esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Base Concepts
### Fuzzy Logic  
Researchers' studies show that real and complicated processes cannot be modeled, measured, or managed accurately because they include various uncertainties such as uncompleted data, random data, noise data, outliers, and data loss. A robust solution for solving this issue is to use a useful mathematical technique called fuzzy logic (FL). It can describe human thinking in an approximate manner. [PERSON] first introduced this theory in 1965. Note that the classical set theory presents a precise and certain definition of membership. Based on this definition, an element either belongs to a set or not. In contrast, the fuzzy theory emphasizes a novel concept called partial membership [34; 35]. According to this concept, an element may partly be belonging to a set. Thus, the results are not right or wrong absolutely.  
In the fuzzy theory, assume that \(X\) indicates a reference set, which includes a set of elements such as \(x\). In this mode, Equation (2) defines the fuzzy set (i.e., \(A\)) based on \(X\).  
\[A=\{(x,\mu_{A}(x))|x\in X\}=\sum_{i=1}^{n}\mu_{A}(x_{i})/x_{i} \tag{2}\]  
where, \(\mu_{A}:X\rightarrow[0,1]\) means the membership function (MF). It determines the membership degree (i.e., \(\mu_{A}(x_{i})\)) for each element (i.e., \(x\)) in \(A\). \"/\" is a symbol for separating \(\mu_{A}(x_{i})\) from \(x\). Note that MF is a key component in fuzzy sets. For example, the triangular function, trapezoidal function, and Gaussian function are known as the most common MFs.  
Today, various applications utilize fuzzy inference mechanisms (FIMs) to improve their performance. The most famous FIMs are Mamdani and Sugeno (TSK). In each fuzzy system, there are four main parts, including the fuzzifier, defuzzifier, rule base, and fuzzy engine.  
Figure 2: Reinforcement learning process.  
The fuzzifier produces fuzzy inputs based on crisp values and allocates a membership degree to each element using the defined membership functions. The fuzzy engine uses fuzzy rules stored in the rule base to simulate fuzzy inputs. Finally, the results obtained from the fuzzy engine are converted to crisp values using the defuzzifier. The most common defuzzification techniques are averaging and centroid [35; 36].
## 3 Results and Discussion
### Slump Flow Characteristics of the Mixtures  
The workability of fresh admixtures is an important factor to assess and evaluate the overall performance of concretes. Any given mix design is expected to maintain its fluid nature to enable mixing, transportation, placement and finishing procedures. The GP pastes in present study exhibited excellent workability parameters, which was expected as the mix matrix comprised of very fine and spherical shaped particles which are bestowed with a smooth flow character. The slump flow of GP 01-05 was lower than for GP 06-10, except for GP 07; this can be attributed to the higher alkaline solution to FA ratio of the latter half of the mixtures. The higher this ratio implies a higher fluid content in the mixture which produce a more liquid paste (see the results given in Table 6).  
The Slump Flow results for RPC mixes are given in Table 7. Compared to the GP mixtures, RPC specimens displayed relatively low workability attributes, with a minimum slump flow of 111.0 mm and maximum of 132.5 mm. This condition can be expected owing to the inclusion of aggregate particles which can increase the friction among the constituents and hence result in a decreased flow characteristics. Moreover, the RPC mixtures were observed to be denser compared to the GP pastes, which further reduces the workability characteristics of the mixture. The Flow Table results obtained in this study were found to be lower than the ASTM C230 standard requirement, which is 190 mm-250 mm after 20 drops. According to [PERSON] and [PERSON] (2017), this flow can be achieved with a mixing time of about 40 min at laboratory conditions.  
