esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### CEFR A2 level words  
After limiting the word list range by setting CEFR A2 level words as the \"specific words\" on AntConc 3.5.9 (see Figure 7), it generated 2557 word types and 22,589 tokens of CEFR A2 level words that existed on the target corpus (see Figure 8). After calculating their proportions on the target corpus, it was discovered that the word types of CEFR A2 level words account for approximately 47% of word types of the target corpus, while their lexical coverage is 28%.  
Figure 6: The function words that exist on the target corpus.  
Figure 7: Set CEFR A2 level word list as “specific words”.  
(3) CEFR B1 level words  
After limiting the word list range by setting CEFR B1 level words as the \"specific word\" on AntConc 3.5.9 (see Figure 9), it generated 1937 word types and 5914 tokens of CEFR B1 level words that existed on the target corpus (see Figure 10). After calculating their proportions on the target corpus, it was discovered that the word types of CEFR B1 level words account for approximately 35% of word types of the target corpus, while its lexical coverage is 7%.  
Figure 8: CEFR A2 level words that exist on the target corpus.  
Figure 9: Set CEFR B1 level word list as “specific words”.  
(4) Off-list words  
After finishing the vocabulary taxonomy, the composition of the off-list words included the following classifications: other EGP words embraced 405 word types (proportion = 7.41%) and 760 tokens (lexical coverage = 1%), Name embraced 186 word types (proportion = 3.4%) and 683 tokens (lexical coverage = 0.9%), Countries / Locations embraced 109 word types (proportion = 1.99%) and 308 tokens (lexical coverage = 0.4%), Military embraced 27 word types (proportion = 0.49%) and 70 tokens (lexical coverage = 0.09%), and Medical embraced 7 word types (proportion = 0.13%) and 12 tokens (lexical coverage = 0.02%) (see Table 1).  
To summarize, after the composition of the target corpus was comprehensively investigated by the proposed method, the results indicated that from the perspective of word types, CEFR A2 level words account for the largest proportion (46.79%) on the target corpus, while Medical had the lowest proportion (0.13%); in addition, from the perspective of lexical coverage, the function words has the largest lexical coverage (61.85%), while, Medical also had the lowest lexical coverage (0.02%) (see Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Word Classification Criteria** & **Word Types** & **Word Types** & **Tokens** & **Lexical Coverage** \\ \hline The function words & 237 & 4.34\% & 49,190 & 61.85\% \\ CEFR A2 level words & 2557 & 46.79\% & 22,589 & 28.4\% \\ CEFR B1 level words & 1937 & 35.44\% & 5914 & 7.44\% \\ Other EGP words & 405 & 7.41\% & 760 & 1\% \\ Name & 186 & 3.4\% & 683 & 0.9\% \\ Country/Location & 109 & 1.99\% & 308 & 0.4\% \\ Military & 27 & 0.49\% & 70 & 0.09\% \\ Medical & 7 & 0.13\% & 12 & 0.02\% \\ \hline Total & 5465 & 100\% & 79,526 & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lexical composition of the target corpus.  
Figure 10: CEFR B1 level words that exist on the target corpus.
# Relationship between Body Composition and Gross Motor Coordination in Six-Year-Old Boys and Girls
###### Abstract  
The aim of this study was to investigate the association between morphological characteristics (body composition and anthropometric data) and gross motor coordination in preschool children (42 boys and 40 girls, aged 6.22 \(\pm\) 0.43 years, height: 1.22 \(\pm\) 0.48 m, body mass index: 20.22 \(\pm\) 2.34 kg/m\({}^{2}\), muscle mass: 11.50 \(\pm\) 2.08 kg, and fat mass: 5.43 \(\pm\) 4.02 kg). Motor coordination was determined by the obstacle course backward test (OCB), while body composition was measured using the bioelectrical impedance (BIA) method. Sex differences in the OCB test and body morphology were determined by an independent \(t\)-test. Multiple linear regression was used to examine whether morphological characteristics could predict OCB scores. Boys were significantly taller, with greater muscle mass, protein mass, and total body water compared to girls (t = 2.01-3.73, \(p\) = 0.05). Inversely, mineral mass was greater in girls than in boys (t = 2.98, \(p\) = 0.01). No significant sex differences were observed in the results obtained for the OCB test (t = 0.74, \(p\) = 0.46). All morphological variables showed trivial-to-weak (r = 0.01-0.15) associations with the OCB results, without reaching statistical significance (\(p\)\(\leq\) 0.16). The model of predictor variables did not have a statistically significant effect on the OCB scores in boys and girls (R\({}^{2}\) = 0.09, \(p\) = 0.91 and R\({}^{2}\) = 0.012, \(p\) = 0.92, respectively). These results indicate that sex dimorphism in body composition is present at an early age before puberty, while morphological characteristics have a negligible influence on motor coordination in 6-year-old children.  
