esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Principal Component Analysis
In the current study, the score plot of the PCA superimposed on the above matrix of variables revealed strong clustering of the two perilla genotypes grown under mild to moderate salt stress conditions, with green-pigmented perilla concentrating plant growth parameters, most of the mineral composition (except the K\({}^{+}\)) and leaf colorimetry, while the red cultivar stands out for target and total polyphenols as well as several major aroma volatile components (Figure 1). Particularly, the green-pigmented perilla was positioned on the positive side of PC1 in the upper and lower right quadrants of the PCA score plot. The upper right quadrant included green cultivar treated with 20 and 30 mM NaCl that delivered leaves with high concentration of toxic ions (Na\({}^{+}\) and Cl\({}^{-}\)) as well as high dry matter percentage. Green perilla grown under non-saline solution or treated with 10 mM NaCl were positioned in the lower right quadrant, characterized overall by higher plant growth parameters (leaf fresh yield and shoot dry biomass), protein and Mg\({}^{2+}\) concentration (Figure 1). The upper left quadrant depicted treatments (red perilla treated with 20 and 30 mM NaCl) characterized by high levels of 1-octen-3-ol, 2-hexenal and \(\beta\)-linalool. Finally, treatments in the lower left quadrant (red perilla at 1 mM and especially 10 mM NaCl) had the highest sinapic acid, total polyphenols and key volatile compounds such as perilla aldehyde (Figure 1). Altogether, the PCA outputs provide an important piece of information, namely that the targeted selection of perilla cultivar along with the application of a chemical eustressor, such as mild salt stress, could enhance the nutritional and functional quality of perilla grown in controlled environments. Figure 1: Principal component loading plot and scores of principal component analysis (PCA) of colorimetric data (L\({}^{+}\), a\({}^{+}\), b\({}^{+}\)), fresh yield, dry biomass, dry matter content, mineral concentrations (NO\({}_{3}\)\({}^{-}\), N, PO\({}_{4}\)\({}^{3-}\), K\({}^{+}\), SO\({}_{4}\)\({}^{2-}\), Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\), Cl\({}^{-}\)), protein and chlorophyll a contents, phenolic composition (rosmarinic, caffeic and sinapic acids), total phenolic content and aroma volatile compounds in green and red perilla grown under increasing NaCl concentration in the nutrient solution (1, 10, 20 or 30 mM NaCl).
###### Abstract  
The design phase is critical in construction projects, as it directly impacts cost, quality, and execution efficiency. However, it suffers from structural deficiencies in communication, coordination, and early problem detection, leading to delays, cost overruns, and inefficiencies. While Lean Construction has been widely applied in execution phases, its adoption in design remains fragmented, lacking a clear framework for identifying and evaluating Lean tools in this context. This study aims to identify, classify, and evaluate Lean tools applicable to the building design phase, emphasizing their functionalities, benefits, and limitations. A systematic literature review and expert validation process led to the identification of 16 Lean tools and 26 design-related functionalities. Among these tools, Building Information Modeling (BIM), Last Planner System (LPS), and Agile Design Management (ADM) were identified as the most impactful, collectively addressing 88% of design functionalities. Expert insights revealed that ADM improves task control and decision-making clarity, LPS reduces uncertainty and enhances workflow reliability, and BIM strengthens coordination and early conflict detection. This study provides a structured perspective on Lean tool integration during design, highlighting their benefits and limitations and offering guidance for their implementation. The findings contribute to improving design efficiency, minimizing waste, and fostering collaboration in construction projects.  
