esa-sceva/satcom-chunk-collection huggingface dataset:
# A Finite Element Investigation into the Effect of Slope of Grain on Wood Baseball Bat Durability
###### Abstract  
Bat durability is defined as the relative bat/ball speed that results in bat breakage, i.e., the higher the speed required to initiate bat cracking, the better the durability. In 2008, Major League Baseball added a regulation to the Wooden Baseball Bat Standards concerning Slope-of-Grain (SoG), defined to be the angle of the grain of the wood in the bat with respect to a line parallel to the longitudinal axis of the bat, as part of an overall strategy to reverse what was perceived to be an increasing rate of wood bats breaking into multiple pieces during games. The combination of a set of regulations concerning wood density, prescribed hitting surface, and SoG led to a 30% reduction in the rate of multi-piece failures. In an effort to develop a fundamental understanding of how changes in the SoG impact the resulting bat durability, a popular professional bat profile was examined using the finite element method in a parametric study to quantify the relationship between SoG and bat durability. The parametric study was completed for a span of combinations of wood SoGs, wood species (ash, maple, and yellow birch), inside-pitch and outside-pitch impact locations, and bat/ball impact speeds ranging from 90 to 180 mph (145 to 290 kph). The \"MAT_WOOD (MAT_143) material model in LS-DYNA was used for implementing the wood material behavior in the finite element models. A strain-to-failure criterion was also used in the \"MAT_ADD_EROSION option to capture the initiation point and subsequent crack propagation as the wood breaks. Differences among the durability responses of the three wood species are presented and discussed. Maple is concluded to be the most likely of the three wood species to result in a Multi-Piece Failure. The finite element models show that while a 0\({}^{\circ}\)-SoG bat is not necessarily the most durable configuration, it is the most versatile with respect to bat durability. This study is the first comprehensive numerical investigation as to the relationship between SoG and bat durability. Before this numerical study, only limited empirical data from bats broken during games were available to imply a qualitative relationship between SoG and bat durability. This novel study can serve as the basis for developing future parametric studies using finite element modeling to explore a large set of bat profiles and thereby to develop a deeper fundamental understanding of the relationship among bat profile, wood species, wood SoG, wood density, and on-field durability.  
b acseball; bat; durability; finite element; impact; slope of grain; wood +
Footnote †: journal: Applied Sciences
## 2 Numerical Model
### The Coriolis Correction  
The Coriolis force due to the rotation of the Earth is proportional to the velocity and since the velocity differs with height this leads to a turning of the wind with height, a so-called wind veer. In the FBL the mean profile of the wind shear including wind veer is added using body forces. Deviation from the mean due to wakes and turbulence is not handled by the FBL and an extra Coriolis correction body force \(\mathbf{f}_{Corr_{mn}}\) needs to be added in Equation (1) to include this. Equations (3) and (4) show the added forces (before non-dimensionalization) associated to the Coriolis correction in the \(x\) respectively \(z\) directions. Here \(\Omega\) is the rotation of the Earth, \(\phi\) is the latitude, \(u\), \(w\) are the instantaneous local velocities and \(U\), \(W\) are the mean values used in the FBL. The latitude 43.3 degrees is used in the studied case which corresponds to the Coriolis parameter \(f=10^{-4}\ \frac{1}{s}\), also used by [PERSON] [15]).  
The non dimensionalized (with respect to \(R\) and \(U_{0}\) ) form, \(\mathbf{f}_{Corr_{nan}}\), of Equations (3) and (4) are added in Equation (1).  
\[\mathbf{f}_{x,Corr}=-2*\Omega*sin(\phi)(w-W)=-f*(w-W) \tag{3}\]  
\[\mathbf{f}_{z,Corr}=2*\Omega*sin(\phi)(u-U)=f*(u-U) \tag{4}\]
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Deposition Techniques
#### 5.1.1 Chemical Vapour Deposition  
Chemical vapour deposition is not really suitable for deposition on flexible polymer materials, as they cannot withstand the ultrahigh temperature treatment that comes with this method [50]. Moreover, it is very challenging to achieve selective deposition of carbon-based material on a specific area with chemical vapour deposition. However, some micro-supercapacitors were produced using this method [135; 136; 137; 138; 139].  
