esa-sceva/satcom-chunk-collection huggingface dataset:
# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
## 5 Discussion  
### Evaluation and Implications  
This study has developed the WordMap system by using a highly integrated framework, aiming to evaluate both its performance and user acceptance. This section discusses the performance and user feedback of the WordMap system, as well as the benefits offered by its integrated framework.  
In theoretical terms, this study verified the applicability of WordMap text mining applications among users through the TAM and contributed to advancements in text mining research. The empirical results indicated that PEOU has a direct positive impact on both PU and ATT, indicating that an intuitive system can effectively enhance users' perception of its value and generate a positive attitude towards use. Furthermore, PU significantly influences ATT, and ATT has a significant predictive effect on BI. This means that users' perception of the system's practicality can be transformed into a positive attitude, which serves as the core driving force for users to continue using WordMap. In practical terms, the intuitive interactive interface and integrated framework of WordMap provide both usability for processing text tasks and convenience for non-technical users. WordMap demonstrates strong potential in specific application scenarios. Specifically, we evaluated the system using real-world news datasets, which included articles from various domains such as education, technology, and art. These evaluations confirmed WordMap's ability to perform corpus segmentation, topic analysis, and visualization across distinct subject areas. WordMap's capacity to efficiently process large-scale unstructured text suggests strong potential for applications in areas such as sentiment analysis (e.g., [42]) and social media data-driven analysis (e.g., [43]). Moreover, the system's extensible framework makes it well-suited for cross-domain text mining tasks, including financial analysis, healthcare analytics, marketing research, and legal document processing.  
This study validated the effectiveness and user acceptance of the WordMap system while demonstrating its adaptability to real-world applications. By integrating a user-friendly interface with powerful analytical capabilities, WordMap represents a meaningful advancement in making text mining more accessible, practical, and relevant across various domains.
## 3 Results
### Leaflet Morphology and Mobility  
The morphologic features of the nine MV models with different SC insertion locations at peak systole were quantitated to assess the structure and mobility of the anterior leaflet (Figure 6). The cross-sectional views of the anteroposterior (A-P) leaflet edges are presented as blue (anterior leaflet) and red (posterior leaflet) lines to visualize and quantitate the coaptation lengths, coaptation angles, and anterior bulging heights. Dividing the nine MV models into three groups (top, center, and bottom), the center (narrow = 7.03 mm, middle = 5.93 mm, wide = 4.77 mm) and bottom (narrow = 6.86 mm, middle = 5.94 mm, wide = 4.79 mm) groups demonstrated decreased coaptation lengths in proportion to the width of the SC insertion location. The center and bottom groups also displayed an increase in coaptation angles in proportion to the width of the insertion location (center: narrow = 7.71\({}^{\circ}\), middle = 8.63\({}^{\circ}\), wide = 11.91\({}^{\circ}\); bottom: narrow = 6.19\({}^{\circ}\), middle = 8.66\({}^{\circ}\), wide = 11.97\({}^{\circ}\)). In the top group, the middle and wide MV models showed similar coaptation lengths and coaptation angles (coaptation length: middle = 4.78 mm, wide = 4.79 mm; coaptation angle: middle = 11.91\({}^{\circ}\), wide = 12.09\({}^{\circ}\)). The top-wide MV model demonstrated the greatest anterior bulging height (4.13 mm), and the bottom-middle MV model showed the minimum anterior bulging height (3.88 mm). In the wide group, all three MV models had similar bulging heights (top = 4.13 mm, center = 4.09 mm, bottom = 4.1 mm). In the middle group, the bulging heights decreased markedly when the SC insertion location moved from the top to the middle and bottom.
## 2 Materials and Methods
#### 2.2.1 General Concepts of Error Control Codes  
* Error control codes, also known as channel (noise-tolerant) codes, serve to detect and correct (or only detect) any errors received during data transmission or storage at the expense of adding information redundancy to the data [23]. Channel codes can be error-detecting-only codes or error-detecting and error-correcting codes. On the other hand, they are divided into block and convolutional (continuous) codes. Block codes are those in which each character of a given message corresponds to a precisely defined code combination. They are divided into separable (when it is known which bits in the code combination are information and which are control) and inseparable (all bits are both information and control) [24].
* In the theory of channel coding, the following concepts are defined [24]:
* Information combination (IC)--these are the information bits that will be coded, and the number of bits in one IC will conventionally be denoted by \(m\).
