esa-sceva/satcom-chunk-collection huggingface dataset:
# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 2 Related Work
### CWS Technology Based on Information Entropy  
Reviewing the development of CWS, many researchers have solved the task of CWS from the perspectives of word, grammar, and sentence dependency relationships based on the preconceived knowledge system they have learned, which naturally opened the way for dictionary-based or deep-learning-based model word segmentation technology. When knowledge thinking and external information input are discarded, zero-sample geological text word segmentation is transformed into a word recognition problem only through the own text information of geological text data. Text is composed of several characters and is the result of the connection and composition of characters. Characters exist before the grammatical structure and words, and words themselves are also the results of the combination and connection of characters. The word segmentation method based on texts and characters transforms the problem of term recognition into the problem of character connection judgment, focusing on quantifying the uncertainty of the adjacency before and after the candidate term. As a classical uncertainty theory in information theory, information entropy is a very good quantitative indicator.  
In thermodynamics, entropy is a physical quantity that represents the degree of disorder in the molecular state, and the theory of information entropy was established by [PERSON]. Information entropy was first introduced into the field of CWS when [PERSON] [28] carried out research on word and character relationships and filtered words by a given empirical threshold. Later, [PERSON] [29] extracted high-frequency words from the text by comparing information entropy with the manually set threshold, which achieved good results. Although information entropy is a classical uncertainty theory, the research on information entropy+ is mainly distributed in information resource management [30],hydrological simulation [31], no-reference image quality evaluation technology [32], and link prediction [33]. In recent years, [PERSON] et al., discovered new words from a large number of classical literary corpus based on left-right entropy and mutual information to build a segmentation dictionary of ancient Chinese, but the accuracy rate, recall, and F-value of new-word discovery were low [34]. Based on the text mining theory of information entropy, [PERSON] analyzed the praise and criticism tendencies of the overall text information [35]. Existing research and experiments have proved the feasibility of information entropy theory in CWS. There is little research on information entropy in CWS, and it is hardly applied in geological word segmentation. This provides a good opportunity to introduce information entropy into geological text word segmentation tasks.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 5 Optimal Offloading Decision for Collaborative Task Execution
### Optimal Offloading Based on LARAC  
It has been previously proposed that the constrained shortest path problem can be solved by LARAC algorithm [33]. We first denote a LARAC function  
\[L(\lambda)=\min_{p\in P}[b_{\lambda}(p)]-\lambda T_{d} \tag{16}\]  
where \(b_{\lambda}(p)=b(p)+\lambda d(p)\) and \(\lambda\) is the Lagrangian multiplier. By using the Lagrangian duality principle, we can proof \(L(\lambda)\leq b(p^{*})\).  
Next, we employ Algorithm 1 to find the path with smallest \(b_{\lambda}\) between \(S\) and \(D\). In Algorithm 1, the _PathAlgorithm_ is a process (e.g., Bellman-Ford algorithm [34]) to find the shortest path between \(S\) and \(D\) with the cost \(C\). The details of the _PathAlgorithm_ adopted in this paper will be explained in Section 5.2. If we can find the minimum-cost path under all constraints, this path will be the offloading strategy, otherwise update \(p_{d}\) and \(p_{b}\) repeatedly to find the optimal \(\lambda\).  
Figure 5: The task execution flow for collaborative offloading decision.  
The computational complexity of the dynamic programming algorithm depends on the number of nodes, namely \(O(|V|)\). In addition, as shown in Reference [35], the Lagrangian multiplier of optimum is obtained after \(O\Big{(}|N|\log^{2}|N|\Big{)}\) iterations, so the overall computational complexity of the Algorithm 1 is \(O\Big{(}|V||N|\log^{2}|N|\Big{)}\), namely \(O\Big{(}n^{2}\log^{2}n\Big{)}\).  
Although the algorithm cannot guarantee to find the optimal path, it can obtain a lower bound of the optimal solution. Moreover, its running time is shown to be polynomial [33].
# Optimization Design for Sparse Planar Array in Satellite Communications
## 3 Sparse Surface Array and Material Design
### Sparse Planar Array  
When designing sparse planar arrays, the array elements and the layout mode determine the array performance. The array layout involves studying the relationship between array performance and array element distribution. The antenna characteristics can be changed by changing the array layout mode to obtain different antenna patterns. According to the layout mode of the array, the sparse planar arrays can be divided into two basic methods: sparse planar arrays and thinned planar arrays, with the main distinction being periodicity. The former is still periodic, while the latter can be arbitrarily distributed after sparseness and generally does not have periodicity. In the absence of special requirements, the two can also be collectively referred to as sparse plane arrays.  