Vertically aligned graphitic nanofibers were grown using chemical vapour deposition, and their surface was then coated with ruthenium oxide thin films through an electrochemical deposition process; these coated fibres acted as electrodes in supercapacitors [140]. A solid-state micro-supercapacitor, with thermally reduced graphene oxide, was produced through atmospheric pressure chemical vapour deposition [141].
## Materials and Methods
### Analysis of PLS Model  
The partial least squares organically combines the model and cognitive methods. Under regression modeling (multiple linear regression), data structure simplification (principal component analysis) and correlation analysis between two sets of variables can be performed simultaneously [36]. In this study, the prediction model was built on the PLS algorithm.  
In this study, the following parameters were selected to evaluate the accuracy of the model: correction determination coefficient (\(R_{c}^{2}\)), cross-validated determination coefficient (\(R_{c}^{2}\)), prediction determination coefficient (\(R_{p}^{2}\)), the root mean square error of calibration (RMSEC), the root mean square error of cross-validation (RMSECV), and the root mean square error of prediction (RMSEP). The larger the \(R^{2}\), the more accurate the predictive ability of the mode, and the RMSE represents the stability of the model [37]. The lower the value of these three values, the higher the reliability of the model. The correlation coefficient (\(R\)) is used to measure the correlation between two variables; the closer \(R\) is to 1, the higher the correlation. In this paper, when \(R>0.8\), the corresponding variables are defined as strongly correlated variables.  
\[R^{2}=\frac{\sum_{i=1}^{n}(\hat{y}_{i}-\overline{\hat{y}})(y_{i}-\overline{y}) ^{2}}{(n-1)\sum_{i=1}^{n}\left(y_{i}-\hat{y}\right)^{2}\sum_{i=1}^{n}\left( \hat{y}_{i}-\overline{\hat{y}}\right)^{2}} \tag{2}\]  
\[\text{RMSEC}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{3}\]  
\[\text{RMSECV}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i*}\right)^{2}}{n}} \tag{4}\]  
\[\text{RMSEP}=\sqrt{\frac{\sum_{i=1}^{m}\left(\hat{y}_{i}-y_{i}\right)^{2}}{m}} \tag{5}\]  
where \(\hat{y}_{i}\) is the value predicted by the calibration model, \(y_{i}\) is the reference value, \(\hat{y}_{i*}\) is the value predicted by the cross-validation model, \(\overline{y}\) is s the mean of the reference values, \(\overline{\hat{y}}\) is the mean of the predicted values, \(n\) is the number of samples in the calibration or validation steps, and \(m\) is the number of predicted samples.
# Exploring Convective Drying Behavior of Hydroxide Sludges Through Micro-Drying Systems
## 3 Results and Discussion
### Activation Energy  
The activation energy can be determined by plotting In (Deff) as a function of 1/T, which represents the influence of different drying air temperatures on the effective diffusivity. The effective moisture diffusivity Deff was calculated using Equation (4) and is shown in Table 3. The effective diffusivity values of dried samples at 70-110 \({}^{\circ}\)C varied in the range of 3.73 \(\times\) 10\({}^{-9}\) and 1.75 \(\times\) 10\({}^{-8}\) m\({}^{2}\)/s. that Deff values increased greatly with increasing temperature. Drying at 110 \({}^{\circ}\)C gave the highest Deff values. Comparable findings have been reported in numerous studies. The effective moisture diffusivity Deff values range from 8.486 \(\times\) 10\({}^{-10}\) to 4.386 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s for sewage sludge [20], 2.2 \(\times\) 10\({}^{-10}\) to 3.96 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s for paper mill sludge [21], and 1.91 \(\times\) 10\({}^{-8}\) to 9.12 \(\times\) 10\({}^{-8}\) m\({}^{2}\)/s for wastewater sewage sludge [22]. All studies found that increasing air temperature led to higher effective diffusivity values, while the effect of air velocity varied among materials. Furthermore, the effective diffusivity values obtained in our study are consistent with those reported by [18; 23] who investigated convective drying under similar thermal conditions.  
Figure 9: Drying characteristic curve.  
This energy indicates the sensitivity of the reaction rate to temperature, as shown in Figure 10. The activation energy determined under varying air speed and temperature drying conditions ranged between 31.54 and 33.78 kJ/mol. A significant influence of air velocity and temperature on the activation energy is observed in several studies [16; 20; 21; 22].