* Control bits (\(X\))--these are the bits that are added to the information bits and thus form the code combination. The number of control bits will conventionally be denoted by \(k\)* Code combination (CC) or code word \([X]\)--this is the coded combination, including the information and control bits. The number of bits in one code combination (CC) will conventionally be denoted by \(n\), as: \[n=m+k.\] (1)
* Multiplicity of errors (\(t\))--number of erroneous bits in one code combination.
* Code (Hamming) distance between two combinations (\(d\))--number of bits by which the two combinations differ. For codes without redundancy, it is characteristic that \(d=1\), and for codes with redundancy--\(d>1\).
* Minimum code distance of the code (\(d_{min}\))--the smallest possible code distance between two code combinations of a given code.
* Information redundancy--extra (check) bits added to the information, with the help of which it is checked whether errors occurred during the transmission/storage of the message.
* Transmission rate of the code combination (\(U_{m}\)): \[U_{m}=\frac{m}{n}.\] (2) where: \(m\)--number of information bits in the code combination; \(n\)--total number of bits in the code combination.
* Weight of the code combination in binary coding--number of ones (1s) in the code word.  
To determine the noise immunity of a code, the concept of error-detection coefficient is also introduced:  
\[K_{0}=\frac{L}{M}. \tag{3}\]  
where: \(L\)--number of wrong combinations in which the errors were detected.  
\(M\)--total number of wrong combinations.
* Correction capability (\(l_{0}\))--the correction capability of a given code is the number of errors that the code can correct and is closely related to the minimum code distance \(d_{min}\) with the following dependence [20]: \[l_{0}=\left\lfloor\frac{d_{min}-1}{2}\right\rfloor,\] (4) where \(\left\lfloor x\right\rfloor\) denotes the largest integer not exceeding the value of \(x\).
* Detectability (\(l_{n}\))--the detectability of a given code is the number of errors it can detect and has the following relationship with \(d_{min}\): \[l_{n}=d_{min}-1.\] (5)
# Bi-Photon Entangled Airy Beams through Unstable Ocean Turbulence
## 1 Introduction
Analyzing the results of numerical simulations, we compare the bi-photon entangled system and non-entangled ones and study how OAM modes of the bi-photon entangled Airy beams are affected by specific parameters of beam and turbulence. Through analytical simulations under various parameter settings, entangled Airy beams show stronger robustness to oceanic turbulence than single ones.
# A Supernet-Only Framework for Federated Learning in Computationally Heterogeneous Scenarios
## 2 Background and Related Work
### Federated Learning  
Federated Learning (FL) is a distributed machine learning paradigm that enables multiple devices to collaboratively train a global model without sharing raw data. A central server orchestrates the model, while clients train locally using their data and send updates--typically gradients or trained model parameters--to the server in a privacy-preserving manner. Assume there are \(K\) clients (participants), and each client \(k\) holds a local dataset \(D_{k}\). The goal is to optimize the global model parameter \(\mathcal{P}\) by minimizing the global loss function \(\mathcal{L}\) as the weighted sum of \(K\) local loss \(\mathcal{L}_{k}\):  
\[\min_{\mathcal{P}}\mathcal{L}(\mathcal{P})=\min_{\mathcal{P}}\sum_{k=1}^{K} \frac{|D_{k}|}{\sum_{l=1}^{K}|D_{l}|}\mathcal{L}_{k}(\mathcal{P}) \tag{1}\]  
FL encompasses several fundamental approaches [2], such as ring-based aggregation FL [12], gradient-based FedSGD [13] and model-based aggregation FL [14]. FL can mitigate the issues of data non-IID and data privatization. However, it generally requires different clients to use the same network architecture, making it difficult to handle the challenge of highly heterogeneous devices.  
Some studies attempt to address the issue of device heterogeneity in federated learning by dynamically adjusting model complexity through pruning strategies. They send networks of different sizes to different clients, where networks of varying sizes share a portion of the channels [15], the early layers [16], or a combination of both [11, 17]. Since it is necessary to train directly on networks of different sizes, they face various challenges such as optimal decision and generalization issues [18]
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers
#### 5.3.6 Wormhole Attack  
Wormhole attack is a data link layer intrusion. In this type of attack, a malicious or fake node registers all the information and diverts it to wrong path. This attack can be formed without the knowledge of cryptography of actual wireless sensor node [84; 85; 86].