lean design; BIM; last planner system; agile design management; construction efficiency
# A Semantic and Knowledge-Based Approach for Handover Management
## 2 Related Work  
This section presents research on HM approaches based on both a single criterion and on multiple criteria. Table 1 briefly summarises each approach and presents the type of data/information model employed and how they make handover decisions. According to [25], the handover control can be: Network-Controlled HandOver (NCHO), Mobile-Controlled HandOver (MCHO), Mobile-Assisted HandOver (MAHO) and Network-Assisted HandOver (NAHO). In NCHO, the network starts and controls the handover; operators usually adopt it for load balancing and traffic management. In MCHO--traditionally used by IEEE 802.11 technologies--the end-user device initiates and controls the handover. In MAHO, the end-user device helps in the handover process controlled by the network; MAHO is typically used in cellular networks. In NAHO, the network helps with the handover process controlled by the end-user device; NAHO is intended for heterogeneous wireless networks. Next, we point out the main shortcomings of the related work. The work in [9] employed SSF to make handover decisions. Since it does not exploit contextual information, such as movement velocity and application requirements for enhancing HM, SSF can lead to unnecessary and frequent handovers, decreasing throughput, increasing packet loss and even causing network service disruption. In [13; 14; 15; 16; 17; 18; 19; 20; 21; 22] handover decisions are made by using multicriteria and techniques such as Fuzzy Logic, Network Management Policies or Multiple Attribute Decision Making (MADM) Algorithms. Nonetheless, these approachesneglect an information model that disregards criteria from one or more information domains, such as network characteristics and status, application requirements, end-user profile, end-user device features, or handover history, which are relevant for advancing HM. Thus, they can also lead to handover issues (generating failure, unnecessary and frequent handovers) and, consequently, network performance degradation. The work in [24] presents a solution that combines multicriteria approaches for improving HM. Nevertheless, its computational complexity is higher than that of the unique criterion and individual multicriteria methods. Unlike the related work, SIM-Know introduces an SIM to enable context-aware handover decisions by considering and relating criteria from a comprehensive set of context information domains: Network, Application, User, UserDevice and Handover. SIM-Know also proposes KBP, to realize cognitive and proactive handover decisions by providing local and global knowledge.
## 6 Proposed MAC Protocol for Wireless Healthcare Environments--WhMAC
### Addressing  
Every sensor would have its own identifier address based on its function, which would be determined by the standard (for example a temperature sensor has address 1, SPO2 has 2, ECG has 3, _etc._). There might also be a group of sensors of the same type (for example accelerometers), and they should have unique identifier based on the position on the body. Also, identifiers would be different if a sensor is \"simple\" and only sends raw data, or if it is DSP enabled. Therefore, the address should describe: (1) Type of the sensor; (2) Position on the body; (3) Standardised sensor characteristics. Since medical equipment has to be standardised anyway, this wireless address standardisation should not be difficult to achieve.
## 3 Infrared Image Segmentation
### Copper Rod Segmentation Based on the Otsu Algorithm  
In the raw infrared images, obvious brightness differences between the copper rods and electrolyte area exist, with obvious edges and contour features. Therefore, this paper attempts to use global threshold methods for infrared image segmentation. The Otsu algorithm is one of the most common global threshold methods and it searches for the best global threshold by traversing the gray histogram, which maximizes the variance between the background and foreground [11]. Figure 5 shows the results of the copper rod segmentation in different conductive states based on the Otsu algorithm.  
In Figure 5, some pipe areas are classified as foreground in the infrared image. Moreover, the copper rod under the abnormal heating condition is not segmented well because it is much brighter than the other copper rods and easily regarded as the electrolyte background.  
However, the contour and edge of the cell wall area can be segmented well using the Otsu method. So, the approximate vertical position of the cell wall edge can be located  
Figure 4: Infrared images of copper rods under different conductive states.  
Figure 5: The results of copper rod segmentation based on the Otsu algorithm.  
by searching for the position of the white-black jump points in a certain range from the bottom of the binary image [12; 13]. Figure 6 shows the searching result.  
In Figure 6, the black lines represent the approximate vertical position of the cell wall edge in the grayscale image. It only covers the areas of the copper rods, insulated partitions, and busbars, and avoids the electrolyte areas being covered as much as possible. The areas of the conductive states that we are focused on are below the cell wall edge lines.