morphology; bioelectrical impedance; muscle mass; obstacle course backward
# AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems
## 2 Related Work and Problem Statement
### Related Work
Furthermore, they introduced a user association algorithm rooted in primal-dual decomposition. In a different vein, ref. [32] saw [PERSON] et al. conceptualizing traffic load balancing as a congestion game, where the association decisions act as actions and the network users assume the roles of players. The authors of [33] offered a user association algorithm which achieves load-balancing in HetNets while utilizing green energy-powered BSs to decrease on-grid energy consumption. In [34], the authors presented simple and efficient user association rules in the form of closed-form formulas which magnify the utility of the network. In [35], the author presented an evolutionary approach which considers both user association and power allocation while balancing loads in HetNets. In [36], flexible cell-breathing techniques were introduced for application within HetNets. These techniques enable the transfer of loads from overloaded base stations to neighboring, lightly loaded BSs. The primary objectives of these techniques are to enhance energy efficiency and achieve load balancing. Extensive surveys have been conducted by the authors of [30; 37], encompassing a comprehensive review of available methods for traffic load balancing and BS biasing within HetNets, all aimed at improving energy efficiency and overall performance. Furthermore, the authorsof [38; 39; 40; 41] covered a range of various similar aspects within HetNets, including SDR architectures, interference coordination, utilizing SDR in small-cell deployments, SDR energy efficiency, and the application of machine learning techniques for resource management. They provided comprehensive insights into current research trends and challenges in heterogeneous network environments. The majority of the aforementioned research endeavors have concentrated on employing various technologies such as mmWave [42] bands, FTTB, FTTN, VDSL2, and SDR to alleviate backhaul traffic, reduce costs and latency, expand coverage areas, and enhance energy efficiency. However, it is worth noting that most of these works have relied on wireless network designs which are heavily constrained by preconfigured hardware and constructed upon closed, rigid, and non-adaptable architectures. Such designs typically pose significant challenges in adapting to unpredictable, variable, and continuously growing traffic patterns [43]. Furthermore, due to the inflexible hardware-based design and the scarcity of licensed spectrum resources, the focus of these works has primarily been on maximizing the efficiency of the allocated licensed spectrum rather than optimizing the achievable data rates for users. Moreover, influenced by the closed hardware-centric approach, many of the aforementioned authors have not explored the possibility of using BSs that can be shared by multiple operators, which could potentially reduce operational costs and overall energy consumption.
## 2 Background of the Research and Case Study  
In this section, the authors focused on identifying previous research conducted on the Minpu bridge in China, where the role of the composite connection has been investigated. Specifically, proper numerical models and experimental data have been collected in order to compare the modeling strategy and validate the numerical results of the authors.  
The Minpu bridge in China can be acknowledged as the first practical example of a large-span bridge with composite connections. This bridge has eight spans of 63 m each, with a main span of 708 m and a total length of 1212 m. For clarity purposes, the side view of the bridge is reported in Figure 1.  
[PERSON] et al. [13] identified the most stressed joint of the bridge (shown in Figure 2) by means of numerical analyses and reproduced it in a laboratory to perform load tests.  
Figure 1: Cross-section view of the Minpu bridge (China).  
Figure 2: Joint of Minpu Bridge.  
The joint was tested by [PERSON] in the laboratory under the load condition shown in Figure 3. Force cells were positioned at the interfaces between the joint and the different members arriving in the connection. The structure was loaded up to 1.7 times the design loads, the forces were applied by hydraulic jacks using 17 steps with a load step increment of 10%. Each load step took 5 min and the load was maintained constantly for 10 min before proceeding with the next step. The maximum loads reached during the laboratory test are pictured in Figure 3.  
