esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of Moisture Identification and Algorithm Comparison
### Comparison of Selection Algorithms
#### 4.2.1 Pearson Coefficient Method  
The Pearson coefficient method is used to judge the correlation between variables, and the correlation size is measured by the Pearson correlation coefficient \(r\). Assume that \(x\) and \(y\) are variables, and the Pearson correlation coefficient can be calculated as follows:  
\[r=\frac{\sum\limits_{i}z_{x}z_{y}}{N-1}=\frac{\sum\limits_{i} \left(x-\overline{x}\right)\left(y-\overline{y}\right)}{\sqrt{\sum\limits_{i} \left(x-\overline{x}\right)^{2}\sum\limits_{i}\left(y-\overline{y}\right)^{2} }}, \tag{8}\]  
where \(z_{x}=\frac{\left(x-\overline{y}\right)}{\sigma_{x}}\), \(z_{y}=\frac{\left(y-\overline{y}\right)}{\sigma_{y}}\), \(\overline{x}\), \(\overline{y}\) are the mean values, and \(\sigma_{x}\), \(\sigma_{y}\) are the standard deviations of \(x\) and \(y\), respectively. The value of \(r\) is between \(-1\) and \(1\), the two variables were linearly negative when \(r\) was \(-1\), and there was a linear positive correlation when \(r\) was \(1\)[35]. The Pearson coefficients were calculated for the above six parameters, and the Pearson correlation coefficient matrix is shown in Table 3.  
As shown in the Pearson correlation coefficient matrix, the Pearson correlation coefficients between Energy and Duration, Energy and Counts Number, Energy and Peak Amplitude, and Duration and Counts Number were all close to or exceeded \(0.9\). It can be assumed that the variables mentioned above were approximately linear, and useful information for classification will not be provided if these parameters are used at the same time. The Energy and Counts Number parameters were discarded to avoid redundancy.
## 4 QoS Aware Multipath Routing Protocols for WMSNs
### [PERSON] & [PERSON] et al.  
A Zigbee cluster-tree based multipath routing protocol Z-MHTR [48] with interference awareness [49] is studied by [PERSON] et al. Apart from the conventional Zigbee parent-child tree routing (TR) path (path 1 as shown in Figure 9), Z-MHTR allows source nodes to explore other node disjoint paths using non-parent neighbors.The protocol requires the source node to maintain a record of branches that have been used for TR. A source node \(S\) begins to build node disjoint paths following three basic scenarios. Suppose the first intermediate node selected by \(S\) is node \(N_{i}\). If the branch that \(N_{i}\) is on hasn't been used for any TR path for \(S\), then a node disjoint path can be established from \(N_{i}\) to the sink (root node) using TR (path 2 as depicted in Figure 9). If the branch \(N_{i  
case \(d_{c}=d_{i}-1\), then the choice of nechtop depends on \(N_{i}\)'s neighbor statuses. If \(N_{i}\) has a neighbor, say node \(N_{x}\) whose branch has not been used for TR, \(N_{x}\) will be used as nechtop (path 4 in Figure 9). If no such node exists, i.e., all \(N_{i}\)'s neighbors have had their respective branches used for TR, then \(N_{i}\) selects the neighbor node (if it exists) that is not included in any TR path (path 5 in Figure 9). The same rules are executed at following intermediate nodes until the path reaches the sink. And the number of disjoint paths is equivalent to the number of branches in the topology.  
To reduce interference among disjoint paths, the authors proposed an interference level metric in their follow up work [49]. Nodes are required to record their list of interfering neighbors, excluding those on the same path, by overhearing the route discovery messages and data packets which are not intended for them. As the route reply message or data acknowledgement travels along the reserve path, the total number of nodes interfering with those that form the path is accumulated. Thus, at the source node, the interference level of a path is computed as the ratio of total interfering nodes to the number of intermediate nodes. And the set of disjoint paths that minimizes the inter-path interference will be selected.  
Pros and Cons. Based on the Zigbee cluster tree routing address assignment mechanism, Z-MHTR achieves multipath routing by adding a neighbor table and by maintaining a record of whether or not TR has been used on a branch. In their following work, an interference-aware mechanism is also proposed to mitigate the route coupling problem between multiple paths. Such a stateless and interference-aware design makes the protocol rather energy efficient. However, the protocol is only fit for the Zigbee cluster tree topology, and the number of paths that could be established is inherently limited by the number of branches. Moreover, it does not consider key QoS requirements such as delay and reliability.
