esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Our investigation using Jira, Twitter, and Reddit short messages has demonstrated that the user-generated DSSMs are different in terms of lexicon, vocabulary, and terminology, from DISMs. We were able to determine this by using sentiment analysis in a cross-domain environment and relying on the RoBERTa model as the backbone of our methodology. Based on the experimental results, we can observe that while different domains share a common lexicon, they also have unique characteristics specific to each domain. Jira messages, which are typically used by professionals, tend to be more complex and use specific terminology, while Twitter messages are often brief and to the point, with a focus on common vocabulary. Reddit messages fall somewhere in between: they contain various communities with messages from different levels of specificity and independence.  
By recognizing the distinct emotional needs and preferences of users in different contexts, researchers, teams, or applications can benefit by meeting user expectations, enhancing satisfaction, or tailoring better features. It can also facilitate cross-communication and collaboration. By understanding how emotions vary, individuals and organizations can adapt their interactions to effectively engage in diverse fields.  
In the future, we aim to adjust the models to recognize emoticons within messages. By integrating emoticon recognition capabilities, we anticipate improving the accuracy and performance of sentiment analysis models within DSSMs. Additionally, we may use more advanced models like GPT-4 or T5 to obtain more precise insights.  
Conceptualization, T.-R.P.; methodology, T.-R.P., C.-D.C. and D.-I.C.; software, T.-R.P. and C.-D.C.; validation, T.-R.P. and C.-D.C.; formal analysis, T.-R.P., C.-D.C. and D.-I.C.; investigation, T.-R.P., C.-D.C. and D.-I.C.; resources, D.-I.C.; data curation, T.-R.P., C.-D.C. and D.-I.C.; writing--original draft preparation, T.-R.P. and C.-D.C.; writing--review and editing, T.-R.P., C.-D.C. and D.-I.C.; supervision, D.-I.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Tuning ANN Hyperparameters for Forecasting Drinking Water Demand
## 3 Results and Discussion
### Results Analysis and Comparison
#### 3.2.2 The Influence of Set of Inputs on Tuning  
2 sets of inputs have been studied with the proposed tuning methodology; set of inputs 1 and 2 representing a shorter and a larger selection of time series lags, respectively. Both \(set1\) and \(set2\) have been defined for each prediction horizon as reported in Table 1.  
Figure 9 shows the MAPE of the best models for each ANN architectures on \(ts2\) over the 4 horizons. Generally, the ANN tuning illustrates slightly better results of the models using \(set2\), meaning a larger number of lags. This trend is inverted for the 168 h horizon even though the difference is very limited. Nevertheless, FFNN models present better results with lower number of lags (\(set1\)) for all the horizons except for 1 h.  
The inference we can make about the number of input analyses is that not always so many inputs are better. Thus, not only the selection of the more significant inputs is important for a proper ANN tuning and then forecasting but also testing different numbers of significant inputs can help this process. Although generally the problem is more complex when the set of inputs has to be larger, testing different numbers of inputs is recommended given that it is not always true and depends on ANN architectures and prediction horizons.
## 1 Introduction
Three-dimensional reconstruction based on CT scan images can provide a good understanding of the development and propagation of internal cracks in the loaded rock samples [15; 16; 17] and can quantitatively analyze the internal fissure [18]. [PERSON] et al. [19] used CT technology to define the damage ratio, integrity, and porosity of rock triaxial compression and studied the evolution process of two-dimensional and three-dimensional fractures based on two-dimensional images and three-dimensional models. [PERSON] et al. [20] explored the rock crack propagation mode during cyclic loading and unloading by three-dimensional reconstruction, quantitatively describing the crack. Scholars have also conducted extensive exploratory research on methods for reconstructing three-dimensional models [21; 22]. Although there are many methods for three-dimensional reconstruction, most of the results are still validated against analyses from Avizo software, indicating that Avizo has significant advantages in rock three-dimensional reconstruction [23; 24]. There are many studies in the existing literature on rock dynamic properties and the three-dimensional reconstruction of rocks. However, there are very few studies that combine confining pressure, dynamic tests, and three-dimensional reconstruction techniques. In response to this issue, this paper takes granite as the research object and creates initial damage to the rock sample by impact. The dynamic mechanical properties of rocks with initial damage under different confining pressures are studied using the Hopkinson test. A group of impacted rock samples is selected for the CT test to analyze the influence of confining pressure on the evolution of internal fissures in the rock samples, and the quantitative representation of the fissure and rock sample three-dimensional fractal dimension are carried out. These research results can provide a certain theoretical reference for the support of surrounding rock in deep engineering.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 4 Evaluations and Results
### Analysis of the Influence of Node Density on TCD  
In this section, the impact of changes in the number of nodes on TCD results will be analysed.  