The RPGC mixtures recorded a higher slump flow compared to RPC mixes, which could be due to the finer and spherical nature of the FA and this in turn can be assumed to improve the fluidity and workability conditions of the former. In contrast, RPGC formulations were appeared to be more viscous compared to GP paste mixes, thus recording poorer flow readings which could be due to the inclusion on aggregate particles which effectively could thicken the mixture, thus reducing the workability conditions. In addition,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample ID** & **d1 (mm)** & **d2 (mm)** & **Slump Flow (mm)** \\ \hline GP 01 & 300 & 300 & 300.0 \\ GP 02 & 300 & 305 & 302.5 \\ GP 03 & 315 & 310 & 312.5 \\ GP 04 & 300 & 300 & 300.0 \\ GP 05 & 290 & 300 & 295.0 \\ GP 06 & 305 & 310 & 307.5 \\ GP 07 & 280 & 280 & 280.0 \\ GP 08 & 310 & 320 & 315.0 \\ GP 09 & 330 & 340 & 335.0 \\ GP 10 & 320 & 330 & 325.0 \\ Minimum & 280 & 280 & 280.0 \\ Maximum & 330 & 340 & 335.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Workability results for GP paste mixtures.  
it can be noted that the flow-ability of RPGC 01 and 02 are much lower than RPGC 03 and 04. This can be attributed to the value of the alkaline solution to FA ratio of the latter two mixtures (0.57), as compared to the former two mixtures (0.4), thus producing a more workable mix. The slump flow results are given in Table 8.
## 6 Critical Evaluation of the Utilisation of PCMs in the Concrete Industry and Their Improvements
Energy piles may be used if the suggested fibre-reinforced PCM-HSB can greatly increase concrete's heat capacity and bearing capacity. Density, compressive strength, and flexural strength all showed upward increases with PCM aggregate content. As PCM aggregates increase, so does their thermal conductivity. 5. To store and release more thermal energy during phase transitions, scientists have been creating novel PCMs such as Microencapsulated PCMs and Macroencapsulated PCMs with an 18 \({}^{\circ}\)C phase transition temperature and greater latent heat capacities. In concrete structures, high-performance PCMs can result in improved temperature control and increased energy savings. 6. Research into creating PCMs with phase change temperatures that are best suited for particular regions and structural needs is expanding. PCM-enhanced concrete can be tailored to the appropriate indoor temperature range by adjusting the melting and solidification points. 7. Efforts have been undertaken to improve PCM compatibility with different concrete mixtures, making sure that their incorporation does not compromise the concrete's structural integrity or lifespan. Based on the above discussion, it is crucial to carefully analyse the kind of PCM used, how it is distributed within the concrete, and any potential downsides, such as price, a limited capacity for heat transfer, and material compatibility. For concrete construction to successfully integrate and gain long-term benefits, proper design, material selection, and consideration of building requirements are essential. In construction projects looking for ecological and energy-efficient solutions, PCM-enhanced concrete may become more common as technology develops and costs fall. More importantly, it is plausible to remember that PCM technology is always evolving, and there may have been more breakthroughs beyond those mentioned above. Researchers and business experts are still focusing on improving PCM embedding in concrete structures to increase their sustainability, energy efficiency, and comfort for residents. For instance, the innovative solid-solid phase change materials (SS-PCMs), which overcome the leakage issues normally seen in solid-liquid PCMs, are of tremendous significance for implementation in thermal energy storage systems. Due to this property, they can be incorporated into other materials without the necessity for encapsulation, such as for thermal energy storage in gypsum or concrete used in buildings [102; 103].
## 2 Experimental Test
### Capillary-Diffusive Model (Cuba Model)  
It is worth noting that sorptivity is the key parameter of water transport in concrete, representing the water uptake via capillary pores at early ages. Despite the ability of this main characteristic to evaluate the near-surface properties, it is crucial to study the water flow through very small pores at larger times for durability purposes, referred to as the diffusion mechanism [49; 50]. The diffusion process fundamentally reflects the movement of  
Figure 3: All experimental tests: (**a**) compressive strength test; (**b**) flexural strength test; (**c**) TWA test; (**d**) CWA test.  