In 2010, the experimental results pointed out in [13] were used by the same authors for the realization of a finite element model of the same composite joint (using ANSYS). Even if the model faithfully reproduced the geometry of the connection, the effects of cracks in concrete were not considered during the analysis. The strains and stresses obtained from the F.E. software were compared with the results obtained from the laboratory tests to validate the accuracy and reliability of the model.
## 3 Results and Discussion  
### Characterization of Materials  
Figure 1 illustrates the XRD spectrum of the different materials. It can be found from the comparison of the standard peaks that the prepared LC material shows obvious PbO characteristic peaks. HPC has broad peaks only at around 26\({}^{\circ}\) and 44\({}^{\circ}\), which is in line with the previous report [32]. The broad peaks indicate that the structure of HPC material is amorphous. According to the results, PbCO\({}_{3}\) can be converted to PbO during the pyrolysis process at 500 \({}^{\circ}\)C, and partial PbO was reduced to Pb.  
The SEM images of the HPC and LC materials are shown in Figure 2. The microscopic appearance of HPC shows a yarn-like porous structure. It has abundant pores that provide the attachment points for PbO. The ion-buffering reservoirs composed of mesoporous exist on the wall of the HPC, which is beneficial to the circulation of electrolytes [33]. As shown in Figure 2e, the average diameter of PbO was about 100 nm. The interplanar spacing was 0.306 nm (Figure 2f).  
Figure 3 shows the element distribution of LC material, in which it can be seen that C, O and Pb were fused together. The ratio of them in LC material was 72.6%, 5.9% and 14.3%, respectively.  
Figure 1: XRD pattern of different materials.  
Figure 2: SEM images of (**a**) HPC, (**b**) LC and (**c**) PLC. TEM images of (**d**) HPC and (**e**) LC. HRTEM image of (**f**) LC.  
The XPS was conducted to identify the relationship between C and Pb elements (Figure 4). The full spectrum indicates the existence of C, O, and Pb elements, which is consistent with the XRD results. C 1s shows the presence of hydrophilic groups of -C-O and -COO which would benefit the dispersal of the LC material in the NAM. A certain number of oxygen-containing functional groups increased the electrode capacity of lead-carbon cells and the lead deposition on the negative electrodes [32; 34; 35]. Pb-COO chemical bond was proved to be included between Pb and C (Figure 4c) [5].
## 4 Our Construction
Actually, \(LF\) is a table, and one line of it can be showed as follows:  
* The user \(u_{i}\) uploads the data blocks and its tags to the CSP. The CSP can check the validation of each tag using the following equation: \[e(\sigma_{j},g)=e(H_{1}(ID_{i}\ +\ \delta)^{m_{j}},P_{pub})\cdot e(H_{2}( \omega_{j}),Y_{ID_{i}})\] (1)
* Challenge. This algorithm is run by the TPA, who randomly picks c-element subset \(J\) of the set \([1,n]\) by pseudo-random permutation (PRP) \(\pi\); each element in \(J\) denotes the index of the challenged data block. The TPA chooses a random element \(v_{j}\in Z_{q}^{*}\) for each element in \(J\) by pseudo-random function (PRF) \(\phi\). Let \(Q\) be the set \(\left\{(j,v_{j})\right\}_{j\in J}\). To generate a challenge, the TPA will search the log file \(LF\) according the set \(J\) to get the information \(\left\{(j,H_{1}(ID_{i}\ +\ \delta))\right\}\). The TPA picks a random value \(t\in Z_{q}^{*}\) as secret value and computes \(T_{1}=g^{t}\), \(T_{2j}=e(H(ID_{i}\ +\ \delta),P_{pub})^{t}\). Let \(T_{2}=\left\{T_{2j}\right\},j\in J\). The TPA sends the \(chal=\left\{Q,T_{1},T_{2}\right\}\) to the server. * Proof-Gen. Upon receiving the \(chal=\left\{Q,T_{1},T_{2}\right\}\), the CSP computes \(\mu=\prod\limits_{j\in J}T_{2j}^{v_{j}m_{j}}\), \(\sigma=\prod\limits_{j\in J}\sigma_{j}^{v_{j}}\) and \(proof=H_{3}(e(\sigma,T_{1})\cdot\mu^{-1})\), then sends \(proof\) as a response to the \(chal\) from the TPA. * Proof-Check. Upon receiving the \(proof\) from the CSP, the TPA first searches the publish log file \(LF\) to get the information \(\left\{(j,Y_{ID_{i}})\right\}\) and checks the equation: \[proof=H_{3}(\prod_{ \begin{array}{c}j\in J\\ \text{j}\to i\end{array} }e(H_{2}\left(j||fname\right)^{\text{\tiny{$\triangledown $}}},Y_{ID_{i}}^{t}))\] in which \(j\to i\) means the information of \(u_{i}\) can be find from public log file \(LF\) by the index \(j\) of data block \(m_{j}\). If the equation holds, the TPA accepts the proof; otherwise, the proof is invalid. The process of Challenge, Proof-Gen, and Proof-Check are summarized as Figure 2.