We believe that the number of nodes should be considered together with the range of node activity, which is reflected in the indicator of node density. Here, the node density in the network is defined as the number of nodes per unit area within the effective area of the node's movement in the network.  
\[Node\ Density=\frac{Node\ Number}{S_{region}} \tag{9}\]  
The revised version gives comparisons of the \(TCD_{nturk}\) when the node density changes. In the case of group movement, the nodes are relatively concentrated and follow the reference point motion. Taking the RWP model as an example, we analyze the change of \(TCD_{nturk}\) when the number of nodes changes. There are several cases.  
Figure 11: Comparisons of topology change degree values with different mobility patterns: (**a**) \(TCD_{nturk}\); (**b**) the average of \(TCD_{nturk}\) (1-RWP, 2-RPGM (g = 50), 3-RPGM (g = 25), 4-RPGM (g = 10), 5-RPGM (g = 5), 6-Pursue).  
Case 1: The area of simulation region is fixed, and the number of nodes changes (the node density changes). We take the RWP model as an example. Figure 12a shows the result. It can be seen that in the same area of region, the value \(TCD_{nturk}\) increases as the density of nodes increases. This phenomenon can be explained by Equation (2). As the node density increases, the number of node neighbors also increases.  
Case 2: The region is proportional to the number of nodes (the node density is constant). Here is also an example of RWP model. In Figure 12b, the node density is fixed to 0.000125. It shows that when the node density is constant, the change in TCD is not so large.  
The value of TCD reflects the degree of topology change between nodes in the network. In fact, it also reflects the degree of aggregation of nodes and the degree of unified action between nodes.  
It can be seen from Equations (1) to (3) that:  
* For \(TCD_{ij}\), the factors that affect its value include changes in velocity, direction, and distance between nodes.
* For \(TCD_{i,nbs}\), in addition to each single \(TCD_{ij}\) value, the number of neighbor nodes within one hop is also one of the influencing factors.
* For \(TCD_{nturk}\), the number of nodes in the network directly affects the value of \(TCD_{nturk}\).
## 4 Retrieved XCO\({}_{2}\) Product Inter-Comparison  
### Methodology of Retrievals Inter-Comparison  
The retrieved XCO\({}_{2}\) and Psurf from the OCO-2 V7r Lite product, paired to the GOSAT/ACOS V7.3 Lite data is used for high gain scenes, for which a bias correction has been applied to XCO\({}_{2}\)[36]. The XCO\({}_{2}\) bias correction has the following three steps; (1) Remove footprint bias (only OCO-2); (2) Identify unphysical XCO\({}_{2}\) variability and (3) Determine global offset from TCCON. The first bias correction uses the median XCO\({}_{2}\), which is calculated with eight co-located OCO-2 footprint averages, as the \"ground truth\" value and subtracts this from the observed XCO\({}_{2}\) for each footprint. On a second step, the spurious XCO\({}_{2}\) variability is corrected using a multivariate linear regression. Finally, the TCCON adjustment was determined relative to the land target observations. For medium gain scenes, for which the GOSAT/ACOS V7.3 bias correction has not yet been developed, the GOSAT/ACOS V7.3 \"Full\" product was used, since these soundings are not included in the \"Lite\" files. Psurf is retrieved mainly from the ABO2 band together with aerosol related parameters. The XCO\({}_{2}\) is retrieved using all three bands. We used data between September 2014 and May 2016, which is the currently available GOSAT/ACOS product. The CAM context image data was not used for additional cloud and aerosol filtering as the pre-screeners had already been applied prior to running the XCO\({}_{2}\) retrieval. The XCO\({}_{2}\) data quality flag was not used in this work to use as many matched-up data and investigate the observation conditions that create biases and fluctuation.