Taking a rectangular array as an example, assuming that the array aperture is \(D=D_{x}\times D_{y}\) and the planar array contains \(Q=M\times N\) elements, the sparse planar array is shown in Figure 10a, and its array elements are arranged equidistant and uniform on the plane, while the thinned planar array is shown in Figure 10b, and its array elements are arranged in the plane with nonuniform and unequal spacing.  
In the design of sparse arrays, careful consideration must be given to the space between elements due to the coupling effect and the potential for grating lobe occurrence. As the antenna scanning angle remains fixed, larger element spacings increase the likelihood of grating lobes. Conversely, smaller element spacings result in inter-element coupling effects. Typically, when the spacing between the elements is greater than \(\lambda\), the coupling effect between the elements is negligible. Therefore, the above two factors need to be balanced in the sparse area array element array to obtain the desired antenna pattern.
## 3 The Gain Parameter Adaption and Scheduling Control Method
### The Double-Face Influence Coefficient Principle  
In order to build the gain table, the double-face influence coefficient should be estimated by the trial weight method at a constant speed. The operating steps are stated as follows:  
Step 1, the test speed, assumed as \(\omega\), must be selected, and then the initial unbalance vibration signal of testing points 1 and 2 is obtained by vibration signal measurment and analysis. The unbalance vibration vectors of points 1 and 2 are represented by \(\overrightarrow{V_{01}}\) and \(\overrightarrow{V_{02}}\).  
Step 2, the influence coefficients of correcting face 1 to points 1 and 2 can be calculated through trial weight experiments in correcting face 1. If the unbalance vibration vectors of points 1 and 2 are respectively assumed as \(\overrightarrow{V_{11}}\) and \(\overrightarrow{V_{12}}\) after trial weight in face 1, and the corresponding trial weight vector is \(\overrightarrow{P_{1}}\), the influence coefficients can be represented as:  
\[\overrightarrow{R_{11}}=\frac{\overrightarrow{V_{11}}-\overrightarrow{V_{01}} }{\overrightarrow{P_{1}}},\overrightarrow{R_{12}}=\frac{\overrightarrow{V_{1 2}}-\overrightarrow{V_{02}}}{\overrightarrow{P_{1}}} \tag{5}\]  
In Equation (5), \(\overrightarrow{R_{11}}\) is the influence coefficient of correcting face 1 to point 1, \(\overrightarrow{R_{12}}\) is the influence coefficient of correcting face 1 to point 2.  
Step 3, the influence coefficients of correcting face 2 to points 1 and 2 can be calculated through trial weight experiments in correcting face 2. If the unbalance vibration vectors of points 1 and 2 are respectively assumed as \(\overrightarrow{V_{21}}\) and \(\overrightarrow{V_{22}}\) after trial weight in face 2, and the corresponding trial weight vector is \(\overrightarrow{P_{2}}\), the influence coefficients can be represented as:  
\[\overrightarrow{R_{21}}=\frac{\overrightarrow{V_{21}}-\overrightarrow{V_{01}} }{\overrightarrow{P_{2}}},\overrightarrow{R_{22}}=\frac{\overrightarrow{V_{2 2}}-\overrightarrow{V_{02}}}{\overrightarrow{P_{2}}} \tag{6}\]  
In Equation (5), \(\overrightarrow{R_{21}}\) is the influence coefficient of correcting face 2 to point 1, \(\overrightarrow{R_{22}}\) is the influence coefficient of correcting face 2 to point 2.  
Combining Formulas (5) and (6), the double-face influence coefficient vector matrix (\(R\)) in the balance speed can be expressed as:  
\[R=\left|\begin{array}{cc}\overset{\rightarrow}{R_{11}}&\overset{\rightarrow} {R_{21}}\\ \overset{\rightarrow}{R_{12}}&\overset{\rightarrow}{R_{22}}\\ \end{array}\right| \tag{7}\]  
Based on the above trial weight method, the work speed range of the spindle can be discretized to many balance speeds, the influence coefficient vector matrix of each balance speed can be tested and calculated. In order to improve the accuracy of the influence coefficient offline estimation, the method of averaging many times experimental values can be applied at each balance speed.
