esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Optimization Approaches Based on MEC for Virtualization in Energy Utilization  
Leveraging an algorithm to enhance energy efficiency in the networking environment, which enables edge cooperation, has been investigated. In [33], the focus is on resource management in end-to-end wireless-powered MEC networks for devices. They addressed the optimization challenges posed by dynamic task arrivals and battery power fluctuations. Employing Lyapunov optimization and virtual queues transformed the long-term optimization problem into a deterministic time slot drift-plus-penalty subproblem, making it more tractable and ensuring optimal performance in the long run. Moreover, in [34], they investigated the trade-off between energy efficiency and delay in multi-user wireless power systems. By incorporating wireless energy transfer into the MEC system, MEC enhances computing capabilities to prolong device battery life. Using Lyapunov optimization theory, they optimized network energy efficiency while ensuring network stability, available communication resources, and meeting energy causality constraints. In [35], green content caching is based on user association mechanisms that aim to minimize content requests by enabling small cell networks while ensuring the QoS of mobile terminals.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.9 Standardized Service Communication Interfaces  
The use of lightweight service communication interfaces based on standardized protocols such as HTTP for service invocation is recommended. The use of application programming interfaces such as those supported in the REST (Representational State Transfer architecture) favors the efficient exchange of messages under high load conditions. Modern microservices-based architectures use this type of communication interface. The Web services community is increasingly using REST in place of heavier and more complicated approaches such as SOAP (Simple Object Access Protocol) [56].
# An Applicable Predictive Maintenance Framework for the Absence of Run-to-Failure Data
## 3 Proposed Framework
### Building a Model for HI  
The unsupervised learning model learns based on preprocessed training data. In this paper, a trained autoencoder is learning the structure of normal data. When the model is built, the following steps are taken:  
* As mentioned in Section 2, it can be calculated by defining the HI with the MAE of the input and output data. Observe the calculated HI which is based on the result of the trained autoencoder. This is to guarantee how the HI is normal and to generate the initial threshold. In general, the HI for the train data will usually be small and uniform if the training is done well.
* The determination of an initial threshold is very challenging. If there is historical run-to-failure data, the initial threshold can be set using the failure data [13]. However, if there is no fault history, the exact threshold is unknown. So, in general, it mostly assumes an arbitrary threshold [6]. The initial threshold can be provided by an expert or determined based on the HI calculated from the training data. In this paper, it is proposed to use a gaussian distribution-based value because z-normalization was applied to the preprocessing method. The general manufacturing process manages each variable with 3-sigma based on the process control method [27]. That is, it is heuristically proposed that the threshold is 3 based on the data normalization and the manufacturing process control method. For example, if the 2-sigma method is utilized, we can use 2 as the threshold. Therefore, it is a method that can be used even if run-to-failure data do not exist, and it is expected that the threshold can be updated  
Figure 2: The proposed framework for an applicable Predictive Maintenance System in the absence of run-to-failure data.  
when run-to-failure data accumulates. In this case, we use the most common 3-sigma method in process control; a value of 3 can be used as a threshold.
* Whenever new data are collected from the equipment, it can be preprocessed based on the training data, and HI can be calculated using the trained autoencoder. In other words, preprocessing is based on the mean and standard deviation of the training data. If there are some outliers or noise in the new data, a high value of HI can be made despite the normal condition of the equipment. Therefore, it is necessary to focus on the overall trend rather than focusing on each HI value.
## 4 L-MMCF Manufacturing Process  
L-MMCF are manufactured from dissolved pulps (i.e., acid sulphite or prehydrolysis Kraft pulp) that contain high alpha-cellulose content (greater than 90%). During the preparation of the dissolved pulp, impurities, hemicellulose, and lignin are removed while maintaining high accessibility (cellulose reactivity) to chemicals [62]. The L-MMCF process includes the following steps:  
1. Preparation of feedstock, including characterisation (i.e., carbohydrate, extractive, ash, metal, sulphur, moisture content).
2. Chemical pulp preparation of feedstock (acid sulphite or prehydrolysis Kraft), including characterisation (i.e., carbohydrate, extractive, ash, metal, sulphur, moisture content, intrinsic viscosity (DP), and kappa number).
