esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Clustering Technique in Wireless Sensor Networks (WSNs)  
The proposed clustering technique combines the forward and backward passes repeatedly to generate the optimal solutions. In this regard, the sensor nodes (SN) perform the forward and backward passes repeatedly for a specified number of times to locate a cluster head (CH) with the optimal distance. One advantage of the proposed clustering technique is its ability to search all available nodes and then optimize energy in transmitting data packets between sensor nodes (SN) and cluster heads (CH) and among CHs. Optimizing the energy takes into consideration the distance to communicate in routing data packets to the sink node or base station through another CH.  
The proposed scheme combines WSA, K-means, and Bee Colony Optimization to optimize the performance of WSNs. The integration of K-means with BCO (K-BCO) aims to enhance the clustering process, improve energy efficiency, and extend the network's operational lifetime. The combination of WSA, K-means, and BCO is because there is a need to develop a robust and efficient clustering algorithm that addresses the challenges of energy consumption and network performance in WSNs. This integrated approach aims to provide more stable and optimal solutions, ultimately leading to improved network lifetime and efficiency.
# Chitosan Films with Caffeine and Propolis as Promising and Ecofriendly Packaging Materials
## 4 Conclusions  
Chitosan solutions supplemented with caffeine and propolis extract have been demonstrated to display strong antioxidant properties, contributing to the antioxidant efficacy of chitosan-based solutions. Among the chitosan-based solutions tested, the highest antioxidant properties were observed in solution prepared with citric acid with the addition of CAF and EP, showing a greater free radical scavenging capacity than the standard antioxidant Trolox. Furthermore, the study demonstrated that chitosan-based films dissolved in citric acid exhibited strong antimicrobial activity against harmful bacterial strains, and this effect was further heightened with the inclusion of propolisis extract and caffeine. The addition of acids, caffeine, and propolisis extract can alter the barrier properties of chitosan-based films, consequently influencing their water vapor and oxygen permeability. This modification renders them well-suited for application in the food packaging sector. The choice of solvent was observed to influence the mechanical characteristics of chitosan films, with citric acid resulting in higher elongation at break but lower tensile strength in comparison to acetic acid. This underscores the significance of selecting appropriate natural acids for potential food packaging applications.  
The findings indicate that natural additives like propolisis extract and caffeine possess the capacity to enhance the antioxidant, antimicrobial, and barrier characteristics of chitosan-based materials, rendering them a promising choice for food packaging applications.  
Conceptualization, K.S., M.W., R.D. and I.R.; methodology, K.S., J.M., A.S., L.M. and W.K.; formal analysis, K.S., J.M., A.S., L.M. and W.K.; writing--original draft preparation, K.S. and M.W.; visualization, K.S. and J.M.; supervision, R.D. and I.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion  
### Variation of the Entropy of the Time Series Data  
In this study, Sentinel-2 images with a cloud-cover-percentage lower than 50% were collected, and NDVI, EVI, and GCVI were then calculated as the original time series. Based on the iterated S-G approach, three time series were reconstructed and then served as the input of the CWT, which produced scalograms to present the time-related frequency information of the reconstructed time series. Therefore, exploring how the reconstruction process and CWT affects the amount of information and whether they are class sensitive is necessary for the further application of the proposed approach. The information entropy was used as the only criterion throughout the assessment process.  
For each VI, the average entropies of each class in the original, reconstructed, and scalogram data were calculated, as shown in Figure 8. Clearly, either the three VIs or seven types of planting conditions indicated a uniform trend of the entropy variation across the original, reconstructed, and scalogram data; that is, the entropy value was approximately 5, 8.5, and 15.5 for the original, reconstructed, and scalogram data, respectively. Thus, the reconstruction process and CWT approximately increased the information amount by 70% and 210%, respectively. Most importantly, such a variation of the entropy was not sensitive to the class, and the trend remained the same over the three VIs. The reason for this phenomenon is largely because the three VIs were calculated based on the fixed and same image collection, and the reconstruction approach was not sensitive to the type of VI. This means that once the original time series is fixed, the reconstruction approach and CWT will exert similar effects across different classes and VIs.  
Figure 7: Vineyard identification comparison: (**a**) original image, (**b**) result of the CNN, (**c**) result of RF, and (**d**) difference between CNN and RF.
## 2 Materials and Methods
### Data Extraction and Quality Assessment  
Two researchers independently undertook data abstraction from each study. Data were abstracted onto a customized data extraction sheet. The variables included: author and year; the title of the study; country of origin; type and number sample; event identified; type of CDSS; study associated with the database, APOE, and EHR; description (aim of study and results). The key findings from each study were summarized and presented in tables.  