## 5 A Case Study--Application as a Grid Voltage Compensator
Therefore, the peak value of the reference voltage was set to 100 V. If there is a problem with a voltage sag with a depth level of 20%, then the line or load voltage is reduced to 80 V. In this situation, the required peak value of the injecting voltage is 20 V, which has to be produced by the AC voltage controller in non-inverting mode from 80 V peak input. For this purpose, the required voltage gain is 0.25, which generates a 20 V peak non-inverted output, as may be observed from Figure 11a. If there is a voltage swell issue with a similar depth level, then line peak voltage is increased to 120 V. Once again, AC voltage has to produce a 20 V peak output with voltage inverted form. The voltage gain for this output is 0.1667, as the input voltage of the converter is now 120 V. These values of the voltage gain are also confirmed from the calculation of Equation (22). It was concluded that any voltage change in the grid or line voltage might be corrected with the compensating voltage generated by the AC voltage controller that can deliver non-inverted and inverted outputs with the required voltage gain. For the correction of the line or grid voltage for voltage sag and swell issues, the converter's output voltages have to be added and subtracted from the line with the help of a series connected transformer. The flow chart in Figure 12 also facilitates obtaining a quick review of the line voltage correction process. Figure 11: Grid voltage compensation during the problem of (**a**) voltage sag and (**b**) voltage swell.
## 2 Materials and Methods
### Outcome Measures
#### 2.4.1 Postural Control  
1. K-TIS  
The K-TIS was developed to assess trunk motor impairment in individuals with stroke [28]. It has been validated for use across various stages of stroke recovery, including acute and subacute patients within 3 months of onset, as well as those in the chronic stage beyond 6 months [29]. The K-TIS evaluates static and dynamic sitting balance, coordination of the trunk, and postural control. The total score ranges from 0 to 23, with high scores indicating better trunk function. It has demonstrated high reliability, with inter-rater reliability coefficients ranging from \(r\) = 0.87 to 0.96 and intra-rater reliability coefficients ranging from \(r\) = 0.85 to 0.99. Owing to its strong psychometric properties, the K-TIS serves as a clinically useful tool for the qualitative assessment of trunk control and evaluation of therapeutic outcomes.  
2. Postural Assessment Scale for Stroke (PASS)  
The PASS was developed to complement the BL Motor Assessment and to specifically evaluate balance and trunk control in patients with stroke [30]. The PASS consists of 12 items across two subdomains: maintaining posture (including sitting unsupported and transitioning from supine to sitting) and changing posture (including sitting to lying down). Each item is rated on a scale from 0 to 3, with a maximum total score of 36; high scores reflect a greater ability to control dynamic balance and posture. The PASS has demonstrated excellent reliability, with inter-rater reliability reported at \(r\) = 0.97 and intra-rater reliability reported at \(r\) = 0.94 [31].
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
###### Abstract  
The corrosion of reinforced concrete (RC) is one of the most serious durability problems in civil engineering structures, and the corrosion detection of internal reinforcements is an important basis for structural durability assessment. In this paper, the appropriate frequency required to cause excitation signals in the specimen is first analyzed by means of frequency dispersion curves. Subsequently, the effectiveness of five damage indexes (DIs) is discussed using random corrosion in finite elements. Finally, guided ultrasonic wave (GUW) tests are conducted on reinforcement and RC specimens at different corrosion degrees, and the test results are verified using a theoretical corrosion model. The results show that the larger the covered thickness is at the same frequency, the higher the modal order of the GUW in the frequency dispersion curve is, and the smaller the group velocity is. The SAD is the most sensitive to the corrosion state of the reinforcement compared with the other DIs, and it shows a linear increasing trend with the increase in the corrosion degree of the reinforcement. The SAD values of the RC specimens showed a three-stage change with the increase in the corrosion time, and the time until the appearance of corrosion cracks was increased with the increase in the covered thickness. It can be seen that increasing the covered thickness is an effective method to delay the time until the appearance of corrosion cracks in RC specimens.  