3. Removal of contaminants through washing, chelation, and bleaching.
4. Dissolution of pulp in n-methyl-morpholine-n-oxide (NMMO, specific to L-MMCF).
5. Fibre spinning/extrusion, drawing, washing, drying, and winding.  
These steps are discussed in detail in the following sections, including unique differences in handling traditional and alternative feedstocks (i.e., wood vs. industrial hemp). Figure 5 provides a flow diagram outlining all processing steps and potential end-use applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feedstock** & **Cellulosic** & **\(\alpha\)-Cellulose** & **Hemicellulose** & **Lignin** & \\
**Content (\%)** & **Content (\%)** & **Content (\%)** & **Content (\%)** & **Content (\%)** & **References** \\ \hline Hemp hurd & 34–46 & 77.6–89.6 & 21.5–34 & 16–25 & [7; 26; 59; 41] \\ Hemp bast & 55–72 & 92–96.2 & 11–15 & 3–4.6 & [7; 25; 41; 60] \\ Hardwood & 40–55 & 95.6–96 & 24–40 & 18–25 & [19; 38; 61] \\ Eucalyptus & 41.6–52 & 92–95 & 22 & 21.7–31.4 & [7; 62; 63; 64] \\ Softwood & 0–50 & 92 & 24–35 & 20–35 & [37; 38; 61; 65; 66] \\ Sugar cane bagasse & 42–52.3 & 94.6–95.2 & 25–28.7 & 20–23.4 & [23; 61; 63; 67] \\ Bamboo & 33.8–60.3 & 90 & 15–32 & 17–32 & [7; 18; 63; 68] \\ Corn cob & 40–45 & 88.8–93.6 & 25–35 & 15–20 & [24; 61; 67] \\ Cotton linter & 80–95 & 95–99 & 2–20 & 0 & [11; 19; 61; 65; 67; 69] \\ Wheat straw & 29–35.9 & 70.3 & 26–32 & 16–21 & [19; 61; 66; 67; 70] \\ Orange peel & 13 & n/a & 6 & \textless{}2 & [19] \\ Flax fibre/noils & 75 & 92 & 15 & \textless{}1 & [30; 31; 41] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cellulosic content of various feedstocks.
## 4 Results and Analysis
### Experimental Results and Analysis
#### 4.2.1 Steady-State Response  
Figure 20 depicts Experimental steady-state curves for the grid-phase voltages and the grid-phase currents. The proposed ADRC control algorithm provides no table performance because the system perfectly tracks the reference current.
## 3 Methodology
### Image Generation
#### 3.3.1 The Architecture of the OCT Generator  
Figure 11 shows the architecture of the image generation model. In order to avoid the problem of loss of details in image generation, we refer to the concept of U-Net feature channels, and concat the features extracted by the second and third convolutional layers of the encoder to the corresponding convolutional layers of the decoder.  
Since the convolutional neural network (CNN) [23] calculates the local area's features, most of the feature loss problems can be solved through feature splicing. Furthermore, to retain more details in the generation stage, we add Non-Local Blocks [24] to the generative model to strengthen the information relationship between each pixel, allowing the generator to generate more realistic images.  
In this paper, the real and generated OCT images are denoted by \(x\) and \(y\), respectively. We divide each image into \(11\times 11\) blocks and use the average value of image brightness (\(l\)), contrast (\(c\)), and structure (\(s\)) as the evaluation criteria, where \(M\) represents the number of image blocks.  
Figure 11: The architecture of the OCT generator model.  
Figure 10: A schematic diagram of the proposed GAN model.  
The multiscale structural similarity (MS-SSIM) [25] is adopted as the loss function for the generator model to evaluate the image quality objectively. The MS-SSIM is defined by Equation (1):  
\[L(x,y)=l(x_{M},y_{M})^{M}\cdot\prod\
olimits_{j=1}^{M}c(x_{j},y_{j})^{j}s(x_{j},y_{j})^{j}, \tag{1}\]  
Using Equation (1), the quality of the generated image can be determined, with a higher score representing a higher similarity.  
The brightness index equation is defined as:  
\[l(x,y)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+C_{1}}, \tag{2}\]  
where \(\mu_{x}\) and \(\mu_{y}\) are the average pixel values of the real and generated images \(x\) and \(y\), respectively, and \(C_{1}\) is a constant added to prevent the denominator from approaching zero.  