# Semantic Decomposition and Anomaly Detection of Tympan Membrane Endoscopic Images
## 3 Semantic Decomposition
### Adaptive Histogram Equalization  
In general, grayscale data, such as those in the MNIST dataset, perform better than RGB data when used in deep learning models. RGB images include a wider variety of information than grayscale images. [PERSON] et al. recognized polyps in colonoscopy images; however, their detection was more precise in grayscale images than in RGB/NBI images [18]. With the exception of complicated RGB data, we attempted to construct images that had only structural information, such as the depth of the damaged region and transparency of the tympanic membrane. After converting tympanic membrane endoscopic data into grayscale, the structure and depth of the tympanic membrane were highlighted using AHE. Figure 2 shows the original data with RGB values before applying AHE.  
Figure 1: Overview of semantic decomposition of endoscopic data and anomaly detection methods using VAE.  
Histogram equalization (HE) achieves image equalization using a single histogram. If sections have distinct pixel intensity distributions and the image is equalized using a single histogram, the image may become deformed. Depending on the light utilized for photography, certain regions of the endoscopic images of the tympanic membrane may have high-intensity measures. AHE breaks images into numerous grids and equalizes them using histograms on each grid such that they can be processed without being distorted by the high-intensity regions caused by the reflection of light from the tympanic membrane endoscopic image. Consequently, we used AHE to prevent data loss owing to data distortion. Figures 3 and 4 show images of applying AHE.
# Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data
## 1 Introduction
Some of the computer search algorithms available include pairwise swap procedure, and its variants where a single or multiple pairs of treatments are swapped at a time ([PERSON] and [PERSON], 2013), and simulated annealing where a cooling strategy is employed ([PERSON] et al., 2014). Most of the applications of these algorithms focus on the analysis of data and little has been done on their applications to improve the designs of genetic experiments, yet, estimated parameters from improved designs can be obtained with increased precision if variability of treatment effects is minimized ([PERSON] et al., 2014). Although there are statistical software such as CycDesigN ([PERSON], 2015), GenStat ([PERSON] et al., 2014), SAS ([PERSON] and [PERSON], 2013) and DiGger ([PERSON] et al., 2014); these programs are not freely available and do not account for both spatial and genetic relatedness of experiments at the design stage. The focus of the present study is to evaluate performance of different algorithms based on a linear mixed model framework which optimally accounts for both sources of correlation in an experiment at the design stage. Hence, the main objective of this study is to evaluate the efficiency of diverse search algorithms to generate improved randomized complete block (RCB) designs applying \(A\)- or \(D\)-optimality criteria, while accounting for both spatial and genetic correlations using linear mixed models with applications in plant breeding trials. This will be done by initially generating experimental layouts through a random process and later applying an array of proposed search algorithms to improve the initial experimental layouts. The procedure also allows optimizing designs initially generated from other software. Several varying field conditions that include a range of heritabilities, genetic relatedness structures and spatial correlations were evaluated in order to throughly assess the practicality of the algorithms presented. An illustration, given in Section 2.3, describes a practical example where the inclusion of a microsite random error, also known as the nugget effect or unstructured residual error is provided. The importance of including a nugget effect has been previously noted on other studies such as [PERSON] (2010) and [PERSON] et al. (2013) where the latter study showed that in modeling spatial data, ommision of the nugget error could lead to a bias in the correlation parameters of the error structure. Hence, the nugget error component could be used successfully to model potential microsite variability between observations that are closely spaced.
## 2 Short Chain Alcohols
### 1-Butanol  
1-Butanol is a natural fermentation product from certain _Clostridium_ species. 1-Butanol production in non-native organisms has been difficult due the enzyme oxygen sensitivity and less well-defined redox reactions. One of the key enzymes of the _Clostridium_ butanol pathway is the butyryl-CoA dehydrogenase electron transferring protein complex. This enzyme was difficult to express in heterologous organisms because it requires association with ferredoxin and is oxygen sensitive. This difficulty was overcome byreplacing this enzyme complex with a trans-enoyl-CoA reductase (Ter) [67,68]. Ter directly utilizes NADH as the reducing cofactor for the reduction of crotonyl-CoA to butyryl-CoA (Figure 3) with a large negative change in free energy, making it irreversible and thus serving as an effective driving force for this pathway.  
