X}\) of GPS trajectories is finally provided by the averaging procedure depicted in Figure 1.  
Figure 5 gives an example of this outlier removal procedure used during Step 3. Note that this procedure does not guarantee that the centroid estimate would be closer to the ground truth. Sometimes, once the outlier removal has been applied, the centroid estimate worsens the assessment measure. However, at least on the training data, it brought on average some assessment measure improvement.
# Study on the Development Rule of Mudstone Cracks in Open-Pit Mine Dumps Improved with Xanthan Gum
###### Abstract  
The stability of open-pit mine slopes is crucial for safety, especially for spoil dump slopes, which are prone to cracks leading to landslides. This study investigates the use of xanthan gum (XG) to enhance the stability of mudstone in spoil dumps. Various concentrations of xanthan gum were mixed with mudstone and subjected to dry-wet cycle tests to assess the impact on crack development. Pore and crack analysis system (PCAS) was utilized for image recognition and crack analysis, comparing the efficiency of crack rate and length modification. The study found that xanthan gum addition significantly improved mudstone's resistance to crack development post-drying shrinkage. A 2% xanthan gum content reduced the mudstone crack rate by 45% on average, while 1.5% xanthan gum reduced crack length by 46.2% and crack width by 26.3%. Xanthan gum also influenced the fractal dimension and water retention of mudstone cracks. The optimal xanthan gum content for mudstone modification was identified as between 1.5% and 2%. Scanning electron microscopy imaging and X-ray diffraction tests supported the findings, indicating that xanthan gum modifies mudstone by encapsulation and penetration in wet conditions and matrix concentration and connection in dry conditions. These results are expected to aid in the development of crack prevention methods and engineering applications for open-pit mine spoil dump slopes.  
open-pit mine; xanthan gum; modification efficiency; crack ratio; modification mechanism
## 3 Research Design
### Relationship Strength Quantification Methods
#### 3.4.1 Relationship Strength Function  
The _calculate_relationship_strength_ function is formally defined as follows. Let  
\[calculate\_relationship\_strength:AOI_{i}\times AOI_{j}\times G\times w\to\mathbb{N}\]  
be a function that computes the relationship strength between two Areas of Interest, defined by  
\[calculate\_relationship\_strength(AOI_{i},AOI_{j},G,w)=\big{|}\big{\{}g\in G:g\in E(L _{i,j},w)\big{\}}\big{|}\]  
where  
* \(AOI_{i},\ AOI_{j}\) are regions of interest with centroids \(c_{i},c_{j}\in\mathbb{R}^{2}\);
* \(G\ =\ \{g_{1},g_{2},\ldots,g_{n}\}\subset\mathbb{R}^{2}\) is the set of fixation points;
* \(w\in\mathbb{R}^{+}\) is the buffer zone width parameter;
* \(L_{i,j}\) denotes the line segment connecting centroids \(c_{i}\) and \(c_{j}\);
* \(E(L_{i,j},w)\) is the expanded buffer region centered on \(L_{i,j}\) with width \(w\).  
The core implementation of this function consists of three main steps: buffer zone definition, fixation point counting, and strength quantification. First, the function defines an adaptive buffer zone with a width of one-tenth of the image size:  
\[w\ =\ \frac{\min(\text{image\_width},\text{image\_height})}{10}\]  
The algorithm iterates over all possible AOI center pairs and creates a connection line between each pair \((A,B)\), extending it into a buffer zone. As shown by the red points in Figure 8, the system counts the number of fixation points that fall within the buffer zone as an indicator of relationship strength, while gray points represent fixation points outside the buffer zone that are not considered. This method not only accounts for the direct connection between AOI centers but also incorporates the surrounding distribution of visual attention, providing a more comprehensive evaluation of relationship strength.  
The calculation of relationship strength can be described by the following mathematical expression:  
\[S(i,j)\ =\ \big{|}\big{\{}g\in G\ :\ g\in E\big{(}L_{i,j},w\big{)}\big{\}} \big{|}\]  
where \(S(i,j)\) is the relationship strength between AOIs \(i\) and \(j\), \(G\) denotes the set of fixation points, and \(E(L_{i,j},w)\) defines the expanded area centered on line segment \(L_{i,j}\) with a width of \(w\).
## 2 Materials and Methods
### Experimental Setup  
The schematic of the PDC experimental facility is presented in Figure 2. The main features are the high-pressure fuel (hydrogen) and oxidizer (air or oxygen) supply lines, a command-and-control panel, and the data acquisition system. The main air supply line can provide a maximum pressure of 11 bar, at a maximum temperature of 750 K (via an electric heater placed on the line) and a maximum mass flow rate of up to 1 kg/s. The oxygen supply line can provide a maximum pressure of 20 bar at ambient temperature and a maximum mass flow rate of 0.2 kg/s. The hydrogen supply line can provide a maximum pressure of 20 bar at ambient temperature and a maximum mass flow rate of 0.05 kg/s. The rig employs a variable frequency spark plug for ignition. ALICAT MQ-250 SLPM-D (from ALICAT Scientific Inc., Tucson, AZ, USA) for hydrogen (position 58) and ALICAT MQ-2000 SLPM-D (from ALICAT Scientific Inc., Tucson, AZ, USA) for oxidant (position 41) flowmeters are incorporated into the hydrogen and air/oxygen supply lines to record the operating conditions in terms of pressure, flow rate and temperature, hence the equivalence ratio can be determined. As a safety measure, hydrogen sensors are continuously monitored within the test setup. One-way valves and flame arrestors are also mounted on the gas lines for safety purposes. The rest of the positions in Figure 2 are described extensively in [27].  
