esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Out-of-Plane Flexural Behavior of SC Composite Beams with Different Shear Connection Ratios
### The Minimum Shear Connection Ratio  
The flexural behavior of the SC composite beam could mainly depend on the tension of the steel plate and the interfacial shear of tie-bars. The shear behavior of tie-bars under cyclic loading are brittle, which significantly affects the flexural behavior of the SC composite beam. For determining the lowest shear connection ratio of SC composite beams subjected to out-of-plane cyclic loading, the flexural behavior of SC composite beams with different shear connection ratios from 1.30-1.60 were investigated.  
Figure 13a shows the tension of the steel plate versus the displacement curves of the SC composite beams. It indicates that the ultimate tension of the steel plate and the ultimate displacement increase with the increase in the shear connection ratio. Figure 13b shows the interfacial shear slip distribution when the tensile steel plate was yield. The minimum shear connection ratio is when the tensile steel plate was yield and the SC composite beam reached theoretical value. According to the simulation results, the minimum shear connection ratio of the SC composite beam with a 6.67 shear span ratio is 1.55. Table 3 shows the tension of the tensile steel plate at yield displacement and ultimate displacement as well as the ductile coefficient of SC composite beams with different shear connection ratios. The ductile coefficient of the SC composite beam with 1.55 shear connection ratio reaches 1.83, which indicates that the SC composite beam subjected to cyclic loading has good ductility and seismic performance.  
Figure 12: Interfacial shear slip distribution curves of SC composite beams with different shear connection ratio.
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 4 System Validation
### Experimental Comparison between Passive and Active Technologies
#### 4.1.3 Comparative of Results: Selection of the RFID Technology  
Although active RFID technologies have some important advantages, mainly a higher sensitivity which allows a longer distance range, they also have important drawbacks, such as:  
* cost per tag
* need for battery replacement and higher maintenance costs
* higher probability of interference with other ISM networks
* proprietary communications protocols  
By contrast, using passive technology:
* The obtained distance range (around 7 m from static experiments) seems satisfactory for inventory of traffic signs and other urban facilities, since the identifiers will be read progressively while the reader is approaching to the tag, employing a usual vehicle speed (50-100 Km/h, i.e., 14-28 m/s) and considering the theoretical data rate (40 Kbps according to the EPC Global 1G2 ISO 18000-6C [42], i.e., around 400 tags/s for the employed tag).
* A complex anti-collision protocol is not necessary, since it is possible to detect several tags placed at the same metallic plate.  
Therefore, at this point, we can conclude that active technology will probably oversize the system, exceeding the reasonable application requirements with an additional higher cost, which yields passive technologies as a more convenient option for this application, since they are open-standard systems and more cost-efficient. These conclusions will be validated by the in motion test at simulation (Section 4.2) and experimental level (Section 4.3).
# Gait Phase Estimation Based on User-Walker Interaction Force
## 2 Method for Asymmetric Gait Phase Estimation
### Adaptive Rules for Transient Parameters
#### 2.4.2 Adaptive Rule for a Parameter  
The component of the interaction force used for gait phase estimation is the slower oscillation that reflects walking, whose amplitude can vary with the walking posture or elbow movements.The phase dynamics of the AO can be expressed as follows:  
\[\phi=\omega-\frac{\sigma}{r}\times F\times\sin\varphi \tag{9}\]  
when the residual \(F\) is large enough and \(F\times\sin\varphi>0\),  
\[\phi=\omega-\frac{\sigma}{r}\times F\times\sin\varphi<0 \tag{10}\]  
Although the AO phase is supposed to increase monotonically, the estimated phase decreases when the condition above is satisfied.  
To prevent this problem, feedback gain \(\sigma\) can be reduced for \(\dot{\varphi}\) to be positive when the transient change occurs, thus limiting fluctuations in the estimated phase. However, a small \(\sigma\) increases the time taken by the oscillator to synchronize with the input signal. This not only influences the estimation accuracy initially but also limits the ability to track gait changes [20]. Alternatively, we apply an adaptive rule for \(\sigma\) when a sudden change in amplitude occurs.  