## 4 Results
### Descriptive Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Distribution of Responses (\%)**} \\ \cline{4-8}  & & **Mean** & **SD** & **1** & **2** & **3** & **4** & **5** \\ \hline \multicolumn{8}{c}{**Airlift**} \\ \hline \multirow{3}{*}{Service Reliability} & 4.10 & 0.99 & 3.0 & 4.5 & 12.5 & 40.0 & 40.0 \\  & 3.96 & 0.90 & 3.5 & 2.0 & 15.5 & 53.5 & 25.5 \\ \multirow{3}{*}{Route Alignment or Coverage} & 4.00 & 0.93 & 3.0 & 4.5 & 11.6 & 51.0 & 29.8 \\ \multirow{3}{*}{Comfort} & 3.82 & 1.01 & 4.5 & 3.0 & 25.1 & 40.2 & 27.1 \\ \multirow{3}{*}{Security} & 4.07 & 0.90 & 3.0 & 3.0 & 9.5 & 52.8 & 31.7 \\ \multirow{3}{*}{Complain-handling} & Security & 3.95 & 0.91 & 3.5 & 2.0 & 16.5 & 52.0 & 26.0 \\ \multirow{3}{*}{Affordability} & 3.84 & 1.07 & 5.6 & 3.5 & 22.7 & 37.9 & 30.3 \\ \multirow{3}{*}{Accessibility} & 3.99 & 1.03 & 4.5 & 4.0 & 14.0 & 43.0 & 34.5 \\ \multirow{3}{*}{Cleanliness} & 3.97 & 1.00 & 3.5 & 5.5 & 14.0 & 44.5 & 32.5 \\ \multirow{3}{*}{Travel Time Saving} & 4.07 & 0.95 & 4.0 & 2.0 & 11.1 & 49.0 & 33.8 \\ \multirow{3}{*}{Environmental Impacts} & 3.83 & 0.94 & 3.0 & 3.5 & 25.1 & 44.2 & 24.1 \\ \multirow{3}{*}{Equity (for impaired or disabled people)} & 3.92 & 0.99 & 4.0 & 4.5 & 15.6 & 47.2 & 28.6 \\ \multirow{3}{*}{Speed} & 3.78 & 0.92 & 3.5 & 4.5 & 21.0 & 53.0 & 18.0 \\ \multirow{3}{*}{Attractiveness} & 3.95 & 0.91 & 3.5 & 3.0 & 14.5 & 53.5 & 25.5 \\ \multirow{3}{*}{Information System} & 3.94 & 0.88 & 3.5 & 2.5 & 13.5 & 57.5 & 23.0 \\ \multirow{3}{*}{Incomite System} & 3.88 & 0.87 & 2.5 & 4.5 & 15.6 & 56.8 & 20.6 \\ \multirow{3}{*}{Incomite System} & 3.93 & 0.94 & 1.5 & 4.5 & 25.5 & 37.0 & 31.5 \\ \multirow{3}{*}{Privacy Concerns} & 3.93 & 0.92 & 1.5 & 7.5 & 14.6 & 49.2 & 27.1 \\ \multirow{3}{*}{Service Schedule} & 3.84 & 1.03 & 3.5 & 6.0 & 23.5 & 37.0 & 30.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Questionnaire items: means, standard deviations (SD), and distribution of responses. It can be seen that most of the respondents were young males, which could be explained by the fact that younger people are more likely to be familiar with new technologies since these DRT services employ smart-phone apps for booking rides, and a relatively smaller proportion of females work in Pakistan as compared to their counterparts.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis  
To verify the validity of the model, the theoretical results were compared with the results obtained from stochastic simulations in the hypernetwork. We also investigated the effects of several parameters, such as the hypernetwork scale, the spreading and recovering rates, the initial propagation seed, and the structural parameters of the hypernetwork, on the information propagation. The hypernetworks were generated based on the construction method described in Section 2.2. Since scholars generally engage in stable collaborations, we assumed that the hypernetwork was static. Initially, one node was selected as the I-state node, and information propagation continued until the system stabilized. To eliminate stochastic effects, the results of each simulation were obtained by averaging the outcomes of 100 independent runs under similar conditions. In order to depict the dynamic process of information flow within the hypernetwork, we determined the trend of the I-state node density as a function of time.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 2 Display to Camera Communication System
