esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Redundant Symmetric Lifting Schemes and the Improved Neighboring Coefficients
### The Proposed Method Using Customized Multiwavelets and the Improved Neighboring Coefficients
In order to improve the limitation of kurtosis, the proposed method chooses \(KE\), the quotient of kurtosis and entropy, as the performance measurement of the lifting scheme and optimized the lifting coefficients with a genetic algorithm to maximize \(KE\) of the detail coefficients:  
\[KE=\frac{K_{p}}{En_{mwt}} \tag{23}\]  
Genetic algorithms (GAs) are based on the idea of natural selection. The major advantages are their flexibility and robustness as an adaptive global search method. GAs can deal with highly nonlinear problems and non-differentiable functions, as well as functions with multiple local optima. They are parallel implementation in nature. Thus, they are utilized as the tool to optimize free parameters. According to our experimental experience, GA parameters are set as follows: arithmetic crossover and non-uniform mutation operators are adopted, the range of the parameter is chosen to be [-3,3] except 0, the population scale is set to 50, the number of iteration to 30, the probability of crossover to 0.6 and the probability of mutation to 0.05. Figure 6 shows the flow chart of the proposed method using customized multiwavelets and the improved neighboring coefficients (INC). The steps are as follows:  
1. Preprocessing method is performed to translate the one-stream input signal into multiple streams. 2. The multiple streams are decomposed by using the customized multiwavelets. 3. Apply INC to shrink the multiwavelet coefficients. 4. The thresholded multiwavelet coefficients are reconstructed. 5. Post-processing method is performed to translate the multiple streams into one-stream. The denoising result is obtained to detect the fault features. Figure 7 shows the flow chart of the customized multiwavelets. First, with the given vanishing moment \(p\), the translations of \(\varPhi\) and \(\psi\) are selected using the \"symmetric selection\" method. Second, assign the values to free parameters and solve the lifting coefficients equations. Third, optimize the free parameters using genetic algorithm. Finally, obtain the optimal symmetric biorthogonal multiwavelet. In the process of the customized multiwavelet cnstructions, optimization is an important step for modifying the coefficients to match the signal. Suppose the free parameters are \(\left\{f_{1}f_{2},\ldots f_{N\!J}\right\}\). The process of the optimization using GA is shown as below:  
(1) Initialize the free parameters \(\left\{f_{1},f_{2},\cdots f_{N_{f}}\right\}\). (2) Substitute \(\left\{f_{1},f_{2},\cdots f_{N_{f}}\right\}\) into matrix \(M_{B}\) and make the matrix satisfy \(Rank(M_{B})=p\,\)'\(\,-\,p\,\). (3) Compute the lifting coefficients and get \(T(z)\) and \(S(z)\). (4) Decompose the signal with new multiwavelet. (5) Compute \(KE\) of the detail coefficients and compare it with the maximum value. (6) Generate the more optimal values of the free parameters and return to step 2), otherwise finish the computation. Figure 6: The flow chart of the proposed method.
# Electricity Generation in LCA of Electric Vehicles: A Review
## 3 Spatial boundary.
### Data Quality
#### 3.3.1 LCA Databases  
LCA databases allow for transparent and replicable results. They have accessible and verifiable data and simplify calculations, since most of their datasets are supplemented with characterisation methods. They also include impacts form upstream processes, contrary to other sources which only provide combustion emissions.  
The most used database is EcoInvent, with 8 studies relying on it for the selection of the electricity mix. Other studies depend on this database for other data, but only those using the EcoInvent mixes are represented in Figure 7. [PERSON] et al. [20] and [PERSON] et al. [15] source the generation datasets from EcoInvent, but their mixes are updated with data from IEA and Entso-E, respectively.  
EcoInvent is a non-profit association founded by institutes of the ETH Domain and the Swiss Federal Offices, whose data are updated every time a new version is released. Except from one study, whose version is not specified, all the articles published so far rely on EcoInvent version 2.2.  
Electricity country mix from this version dates to 2004, for all the countries considered in the literature. It is noteworthy that studies published in 2017 still rely on data from 2004.  
Gabi Database is created by Thinkstep and updated every year [91]. In the literature, two studies employ this database: [PERSON] et al. [55] do not specify the version, and [PERSON] et al. [45] use the version from 2015 to assess Italian electricity mix.  