A phase fluctuation can be identified by observing the difference between \(\dot{\varphi}\) and \(\omega\). For a small difference, \(\sigma\) stays at its initial value. For a large difference, we replace \(\sigma\) with \(\sigma\) as follows:  
\[\sigma=\lambda\times\frac{(r\times\omega)}{F\times\sin\varphi} \tag{11}\]  
where \(\lambda\) is a positive constant less than 1. \(\dot{\varphi}\) is equal to \((1-\lambda)\times\omega\), guaranteeing that the phase changes almost as fast as the frequency. As a transient phase change is uncommon during walking, we consider a large phase change as a disturbance in the system dynamics. Therefore, we change the parameters to minimize the influence of such a disturbance. In this study, the difference between \(\dot{\varphi}\) and \(\omega\) is considered large when it is larger than 30% of \(\omega\), which means the adaptive rule is applied when the change rate of phase is lower than 70% of the estimated frequency. As a result, a transient change can be bounded to improve the robustness against the disturbance and achieve smooth gait phase estimation. In addition, fast synchronization can be achieved when the difference in phase change is small, allowing synchronization to be maintained during walking and promoting accurate gait phase estimation.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 3 Synthesis Methods and Their Influence on the Structure and Properties of HEA Alloys
### Influence of the Synthesis Method on the Structure and Properties of HEA Alloys
#### 3.2.1 Influence on the Microstructure of Different HEA Alloys
A group of researchers from Japan [75; 76] published two studies several years apart about the synthesis of the AlCrFeCoNi HEA alloy using the additive manufacturing technique known as SEBM (Selective Electron Beam Melting) or selective laser melting. Their study revealed that the HEA produced using SEBM shows a microstructure with both BCC and B2 phases, similar to what has been reported for alloys made via melting and casting [77], despite the rapid solidification associated with SEBM. Additionally, an FCC phase was detected at the base of the HEA fabricated by SEBM. This FCC phase may have precipitated from the BCC or B2 phase at lower temperatures during the SEBM process. It is also possible that phase changes occurred during the preheating phase of the SEBM process. The presence of both BCC and FCC phases in the AlCrFeCoNi alloy was also confirmed by [PERSON] et al. [78] who used a powder metallurgy approach (MA + SPS) to create the same HEA alloy. Using the laser powder bed fusion (LPBF) additive manufacturing technique, [PERSON] et al. [79] created an AlCrFeCoMnNi-type HEA alloy. The microstructure of this alloy displayed the presence of B2 (enriched with Ni-Al) and BCC (enriched with Fe-Cr) solid solutions. This outcome differs from the BCC and FCC phases observed in the alloy when synthesized through powder metallurgy or melting and casting methods [80; 81]. When [PERSON] et al. [82] utilized laser-based 3D printing technology to produce a CoCrFeMnNi-type HEA alloy, they observed fine BCC grains distributed along the grain boundaries of the FCC matrix. This observation contrasts with the single FCC phase reported by [PERSON] et al. [83], [PERSON] et al. [15], [PERSON] et al. [84], and [PERSON] et al. [85], who processed the same HEA alloy using various melting and casting methods. Similarly, [PERSON] et al. [86], confirmed the exclusive presence of the FCC phase in the CoCrFeMnNi alloy when produced using powder metallurgy methods.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 7 Conclusions  
In this work, we explored the use of 1D CNNs for performance prediction based on the CIR in UWA communications. We compared the results with those of conventional machine learning methods using traditionally available channel features as well as the features derived only from the CIRs. A brief investigation of using the trained CNN as a featurizer was also performed. This featurizer method opens up opportunities for easier implementation, as the convolutional layers could be implemented as FIR filters and less computation could be required within an adaptive modem or other application of this method. It was demonstrated that on a varied dataset such as SPACE08, the CNN-based methods can outperform conventional ones and their performances are comparable when using on a homogeneous dataset. The results in this work revealed that the CNN is a promising technique to extract useful information from UWA channels.  