# An Architecture for the Performance Management of Smart Healthcare Applications
## 4 The PRIMUS Architecture
### PRIMUS Architecture
#### 4.2.3 Resource Management Module  
It manages the virtual resources of the network infrastructure and determines the association of resources to the slices. This association is based on the request received from the other PRIMUS module. To be aware of available and utilized virtual resources, resource management communicates with the NFV, which controls the creation of virtual network functions. NFV controls the network's physical and virtual resources by analyzing and controlling VNFs, as illustrated in Figure 3. Hence, the PRIMUS allocates the necessary network resources for each application. This resource allocation comprises VNFs that establish better end-to-end routes. Applications with strict requirements, such as intensive care monitoring, are given priority over network resources. When there are insufficient or available network resources, the resource management analyzes the slice types running to delete, share resources, or split traffic across different routes to free up new resources for the restricted application.  
The resource management computes performance metrics, such as packet loss rate and latency, to get information about NFV performance. Finally, PRIMUS has two main types of control messages: announce and monitor, as shown in Table 3. Both control messages assist in controlling and establishing the slice. Announce messages inform the application type and its requirements, based on identifying our previous work [21]. This message is essential to announce s-health applications that require URLLC slice type. In contrast, the monitor message updates the information about network resources availability. Thus, PRIMUS changes information about sharing, availability, and allocation of network resources through monitor control messages. The PRIMUS modules receive updates by such messages. Both control messages follow the TCP format, and they are exchanged between PRIMUS modules on a predetermined network port. Therefore, PRIMUS controls the network resources allocation and synchronizes the information.  
Figure 4: Network slice life cycle.
# Analysis of the Effect of Capillary Water Absorption on the Resistivity of Cementitious Materials
## 4 Conclusions and Perspectives  
### Conclusions  
1. The secondary hydration of mineral admixture can increase the number of gel pores and transition pores in the specimen. The lower activity and certain filling effects of fly ash and SL can have a certain refining effect on the pore size. The silica fume can significantly reduce the porosity of the specimen and increase the proportion of gel pores and transition pores.
2. In the dry state, capillary pores and large pores contribute to the resistivity; the more capillary pores and large pores in the specimen, the higher the resistivity, the highest resistivity was found in the dry state of JZ group cement mortar, and the resistivity of cement mortar mixed with mineral admixture had different degrees of decline.
3. With the increase in the amount of mineral admixture and the increase in the number of gel pores and transition pores inside the specimen, the degree of curvature inside the specimen also increased, leading to a continuous decrease in the capillary water absorption coefficient of the specimen. The resistivity of the specimen after capillary water absorption increased with the decrease in the capillary water absorption coefficient. Among the three admixtures, the addition of silica fume has the best effect on improving the resistivity of cement mortar after 36 days of capillary water absorption, compared with the JZ group.  
Figure 13: Content of chemically combined water and Ca(OH)\({}_{2}\) in cement mortar slurry.  
With the increase in capillary water absorption time, the change curve of the resistivity of cement mortar can be divided into two stages: rapid decrease and slow decrease. It tends to be stable, while the resistivity of cement mortar with silica fume decreases relatively slowly with capillary water absorption. This is mainly due to the low porosity and high proportion of gel pores of the silica fume formation, which lead to a weakening of its water absorption capacity, a significant reduction in the number of conductive paths, and a slow change in resistivity after capillary water absorption.
5. Under the action of capillary water absorption, the specimen can be divided into a saturated zone, an unsaturated zone, and a dry zone. The saturated zone causes the resistivity of the specimen to decrease greatly, while the unsaturated zone and dry zone have little effect on the decrease in the resistivity of the specimen.
6. To improve the resistivity of cement-based materials after capillary water absorption, two aspects can be considered. One is to increase the number of gel pores (5-10 nm) and transition pores (10-100 nm) when the pore volume is not very different, because the contribution of gel pores to the resistivity after capillary water absorption is much higher than that of transition pores; the other is to greatly reduce the volume of pores. This is helpful for improving the resistivity of concrete elements in subways and reducing stray current corrosion.