## 7 Conclusions and Discussion
### Theoretical Contribution and Practical Enlightenment  
In terms of theoretical contributions, this study first develops a model for understanding user willingness to engage with cultural heritage metaverse educational systems, drawing on the UTAUT, the TTF model, and Flow Theory. By incorporating presence, interactivity, and narrativity as external variables, the research extends the existing theoretical framework and offers new insights into user behavior in these environments. Secondly, it systematically explores for the first time how presence, interactivity, and narrativity impact the flow experience and how these factors indirectly influence user intention through flow. This provides a robust theoretical basis and empirical support for future research. Finally, the study highlights the critical role of technology-task fit as the most significant factor in shaping user willingness, enriching the technology acceptance model and underscoring the importance of aligning technology with task requirements.  
In terms of practical contributions, this study offers valuable design guidelines for developers of cultural education metaverse systems. It provides concrete strategies for enhancing the user's flow experience by improving presence, interactivity, and narrative, thereby increasing user willingness to engage with the platform. At the user level, the findings help developers prioritize user acceptance by optimizing technology-task fit, improving performance expectations, and considering social influences. These measures are crucial in creating a highly interactive and immersive metaverse educational experience that can spark learners' curiosity, motivation, and initiative. On a societal level, the study suggests that increasing user satisfaction and engagement with metaverse educational systems can enhance learning outcomes and promote the transmission and preservation of cultural heritage. The metaverse allows students from different regions to transcend geographical boundaries and engage in cultural exchange, fostering mutual understanding and respect. The integration of research and practice supports the long-term preservation of cultural heritage. It cultivates a sense of responsibility in younger generations, contributing to achieving the Sustainable Development Goals.
## 2 Linearization Method
### Linearization in RMA SAR Processing  
In practice, the phase error in Equation (8) above, as determined by a calibration target, only applies to scatterers in a Synthetic Aperture Radar (SAR) scene at the same slant range. The RMA process offers an opportunity to correct for the range-dependent phase error: in calculation and application of the matched filer Equation (9) or in the Stolt transform and subsequent interpolation of \(k_{\gamma}\). By realizing that \(k_{r}\) is:  
\[k_{\gamma}=\sqrt{k_{r}^{2}-k_{x}^{2}}-k_{e} \tag{11}\]  
where \(k_{e}\) is the phase correction factor measured with a calibration target in Equation (8) and equal to  
\[k_{e}=\frac{2\left(\Delta r\right)}{R_{e}}\,\varphi_{n} \tag{12}\]  
The interpolation operation expressed by Equation (10) must be performed in the RMA image formation process, the addition of the phase correction term Equation (12) results in little to no cost in computation time or memory.  
The calibration target at a distance \(R_{e}\) from the radar and \(\Delta r\) is the range resolution. The term \(k_{e}\) can be interpreted as the incremental phase error imposed by each range cell in  
Figure 3: SAR geometry details (asf.alaska.edu, accessed on 20 October 2020).  
turn and correcting for the roundtrip propagation. Keep in mind that the terms \(k_{\gamma}\), \(k_{x}\), and \(k_{e}\) are 1 by \(N\) arrays, where \(N\) is the number of data samples for each ramp (i.e., fast time).
# Embedded One-Dimensional Orifice Elements for Slosh Load Calculations in Volume-Of-Fluid CFD
## 2 Materials and Methods
### Numerical Method
#### 2.3.2 Spatial Discretization of Diffusive Term  
Figure 4 shows 2D meshes containing orifice elements: a structured mesh on the left and an unstructured mesh on the right. The dual-cell control volumes are demarcated with dotted lines. Nodes that do not form part of the boundary nor boundary elements are termed interior nodes (e.g., \(D\) and \(G\)). The orifice boundary nodes are denoted as \(O_{Ci}\) and the internal orifice nodes as \(O_{Ai}\) and \(O_{Bi}\) for left- and right-hand side orifice nodes, respectively.  