## 7 Discussion
### Limitations and Future Research  
This study suffers from some limitations that could be addressed in future research. First, from a contingency perspective [5; 6], a major shortcoming was our inability to confirm or quantify the relationship between green policymaking initiatives and green performance, as explained in Section 3.5. Although this might be challenging because of data availability, we believe it is a highly interesting avenue for further research.  
Second, the present study focused on fact finding. With the MOEJ dataset, we could retrieve some statistical relationships (i.e., what) but we were limited in explaining these relationships (i.e., why). Although we could determine that green policymaking initiatives differed by location, size, and capabilities, we could only suggest explanations based on previous literature. Therefore, we encourage researchers to examine these relationships in more depth.  
Finally, the contextual factors, barriers, and enablers under consideration in this study were predetermined by the used dataset. We acknowledge that other important variables might be at stake. By incorporating additional data sources (e.g., data on population and households, natural environment, economic base, administrative base, etc.) the present contextual factors could be enriched, or novel factors could be added. We further recognize that the list of potential barriers and enablers is not complete. For instance, political support might also be an important factor [70]. We believe future research can also deliver important contributions by addressing these shortcomings.
## 6 Results
### Performance Comparison of Genetic Algorithm Variants  
The comparative analysis of the proposed genetic algorithm variants for sales route optimization demonstrates significant improvements in route efficiency while considering computational costs. The experiments were conducted with two different problem sizes: 15 and 25 locations, allowing for an evaluation of the scalability and effectiveness of each approach.  
For the 15-location problem (Table 2), the GAAM-TS (Genetic Algorithm with Adaptive Mutation and Tabu Search) achieved the shortest travel distance (117.234 km) and lowest travel time (243.094 min), outperforming both the Standard GA (125.216 km, 246.969 min) and GA-AM (117.347 km, 243.873 min). However, this came at the cost of increased computational time, rising to 286.250 s compared to 30.855 s for Standard GA and 59.128 s for GA-AM. In terms of population diversity, GA-AM maintained the highest level (0.74), while GAAM-TS exhibited a slight reduction (0.62), suggesting a trade-off between diversity retention and local search efficiency.  
For the 25-location problem (Table 3), the GAAM-TS approach again demonstrated superior optimization, reducing travel distance to 122.381 km and travel time to 300.090 min, compared to 153.286 km and 340.595 min for Standard GA, and 147.173 km and 335.338 min for GA-AM. The most significant downside of GAAM-TS is its computational cost, which increased dramatically to 1759.560 s, whereas Standard GA and GA-AM required only 55.552 and 54.528 s, respectively. Despite this, GAAM-TS exhibited the highest diversity level (0.73), indicating that the incorporation of Tabu Search helped maintain solution diversity while achieving better route optimization.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variant** & **Distance (km)** & **Time (min)** & **Comp. Time (s)** & **Diversity** \\ \hline Standard GA & 125.216 & 246.969 & **30.855** & 0.60 \\ GA-AM & 117.347 & 243.873 & 59.128 & **0.74** \\ GAAM-TS & **117.234** & **243.094** & 286.250 & 0.62 \\ \hline \hline \end{tabular}  
**Note:** Bold values indicate the best (optimal) results in each corresponding column.  
\end{table}
Table 2: Performance comparison for 15 locations.  