# Comparative Study to Evaluate Mixing Efficiency of Very Fine Particles
## 4 Numerical Analysis Models
### Lmi  
The LMI, a statistical measure commonly used to evaluate the homogeneity of particle mixing, was employed in this study. It is defined as follows:  
\[LMI=({S_{0}}^{2}-{S^{2}})/({S_{0}}^{2}-{S_{R}}^{2}), \tag{14}\]  
Figure 10: Initial spatial distributions of particles 1 and 2 before mixing: (**a**) tumbling V mixer; (**b**) ribbon-blade mixer; (**c**) paddle-blade mixer; (**d**) vertical-blade mixer; (**e**) conical-screw mixer.  
\[{S_{0}}^{2}=\omega_{system}(1-\omega_{system}), \tag{15}\] \[{S_{R}}^{2}=\omega_{system}\frac{(1-\omega_{system})}{n},\] (16) \[S^{2}=\frac{1}{N-1}{\sum_{i=1}^{N}}\Big{(}\omega_{i}-\overline{ \omega})^{2},\] (17) \[\omega_{i}=m_{Additive,i}/m_{Additive,i}+m_{Main\ Product,i}, \tag{18}\]  
where \({S_{0}}^{2}\) represents the variance in a completely segregated state, \(S^{2}\) is the variance in the current mixing state, \({S_{R}}^{2}\) is the variance in a fully randomized mixture, \(\overline{\omega}\) is the average additive mass fraction across cells, \(\omega_{i}\) is the average mass fraction in cell \(i\), \(n\) is the average number of particles across cells, \(m_{Additive,i}\) is the additive mass in cell \(i\), and \(m_{MainProduct,i}\) is the main product mass in cell \(i\)[29]. The LMI has a value between 0 and 1, where 0 indicates complete segregation (i.e., no mixing has occurred), and 1 represents perfect mixing (i.e., particles are uniformly distributed throughout the system).  
[PERSON]'s approach represents a quantitative method for evaluating mixing efficiency. However, its physical meaning must be carefully interpreted due to certain limitations. Specifically, the LMI depends on the size and distribution of the sampling grid, which may lead to discrepancies between calculated values and the actual mixing state [30]. Moreover, in cases where component ratios are highly unbalanced or during the early stages of mixing, the LMI may exceed 1 or even exhibit negative values, which would necessitate careful interpretation [31].  
Under certain CGM conditions, negative LMI values may appear during the initial mixing stages. Such values do not reflect any physically meaningful phenomenon but instead result from a statistical artifact inherent to the LMI formulation. Specifically, when the observed variance within sampling bins exceeds the theoretical variance of a fully randomized mixture, the computed LMI may temporarily become negative. This typically occurs in cases where the number of particles per bin is insufficient for robust statistical evaluation or when the initial particle distribution exhibits extreme segregation. To address this issue, bins containing fewer than 10 particles were excluded from the LMI calculation, which minimized statistical noise and ensured reliable quantification of mixing behavior.  
The LMI values were calculated using a discretized spatial domain, divided into uniformly sized cubic cells. This approach provided an accurate measure of the local particle distributions and mixing uniformity throughout all mixing systems.
# Recent Advances in the Targeting of Head and Neck Cancer Stem Cells
## 3 Identification of CSC
### Marker-Based Isolation of CSCs
#### 3.1.2 CD133  
CD133 (prominin-1) is a penta-transmembrane glycoprotein that was originally recognized as a hematopoietic stem cell marker (Figure 2). First reported as a CSC marker in CRC [74], CD133 has become the most extensively used cell surface marker for the isolation of putative CSCs from a variety of solid tumors [75]. In HNSCC, a subpopulation of CD133+ CSCs displayed greater cell viability, migratory, and invasive capability as well as chemoresistance when compared with CD133- cells [76; 77; 78]. Nevertheless, the CD133-mediated molecular processes in regulating stemness properties of HNSCC CSCs are still unclear. [PERSON] et al. demonstrated the importance of CD133/Src signaling in modifying stemness, EMT, and tumorigenicity of HNSCC CSCs by showing that the downregulation of CD133 resulted in a reduced self-renewal ability and downregulated expression of stemness genes while promoting differentiation and apoptosis in CSCs [79].  
Figure 2: CSC markers in HNSCC. Schematic representation shows the subcellular localization of the most commonly used CSC markers (CD44, CD133, and ALDH) with their functional properties in HNSCC.  