* Revocation-Tag-Gen. If user \(u_{m}\), \(2\leq m\leq z\) leaves the group, and user \(u_{n}\) will be the successor of \(u_{m}\). The following procedure will efficiently update the tags generated by \(u_{m}\). It needs \(u_{m}\), \(u_{n}\) and the CSP online simultaneously. 1. The CSP randomly selects \(\alpha\in Z_{q}^{*}\), and sends it to \(u_{n}\). 2. Upon receiving \(\alpha\), \(u_{n}\) computes \((K_{1}=(psk_{ID_{n}})^{\frac{1}{yID_{n}}},K_{2}=\alpha\cdot y_{ID_{n}})\) and sends \((K_{1},K_{2})\) to \(u_{m}\). 3. \(u_{m}\) computes \((R_{1}=\frac{K_{1}yID_{m}}{sk_{ID_{m}}},R_{2}=\frac{K_{2}}{yID_{m}})\) and sends it to the CSP. 4. When receiving \((R_{1},R_{2})\), the CSP computes \(R_{3}=\frac{R_{2}}{\alpha}=\frac{y_{ID_{n}}}{yID_{m}}\). The CSP will update the tag of the data block \(m_{i^{\prime}}\) by computing the equation \(\sigma_{i^{\prime}}^{\prime}=(R_{1}m_{i^{\prime}}\cdot\sigma_{i^{\prime}})^{ R_{3}}\), in which \(\sigma_{i^{\prime}}\) is the tag generated by \(u_{m}\).
# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 3 Materials and Methods
### Parameter-Shared Intelligent Media Analytics Framework
#### 3.3.1 Parameter Sharing Layer  
This layer, as its name implies, constitutes the base module of our framework, consisting of the LM and the encoder block, where the model learns shared contextual representations for all downstream tasks, namely sentiment, emotion, irony, and hate speech classification, in the context of a single input sequence.  
The multi-task classification dataset contains sentences of different lengths which need to be encoded into contiguous batches of sequences. When creating these batches, padding was used to make sure that all sequences fit a given length by providing a placeholder value in the form of a \"PAD\" string. The LM uses a binary vector in the attention mechanism, indicating the position of these padded indices to indicate which tokens in the input sequence should be attended to.  
Initially, the entire tokenized input sequence is read by the LM, generating representations while attempting to predict the masked word based on the context provided by the non-masked words in the sequence. The attention weights for each token in the input sequence, which is a combination of three embeddings (position, segment, and token embeddings), is constructed by summing them together and calculating them simultaneously. As a result, the parameter sharing layer with \(N\) multi-head self attention layers attends to each word in the sequence and attention weights are learned through parameter optimization.  
The attention mechanism works by assigning weights to each word in the input sequence based on its relevance to the output. The weights determine how much attention the model should pay to each word when making predictions. In the example sentence in Table 4, the model's attention mechanism would likely assign higher weights to certain words based on their relevance to each classification task. The words (excellent) and (hope) would likely receive higher weights as they are strong indicators of positive sentiment and emotion. The word _pantene_ might also receive a relatively high weight, as it is the subject of the sentence and may provide important contextual information.  