The image contrast index equation is defined as:  
\[c(x,y)=\frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}}, \tag{3}\]  
where \(\sigma_{x}\) and \(\sigma_{y}\) are the pixel standard deviations of the real and generated images \(x\) and \(y\), and \(C_{2}\) is a constant added to prevent the denominator from approaching zero.  
The structural index equation is defined as:  
\[s(x,y)=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}}, \tag{4}\]  
where \(\sigma_{xy}\) is the covariance of the real and generated images \(x\) and \(y\), and \(C_{3}\) is a constant added to prevent the denominator from approaching zero.  
The Loss function of the generator is defined as:  
\[Generator\ Loss=1-L(x,y), \tag{5}\]
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Data Sequence  
When a mobile node is disconnected from a WSN, two sequences are extracted from the history of its neighbor counts. The neighbor counts of a mobile node are a set of observations \(P_{t}\), each of which is recorded at time \(t\), because they are reported continually at every update period. Therefore, the history of neighbor counts is represented as the time series \(P=P_{1},P_{2},\ldots,P_{z-1},P_{z}\), where \(P_{z}\) is the last value reported by a mobile node. Each \(P_{i}\) has the property, \(1\leq P_{i}\leq|A|\), where \(A\) is the set of anchor nodes and \(1\leq i\leq z\). After the time \(z\), the mobile node is disconnected from the network. As the history contains a large number of values, it is not feasible to extract the trends from the entire history \(P\) given the resource limitations on a sensor node platform. Therefore, a short sequence \(Q\) is extracted from \(P\). To select \(Q\), a data window expands from \(P_{z-1}\) of the history \(P\) of a disconnected mobile node to the first value that is greater than \(T_{nc}\). \(Q\) is represented as \(Q=Q_{1},Q_{2},\ldots,Q_{w-1},Q_{w}\), where \(Q_{1}=P_{i},\ldots,Q_{w-1}=P_{z-1},Q_{w}=P_{z}\), where \(|Q|\leq|P|,i=z-w+1\). Another sequence \(R\) is extracted from \(Q\), and it is represented as \(R=R_{1},R_{2},\ldots,R_{w-1},R_{w}\), where \(R_{j}=B_{j}/Q_{j},|R|=|Q|\), \(0\leq B_{j}\leq Q_{j},1\leq j\leq w\) and \(B_{j}\) indicates the number of boundary nodes contained in \(Q_{j}\).
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 3 Corner Node Algorithm
### Node Creation  
To create a node, the process of corner detection must first be completed. To detect a corner, all of the obstacle data stored in the predefined list are sequentially processed. After selecting a free space from the data adjacent to the diagonal line from the selected obstacle data, the adjacent data are searched again in the selected free space. If there is only one obstacle data point, it is determined to be a corner and a node is created.  
Figure 4a represents the process of searching for data adjacent to a diagonal line by selecting data node (No.) 7 from among 16 cells. The reason for searching only the diagonal data of the eight directions is that if a node is created in the diagonal direction, it can be created in any corner, and the number of operations can be reduced by not exploring all eight directions.  
Figure 3: Difference between the pathfinding process with and without a topology map when using an image-based map. (**a**) A significant number of nodes must be handled, because each pixel in the image is used as a node. (**b**) Pathfinding can be performed much more easily with a topology map, as the number of nodes to be processed is significantly reduced.  
With the selection of a data point adjacent to the obstacle, if it is a free space, the algorithm checks to see if the data can represent a corner node. For example, since data Nos. 2, 10, and 12 are free spaces corresponding to data No. 7, it is possible to determine whether a node can be created. Conversely, because data No.4 corresponds to an obstacle, it cannot be used to determine whether a node can be created or not.  
Figure 4b shows an example in which No. 10 was selected from among the three data described above. Since No. 7 is the only obstacle data point among adjacent data, it is determined to be a corner, and a node is generated, as shown in Figure 4c.  
Figure 4d is an example in which No. 2 was selected. Because the data outside the image are not determined, only adjacent data in five directions are processed. In this case, there are two adjacent obstacle data; thus, No. 2 is not a corner, and no node is generated.  