water inside the matrix and can be measured over a couple of years, as asserted in a previous study that applied a three-year program to monitor the pore diffusion coefficients for concrete, cement paste, and mortar [50]. As reported in the literature review, a wide range of factors control the evolution of the flow of water through small or large pores, including the pore structure, the curing regime, and the preconditioning temperature [51; 52; 53]. More accurately, the curing in water promotes the refinement of the pores, thus resulting in a high level of impermeability, but this result is not found in the samples cured in open air [51; 52]. Also, the sorptivity is improved with the elevation in the preconditioning temperature. This means that a dry specimen exposed to a temperature of 100 \({}^{\circ}\)C experiences greater capillary forces applied on its pores to allow the water to flow and that these are greater than a partially dried specimen at 50 \({}^{\circ}\)C. However, the variation in temperature does not affect the diffusion coefficients [52]. The capillary-diffusive model, also known as Cuba's model, is used to predict both the sorptivity and diffusion processes [48], which can be used in various fields such as material science, environmental engineering, and soil science. The equation of the model is the summation of the mass per unit of area of water absorbed by capillarity and diffusion, which can be represented as  
\[\frac{M}{A}=C\rho\left(1-\exp\frac{-St^{1/2}}{C\rho}\right)+C_{0}L\left(1- \frac{8}{\pi^{2}}\sum_{n=0}\frac{1}{\left(2n+1\right)^{2}}\exp\left[-Dt\frac {\left(2n+1\right)^{2}\pi^{2}}{4L^{2}}\right]\right)\]  
where  
\[M = \text{mass of water absorbed (kg)}\] \[A = \text{surface area of the cube through which water penetrates (m${}^{2}$)}\] \[C = \text{constant linked to the distance from the concrete surface where the pores control the initial sorption (m)}\] \[\rho = \text{density of the water (kg/m${}^{3}$)}\] \[S = \text{sorptivity coefficient (kg/m${}^{2}$\cdot$\text{s}$\text{i}$)}\] \[t = \text{time for recording the sorptivity (s)}\] \[C_{0} = \text{invariance of water concentration (kg/m${}^{3}$)}\] \[L = \text{sample depth (m)}\] \[D = \text{diffusion coefficient (m${}^{2}$/s$)}\]  
The flowchart of the experimental work is illustrated in Figure 4, noting that three replicates are prepared for each test.  
Figure 4: Flowchart of the experimental work.
## 5 Performance Evaluation
### Synthetic Data
#### 5.2.2 Effect of Adaptive Data Collection  
Figure 10 shows the changing fidelity for Scenario 2 during the data collection by using the same parameter settings in Figure 7, _i.e._, \(t=0.4\), \(T_{l}=50\), \(T_{d}=4T_{l}=200\). During the learning time of \(T_{l}\), about \(35.8\%\) nodes can be determined to be active when the fidelity threshold is set to \(96\%\). Then a fidelity decline from 1 to \(96\%\) can be found in Figure 10 because only \(35.8\%\) member nodes communicate with the cluster head. With the change in the spatial correlation of nodes over time, there is a slight and continuing decrease in the fidelity. When the cluster head detected that the cluster trend is deteriorating, the cluster head and active nodes will be reselected to still maintain a high level of fidelity. Compared to Figure 7, in addition, there is not any fidelity outlier appearing in Figure 10 because of the ideal property of synthetic data.  
We also calculated the energy saving for both of two scenario by the Equation (17) and got the following results: \(P_{E}=32.8\%\) for Scenario 1 and \(P_{E}=51.36\%\) for Scenario 2. The main reason for more energy saving in Scenario 2 with a denser node deployment is that a larger SNR is achieved.
# Reference Models for Digital Manufacturing Platforms
## 3 Reference Models Alignment
### Reference Model for Industrie 4.0 (RAMI 4.0)
#### 3.1.1 Layers  
RAMI 4.0 Layers represent the different Information Technology (IT) management layers of I4.0 project implementation, organized in a vertical axis. Each layer gathers different manageable parts of the system (e.g., data maps, communications, hardware), provides services to the upper layer, and orchestrates services from bottom layers. The different layers defined are:  
* Business layer: maps the business model, the overall process, and the rules that the system has to follow. The business model layer ensures the integrity of the functions in the value stream. It also provides the regulatory and legal framework conditions. The business layer  
Figure 1: Reference Model for Industrie 4.0 (RAMI 4.0) reference model layers.  
orchestrates the services of the functional layer and receives events that inform of the progress of the business process.
* Functional layer: provides the runtime and modelling environment for the services that support the business layer. Remote access and horizontal integration take place in the functional layer, except for processes that are only relevant for lower layers (e.g., reading diagnosis data) or that are not relevant to permanent functional or horizontal integration (e.g., maintenance).