The LM's output is then propagated to the BERT-based encoder block that has randomly initialized weights, and thus is not pre-trained. Every linear layer in the model, including the ones inside the encoder blocks, are initialized using \"normalized initialization\" or more commonly called \"Xavier initialization\" [49], which maintains the variance of activations and back-propagated gradients all the way up or down the layers of the model. The encoder block, consisting of \(N\) encoder layers, where \(N=1\ldots 12\), converts the LM's output into representations that leverage syntactic and semantic meaning by focusing on all tasks simultaneously.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 2 Materials and Methods
### Petri Nets
#### 2.2.3 Circulating Monocytes and Their Recruitment in Atherosclerosis  
Monocytes are derived from HSPCs in the bone marrow and undergo a series of consecutive differentiation stages: the common myeloid progenitor (CMP) and the granulocyte-macro-phage progenitor (GMP), which ultimately give rise to Ly6C\({}^{high}\) monocytes [43]. The differentiation and functions of hematopoietic progenitors in monocytes and macrophages are regulated by a cytokine known as M-CSF. It plays a crucial role in promoting the growth of these progenitors, inducing inflammation, and triggering tissue factor production, which leads to plaque instability and significantly contributes to the development of atherosclerosis [44; 45; 46].  
Humans have two main monocyte categories: classical/inflammatory monocytes with high Ly6C levels (Ly6C\({}^{high}\)) and non-classical/patrolling monocytes with low Ly6C levels (Ly6C\({}^{low}\)). The distinction also lies in their expression of CCR2 and CX3 CR1, where classical monocytes are CCR2\({}^{high}\) and CX3 CR1\({}^{low}\), and non-classical monocytes are CCR2\({}^{low}\) and CX3 CR1\({}^{high}\)[5; 47].  
Recent studies have shown that classical monocytes (Ly6C\({}^{high}\)) are drawn to inflammation sites, where they promote inflammation. It is also believed that these monocytes can become non-classical monocytes (Ly6C\({}^{low}\)), aiding in anti-inflammatory and repair activities. Normally, non-classical monocytes act as vascular scavengers, keeping blood vessels clean by monitoring the endothelium and removing microparticles [8; 9; 47].
## 4 Results of the Experiment
### Analysis of Spatial Variability in Drought
#### 4.4.1 Month-by-Month Drought Changes in Vegetation in Laibin City
Notably, August consistently experienced moderate drought conditions over the three-year period and emerged as a month with high drought frequency. Conversely, the occurrence of severe droughts was infrequent during the months of January to March, with no instances of light drought or higher severity levels observed in January and March over the same period. These months generally exhibited wetter conditions compared to other months, with February being the only exception, experiencing mild drought conditions. Overall, January and March in the past three years have not encountered drought conditions beyond the mild category, with no significant drought-related disasters reported, indicating a relatively wetter climate during these months.
## 3 Multi-Objective Optimization
### Multi-Objective Optimization Design
#### 3.2.2 Mean Value and Weight Ratio Analysis  
The mean values of the experimental results were statistically analyzed the effects of parameter changes on each performance index. The formula for calculating the mean value of all the finite element results for each performance index is shown in Equation (9), and the results are shown in Table 6.  
\[h=\sum_{a}^{n}\frac{S_{a}}{n} \tag{9}\]  
where \(n\) is the number of experiments, \(S_{a}\) is the value of a certain target performance index for the \(a\)-th experiment.  
The average value of each motor optimization parameter corresponding to a particular optimization target at each level value is calculated as  
\[h_{xa}=\frac{h_{x}(g)+h_{x}(p)+h_{x}(k)+h_{x}(z)}{4} \tag{10}\]  
where \(x\) represents the motor optimization parameter, \(h_{xa}\) denotes the average value of the target performance index under the \(a\)-th influence factor of parameter \(x\), \(h_{x}\) denotes the target performance index under a certain experiment of parameter \(x\), and \(g\), \(p\), \(k\), and \(z\) denote the experiment serial numbers.  
According to the formula, the average values of motor flux density, no-load back EMF, and output torque for each optimization variable taken at different levels are shown in Tables 7-9.  
The variance is used to assess the extent to which a number deviates from the mean. By analyzing the variance, the proportion of the effect of the parameter on the performance index can be calculated, and the formula for calculating the variance is (11).  
\[S_{s}=4\times\sum_{a=1}^{4}(h_{xa}-h)^{2} \tag{11}\]  
The weight of the influence is shown in Table 10. It can be concluded that the pole arc coefficient has the widest influence on the optimization target, and has a great influence on the flux density, no load back EMF and output torque of the motor. The influence of the permanent magnet thickness on the no load back EMF and output torque is basically the same. The influence of the permanent magnet arc radius on the output torque is large, and the influence of the air-gap length on the output torque is large.