Figure 4e is an example in which No. 12 was selected. Likewise, as there are two adjacent obstacle data points, we determine that No. 12 is not a corner, and no node is created. After searching all of the data adjacent to the diagonal for all obstacle data, the process is terminated.
## 4 Conclusions  
Natural fibres from palmyra palm fruits were studied as a biosorbent in this work. The untreated and chemically treated samples are referred to as N-PPF and C-PPF, respectively. In this work, the starting concentration, an adsorbent dose of 2.0 g, pH 4, and a contact period of 300 min were used. The adsorption capacity values correlated to the Langmuir, Freundlich, and Temkin models, with the Freundlich model providing the greatest fit. The N-PPF and C-PPF Langmuir adsorption capacities were determined to be 0.0833 and 0.4147 mg/g, respectively. Iron ions absorbed in C-PPF were greater than in N-PPF due to their pore size and greater pore volume than in N-PPF. The Temkin model was used to calculate the sorption heat of all the molecules on the surface, which were +0.0163, +0.0495 J/mol, which is an indication of the physisorption process of N-PPF and C-PPF, respectively, and \(n=1.19\) showed that the adsorbent surface is fairly heterogeneous. Adsorption occurred according to the pseudo-first-order kinetic model, which suited the adsorption process perfectly. It represents the physisorption of N-PPF. The pseudo-first-order and pseudo-second-order kinetic models completely fit the adsorption process, verifying the C-PPF physisorption and chemisorption processes. As a result, it can be inferred that these adsorbents can be used as inexpensive, easily accessible, and efficient biosorbents for iron removal from wastewater.  
Conceptualization, S.S.; data curation, S.S.; formal analysis, S.S.; investigation, S.S.; methodology, S.S. and A.R.; project administration, S.S.; supervision, N.M.; validation, S.S., P.P. and A.R.; writing--original draft, S.S.; writing--review & editing, P.P. and N.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Research and Development at Chandrakasem Rajabhat University.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Biosorbents** & **pH** & \(q_{\text{max}}\) **(mg/g)** & **References** \\ \hline Natural palmyra palm fruit (N–PPF) & 4 & 0.0833 & This study \\ Palmyra palm fruit (C–PPF) & 4 & 0.4147 & This study \\ Cocouth husk & 2 & 0.712 & [19] \\ Sugar cane bagasse & 2 & 18.54 & [18; 34] \\ Rice straw(untreated) & 7 & 0.75 & [10] \\ Rice straw & 7 & 0.81 & [17] \\ Rice straw & 7 & 0.86 & [17] \\ Rice straw & 7 & 0.59 & [17] \\ Rice straw & 1 & 58.82 & [3] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adsorption capacities of Fe(III) ions in aqueous solutions using different natural adsorbents.  
**Acknowledgments:** The authors gratefully acknowledge the funding provided by the Institute of Research and Development at Chandrakasem Rajabhat University (Grants No. CRU18/2019). The authors are also thankful to the board of the Faculty of Science, as well as the board and technical staff of the Department of Chemistry, Faculty of Science, Chandrakasem Rajabhat University, for supporting the use of the liveable laboratory.  
No potential conflict of interest were reported by the authors.
## 9 Discussion of the Results  
In this section, the obtained results are discussed. Firstly, it is important to stress again that the developed algorithm is an off-line strategy. It aims to plan temperatures references signals for each room for the whole day based on a mathematical model that, with a good approximation, predicts the thermal behavior of the whole building taking into account all the main exogenous inputs and the thermal coupling between adjacent areas. Basically, the algorithm reshapes user defined temperature set-point signal, in which hard constraints in terms of temperature values in specific time instants are posed, in order to fulfill all the occupant constraints. To fasten the convergence of the algorithm the set-points signals are constrained to vary as a step or as a ramp. In fact, the solution may be calculated in less than 1 h, approximately in 700 s in case of steps and 1200 s in case of ramps.  
In Section 6 a comparison between these two strategies shows that in case of radiators, the use of ramps instead of steps allows a decrement of the energy consumption up to 15%. However, it mainly depends on the kind of the HVAC system employed, in fact, in case of fan-coil units, the decrement is less significant, as it is up to 2-3%. However, the ramp signal offers more degrees of freedom than the step one, and this helps the algorithm to explore more possible temperature evolutions and therefore better solutions.  