reinforced concrete; corrosion monitoring; guided ultrasonic wave; spectrum amplitude difference; corrosion model +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### The Proposed Method
\begin{split} Obj&=\min\sum_{i,j}^{n}\parallel \mathbf{f}_{i}-\mathbf{f}_{j}\parallel_{2}^{2}\mathbf{S}_{ij}\\ &=\min\sum_{i,j}^{n}((\mathbf{f}_{i}-\mathbf{f}_{j})^{T}( \mathbf{f}_{i}-\mathbf{f}_{j}))\mathbf{S}_{ij}\\ &=\min\sum_{i,j}^{n}(\mathbf{f}_{i}^{T}\mathbf{f}_{i}-2\mathbf{f}_ {i}^{T}\mathbf{f}_{j}+\mathbf{f}_{j}^{T}\mathbf{f}_{j})\mathbf{S}_{ij}\\ &=\min\sum_{i}^{n}\mathbf{f}_{i}^{T}\mathbf{f}_{i}\mathbf{D}_{ii} +\sum_{j}^{n}\mathbf{f}_{j}^{T}\mathbf{f}_{j}\mathbf{D}_{jj}-2\sum_{ij}^{n} \mathbf{f}_{i}^{T}\mathbf{f}_{j}\mathbf{S}_{ij}\\ &=\min 2\sum_{ij}^{n}\mathbf{f}_{i}^{T}\mathbf{f}_{j}(\mathbf{D}_{ij} -\mathbf{S}_{ij})\\ &=\min 2\sum_{ij}^{n}\mathbf{f}_{i}^{T}\mathbf{f}_{j}\mathbf{L}_{ij} \end{split}  \tag{16}\]  
Noting that \(\mathbf{f}_{i}\) and \(\mathbf{f}_{j}\) are column vectors of matrix \(\mathbf{F}\), expressing the above conclusion in a matrix form yields:  
\[Obj=\min Tr(\mathbf{F}^{T}\mathbf{L}\mathbf{F}), \tag{17}\]  
which is equivalent to Equation (15). It is worth noting that, in order to enhance the robustness against outliers and improve computational efficiency, non fully connected graphs are commonly constructed in practice. Generally speaking, the range of neighborhoods can be defined based on the \(k\)-nearestor \(\epsilon\)-distance rules. The similarities of vertexes beyond the neighborhood's range will be assigned as zero. Once the minimization of \(Obj\) is achieved, the optimal low-dimensional mapping \(\mathbf{F}\) is obtained, with a significantly reduced dimensionality while preserving the manifold relationships. The problem (15) can be converted into the eigenvalue decomposition problem and be solved effectively. The low-dimensional data \(\mathbf{F}\) can be conveniently applied to solve various problems, such as feature selection, clustering and semi-supervised classification. In order to further demonstrate effectiveness, FMDR is adopted for a semi-supervised classification problem. Given \(n\) data points with \(l\) labeled samples and \(u\) unlabeled samples (\(1\leq l\leq n\)), where \(\mathbf{Y}_{l}\) represents the label matrix such that each row \(\mathbf{y}_{i}\) denotes the label vector corresponding to the \(i\)th sample. The matrix \(\mathbf{F}\) is composed of two parts, \(\mathbf{F}=[\mathbf{F}_{l};\mathbf{F}_{n-l}]\). The semi-supervised graph-based learning problem can be formulated as follows:  
\[\min_{\mathbf{F}}Tr(\mathbf{F}^{T}\mathbf{L}\mathbf{F})\quad s.t.\mathbf{F}_ {l}=\mathbf{Y}_{l}, \tag{18}\]  
where the Laplacian matrix \(\mathbf{L}\) consists of four parts:  
\[\mathbf{L}= \begin{bmatrix}\mathbf{L}_{l,l}&\mathbf{L}_{l,n-l}\\ \mathbf{L}_{n-l,l}&\mathbf{L}_{n-l,n-l}\end{bmatrix} . \tag{19}\]  
Based on the conclusion from [29], there is a harmonic solution to problem (18), which is defined as:  
\[\mathbf{F}_{n-l}=-(\mathbf{L}_{n-l,n-l})^{-1}\mathbf{L}_{n-l,l}\mathbf{Y}_{l}. \tag{20}\]  
Therefore, the labels of unknown samples can be directly indicated by \(\mathbf{F}_{n-l}\). The algorithm of FMDR for semi-supervised classification is summarized in Algorithm 1.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 2 Materials and Methods
### Feedstock Availability, Transportation Model, and Cost Assumptions  
BR generated as by-products from harvesting and processing activities, represent a valuable yet underutilized lignocellulosic biomass resource in China. This study focuses on ten provinces--Fujian, Guangxi, Guangdong, Jiangxi, Zhejiang, Hunan, Chongqing, Sichuan, Yunnan, and Anhui--where bamboo residue availability exceeds 1 million tons per year. Provincial selection was guided by the national-scale spatial inventory methodology proposed by [PERSON] et al., which integrates statistical regression, crop-to-residue ratio modeling, and GIS-based downscaling to construct a high-resolution geospatial distribution of biomass residues across China [18].  