### Decoding  
The receiver camera captures the transmitted image sequence while looking at the electronic display. The captured images \(\hat{\mathbf{I}}_{\mathbf{E}}\) and \(\hat{\mathbf{I}}_{\mathbf{ref}}\) undergo geometric correction to nullify spatial domain distortions, such as geometrical distortions and RST effects instigated by the transmission channel. Figure 5a,b shows the captured image \(\hat{\mathbf{I}}_{\mathbf{E}}\) and reconstructed image \(\hat{\mathbf{I}}_{\mathbf{G}(\mathbf{E})}\) after geometric correction, respectively. The corners of \(\hat{\mathbf{I}}_{\mathbf{E}}\) are irregular, and the image is geometrically distorted. For geometric correction, we employed a perspective transformation technique using the four corners of the region of interest (ROI) in the captured image. This process was also applied to the captured reference image \(\hat{\mathbf{I}}_{\mathbf{ref}}\) to generate the same-sized image. Next, the corrected images \(\hat{\mathbf{I}}_{\mathbf{G}(\mathbf{E})}\) and \(\hat{\mathbf{I}}_{\mathbf{G}(\mathbf{ref})}\) were subjected to DCT and transformed into their respective spectral domains \(\hat{\mathbf{I}}_{\mathbf{F}}\) and \(\hat{\mathbf{I}}_{\mathbf{F}(\mathbf{ref})}\). From the available coefficients, the MF region was selected in a manner as described in Section 2.1, and the respective coefficient vectors \(\hat{\mathbf{X}}_{\mathbf{E}}=\left[\hat{\mathbf{X}}_{1},\hat{\mathbf{X}}_{2 },\ldots,\hat{\mathbf{X}}_{m}\right]\) and \(\hat{\mathbf{X}}_{\mathbf{F}(\mathbf{ref})}=\left[\hat{\mathbf{X}}_{\mathbf{ F}(\mathbf{ref})}^{1},\hat{\mathbf{X}}_{\mathbf{F}(\mathbf{ref})}^{2}, \ldots,\hat{\mathbf{X}}_{\mathbf{F}(\mathbf{ref})}^{m}\right]\) were extracted. Finally, by subtracting \(\hat{\mathbf{X}}_{\mathbf{F}(\mathbf{ref})}\) from \(\hat{\mathbf{X}}_{\mathbf{E}}\), the data were extracted in the form of symbols as:  
\[\hat{\mathbf{S}}=\hat{\mathbf{X}}_{\mathbf{E}}-\hat{\mathbf{X}}_{\mathbf{F}( \mathbf{ref})} \tag{6}\]  
The symbol \(\hat{\mathbf{S}}\) obtained from the above simple subtraction contains information on the optical wireless channel of the D2C link, the original signal, and the embedding strength. Since the original symbol \(\mathbf{S}\) has levels of \(-1\) and \(1\), the decoding process can be performed through the sign of \(\hat{\mathbf{S}}\). The acquired symbol is a real number and is de-mapped to binary values \(\hat{\mathbf{d}}\) and channel-decoded into output message vector \(\hat{\mathbf{b}}\) as follows:  
\[\hat{\mathbf{b}}=\left\{\begin{array}{ll}1,&\forall\ \hat{\mathbf{S}}:\hat{ \mathbf{S}}\leq 0\\ 0,&\forall\ \hat{\mathbf{S}}:\hat{\mathbf{S}}>0\end{array}\right. \tag{7}\]  
Note that the proposed decoding method requires a very simple operation; thus, it can be easily applied to a communication service requiring a small-scale and complexity-constrained receiver hardware.
## 3 Results and Discussion
### Correlation Analysis  
In order to analyze the correlation between the fish broth's components and its particle size, the thermo-ultrasonic and boiling water treatment groups were analyzed (Figure 7). For both the A and B treatment groups, there was a positive correlation between protein, fat, total sugar and \(L^{\star}\) value, indicating that both thermal treatments alone and the synergistic effects of thermal and ultrasonic treatment can destroy fish head tissues. Various nutrients in the fish head tissues were gradually extracted into the broth with the same increasing trend. The dissolution of substances greatly changed the color characteristics of the fish head broth, especially the brightness. For the thermo-ultrasonic treatment group, the particle size was negatively correlated with protein, fat, total sugar, \(5^{\prime}\)-AMP and \(L^{\star}\) value, while there was no correlation between these indexes in the boiling water group. These results suggest that the effect of thermal and ultrasound treatment promotes nutrient dissolution, while the cavitation effect of the ultrasound makes the particle size of fish broth smaller, which ultimately leads to a more homogeneous and stable emulsification system of the fish broth. On the other hand, there was no correlation between these indexes in the boiling water heating group, which may be because only the effect of the thermal treatment was not enough to dissolve enough proteins and lipids to form small and stable oil droplets.