GREET is a life cycle assessment model, initially born as an Excel datasheet, now available also in the more appealing graphic user interface GREET.NET. It consists of two sub-models, the GREET fuel cycle model and GREET vehicle cycle. For the electricity generation it offers complete life cycle inventory of different production pathways. It has been widely used in the literature, especially for the evaluation of upstream emission of fossil-fuel generated energy [46; 47].
# Burnt-Area Quick Mapping Method with Synthetic Aperture Radar Data
## 2 Materials and Methods
### Related Method
Several neural network models are commonly applied. Classification and regression trees (CART), are known as decision tree classifiers, which apply a multi-stage binary system to classify imageries [42]. The CART principle is based on the tree framework that has been widely used in remote sensing applications. The key principle is to determine the structure of a decision tree to select an input feature and threshold value at each splitting point. The pixels then are divided, according to the binary classification rule. Groups of pixels are divided, based on the tree growing and pruning parameters, until the optimal classification is found [43]. However, CART easily suffers from overfitting, if allowed to grow uncontrolled, to fit all of the training data. Therefore, the limiting process called pruning, can be employed to reduce the tree levels and increase the generalization ability of CART [37]. The large number of input features requires a significant amount of training pixels to generate a robust classification result.
# Optimal Control for a Three-Rotor Unmanned Aerial Vehicle in Programmed Flights
## 2 Control Object  
### Tricopter Characteristics  
The tricopter is a highly complex object in terms of its construction [12] and, consequently, also in terms of control. With the increasing interest in this type of unmanned flying object, it has received support in the flight controller software of the most popular open-source distributions (Figure 1).  
Depending on the concept adopted, the motors may rotate in the same direction or in a configuration where two rotate in one direction and the third in the opposite direction. As mentioned in the introduction, a servo is used on the tail, whose task is to rotate the motor around the tricopter's tail axis. This is necessary to balance the gyroscopic moments generated by the motor movements during operation. In objects without a servo [10], the object will rotate minimally around its axis while hovering. However, in the presence of a servo and motor rotation on the tail, an additional force is present, which is further discussed in Section 4.2.  
Comparing tricopters with other multirotor UAVs, such as quadcopters, we can say that their complexity is one of their drawbacks. Also, in terms of landing maneuver, the tricopter's structure is less stable and, as a result, less durable. Due to the smaller number of motors, its payload capacity is also reduced, but the lower number of motors translates into a lower cost for the entire object, which is usually lower in the case of tricopters. Additionally, the weight of the object is lower than that of a quadrotor, which translates into longer battery life during flight. With the additional servo, the maneuverability in the z-axis (yaw angle) is increased. The analyzed tricopter is presented in the figure below (Figure 2).  
Figure 1: Motor placement in a tricopter [17], where 1, 2, and 3 are the motor numbers, and 4 is the number of the servo controlling the tail angle.  
Figure 2: Tricopter model [18].  
The concept was adopted in which the angle of the tail motor can be changed by a servo mechanism. The two remaining motors rotate in opposite directions. By shifting the center of mass closer to the front of the tricopter, the need to apply a large angle of deflection to the tail motor has been reduced.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
###### Abstract  
The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.  
wearable sensors; stretchable devices; non-invasive sensing; wireless technologies; smart contact lenses; skin-interfaced sensors +
Footnote †: journal: Applied Sciences  
an easy way of charging irrespective of the environmental conditions [11; 12; 13]. Progress in wireless communication technologies and electronic engineering facilitates the data processing-transmission procedure with a miniaturized single electronic chip. Among various wireless communication protocols, near-field communication (NFC) and Bluetooth low energy (BLE) have been used most commonly in wearable devices. Their high compatibility with commercial electronic devices, such as smartphones enables easy and simple data recording without any other display devices attached to the sensor [14; 15; 16]. Combining all the functional components with varying functions, properties, and shapes into small, compact devices should involve careful and elaborate methodological approaches. The integration should form a configuration suitable for each platform and target function such that they can offer convenient and reliable diagnostics.  
Herein, we have discussed the recent progress in wearable sensors for non-invasive sensing. We have summarized recent advances in materials and structural modifications for wearable conductors. Next, we provide an overview of nano/microfabrication techniques that are widely used in the fabrication of wearable devices. Then, we discuss the recent progress in wearable sensors with focuses on their sensing mechanisms and applications. Technologies for wireless energy supply and data transmission are also reviewed. Lastly, we introduce recent studies that demonstrate highly-integrated sensing systems and discuss the challenges and prospects for further development of wearable devices.