Conceptualization, E.L. and Z.W.; methodology, E.L. and Z.W.; software, E.L.; validation, E.L.; formal analysis, E.L. and Z.W.; investigation, Z.W.; resources, Z.W.; data curation, E.L. and Z.W.; writing--original draft preparation, E.L.; writing--review and editing, E.L. and Z.W.; visualization, E.L.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the NSF grant ECCS-1651135 (CAREER).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset** & **MAE** & **Pearson Corr. Coeff.** \\ \hline SPACE08 using KWAUG14 Train & 0.047 (\(5.5\times 10^{-3}\)) & 0.57 (\(2.5\times 10^{-2}\)) \\ KWAUG14 using SPACE08 Train & 0.027 (\(1.3\times 10^{-3}\)) & 0.42 (\(2.1\times 10^{-2}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Convolutional neural network transfer learning regression results.
## 2 Results and Discussion
### Solid State \({}^{29}\)Si CP/MAS NMR Spectroscopy  
Figure 2 compares the \({}^{29}\)Si CP/MAS-NMR spectra of representative NR/WMS-NH\({}_{2}\) with that of WMS-NH\({}_{2}\) to determine the degree of amino groups anchored onto the silica surface. All samples displayed three distinct signals in the range of chemical shifts between \(-\)80 and \(-\)120 ppm, corresponding to different framework silica species (Q\({}^{n}\)). The signals at \(-\)92, \(-\)101 and \(-\)111 ppm were, respectively, assigned to Q\({}^{2}\) (Si(OSi)\({}_{2}\)(OH)\({}_{2}\)), Q\({}^{3}\) (Si(OSi)\({}_{3}\)(OH)) and Q\({}^{4}\) (Si(OSi)\({}_{4}\)). The functionalization with APS gave two more signals at \(-\)57 and \(-\)67 ppm, relating to T\({}^{2}\) (R-Si(OSi)\({}_{2}\)(OH)) and T\({}^{3}\) (R-Si(OSi)\({}_{3}\)) species, respectively, where R represents the aminopropyl groups [28; 29]. This result indicated that the amino-functional moieties were incorporated as part of the silica framework. The  
Figure 1: FTIR spectra of (a) WMS, (b) WMS-0.10, (c) NR/WMS, (d) NR/WMS-0.05, (e) NR/WMS-0.10, and (f) NR/WMS-0.15.  
relative population of silicon environments was calculated by deconvolution of the NMR spectra into individual Gaussian peaks. The NR/WMS composite gave higher relative intensities of Q\({}^{2}\) and Q\({}^{3}\) than pure silica WMS (Table 1), indicating a high amount of free silanol and remnant ethoxy groups on the surface of WMS in the presence of NR. This result suggested that the NR molecule incorporated into the mesostructure resulted in a reduction in the hydrolysis and condensation of TEOS [13]. The fraction of T\({}^{\text{m}}\) species, reported as a \(\Sigma\)T\({}^{\text{m}}\)/\(\Sigma\)(T\({}^{\text{m}}\) + Q\({}^{\text{m}}\)) ratio, of WMS-NH\({}_{2}\) (17.53%) was higher than that of NR/WMS-NH\({}_{2}\) (15.09%), which was in accordance with their nitrogen content, obtained from the elemental analysis (Section 2.3). Although the presence of the rubber phase hampered the surface functionalization, a significant decrease in the relative amount of Q\({}^{2}\) and Q\({}^{3}\) signals of the NR/WMS-NH\({}_{2}\) signified the substitution of surface silanol groups by the incorporated aminopropyl groups.