This modified 1-butanol pathway was subsequently transferred to PCC 7942 [18]. However, the resulting strain did not produce 1-butanol under photosynthetic conditions. 1-Butanol was produced only under dark anaerobic conditions [18]. One difficulty was that that the first reaction of the pathway catalyzed by thiolase is thermodynamically unfavorable with Keq of 10\({}^{-5}\). To circumvent this difficult step, an alternative route using malonyl-CoA was constructed [17]. Malonyl-CoA reacts with acetyl-CoA to form acetoacetyl-CoA in an irreversible reaction catalyzed by acetoacetyl-CoA synthase. Naturally, malonyl-CoA is the ATP activated product of acetyl-CoA through the acetyl-CoA carboxylase. Combining the reactions catalyzed by acetyl-CoA carboxylase and acetoacetyl-CoA synthase, the net reaction is two acetyl-CoA condensation to acetoacetyl-CoA at the expense of one ATP. This ATP hydrolysis serves as an energy input to convert the thermodynamically unfavorable acetyl-CoA condensation into a favorable reaction. The expression of acetoacetyl-CoA synthase with the butanol production pathway enabled the direct photosynthetic production of 1-butanol with a titer of 30 mg/L.  
1-Butanol production was further improved by the expression of an oxygen tolerant CoA-acylating aldehyde dehydrogenase [13]. The _Clostridium_ aldehyde dehydrogenase is oxygen sensitive, which prohibits its functional expression in cyanobacteria under photosynthetic conditions. To solve this difficulty, the oxygen tolerant CoA-acylating aldehyde dehydrogenase of the 1,2-propanediol degradation pathway from _S. enterica_ was used to replace the _Clostridium_ aldehyde dehydrogenase. The resulting strain was able to produce about 300 mg/L of 1-butanol in 12 days, representing a 10-fold increase compared to the 1-butanol produced using the _Clostridium_ aldehyde dehydrogenase. This result shows that the oxygen tolerance of enzymes is also very important for biochemical production using cyanobacteria.
# Jointly Production and Correlated Maintenance Optimization for Parallel Leased Machines
## 4 Optimization Approach
### Steps of the Resolution Method  
* Step 1: Generate randomly a solution Si for production plan: vector of number of machines to be leased and matrix \(X^{N}\) and \(X^{S}\);
* Step 2: Calculate the total production cost and check if the service rate constraint is verified for the current solution;  
Figure 2: Comparison between IPM and GPM policies.  
Step 3: Evaluate the number of preventive maintenance activities for each machine for the current solution by minimizing the total maintenance cost;
* Step 4: Group several maintenance activities using the center of gravity method and save the new interval of maintenance;
* Step 5: Increment the time of repair to the minimal processing time of any machine for the current combination and update the new configuration of production and maintenance plans;
* Step 5.1: If the sum of processing times of machines in regular time is less than the maximal processing time for this equivalent work pattern, then browse through matrix Xn and find the minimum value;
* Step 5.2: If Step 5.1 is not verified and the sum of processing times of machines in overtime is less than the maximal processing time for this equivalent work pattern, then browse matrix Xs and find the minimum value;
* Step 6: Repeat the same steps until founding the optimal solution Sn (return to Step 2);
* Step 7: Save the optimal values of \(M_{k}\), \(X^{N}\), \(X^{S}\) and \(T_{n}\);  
Figure 3 shows the algorithm of the resolution method.  
Figure 3: Steps of the resolution approach.  
equal quantities produced by each machine in each production period. In the second case, we consider a random demand with a high standard deviation; the quantities of goods produced by each machine are then widely different from each other's. The obtained results are shown in Tables 1-8.