* Information layer: contains the data services that enable the ingestion, use, and maintenance of the data used, generated, or modified by the technical functionalities of the assets. This includes data persistence, provisioning, integration, and integrity. It receives events from the physical asset via lower level layers and applies the adequate processing and transformation to support the functional layer services.
* Communication layer: provides uniform communication and data formats that allow the access of information and provides interfaces to access the functions of an asset from other assets.
* Integration layer: represents the transition from the physical world to the information world. The integration layer contains a representation of the properties and process-related functions of an asset and reports events from the physical world. The integration layer includes asset documentation, software and firmware, or Human-Machine Interfaces (HMI).
* Asset layer: represents reality, i.e. the physical instance of the asset which is represented by all other layers.
###### Abstract
Therefore, the interval structure has a strong anti-interference ability to the abnormal values in the interval data. In addition, to testify the ability of the designed prediction architecture, we select 9 indicators to verify the accuracy of the prediction, and implement a series of discussion to judge the effectiveness of the prediction system. The leading innovations of the forecasting system are summed up bellow:  
**(1) A new double forecasting system for power load is established in this paper, which is successfully combined of point forecasting module and interval forecasting module. ** The purpose of the system is to improve the accuracy of load point prediction, and effectively analyze the uncertainty of power load data. **(2) A nonlinear combination method based on the BP algorithm optimized by MOEA/D is proposed to better improve the forecasting performance of the system. ** To obtain the optimal combination pattern of each model, the nonlinear aggregation mechanism based on BP is adopted to combine the models and eliminate the inherent defects of individual model and linear combination. In particular, MOEA/D algorithm is used to search the best parameters of BP, which further improves the prediction accuracy. **(3) An interval prediction method based on fuzzy clustering is established, which provides an effective tool for load uncertainty analysis. ** The interval forecasting method determines the upper and lower bounds of the power load prediction value to quantify the uncertainty information of the load, and provide more comprehensive reference information for the operation risk decision makers of power system. The rest of this paper is: Section 2 shows the methods applied in the proposed double forecasting architecture. In Section 3, the double forecasting system is established. Section 4 introduces the experimental data and displays the experimental results. Section 5 provides further discussion. Finally, the conclusion is given in Section 6.
# Improving Web Readability Using Video Content: A Relevance-Based Approach
## 3 Methodology
### Data Pre-Processing  
Once the video transcripts are obtained, they undergo several pre-processing steps to ensure uniformity and readiness for analysis. The following steps were undertaken to prepare the data:  
* Tokenization: The text was then preprocessed by partitioning the extracted text into tokens or words. Tokenization is also very helpful in word separation which is important when analyzing the frequency and relevance of terms. Special emphasis was placed on the punctuation and spaces that could have been an issue at the time of OCR extraction.
* Stop Word Removal: Regarding the text preprocessing after text extraction, stop words like \"the\", \"is\", and \"and\" were removed from the text. They are extra words, which do not have any significance in terms of term frequency or importance to the content of the text, and, as a result, skew the results of term frequency analysis. This was accomplished using a standard stop word list which was expanded with more stop words that are common in news articles but that do not significantly affect relevance calculations.
* Stemming and Lemmatization: To further enhance the extraction of text, stemming and lemmatization were also applied in this study. These techniques take words back to their root form; for example,'reading' is reduced to'read' and 'works' to 'work', so that different forms of the same word are put together during the analysis. This  
Figure 1: Workflow of methodology.  
step is necessary to reduce noise and improve the cosine similarity calculations in the subsequent steps of the algorithm.
* Case Normalization: To eliminate the impact of case sensitivity, all text was converted into lowercase. For example, \"News\" and \"news\" are the same terms, but during the analysis, they are different due to normalization.
* Character and Formatting Clean-Up: Every symbol that was not a letter in the alphabet was removed from the text. This included any character that OCR may have got wrong, like punctuation marks that were left behind or HTML tags that were scraped from the websites.