Nevertheless, although at first, it seemed that CD133 would be a useful marker for HNSCC CSCs, the development of CSC research revealed certain discrepancies even in this type of cancer. For instance, clinical reports demonstrated that the upregulation of CD133 in HNSCC tumor tissue negatively correlates with the survival of HNSCC patients [80]. However, a study by [PERSON] et al. reported no CD133 expression in tumor samples from HNSCC patients and no correlation between CD133 expression and prognosis or survival [81].
## 3 Classification of Attacks on RPL-Based IoT
### RPL-Specific Attacks  
#### 3.2.1 Rank Attacks  
Rank attacks involve adversaries manipulating the Rank and Objective Function (ROF) values distributed by DIO messages. Within the category of rank attacks, adversaries may employ decreased rank attacks, advertising lower ranks to attract nodes as preferred parents, or increased rank attacks, advertising higher ranks near the root node to disrupt routing topology [42].
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 5 Example Analysis
### Results and Analysis
#### 5.2.1 Scheme Results  
The following is the optimization scheme result of the distribution path under the pallet constraint waiting time window. As can be seen from Table 4, the scheme can still obtain an average vehicle loading rate of 81.68% after using pallets. The average violation time window of each route is 1.43 h.
## 3 Nonparametric Model and Eigenvalue Solution
### Construction of Nonparametric Riccati Whole Transfer Model  
The Riccati whole mean transfer model is constructed in Sections 2.2 and 2.3; furthermore, the construction method of the maximum entropy random matrix is provided in Sections 3.1 and 3.2. Therefore, the nonparametric Riccati whole transfer model can be obtained by introducing a maximum entropy random matrix to the Riccati whole mean transfer model. At the same time, the following formula can be obtained using Equations (11) and (13):  
\[\mathbf{B}^{T}\mathbf{B}=\mathbf{A}^{2} \tag{24}\]  
The whole random transfer matrix, \(\mathbf{U}_{qr}\) (\(q\), \(r\in\{1,2\}\)), does not have strict positive definiteness like the random matrix, \(\mathbf{B}\); thus, \(\mathbf{U}_{qr}\) satisfies the following constraints from Equation (25):  
\[\left(\mathbf{U}_{qr}\right)^{T}\mathbf{U}_{qr}=\mathbf{A}^{2} \tag{25}\]  
where \(\mathbf{U}_{qr}\) is the maximum entropy random matrix corresponding to the mean matrix, \(\underline{\mathbf{U}}_{qr}\), in Equation (5). Similarly, the maximum entropy random matrices, \(\mathbf{U}_{Cqr}\), at the coupling elements can be calculated as follows:  
\[\left(\mathbf{U}_{C,qr}\right)^{T}\mathbf{U}_{C,qr}=\mathbf{A}^{2} \tag{26}\]  
From Equations (25) and (26), \(\mathbf{U}_{qr}\) and \(\mathbf{U}_{C,qr}\) can be obtained and then substituted into Equations (5) and (9), respectively, to obtain the nonparametric Riccati whole transfer model of each element.  
\[\mathbf{S}_{i+1}=\left(\begin{bmatrix}\mathbf{U}_{11}^{I}&0\\ 0&\mathbf{U}_{11}^{II}\end{bmatrix}_{i}\mathbf{S}_{i}+\begin{bmatrix}\mathbf{U}_{12}^{I}&0 \\ 0&\mathbf{U}_{12}^{II}\end{bmatrix}_{i}\right)\cdot\left(\begin{bmatrix}\mathbf{U}_{21}^ {I}&0\\ 0&\mathbf{U}_{21}^{II}\end{bmatrix}_{i}\mathbf{S}_{i}+\begin{bmatrix}\mathbf{U}_{22}^{I}&0 \\ 0&\mathbf{U}_{22}^{II}\end{bmatrix}_{i}\right)^{-1} \tag{27}\]  
\[\mathbf{S}_{C,i+1}=\mathbf{S}_{C,i}+\begin{bmatrix}\mathbf{T}_{C,12}&\mathbf{T}_{C,14}\\ \mathbf{T}_{C,32}&\mathbf{T}_{C,34}\end{bmatrix}_{i} \tag{28}\]  
From Equations (27) and (28), the nonparametric Riccati whole transfer matrix, \(\mathbf{S}_{2}\)... \(\mathbf{S}_{C,i}\)... \(\mathbf{S}_{N+1}\), of each element in the system can be successively obtained. As such, a nonparametric Riccati whole transfer model of a dual-rotor system has been constructed successfully.