These results confirm that while GAAM-TS provides the best route optimization in terms of distance and time, it comes at the cost of higher computational time. In smaller-scale problems, GAAM-TS remains practical, but as the problem size increases, computational efficiency becomes a limiting factor. The GA-AM variant presents a more balanced approach, offering moderate improvements in route optimization while maintaining a significantly lower computational cost compared to GAAM-TS.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preparation *** & **Share [\%]** & **G\({}^{\prime}\) [Pa]** & **G\({}^{\prime\prime}\) [Pa]** & **tan \(\delta\) [G\({}^{\prime\prime}\)/G\({}^{\prime}\)]** \\ \hline Wheat starch & 0 & 42.71 \(\pm\) 1.13 g & 5.58 \(\pm\) 0.59 ab & 0.13 \(\pm\) 0.02 a \\ PFRT & 1 & 31.71 \(\pm\) 1.82 de & 5.41 \(\pm\) 0.10 ab & 0.17 \(\pm\) 0.01 abc \\ PFRT & 3 & 24.59 \(\pm\) 0.13 bc & 6.13 \(\pm\) 0.06 abc & 0.25 \(\pm\) 0.00 d \\ PFRT & 6 & 20.16 \(\pm\) 0.08 ab & 6.83 \(\pm\) 0.21 cd & 0.34 \(\pm\) 0.01 e \\ PF70 & 1 & 33.19 \(\pm\) 0.64 de & 5.16 \(\pm\) 0.69 ab & 0.16 \(\pm\) 0.02 ab \\ PF70 & 3 & 29.31 \(\pm\) 1.10 cd & 6.29 \(\pm\) 0.15 bc & 0.21 \(\pm\) 0.00 cd \\ PF70 & 6 & 23.23 \(\pm\) 0.22 abc & 7.53 \(\pm\) 0.12 d & 0.32 \(\pm\) 0.00 e \\ PSBRT & 1 & 40.48 \(\pm\) 0.85 fg & 5.23 \(\pm\) 0.07 ab & 0.13 \(\pm\) 0.00 a \\ PSBRT & 3 & 35.77 \(\pm\) 0.42 ef & 7.69 \(\pm\) 0.15 d & 0.21 \(\pm\) 0.01 cd \\ PSBRT & 6 & 53.27 \(\pm\) 3.66 h & 10.89 \(\pm\) 0.08 e & 0.20 \(\pm\) 0.01 bcd \\ PSB70 & 1 & 31.63 \(\pm\) 3.24 de & 4.99 \(\pm\) 0.34 a & 0.16 \(\pm\) 0.01 ab \\ PSB70 & 3 & 18.33 \(\pm\) 0.31 a & 7.95 \(\pm\) 0.08 d & 0.43 \(\pm\) 0.00 f \\ PSB70 & 6 & 21.66 \(\pm\) 0.86 ab & 6.89 \(\pm\) 0.38 cd & 0.32 \(\pm\) 0.03 e \\ \hline \hline \end{tabular}  
* PFRT—preparation of polysaccharides extracted at temp. 25 \({}^{\circ}\)C from milled fenugreek seeds; PF70—preparation of polysaccharides extracted at temp. 70 \({}^{\circ}\)C from milled fenugreek seeds; PSBRT—preparation of polysaccharides extracted at temp. 25 \({}^{\circ}\)C from milled sweet basil seeds; PSB70—preparation of polysaccharides extracted at temp. 70 \({}^{\circ}\)C from sweet basil seeds. Mean values marked with the same letters in particular rows are not statistically significantly different at \(p<\) 0.05.  
\end{table} 
Table 4: Parameters characterizing mechanical spectra for starch pastes without and with polysaccharide preparations extracted from fenugreek and sweet basil seeds.
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 4 Conclusions  
In this paper, an MFI scheme based on signal constellation diagrams and SVM is proposed. Firstly, the signal constellation diagrams are divided by the WLS-FD algorithm, and each region's FD is calculated, regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the GLCM, and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km is constructed. The results show that compared with other traditional schemes, the proposed MFI scheme greatly improves the identification accuracy with the same OSNR.  
Conceptualization, Q.Z.; methodology, H.Y. and L.Y.; software, Z.H. and J.J.; validation, Z.H., Q.Z., and R.G.; formal analysis, Z.H., F.T., and B.L.; investigation, Z.H.; resources, F.W. and Q.T.; data curation, Z.H. and Y.W.; writing--original draft preparation, Z.H.; writing--review and editing, Z.H., Q.Z., and H.Y.; visualization, Q.Z. and J.J.; supervision, X.X. and R.G.; project administration, Q.Z. and X.X.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under grant number 2019 YFB1803701.  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 3 Transmission Error Frequency Analysis
### Characteristic Frequencies of Transmission Error
#### 3.3.1 Condition 1 with Fixed Flange
Firstly, these errors complete a full cycle with each engagement with the pin gear, considering the cycloidal gear's counter-rotation (see Figure 3), resulting in a characteristic frequency of (\(f_{\rm pla}+f_{\rm cyc}\)). Secondly, these errors complete a full cycle per self-rotation of the cycloidal gear, with a characteristic frequency of \(f_{\rm cyc}\). (6) Pin gear  
When the pin gear is fixed, its errors affect the engagement with the cycloidal gear. As the cycloidal gear orbits around the pin gear, pin gear errors complete one full cycle, with a characteristic frequency of \(2f_{\rm pla}\) for two cycloidal gears. Based on this analysis, the characteristic frequencies of errors induced by each component are summarized in Table 2. Higher-frequency errors due to gear tooth count and bearing ball count are omitted here as their overall impact on TE is minimal. Table 2 shows that different components may exhibit similar characteristic frequencies, which are organized in Tables 3 and 4 by component and frequency.