To estimate the average transportation distance from decentralized BR sources to centralized carbonization facilities, each province was geometrically simplified as a circular area. The mean straight-line distance from any point within the province to the center was approximated as  
\[D_{E}=0.66\times\sqrt{A} \tag{1}\]  
where \(D_{E}\) is the mean straight-line distance; \(A\) is the area of the province [19]. To correct for real-world road network detours, an empirical mobility correction was applied using the following formula:  
\[D_{R}=\alpha\cdot D_{E}+\beta \tag{2}\]  
where \(\alpha\) is 1.287, \(\beta\) is 0.4 km for expresses with R\({}^{2}\) = 0.900, based on large-sample transportation studies in China [20].  
Feedstock cost assumptions were based on national procurement data, with the price of BR ranging from 200 to 500 CNY per tonne [21; 22; 23]. The transportation cost was set at 0.3 CNY per tonne-kilometer, reflecting the average within the reported range of 0.2-0.4 CNY/tkm from national logistics pricing reports [24]. These cost parameters, combined with the transport distance estimates, were used to compute the delivered feedstock cost to the facility gate for each province.
## 2 Statistical Distribution
### Exponential Distribution  
An exponential distribution is typically represented by one or two parameters. Equation (4) represents the reliability function of the exponential distribution.  
\[R(t)=exp\{-\lambda(t-\gamma)\}, \tag{4}\]  
where \(\gamma\) represents the location parameter, and \(\lambda\) represents the rate parameter. When using only one parameter, the value of \(\gamma\) becomes 0. The exponential distribution assumes a constant failure rate over time and is used when failures occur at a constant rate.
## 3 Technology Developments for Production of Carotenoids
### Pathway Engineering for Production of Carotenoids
A so-called \"randomized BioBrickassembly\" approach has been applied to the optimization of the lycopene synthesis pathway wherein the expression construct was designed to independently express each enzyme from its own promoter, which resulted in an increase by 30% in lycopene production [91]. A longer and more complicated pathway can be modularized into subsets, which contain pathway enzymes with similar turnover numbers. Modulating these subsets would be more convenient and efficient than regulating all components of the entire pathway for improved production [92]. By using this multivariate modular metabolic engineering (MMME) approach, recent work achieved a 15,000-fold increase in production of taxadiene, a precursor of the anti-cancer drug taxol [93]. There are also a variety of promising approaches, such as tunable promoters, tunable intergenic regions, and ribosome binding site design, which can be applied to fine tuning the expression of modules [94; 95; 96]. In the other approaches, a multi-genic operon is transcribed into a single polycistronic mRNA, and then the large transcript can be spliced to small monocistronic transcripts through post-transcriptional RNA processing such as ribozyme cleavage and clustered regularly interspaced short palindromic repeats (CRISPR) editing. Thus, the stability of the monocistronic transcripts can be independently modulated to differentiate the expression level of each enzyme even in a multi-gene operon. These RNA processing tools have been developed as insulating elements between operonic genes to reduce the context dependence of the genes in a polycistronic transcription unit [97]. The diffusion of pathway intermediates can decrease the effective concentrations of intermediates for following enzyme reactions and some intermediates may serve for competing pathways. By learning from Mother Nature, synthetic biologists spatially organize enzymes of the MVA pathway by protein scaffolds in _E. coli_ to minimize diffusion limitation and achieve a 77-fold increase in mevalonic acid production [98]. The propanediol utilization machinery of _Citrobacter freundii_ has been heterologously recasted in _E. coli_[99]. Some intermediates of carotenoid synthesis such as isoprenyl diphosphates are toxic when they accumulate over the concentration threshold [100]. To avoid the accumulation of toxic intermediates, genetic sensors can potentially be coupled with gene expression cassettes to regulate the intermediate flux in a dynamic manner. The native _E. coli_ promoters that respond to the toxic FPP have been successfully used to dynamically regulate the amorphadiene synthesis pathway and improve the production by two-fold over common inducible promoters and constitutive promoters [101]. The Ntr regulon has been engineered to control lycopene synthesis in response to glycolytic flux dynamics, resulting in an 18-fold increase in lycopene production [102].