## 1 Introduction
### Paper Organization  
We have structured the remainder of the paper into distinct sections to provide a clear and organized presentation of our research:  
In Section 2, titled \"Materials and Methods\", we provide an overview of the proposed JCAS system, aiming for the seamless integration of functionalities while addressing the interference and performance balance challenges. We investigate the joint receiver design, emphasizing the simultaneous processing of communication and target echo signals. SSF is advocated for regarding its advantages over TDMA, with discussions on the signal models and interference cancellation techniques. Additionally, we explore OFDM communication and radar sensing, including the radar parameters and performance evaluations. The application of MIMO technology in OFDM systems is examined. Lastly, we delve into deep neural network-based channel estimation and signal detection, focusing on long short-term memory (LSTM) networks and model training methodologies. Section 3, entitled \"Simulation Results\", serves as the empirical backbone of our study. Within this section, we present and analyze the outcomes of our simulations, encompassing various facets such as channel measurements, joint design systems, and the application of deep learning techniques for signal detection. These findings serve as critical evidence supporting the conclusions drawn in our study. Finally, Section 4 serves as the culmination of our research efforts. In this section, titled \"Discussions, Conclusions, and Future Directions\", we engage in a thorough discussion of our results, draw overarching conclusions, and outline potential avenues for future research. By providing insights into the significance of our findings and their implications for the broader research landscape, we aim to contribute meaningfully to the advancement of knowledge in our field.
## 4 Results
### Second Phase of the Study
#### 4.2.1 Participants  
To delve deeper into the industry adoption barriers of AIVGTs, we organized a questionnaire survey targeting professionals in the field of video production. Before distributing the questionnaire, we first verified whether participants had experience in video production and the use of AIVGTs, excluding those who had never used these tools. A total of 535 questionnaires were distributed, with 420 returned, of which 401 were deemed valid, and 19 were discarded as invalid.  
From the descriptive analysis of demographic information (Table 3), the gender distribution of participants was balanced, with 42.6% male and 57.4% female. In terms of age distribution, the highest proportion was participants aged 30-40, accounting for 52.4%, followed by those aged 20-30, representing 34.9%. These two age groups together constituted 87.3% of the sample. In terms of field of work or study, participants also spanned media studies, technical engineering, and digital content strategy, reflecting the interdisciplinary and comprehensive nature of the video production field. Regarding the years of experience in work or study, the majority had 5-15 years, comprising 74% of the participants. In terms of educational background, all participants had higher education.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Indictators** & **Frequency** & **Percent** \\ \hline \hline \multirow{3}{*}{Gender} & Male & 171 & 42.6 \\  & Female & 230 & 57.4 \\ \cline{2-4}  & 20–30 & 140 & 34.9 \\ \multirow{3}{*}{Age} & 30–40 & 210 & 52.4 \\  & 40–50 & 38 & 9.5 \\  & Above 50 & 13 & 3.2 \\ \cline{2-4}  & Video Production and Editing & 25 & 6.2 \\ \multirow{3}{*}{Area of work/study} & Film and TV Production & 26 & 6.5 \\  & Animation and 3D Modelling & 38 & 9.5 \\ \multirow{3}{*}{Area of work/study} & Visual Effects (VFX) Design & 31 & 7.7 \\  & Scriptwriting for Film and TV & 32 & 8 \\ \multirow{3}{*}{Area of work/study} & Multimedia Art and Design & 24 & 6 \\  & Audio Engineering and Production & 28 & 7 \\ \multirow{3}{*}{Area of work/study} & Video Marketing and Advertising & 34 & 8.5 \\  & Interactive Media and Game Design & 27 & 6.7 \\ \multirow{3}{*}{Years of work/study} & Media Research and Criticism & 22 & 5.5 \\  & Media Technology and Engineering & 19 & 4.7 \\ \multirow{3}{*}{Years of work/study} & Digital Content Strategy & 32 & 8 \\  & Broadcasting and News Production & 24 & 6 \\ \multirow{3}{*}{Years of work/study} & Directing and Cinematography & 16 & 4 \\  & Production and Project Management & 23 & 5.7 \\ \hline \multirow{3}{*}{Years of work/study} & Less than 1 & 5 & 1.2 \\  & 1–3 & 27 & 6.7 \\ \multirow{3}{*}{Academic Qualifications} & 3–5 & 37 & 9.2 \\  & 5–10 & 108 & 26.9 \\ \multirow{3}{*}{Academic Qualifications} & 10–15 & 189 & 47.1 \\  & 15–20 & 35 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The description of characteristics in the sample.