## 3 Results
### Response Time of Slave and Master Modules
#### 3.4.3 Mathematical Model with Virtual Wires and Multiprocessing
The software has the ability to function while fixing errors, but when the error is too significant that it can \"break the code,\" the software stops all processes and prioritizes the repair of that error to prevent tasks from being compromised. When this happens, the software has a probability of successful recovery ranging from 0% to 80%, with the latter being the most prevalent in the practical tests conducted, as seen in \[T\_improved\_master(C)=ce^{-dC}g(C) \tag{7}\]7 It is a breakdown representation of the recovery behavior of the software concerning the degree of error, namely:  
1. Normal errors for which the software was programmed, such as changes in position in elements, incorrect memory data, and file name changes, among others (0.1). 2. Errors like memory overflow (0.5). 3. Errors that cause failure in the master modules (1). Where \(P(failure)\) is the software failure probability, and \(P(RF)\) is the probability of successful recovery after a failure (which in this case is 0.8), as we can see in \[T\_improved\_master(C)=ce^{-dC}g(C) \tag{7}\]8  
In Figure 13b, we show the statistical results of the proposal tests with a mean recovery rate of 80%, a standard deviation of 5%, and a 95% confidence interval (78.21080585628285, 81.78919414371715), with a z-score \(<-1.96\) or \(>\)+1.96.
## 3 Results and Discussions
### Analysis of Indoor Air Temperature
#### 3.1.2 Range Analysis of Indoor Air Temperature  
Range analysis is further used to find the relationship between the four influencing factors and indoor air temperature. Range analysis (see Table 6) is carried out using the numerical simulation results in Table 3. From the comparison of the \(R\)-values (range values) of the four factors, it can be seen that, among the four factors, factor A is the most important factor, and factor D has the least influence on indoor air temperature. The influence degree of the four factors on indoor air temperature is as follows: factor A > factor B > factor C > factor D. In this orthogonal experiment, the best level of each factor is the level that corresponds to the highest average indoor air temperature. As shown in Table 6, the level corresponding to the maximum \(\overline{K}\) value of each factor is the optimal level of each factor. When factor A is at the fifth level, factors B, C, and D are all at the first level; that is, when the length of the room, the width of the room, the width of the opening on the north wall of the room, and the distance from the lower edge of the opening to the floor are 12,300 mm, 5100 mm, 1500 mm and 0 mm, respectively, the average indoor air temperature reaches its highest. The optimal combination for the room's indoor air temperature, determined by range analysis, is A\({}_{5}\)B\({}_{1}\)C\({}_{1}\)D\({}_{1}\). The scheme number of the combination A\({}_{5}\)B\({}_{1}\)C\({}_{1}\)D\({}_{1}\) is set to S26.  
The reasons why this is the optimal combination can be explained as follows. For the length of the room (Factor A), the longer the room length, the larger the area of the T-wall in the south wall, so the more air heated by the T-wall and the higher the room temperature. For the width of the room (Factor B), the longer the width, the longer the cold air path which enters the room through the opening on the north wall. Due to the mixing of cold air, the room temperature is lower. For the width of the opening on the north wall of the room (Factor C), the larger the width of the opening, the more cold air enters the room through the opening, and the lower the room temperature. For the distance from the lower edge of the opening to the floor (Factor D), according to the principle of different densities of cold and hot air, the hot air heated by T-wall will gather on the ceiling after entering the room. Over time, the hot air will slowly fill the room. If the opening on the north wall is closer to the ceiling, the hot air will quickly flow out of the room, so the room will not be heated.
## 2 Materials and Methods
### Methodology
#### 2.2.5 Selection of Image Fusion Algorithms  
For the image fusion experiments, five popular image fusion algorithms were selected based on their relative advantages found in different types of literature, as listed in Table 2. Interestingly, they are also available in commercial software packages like ENVI and ERDAS Imagine. Thus, they can be used easily for many applications.