Furthermore, in Section 7, the strategy is applied in case of unoccupied spaces. It turns out that, as the approach considers the whole building and therefore the couplings between adjacent rooms, it is not always better to switch off the heating system of a room if it is empty for a certain time interval. This depends on different factors like the position of the room, its dimensions, and the weather conditions. For example, in the reported example, rooms 3 and 5, which have the same dimensions and shape, have two completely different behaviors. This is mainly due to the fact that the adjacent rooms are different. Indeed, the contribution of room 3 in heating rooms 2 and 4, which are small, is significant, so that heating room 3 is useful in allowing rooms 2 and 4 to use less energy to follow their set-point signals. On the other hand, room 5 is adjacent to room 6 which is much bigger and therefore keeping room 5 heated is less relevant to reduce the energy required to heat room 6.  
Finally, a comparison between this off-line high-level algorithm and the well-known MPC is done in Section 8. This comparison aims to show that the suboptimal results obtained by this off-line strategy can be similar to results obtained with predictive online control algorithms. This means that the choice of ramp signals as set-point variations is appropriate. Furthermore, thanks to the fact that the algorithm works only during the first hour of each day, it is possible to use its resources to further help the occupant by solving other possible optimization problems.
# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
## 4 Results and Discussion
### Recognition and Computational Time Analysis for CURE-TSR
#### 4.2.2 Computational Time for Traffic Sign Recognition  
The computational time of CNNs is influenced by various factors, such as the number of layers, the size of the input data, the number of filters in each layer, and the size of the filters. In these experiments, we refer to both training and testing time as computational time. In these experiments, we use the same number of layers, number of filters, and size of the filters. However, the difference is just the size of the input data, which is mentioned in Section 3.3. Note, in Tables 3 and 4, SH stands for shadow and DK means darkness. In Tables 4 and 6, we provide the bold text where the processing time is the lowest.  
In lens blur, snow, and darkness, an embedded denoising approach with a median filter takes more computational time. However, in lens dirty, rain, and snow, the traditional denoising approach with a median filter takes maximum computational time, as is seen in Table 4. In all the environmental and camera impacts, the median filter takes more computational time than the Gaussian filter due to the way it processes data. We calculated the mean values and standard deviation of computational time in each denoising approach. Embedded denoising with a Gaussian filter takes less computational time (36.6 m) than embedded denoising with a median filter. Also, the standard deviation of the computational time of embedded denoising with a Gaussian filter is the smallest (1.03) among all other denoising approaches because a Gaussian filter replaces each pixel with a weighted average of its neighbors based on a Gaussian distribution, whereas a median filter replaces each pixel in an image with the median value of its neighboring pixels. The neighboring pixels must be sorted to obtain the median value, which is a computationally expensive procedure. The Gaussian weighted average, on the other hand, can be computed quickly because it just requires addition and multiplication operations.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were applied using SPSS (IBM SPSS 20.0 software; SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov test was applied to determine the normality of the data distributions for both analyses (R\({}_{\mathrm{a}}\) and \(\Delta\)E\({}_{00}\)). Because the data were normally distributed, a one-way ANOVA followed by the Tukey post hoc test was used to analyze the R\({}_{\mathrm{a}}\) values. Kruskal-Wallis and Mann-Whitney U tests were used to analyze the non-normally distributed \(\Delta\)E\({}_{00}\) values. R\({}_{\mathrm{a}}\)0 and R\({}_{\mathrm{a}}\)1 values were compared with the paired sample \(t\)-test and the Wilcoxon test was used to compare \(\Delta\)E\({}_{00}\) values at different intervals. A value of \(p\) < 0.05 was used as the statistical significance level.
## 3 Results and Discussion  
### Experimentation Setup  
The proposed methodology was implemented in MATLAB software. For this purpose, MATLAB R2018a was used. The image processing toolkit was used to provide essential image processing functions. The proposed model was evaluated by implementing it in MATLAB, and the efficiency of the algorithms was analyzed. The implementation environment is presented in Table 1.