## 4 Results and Discussion
### Simulation Results and Comparisons  
#### 4.2.1 Flow Regime Analysis and Comparisons  
The dynamic modeling steps of OLGA are described in Section 3. In Section 4, a comparative analysis of the entire process of slug flow formation in the experimental and OLGA models will be conducted. The figures presented in this section (i.e., Figures 23-26) have been compared with Figures 9, 10, 13, 14, 17, 18, 21 and 22, respectively. It is demonstrated that the OLGA software accurately replicates the formation of slug flow by undergoing three essential phases, slug generation, slug injection, and slug flow back, consistent with the experimental outcomes. In Figures 23-26, the red dotted circle indicates the flow diagram at the pipeline's elbow position (P3), while the yellow dotted circle specifies the flow diagram in the pipeline's middle (P4) of the upwardly inclined pipe. Notably, the simulation diagram does not emphasize the detailed flow during the slug flow's formation process, which relates to the node selection in the simulation software. Generally, an increase in the number of nodes arranged on the pipeline provides a more detailed description of the flow. However, this approach also leads to an increment in calculation time and a reduction in operational efficiency.  
Figure 21: The flow back of slug flow image at P3.  
Figure 22: The flow back of slug flow image at P4.  
The flow regime (ID) diagram depicted in Figure 27 provides a comprehensive illustration of the slug flow formation process. The black dotted line represents the geometric model of the pipe, while the yellow line indicates the initiation movement of slug flow, wherein an ID of 1 is observed throughout the pipeline, suggesting a lack of slug flow. The blue line corresponds to the liquid accumulation stage, where a significant liquid slug is identified at the pipe elbow position (P3), with an ID of 3 at this location and an ID of 1 at other positions. The green line showcases the flow regime in the pipeline post slug flow occurrence. At this stage, the ID in the pipeline between P3 and P4 is observed to be 3, while an ID of 1 is noted in other positions. Lastly, the red line represents the flow regime of slug flow backflow in the pipe, with an ID of 3 observed in the upwardly inclined pipe and 1 in other positions.  
Figure 23: The initiation movement.  
Figure 27: The ID at the pipeline at different times.  
Figure 24: The liquid accumulation.  
Figure 25: The eruption of slug flow.
## 3 The Proposed Routing Protocol--CRD
### The Proposed Routing Protocol--CRD
#### 3.4.2 The Proposed Routing Protocol--CRD
* When the RREQ\({}_{2}\) has the same \(seq\_num\) as the RREQ\({}_{1}\). If the \(broadcast\_id\) of RREQ\({}_{2}\) is greater than \(broadcast\_id\) of RREQ\({}_{1}\), node \(v\) will drop the RREQ\({}_{1}\) and forward RREQ\({}_{2}\). * When the RREQ\({}_{2}\) has the same \(seq\_num\) and \(broadcast\_id\) as the RREQ\({}_{1}\). If the \(hop\_count\) of RREQ\({}_{2}\) less than the \(hop\_count\) of the RREQ\({}_{1}\), node \(v\) will drop the RREQ\({}_{1}\) and forward RREQ\({}_{2}\). * In contrast to cases 1, 2 and 3, node \(v\) drops RREQ\({}_{2}\). * Step 4. When each node \(v\) receives a RREP packet, it uses the routing table to forward this RREP packet to next node and then node \(v\) records sender channel and next hop into its routing table. * Step 5. Finally, if source \(S\) receives a RREP packet, it will use the routing table to send data to the destination. If a path from the source to the destination is found, the clustering protocol will not be necessary anymore in the next session. This helps to reduce significantly control overheads, delay and energy of the system. Besides, the use of the focus region in the CFS algorithm also improves the system performances.