## 2 The Typical PV Power System with Three-Level Structure
### Analysis of Energy Characteristics of Different PV Access Points
#### 2.1.1 PV Access Points Located in the Main Switchboards  
First, consider the case of PV access points located in the main switchboards MS\({}_{1}\) and MS\({}_{2}\), whose function is to provide the energy for the whole islanded power system. In Figure 2a, assume that the PV power sources of PV\({}_{1-1}\) and PV\({}_{1-2}\) access the main switch  
Figure 4: Energy flow diagram in the third switchboard: (**a**) PV\({}_{3}\) accessing the switchboard; (**b**) simplified flow diagram of (**a**); (**c**) PV\({}_{3}\) and emergency generator G4 accessing the switchboard; (**d**) simplified flow diagram of (**c**).  
boards, where \(u_{\text{PV1-1}}\), \(u_{\text{PV1-2}}\) and \(S_{\text{PV1-1}}\), \(S_{\text{PV1-2}}\) are the input voltages and apparent powers of the access points PV1-1 and PV1-2; \(u_{\text{G1}}\), \(u_{\text{G2}}\), and \(u_{\text{G3}}\) and \(S_{\text{G1}}\), \(S_{\text{G2}}\), and \(S_{\text{G3}}\) are the corresponding voltages and apparent powers produced by the generators G1, G2, and G3; \(Y_{\text{L1-1}}\) and \(S_{\text{L1-1}}\), \(Y_{\text{L1-2}}\) and \(S_{\text{L1-2}}\), and \(Y_{\text{L1-3}}\) and \(S_{\text{L1-3}}\) are the corresponding admittances and apparent powers as the power flows through the cables to the user loads Load1-1, Load1-2, and Load1-3; \(Y_{\text{12-1}}\) and \(S_{\text{12-1}}\), \(Y_{\text{12-2}}\) and \(S_{\text{12-2}}\), \(Y_{\text{12-3}}\) and \(S_{\text{12-3}}\), \(Y_{\text{12-4}}\) and \(S_{\text{12-4}}\), and \(Y_{\text{12-5}}\) and \(S_{\text{12-5}}\) are the corresponding admittances and apparent powers as the power flows through the cables to the second switchboards SS1, SS2, and SS3. Note that the blue energy path indicates that two-way flow is allowed.  
Here \(u_{\text{PV1}}\), \(S_{\text{PV1}}\) and \(u_{\text{G}}\), \(S_{\text{G}}\) are the total input voltages and apparent powers of the PV power sources PV1-1 and PV1-2 and the three generators G1, G2, and G3; \(Y_{\text{out1-1}}\) and \(S_{\text{out1-1}}\) as well as \(Y_{\text{out1-2}}\) and \(S_{\text{out1-2}}\) are the sum of the total admittance and apparent power for the main switchboards MS1 and MS2. Therefore, \(Y_{\text{out1}}=Y_{\text{out1-2}}+Y_{\text{out1-2}}\), and \(S_{\text{out1}}=S_{\text{out1-2}}+S_{\text{out1-2}}\). Figure 2a can be simplified into Figure 2b. It should be noted that, when MS1 is linked to MS2, PV1-1 and PV1-2 can be combined into a single PV model. Therefore, the influences of PV1-1 and PV1-2 on the main switchboard are the same. It is also the reason that PV1-1 and PV1-2 are connected to the main switchboard, as seen in Figure 2a. Similarly, for the generators, whether the three generators are all open or only one or two of them are depends on the control strategy of the system.
## 3 Results  
### Validation of Model Simulated NPP Results  
Figure 2 shows the validation of model simulated NPP data against field survey data. The linear regression slope was 0.71 (R\({}^{2}\) = 0.85, \(p\) < 0.001). The simulated NPP results were 29% higher than field survey data. The field survey data represented the actual carbon assimilation amount of cropland ecosystem. The actual carbon sequestration amount was derived from ecosystem NPP minus heterotrophic respiration and external disturbances. Moreover, because of farmers' intensive requirement of soybean seed, we harvested all crop components except soybean seed when we herborized the field crops in maize-soybean intercropping cropland. These two above reasons caused the difference between the simulated results and the field survey data.