The diffusive term for an interior node, e.g., \(D\), is discretized as usual:  
\[\mathbf{\
abla}\cdot\mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Big{)}\Big{|} _{D}=\frac{1}{V_{D}}\sum_{f\in A_{D}}\Big{(}\mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{ \
abla}\mathbf{u}^{T}\Big{)}\cdot\mathbf{\Gamma}\Big{)}\Big{|}_{f}\,,\]  
where \(V_{D}\) is the volume of the dual cell at node \(D\), \(A_{D}\) is the surface (in dotted lines in the figure) enclosing \(V_{D}\) and the subscript \(f\) indicates a specific face section of \(A_{D}\). \(\mathbf{\Gamma}\) is the outward pointing normal vector of the face with magnitude equal to the area of the face. The gradient at the face is calculated using the compact stencil formulation [47].  
In the case where the discretization takes place at the orifice nodes, Equation (28) is used to calculate the viscous losses through the orifice. Here, the diffusion contribution at any orifice node (e.g., \(O_{A}\), \(O_{B}\) or \(O_{C1}\)) is replaced with the empirical loss term by setting \(H_{O}=1\). As an example, the diffusion term at node \(O_{A}\) on the right-hand side of Figure 4 would be computed as follows:  
\[\mathbf{\
abla}\cdot\mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T }\Big{)}=\frac{1}{V_{O_{A}}}\bigg{(} \mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Big{)}\cdot\mathbf{ \Gamma}\Big{|}_{f_{G-O_{A}}} \tag{34}\] \[+\left.\mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Big{)} \cdot\mathbf{\Gamma}\right|_{f_{D-O_{A}}}\] \[+\left.\mu\Big{(}\mathbf{\
abla}\mathbf{u}+\mathbf{\
abla}\mathbf{u}^{T}\Big{)} \cdot\mathbf{\Gamma}\right|_{f_{D-O_{A}}}\] \[-V_{O_{A}}\frac{C\rho\tilde{u}_{O}^{2}}{L}\mathbf{n}_{O}\bigg{)}\,.\]  
Figure 4: Structured mesh with multiple orifice element pairs (**left**) and unstructured mesh with three orifice element pairs (**right**).  
Here, the subscript \(f_{G-O_{A}}\) refers to the face connecting nodes \(G\) and \(O_{A}\). Note that the empirical loss term accounts for the contributions of the faces connecting \(O_{\text{C1}}\), \(O_{\text{C2}}\) and \(O_{B}\) to \(O_{A}\).
# DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network
## 3 Methods
### Loss Function  
The proposed method aims at crack segmentation. Simple cracks have a pure edge, while complex cracks have many small branches, irregular shape and pseudo-crack.  
Considering the variety of crack shapes and their imbalance problem about distribution and difficulty, we select Focal Loss to deal with it. The formula for focal loss is presented in Equation (2).  
\[L_{fl}=\{\begin{array}{c}-\alpha(1-y^{\prime})^{\gamma}\log y^{\prime},y=1\\ -(1-\alpha)y^{\prime\gamma}\log(1-y^{\prime}),y=0\end{array} \tag{8}\]  
\(\gamma\) makes the model pay more attention to complex samples and reduces the influence of simple samples. \(\alpha\) alleviated the problem resulting from uneven pixel proportion of object and background [14]. This paper set \(\alpha=0.25\), \(\gamma=2\).
## 3 JPRL-Based Joint Resource Allocation Approach
Unlike the typical reward functions that evaluate the single-step target by setting a threshold, the proposed reward function employs a long short-term criterion that varies autonomously as the performance over time, which allows agents to perform more stable exploration in the multi-cell multi-user system. In the proposed JPRL method, we developed a distributed learning and centralized training framework, as shown in Figure 3, which promised to explore the entire action space fully and encourage each agent to leverage the experience of other agents. Specifically, all agents are guided by the harmonized loss feedback value of the MADDQN and P-MADDPG when learning the bandwidth and power individually. The details of the proposed JPRL method are given as follows, its structure is illustrated in Algorithm 1, and the flow chart is shown in Figure 4. In the learning phase, the state of each agent is input into the the MADDQN and P-MADDPG algorithms synchronously, and then each agent individually performs the bandwidth allocation and power allocation actions. Based on the actions, rewards and new states are generated and stored in the replay buffers of the two algorithms. Note that the reward is calculated by Equation (8), which corresponds to all actions of the bandwidth and power. In the training phase, the values in the buffer are randomly selected to compute correlation values to guide the intelligence in the direction of increasing throughput. The details are described as follows. Figure 3: Framework of centralized training. Figure 2: System model of the JPRL-based bandwidth and power allocations.