# Bell-Clauser-Horne-Shimony-Holt Behavior Under Quantum Loss and Decoherence
## 2 Background, Notation
### Detector Imperfections  
One of the most frequently studied issues with Bell-type experiments is that of detector imperfections. An imperfect detector on [PERSON]'s side can be modeled as follows: suppose [PERSON] has the accurate result of the measurement, but she could only communicate with [PERSON] through a noisy channel, \(T^{(A\to R)}\). Similarly, on [PERSON]'s side, the access to the measurement results of [PERSON] is only available for [PERSON] through \(T^{(B\to S)}\). As the detectors are at different locations, they are independent; thus, we have  
\[T^{(XY\to RS)}=T^{(XY\to AB)}T^{(AB\to RS)}=T^{(XY\to RS)}\Big{(}T^{(A\to R)} \otimes T^{(B\to S)}\Big{)}, \tag{17}\]  
and the signatures of nonlocality, such as the no-signaling conditions in Equation (7) or the violations of the Bell inequalities, are to be evaluated on the so-arising behavior, \(P(rs|xz)\).  
The observable behavior, \(P(rs|xz)\), will depend on the parameters of \(T^{(AB\to RS)}\), such as detector efficiencies. Ref. [8] presents calculations that show which the critical values are, for which the no-signaling polytope of the given setup is shrunk by \(T^{(AB\to RS)}\) to the local polytope--the set of behaviors not violating any Bell-type inequalities. This implies that no device-independent cryptographic protocol can be implemented with detectors worse than the threshold. This is, however, a rough picture which yields too strict a threshold. A more accurate approach is to consider the convex set of quantum correlations. The latter can be characterized by a hierarchy of semidefinite programs and it is contained inside the no-signaling polytope. The authors of [10] consider the effect of detector imperfections on this convex set.  
In the present paper, our intention is to consider the effect of noisy quantum channels acting on quantum systems in a quantum Bell experiment in an analogous fashion. On the one hand, we calculate threshold values of quantum channel parameters for the violation of Bell-CHSH inequalities. More importantly, we analyze whether the presence of quantum loss and noise can be treated in a similar fashion as detector inefficiencies are treated using Equation (17).
## 3 System Model and Medium Access Control Protocols
### MAC Protocols
#### 3.2.1 Frame Slotted-ALOHA  
An example of a DCR using FSA is depicted in Figure 0(a). In every frame, each end-device randomly selects one of the \(m\) slots to transmit one of its \(L\) data packets. At the end of each frame, the coordinator broadcasts an FBP to inform about the state of the \(m\) slots: empty, success or failure. When an end-device succeeds in transmitting a data packet, it randomly selects one of the \(m\) slots of subsequent frames to transmit the next data packet in its sequence of \(L\) packets. This process is repeated, frame after frame, until all of the data packets are successfully transferred to the coordinator, thus contending independently for each data packet.  
The example of Figure 0(a) shows FSA with \(n=\) 3 contending end-devices and \(m=3\) slots per frame. In Frame 1, all end-devices transmit their first data packet. End-devices 1 and 3 collide in Slot 1, and End-device 2 succeeds in transmitting its first packet in Slot 3 of Frame 1. In Frame 2, End-devices 1 and 3 transmit again their first packet, and End-device 2 transmits its second packet in Slot 1. All end-devices succeed in Frame 2. In Frame 3, End-devices 1 and 3 transmit their second packet, and End-device 2 transmits its third packet: End-device 1 succeeds in Slot 1, and End-devices 2 and 3 collide in Slot 2. The process continues after Frame 3, until all end-devices succeed in transmitting their \(L\) packets to the coordinator.
## 3 Data and Methods
### Methodology
#### 3.2.3 Identification of Flood-Affected Zones  
The flood-affected zones were determined based on the inundation frequency of each grid cell. Specifically, a grid cell is considered to fall within a low flood-affected zone if it is inundated in at least 40% of the events, corresponding to 3 or fewer events during peak monsoon rainfall and cyclonic events. Cells inundated in 41% to 80% of events, equivalent to 4 to 6 events, are categorized as moderately flood-affected. Finally, cells inundated in more than 80% of the selected events or more events are classified as highly flood-affected.