## 5 Implementation and Evaluation
### Feature Selection  
As mentioned in Section 4.2, several feature selection methods evaluated in the present study selects the feature indices \(k\) (30, 50 or 80) experimentally, to show the influence of sample size on accuracy. The feature selection algorithms that use mutual information as a relationship measure are explained in Table 2. Among the different feature selection algorithms evaluated, MIFS and CIFE are explained in the following section as they achieve better recognition accuracy and the details are discussed in the subsequent sections.  
\begin{table}
\begin{tabular}{r p{142.3 pt}} \hline \hline
**Algorithms** & **Description** \\ \hline MIFS [20] & A greedy approach that selects only highly informative feature and forms an optimal feature subset. It identifies the non-linear relationship between the selected feature and its output class to reduce the amount of redundancy and uncertainty in the feature vector [61; 62; 63]. \\ mRMR [21; 22] & An incremental feature selection algorithm that forms an optimal feature subset by selecting features with minimum Redundancy and Maximum Relevancy. \\ CIFE [23] & It forms an optimal feature subset that maximizes the class-relevant information between the features by reducing the redundancies among the features. \\ JMI [24] & An increment to mutual information which finds the conditional mutual information to define the joint mutual information among the features and eliminates the redundant features if any. \\ CMI [25] & Selects the features only if it carries additional information and eases the prediction task of the output class. \\  & DISR measures the symmetrical relevance and combines all features variable to describe more information about the output class instead of focusing on individual feature information. \\ ICAP [27] & Features are selected based on the interactions and understand the regularities of the feature set. \\ CONDRED [28] & Identifies the conditional redundancy exists between the features. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mutual information-based feature selection Algorithms.  
Figure 8: The 2D contour plot of third order cumulants (**a**) ‘HORSE’ (Sample 25); (**b**) ‘MONKEY’ (Sample 27); (**c**) ‘SNAKE’ (Sample 30); (**d**) ‘TIGER’ (Sample 31).
# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 6 Dynamic Route-Planning System and Computational Results with Managerial Insights
### Dynamic Route-Planning System  
The routes for movement within a DC are initially determined by the shortest-path model and an A-star algorithm. If the solutions from the A-star algorithm have high accuracy when compared with the solutions from the shortest-path model, the A-star algorithm is used due to the shorter runtime and the computing resources required by the automated guided vehicle. Note that an automated guided vehicle is allowed to carry one shipment at a time, due to the standard size of the pallet that can be loaded into a cargo. The input data, which include real-time positions of obstacles, as well as the generated routes, are collected as a training dataset. Once there are sufficient training datasets, an ANN model for a dynamic routing problem is created and used to determine the routing solution. The quality of the solutions from the ANN model needs to be tested with the solutions from the shortest-path model on a regular basis. A requirement is specified according to the accuracy of the ANN model; if the accuracy falls below 95%, the ANN model needs to be updated.  
Note that, for any internal movement within a DC, the gap between aisles is narrow; as a result, passing obstacles is not possible. If the path segment contains an obstacle, it is excluded from consideration. Moreover, to consider real-time obstacles in the planning process, the route is generated using the logic provided in Figure 8. At a starting point, a route is generated and used to guide the movement of the vehicle. When an automated guided vehicle reaches an intersection, the positions of obstacles are retrieved. If the positions are unchanged or not on the generated path, the route does not need an update; otherwise, the route needs to be regenerated.  
Figure 7: Distribution center 2 (DC2): (**a**) a very narrow aisle layout at DC2; (**b**) a simulated environment of an internal layout at DC2.
###### Abstract  
Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO\({}_{4}\)) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)--a massive waste generated by copper mining activities worldwide--employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO\({}_{4}\). CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7-388 days) and following immersion in a 5 wt.% MgSO\({}_{4}\) solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by \(\leq\)10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO\({}_{4}\) exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28-100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180-360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C-S-H into non-ceentitious M-S-H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials.  
copper slag; magnesium sulfate; recycled aggregate; compressive strength; microstructural analysis +
Footnote †: journal: Applied Sciences  
Article