## 2 HHT Theory and Signal Recognition
### HHT Theory
#### 2.1.1 EMD Principle and Algorithm [38]  
First, find out all the maximum and minimum points on the time series \(X(t)\), and then use the cubic spline function to interpolate the maximum and minimum points of the signal, respectively, to obtain the upper envelope \(X_{\text{max}}(t)\) and lower envelope \(X_{\text{min}}(t)\) of a signal. The mean line \(m_{1}(t)\) obtained by connecting the upper and lower envelope lines in turn is as follows:  
\[m_{1}(t)=[X_{\text{max}}(t)+X_{\text{min}}(t)]/2 \tag{1}\]  
The first component \(h_{1}(t)\) is as follows:  
\[h_{1}(t)=X(t)-m_{1}(t) \tag{2}\]  
In the subsequent sifting processes, \(h_{1}(t)\) can be treated only as a proto-IMF. Then,  
\[h_{11}(t)=h_{1}(t)-m_{11}(t) \tag{3}\]  
After repeated siftings in this manner, up to \(k\) times, \(h_{1k}(t)\) becomes an IMF; that is,  
\[h_{1k}(t)=h_{1(k-1)}(t)-m_{1(k-1)}(t) \tag{4}\]  
The test is defined as  
\[SD=\sum_{t=0}^{T}\left|\frac{\left|h_{1(k-1)}(t)-h_{1(k)}(t)\right|^{2}}{h_{1 (k-1)}^{2}(t)}\right| \tag{5}\]  
[PERSON] et al. [24] have proved that when the SD value is between 0.2 and 0.3, it can not only ensure the linearity and stability of IMF, but can also make it have a clear physical significance, and the extraction effect of waveform frequency characteristics is relatively good.  
When \(h_{1k}(t)\) meets the requirements of SD, the \(h_{1k}(t)\) is taken as the first-order IMF.  
\[c_{1}(t)=h_{1k}(t) \tag{6}\]  
The residual \(r_{1}(t)\) is as follows:  
\[r_{1}(t)=X(t)-c_{1}(t) \tag{7}\]  
Regard \(r_{1}(t)\) as the decomposed signal, and obtain all residuals \(r_{i}(t)\) after multiple operations. The result is  
\[r_{i-1}(t)-c_{i}(t)=r_{i}(t),\ i=2,3,\cdot\cdot\cdot,n \tag{8}\]  
By summing up (7) and (8), we finally obtain  
\[X(t)=\sum_{i=1}^{n}c_{i}(t)+r_{n}(t) \tag{9}\]
# Molecular Insights into the Interactions Between Human Serum Albumin and Phospholipid Membranes
## 2 Methods
### Free Energy of Binding  
The binding free energy was determined by applying the single trajectory method, employing Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) [58] and YASARA Structure. The solvation energy and electrostatic interactions were analyzed using the AMBER14 force field combined with the Adaptive Poisson-Boltzmann Solver (APBS) [59]. The electrostatic surface potential map was generated through this approach. Utilizing the optimized structures, the binding free energy, \(\Delta G_{\text{b}}\), was computed using the following equation:  
\[\Delta G_{b}=\Delta G_{c}-(\Delta G_{m}+\Delta G_{HSA}) \tag{1}\]  
where \(\Delta G_{c}\), \(\Delta G_{m}\), and \(\Delta G_{HSA}\) represent the minimized free energies of the HSA-membrane complex, membrane, and HSA, respectively. The total \(\Delta G_{b}\) has multiple contributions, which under isothermal conditions can be represented by Equation (2):  
\[\Delta G_{b}=\Delta H_{HSA-membrane}+\Delta H_{PS}+\Delta H_{NS}-T\Delta S \tag{2}\]where \(T\Delta S\) represents the conformational entropy contribution and \(\Delta H_{HSA-membrane}\) denotes the interaction enthalpy between the HSA and the phospholipid bilayer. This component encompasses both electrostatic and van der Waals interactions. The terms \(\Delta H_{PS}\) and \(\Delta H_{NS}\) denote the polar and non-polar solvation enthalpies, respectively.