The reviewers coded the variables and resolved any disputes through mutual discussion. We followed three main steps in conducting an interpretive synthesis of our findings: (1) noting the range of functions and the uses of alert systems to promote/improve the situation in ICUs in cardiac patients, (2) developing a synthesis of the findings of included studies, and (3) exploring the relationships in the findings. Automated alerts were used all along the study process in order to keep the review up to date.  
Figure 1: Search and selection flow diagram. ICU, intensive care unit.  
The reviewers assessed the methodological quality of the included studies, based on the criteria of the Effective Public Health Practice Project (EPHPP). This tool was employed for evaluating quantitative studies by previous systematic reviews. Studies are rated based on six components: (a) selection bias, (b) study design, (c) confounders, (d) blinding, (e) data collection methods, and (f) withdrawal and drop-outs.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 5 Defect Classification
### New Methods

Table 1: Main techniques used in the papers cited for readers guideline. image is sparse and can be compressed [53]. Deep learning can learn hierarchical feature replacing the handcrafted features. Their excellent performance in feature learning from image urged people to use them in automatic defect detection [54; 55; 56]. For automatic detection of welding defects, the application of two methods begin to rise, especially the application of convolutional neural network (CNN) which is a typical deep learning model [57; 58; 59]. In the focus of this paper, [PERSON] developed a system for recognizing diverse types of defects from images inspired by the human visual inspection mechanism. The method which is an unsupervised algorithm learned a dictionary from lots of normal images replacing the experienced workers. The dictionary can be used to sparsely reconstruct the background and the weld region of test images without the defective regions. Thus the defects would stand out in difference image between the test image and the reconstructed image [60]. [PERSON] proposed a model-based on CNN to classify the X-ray weld images by improving the convolution kernel and the activation function. The method doesn't need noise reduction, the feature extracting and enhancing [61]. Based on the principle of visual perception, [PERSON] constructed a deep learning network with 10 layers to directly determine the type of suspected defect. The network can judge whether the defect is linear or circular without extracted features [62]. [PERSON] designed online segmentation and recognition method based on the compressed sensing for weld defect. The method firstly established offline database of defects images, then segmented the defect using clustering method, lastly determined the type of defects using the optimal dictionary whose atoms are characteristic values of defects. [PERSON] developed a model based on a deep convolutional neural network (DCNN) to extract the deep features directly from the X-ray images without any preprocessing operation. The performance of deep feature was compared with those of traditional designed feature, such as texture features and histogram of oriented gradients features. The results showed that the separability of deep features is better [63]. Figure 4 shows the difference of traditional computer vision technology based on the above-mentioned technologies and the deep learning approach. The deep models can directly learn the high-level structural features from the original images avoiding the segment of the weld region. Thus unlike previous algorithms, the performance of these models does not depend on accurate weld region extraction. Moreover, the deep models embracing the feature extraction, selection and classification can be jointly optimized. Figure 4: Traditional computer vision technology and deep learning approach comparison.
## 1 Introduction
(2021) [28] analyzed the contribution of the wt.% of reinforcements on the tribological properties and microhardness of an Al/Al\({}_{2}\)O\({}_{3}\)/RGO hybrid composite. In their study, a higher wt.% combination of RGO (\(>\)0.3 wt.%) with Al\({}_{2}\)O\({}_{3}\) (\(\geq\)10 wt.%) decreased the microhardness and tribological effect due to the agglomeration of the reinforcements. [PERSON] et al. (2022) [29] investigated the microstructure and mechanical properties of Al/075/graphite/SiC hybrid composites; they found that 8 vol.% of graphite and 2 vol.% of SiC maximized the mechanical properties. [PERSON] et al. (2022) [30] studied the damping behavior of a fiber composite in aeronautical technology. In addition to ceramic reinforcements, iron oxide (Fe\({}_{2}\)O\({}_{3}\)) is also used to make hybrid composites [31]. Moreover, the hybrid composite has a complex constitution with multiple reinforcement particles. The surface area of the specimen, volume fraction, size and sintering conditions greatly influence the overall properties of hybrid composites. Among these, the surface area of the sample is affected by the compaction pressure, resulting in non-homogeneous densification due to die wall-powder interface friction. Therefore, it is necessary to investigate the porosity level, density and hardness as a function of the sintering time and elastic modulus of the part. In this research study, we carefully synthesized cylindrical test specimens with a different part modulus using the conventional powder metallurgy process. The physical and mechanical properties (hardness and compressive strength) of the sintered aluminum composite material were investigated for a different part modulus and sintering time. An exhaustive examination of the microstructure was carried out to study and concurrently establish the nature of the interface bonding between the hard, elastically deforming ceramic particle reinforcement and the soft and ductile metal matrix. In addition to this, the porosity of the sintered specimens was determined.
# Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems
## 6 Discussion and Limitations
### Flexibility Improvements and Considerations in the SDN Architecture  
The design consideration to improve the flexibility of the SDN architecture is identified as a mesh topology configuration with a control algorithm that dynamically adapts to the changes in the link states between SDN nodes. Simulations are performed using a Mininet SDN network emulator to simulate a network of SDN nodes configured in a mesh topology. The controller code uses a Floyd\(-\)Warshall algorithm to determine the shortest path between SDN nodes continuously. This enables continuous information flow between the consumer and prosumer during the energy transaction in a dynamic network environment.  
The negative impact of the mesh topology configuration is the requirement to enable the mesh configuration. All SDN nodes are required to be reachable on the same network level (data plane), which is difficult to achieve in a multi-tier public network such as the Internet. Another consideration is the technical capability to support the mesh topology configuration. Technologies such as geostationary VSAT communication links have certain limitations for site-to-site communication in a mesh topology.  
The simulation proves that the mesh topology configuration with the control algorithm to determine the shortest data path dynamically improves the flexibility of the SDN architecture.
# Use of Bilberry and Blackcurrant Pomace Powders as Functional Ingredients in Cookies
## 3 Results and Discussion
### Total Phenolic Content and DPPH Radical scavenging Activity  
The total phenolic content and DPPH radical scavenging activity of the control and BIPP- and BCPP-supplemented cookies are illustrated in Tables 10 and 11, respectively. A significant (\(p<0.05\)) progressive increase in the total phenolic content with the increase in the wheat flour replacement level was found both for BIPP and BCPP, along with an increase in radical scavenging activity in cookies. The phenolic content in bilberry pomace powder was very high (around 3.6 g GAE/100 g), primarily due to the high anthocyanin content. The highest replacement level of wheat flour with BIPP (10%) led to a seven-fold increase in the total phenolic content of the cookies, reaching 214.73 mg GAE/100 g, while leading only to a three-fold increase in the total phenolic content when replacing 10% wheat flour with BCPP. The differences could be attributed to the higher phenolic content and antioxidant potential of bilberry as compared with blackcurrant [36]. The antioxidant capacity determined with the DPPH radical was lowest in the control cookies. The increase in pomace powder content led to a strong increase in the DPPH radical scavenging activity, up to 1.26 mmol Trolox/100 g in cookies with a 10% BIPP replacement level. Similar increasing trends of the total phenolic content and antioxidant activity have been reported previously in cookies developed by adding pomace from grape [24; 86], grapefruit [6], apple [16; 18; 19; 70], and pear [11].  
[PERSON] et al. [8] also reported that raspberry and blueberry pomace powders increased the bioactive contents and antioxidant capacity of gluten-free cookies, with a total phenolic content six-fold higher in cookies containing blueberry pomace as compared with those made with raspberry pomace.
## 2 Materials and Methods
### Calibration of IgG and TSH Antigen  
In terms of the overall processes of ELISA, they are composed of four basic steps: the immobilization of capture antibodies, blocking and washing, detection of target analytes, and optical density of analytes. The first step is the immobilization of capture antibodies on the surface of membranes. We used mouse IgG capture antibody (10 ug/mL in 0.1M phosphate buffered saline (PBS)) for the optimization. Then, for the immobilization of capture antibody process, the membrane was incubated overnight at room-temperature. After washing with PBS, blocking solution (5% casein in 0.1M PBS) was injected into the well for 60 min at the incubator to block all non-immobilized surface-binding sites.  