## 2 Crystal Orientation and Practical Determination of Principal Indices of Refraction
### Uniaxial Crystals  
For uniaxial crystals, \(n_{1}=n_{2}\) and the optical indicatrix is rotationally symmetric about the optic axis. The optic axis here corresponds to the direction of the high-symmetry crystallographic axis. A ray travelling along this direction in the crystal encounters an isotropic medium and double diffraction does not occur. Uniaxial crystals have two principal axes of refraction and magnitudes of refractive index associated with them. For trigonal, tetragonal, and hexagonal point-group symmetries, the index ellipsoid is always rotationally symmetric. The consequence is that the decomposition of polarised radiation by double refraction only depends on the angle between the ray direction and the optic axis. When the polarisation direction is along one of the two principal axes of refraction, no double refraction occurs, given that the component in the other direction is zero. For an incident k-vector, the intersection plane between the normal of the k-vector and the indicatrix of a uniaxial crystal produces an ellipse (Figure 1 left & middle). The semi-axes of this ellipse correspond to the orthogonal radiation modes that solve Maxwell's equations for anisotropic media. Thus, for any k-vector direction the polarisation direction of these modes is invariant and the initial polarisation of the incident wave is decomposed along these modes and determines their amplitudes accordingly. This treatment disregards attenuation, which will result in a circular polarisation. For many practical examples, attenuation is limited and this approximation can be made. In practice, micrometre-sized protein crystals have relatively low absorption values (<0.3 OD) due to the large unit cell that contains only a single or a few chromophores. If larger crystals are studied, the measurement of dichroic absorption along the principal axis will allow a straightforward modification of the birefringence decomposition for low attenuation to allow for circular polarisation conditions. For crystals of small molecules, the optical density is much larger and optical penetration may be insufficient, even to allow transmission measurements. Reflection measurements are more appropriate for such materials. Similarly, optical activity is disregarded but would have only a very small effect on the indicatrix. Although in isolated cases optical activity could modify the directions of decomposition, this is rare and possible only if the index differences in the absence of a field are very close in magnitude. Consequently, optical activity is also neglected in the following description. Therefore, explicit measurements of the magnitude of the indices of refraction and of the intrinsic birefringence are not necessary for uniaxial symmetries for the purpose of analysis of linear and non-linear population dynamics.
# Forecasting Multivariate Time Series
### Data Cleaning
#### 2.4.4 Deep Learning Algorithms  
Data imputation using deep learning techniques, particularly recurrent neural networks (RNNs) and generative adversarial networks (GANs), has emerged as a promising approach in recent literature for time series analysis. RNNs and their variations excel in modeling sequence data, while GANs are adept at generating and imputing missing values. Pretimize leverages a GAN for both single and multiple imputations. The GAN operates through two models, the generator (G) and discriminator (D), which work adversarially to generate realistic data samples and distinguish between real and fake samples, respectively. Training continues until the discriminator is unable to differentiate between real and generated samples, indicating the generator's ability to produce credible examples.  
In GANs, \(D\) tries to assign the correct label to the samples with maximum probability, whereas \(G\) tries to minimize \(log\) (\(1-D(G(z))\)). According to [38], \(D\) and \(G\) play the two-player minimax game with value function \(V\) (\(G\), \(D\)), as shown by (4).  
\[min_{G}max_{D}\;V(D,G)=E_{x\sim p_{data}(x)}[\log D(x)]+E_{z\sim p_{z}(z)}[\log( 1-D(G(Z)))] \tag{4}\]  
In Pretimize, a model presented in [39] is used for imputing missing data using a GAN. The method is called Generative Adversarial Imputation Nets (GAIN). In the method, \(G\) observes real data, imputes the missing element, and outputs finalized data. \(D\) then takes a finalized datum and tries to discover which elements are observed and which are imputed.
# Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications
## 4 Simulation Environment
It is due to the consideration of resource consumption while taking forwarding decisions. Furthermore, the link performance is evaluated periodically and upon identification of any fault in the existing communication channel, the proposed solution readjusts the routing paths. However, the security strategy of the proposed protocol offers the reduction of congestion flooding by malicious nodes. Accordingly, the routes are available for sending the data toward the sink node in a timely and precise manner. In Figure 9a,b, we demonstrate the performance analysis of the proposed algorithm and existing solution in terms of energy consumption. The experiments are conducted under varying message generation intervals, and varying mobile devices. Based on the results, it was observed that with time, the energy resource dropped down. However, the proposed algorithm still gives a lower rate of energy consumption of an average of 15% and 16% compared to other existing work. This is because of the lower transmission distance, in terms of hops, while forwarding the data toward the sink node. Moreover, the nodes are balancing their load over the routing path and efficiently exploring the QoS parameters. The delay time is intelligently managed for carrying the real-time data, and  
Figure 8: (**a**) Delay with varying time. (**b**) Number of packets with varying time. Figure 7: (**a**) Reliability with message generation interval. (**b**) Reliability with mobile devices. decreases the ratio of routing requests repeatedly. It reduces the energy load on the devices and accordingly offers an energy-aware solution.