For thrust measurements, a STA-3-100 LCM Systems load cell sensor (from LCM Systems, Newport, UK) with 20 gF accuracy is employed. The PDC breadboard is allowed to slide freely on a sleigh with respect to the test bed, by means of three lubricated ball sliders placed vertically. The load sensor is placed vertically, underneath the experimental model, and connects with its moving end to the experimental model, with its fixed end to the test rig. The load sensor is axially aligned with the experimental model longitudinal axis to minimize thrust measurement errors (see Figure 2).  
Load sensor calibration was performed prior to the experimental campaign using 10 identical 5 lbs flanges. To account for the sensor hysteresis and measurement replication, two loading and two unloading paths were used for calibration. The transfer function from mA to N is the result of a linear regression with R\({}^{2}\) = 0.99. To ensure data accuracy, multiple calibrations were performed each time the exhaust pipe was changed to account for the change in preload introduced by the supply lines. The measured thrust values are time-averaged, as this is the actual propulsion force the thruster is able to provide, and  
Figure 1: Pulsed detonation chamber architecture [27].  
this should be the basis for determining the specific impulse it yields. The time-averaged thrust of the PDC was determined through the dynamic force by time averaging. The data acquisition system includes a Strain Gauge Amplifier - SGA (from Micron Meters, Georgia, GA, USA) [28] connected to an acquisition board and the experimental facility computer.
###### Abstract
In this paper, we propose a simulation method for the demodulated sound of a PAL based on the sequential acoustic ray-tracing method. Discrete acoustic rays are generated sequentially along the propagation paths of the ultrasound, thereby generating the demodulated sound through the interactions among the ultrasound rays. Compared with traditional ray-tracing methods, the proposed method considers the self-demodulation phenomenon of PALs to achieve higher accuracy. Compared with FDTD-based methods, the proposed method can reduce the calculation complexity drastically because only the discrete acoustic rays are involved in the calculation rather than the whole simulation space. As mentioned, in practical applications of PAL such as [31], what we are most concerned about is the area covered by the reproduced sound rather than the detailed sound field. The proposed method is more feasible in such scenes as it has a much lower calculation complexity. The rest of this paper is organized as follows. Section 2 gives a brief introduction of the basic principles of PALs that act as the theoretical support of the proposed method. Section 3 describes the proposed method in detail. Section 4 demonstrates the effectiveness of the proposed method through a comparison between numerical simulation and real-world measurements. Finally, Section 5 provides conclusions.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 3 Experiment and Results
### Comparative Experiment
#### 3.5.3 Routing Algorithm  
In order to verify the advanced nature of the FM routing algorithm used in this paper, we compare it with other commonly used routing algorithms. According to the original contrastive learning architecture, different routing algorithms (three-iteration dynamic routing, three-iteration EM routing, and FM routing algorithms) are used to train in ShapeNetCore with a cycle of 100. The trained models are classified on ModelNet40 using the method described in Section 3.3.  
Table 6 shows the impact of the different routing methods on time consumption and classification accuracy. In terms of time consumption, the FM routing algorithm training cycle requires 41 min, which saves 12 min compared to the best comparison method. This shows that the computational complexity of this method is lower than that of the dynamic routing and EM routing methods. In terms of classification accuracy, the FM routing algorithm, when compared with the other two methods, achieves the best results, indicating that this method enables the model to learn visual representations better.
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 5 Numerical Results and Discussion  
### Effect of Imperfections on Stability  
According to the method described by [PERSON] and [PERSON] [40], the instability modes of contained global buckling, mixed buckling and local buckling. As shown in Figure 15, the out-of-plane displacement of the red region was larger than that of the blue region. When D was 0, the instability mode of the defect-free model was global buckling. When D is 40 mm or 80 mm, the local buckling of the panel occurred in the debonding area. The larger the proportion of the structure occupied by the defect was, the more obvious the local instability mode of the structure was. It is worth noting that the small damages or defects occurring in specimens during the molding, manufacturing, and transportation processes may lead to asymmetry in the structure during the test process, and these factors were not considered in the linear buckling analysis.  
Figure 14: The grid discretization and boundary conditions of the debonding model.  
Figure 15: Buckling modes of different honeycomb panels. (**a**) the first-order and second-order buckling modes of non-defective specimens; (**b**) the first-order and second-order buckling modes of specimens with defective diameter 40 mm; (**c**) the first-order and second-order buckling modes of specimens with defective diameter 80 mm.  
As given in Table 8, the experimental results of the first-order buckling load of the two types of defective honeycomb panels were basically consistent with the simulated values. However, the simulated value of the defect-free honeycomb panel was higher than its failure load, this is because the material failure was not considered during the linear buckling analysis. The sloped area was just a weak link of the edge closed structure, so before the global buckling of the structure occurred, the sloped area was damaged, which resulted in losing bearing capacity in advance. Overall, further nonlinear post-buckling analysis is required in simulation in order to accurately assess the final bearing capacity of the structure.  
### Effect of Imperfections on Stability  
The particularity of the material molding process made the structure itself have certain initial defects. After the buckling deformation, the structure showed obvious nonlinearity. Therefore, the nonlinear analysis of sandwich panels needed to introduce initial geometric defects to accurately obtain the final failure load of the structure. Considering that the first-order buckling mode and buckling load obtained by simulation were close to the test results, the node displacement of first-order buckling was introduced into the post-buckling model of the honeycomb panel. As given in Table 9, the ultimate loads of the honeycomb panel obtained by the numerical analysis method in this paper were in good agreement with the experimental values, and the maximum relative error did not exceed 10%. Compared with CD0, the carrying capacities of the CD40 and CD80 structures decreased by 9.9% and 22.1%, respectively.