The membrane was then rinsed with washing buffer (0.05% Tween 20 in 0.1M PBS) followed by a reaction with capture antibody at 37 \({}^{\circ}\)C for 30 min and by additional washings. For the calibration,  
Figure 1: Schematic of plastic membrane design for this study. (**a**) Most antibodies are conjugated on the bottom surface in a conventional ELISA well plate, (**b**) the fabricated membrane increases the surface area where antibodies are conjugated, and (**c**) a designed and fabricated meshed type plastic membrane.  
the concentration of serially diluted IgG antigens were prepared at 0.1, 1, 10, 100, and 1000 ng/mL in PBS, respectively. As a negative control, PBS solution was prepared without the presence of IgG antigen. After extensive washing, IgG antigen was reacted with 1 \(\mu\)g/mL detection IgG antibody (in 100 \(\mu\)g/mL BSA, 10 mM HEPES (pH 7.4), 10 mM NaCl, and 0.05% Tween-20 in 0.1M PBS) at 37 \({}^{\circ}\)C for 30 min. After another washing process, SA-HRP (1:5000 in 0.1M PBS) was conjugated to the detection antibody which was conjugated with IgG antigen. After all the process, the plate was incubated with TMB substrate at room temperature for 15 min, and the absorbance was measured at 650 nm using an ELISA plate reader.  
For TSH calibration, all the steps were performed using TSH antigen, capture antibody, and detection antibody instead of IgG antigen and antibody proteins.
## 4 Main Result
### Adaptive Coding for Transmission of Position Between Satellites in Formation  
The application of adaptive coding for navigation data transmission between satellites in the formation is often based on the kinematic representation of the vehicle motion by the second-order model for each channel under the assumption that the satellite speed is constant, which leads to the following representation of the data source generator (cf. [91; 92; 93]):  
\[\left\{\begin{array}{l}y[k+1]=y[k]+T_{0}V[k],\\ V[k+1]=V[k]+\xi[k],\end{array}\right. \tag{16}\]  
where \(y[k]\), \(V[k]\) are the satellite position and speed with respect to the given direction, variable \(\xi[k]\) denotes the unmodeled variations of the vehicle speed and is considered as an unknown disturbance.  
To estimate the change of the coded signal, the following embedded observer is introduced to the encoder and decoder ([78; 93]):  
\[\left\{\begin{array}{l}\hat{y}[k+1]=\hat{y}[k]+T_{0}\hat{V}[k]+l_{1}\sigma[k ],\\ \hat{V}[k+1]=\hat{V}[k]+l_{2}\sigma[k],\end{array}\right. \tag{17}\]  
where \(\hat{y}[k]\), \(\hat{V}[k]\) are the estimates of \(\hat{y}[k]\), \(\hat{V}[k]\), respectively, \(l_{1}\), \(l_{2}\) are the observer design parameters.
## 4 Experimental Results and Analysis
### Experimental Design
#### 4.1.1 Results of Experimental Design for Spring (Autumn) Data  
The experimental data of spring and autumn are analyzed together according to references [25; 60], in which most literature uses seasons as experimental data. The experimental data in spring (autumn) met the assumption of normality and the assumption of homogeneity as shown in Figures 2 and 3. Therefore, two-way ANOVA can be used to analyze the variance of the two factors, and the results are shown in the following Table 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level 1** & **Level 2** & **Level 3** \\ \hline \(Y=0.3\) & \(Y=0.6\) & \(Y=0.9\) \\ \(F=0.05+0.00005\times N_{gen}\) & \(F=0.1+0.00005\times N_{gen}\) & \(F=0.15+0.00005\times N_{gen}\) \\ \(CR=0.85+0.00005\times N_{gen}\) & \(CR=0.85+0.00005\times N_{gen}\) & \(CR=0.85+0.00005\times N_{gen}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Three levels of DE parameter setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level 1** & **Level 2** & **Level 3** \\ \hline \(c_{g}=0.45\) & \(c_{g}=0.5\) & \(c_{g}=0.55\) \\ \(c_{p}=0.4\) & \(c_{p}=0.4\) & \(c_{p}=0.35\) \\ \(c_{w}=0.1\) & \(c_{w}=0.05\) & \(c_{w}=0.05\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Three levels of SSO parameter setting.  
The factor of parameter of SSO and factor of parameter of DE have no significant effect, and the interaction of the two factors has no significant effect according to the results of experimental design shown in Table 11. Thus, this study intends to use principal factor analysis, and the results are shown in the following, Figure 4, to determine the parameter configuration of the experimental data for winter.  
Figure 3: Homogeneity test for spring (autumn).  
Figure 2: Normality test for spring (autumn).  
The parameters of DE were set to level 3 that is \(Y=0.9\) and initial \(F=0.15\) and the parameters of SSO were set to level 3, that is, \(c_{g}=0.55\), \(c_{p}=0.35\), and \(c_{w}=0.05\) when it is in progress for the experimental data of spring (autumn) according to Figure 4. The parameter configuration is used in the subsequent experiment for spring (autumn) in Section 4.2.