## 4 Experimental Evaluation
### Results and Analyses
#### 4.2.1 Link-Based Similarity Measures: Best Iterations  
We apply the similarity measures to our five datasets on eight iterations; then, for each similarity measure with a dataset, we find out the _best_ iteration on which the similarity measure shows its _highest_ accuracy. Figure 3 illustrates the results with our five datasets; as an example, SimRank shows its highest accuracy on iterations 2 and 3 with the BlogCatalog and DBLP datasets, respectively. As already noted in Section 2, JPRank shows exactly the same results as JacSim does with undirected graphs; therefore, we do not apply JPRank to the BlogCatalog and Wikipedia datasets (i.e., \(5*4-2=18\) experimental cases are conducted). In addition, in the figure, we do _not_ represent the precision metric since the range of its values is higher than MAP, recall, PRES, and F-score; it makes the other four metrics plotted out very close together in the figure, thereby decreasing the readability of figures.  
As shown in Figure 3, for _all_ similarity measures, the best iteration is observed before the eighth one in all datasets. Table 2 summarizes the best iterations for all similarity measures with our datasets. Note that, hereafter, when we compare the effectiveness of a similarity measure with those of embedding methods for a dataset, we consider the effectiveness of the similarity measure _on its best iteration_ with that dataset; as an example, in the case of SimRank with the BlogCatalog dataset, we consider its effectiveness on the second iteration according to Table 2.  
Figure 3: Accuracy of similarity measures with all datasets on eight iterations.
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 3 Strength Criteria  
### Principal Stress Space  
The Cartesian coordinate system formed by the three principal stresses (maximum principal stress \(\sigma_{1}\), intermediate principal stress \(\sigma_{2}\), minimum principal stress \(\sigma_{3}\)) is referred to as the principal stress space, also known as the Haigh-Westergaard space [36]. The principal stress space is primarily utilized for depicting the geometric shapes of strength criteria, with specific parameters named as shown in Figure 3. Along the hydrostatic pressure axis, the three principal stresses are equal. The deviatoric plane (also referred to as the \(\pi\)-plane) is perpendicular to the hydrostatic pressure axis, where the sum of the three principal stresses at each point on it is a constant value denoted by \(I_{1}\), which can be represented by Equation (14). \(I_{1}\) is also known as the first stress invariant. In the figure, \(r\) represents the distance between point P and the hydrostatic axis, characterizing the magnitude of the shear stress. \(\theta\), also known as the Lode angle, ranges from \(0^{\circ}\) to \(60^{\circ}\) (some researchers use \(-30^{\circ}\) to \(30^{\circ}\)). When \(\theta=0^{\circ}\), the relationship between the three principal stresses is \(\sigma_{1}>\sigma_{2}=\sigma_{3}\), which is referred to as the triaxial compression meridian boundary (CMB). When \(\theta=60^{\circ}\), the relationship between the three principal stresses is \(\sigma_{1}=\sigma_{2}>\sigma_{3}\), which is referred to as the triaxial tensile meridian boundary (TMB). Based on the relevant concepts of shear stress, the principal stress state, stress invariants, as well as parameters like \(\theta\) and \(r\) can be interconnected. The specific transformation relationships are shown in the Equations (14)-(17):  
\[I_{1}=3S_{8}=\sigma_{1}+\sigma_{2}+\sigma_{3} \tag{14}\]  
\[r=\sqrt{2J_{2}}=\frac{\sqrt{3}}{3}T_{8}=\sqrt{\frac{1}{3}\Big{[}\big{(} \sigma_{1}-\sigma_{2}\big{)}^{2}+\big{(}\sigma_{2}-\sigma_{3}\big{)}^{2}+ \big{(}\sigma_{1}-\sigma_{3}\big{)}^{2}\Big{]}} \tag{15}\]  
\[\theta=\tan^{-1}(\sqrt{3}\frac{\sigma_{2}-\sigma_{3}}{\sigma_{1}-\sigma_{3}} /\Big{(}2-\frac{\sigma_{2}-\sigma_{3}}{\sigma_{1}-\sigma_{3}}\Big{)}) \tag{16}\]  
\[\left\{\begin{array}{l}\sigma_{1}=\frac{\sqrt{6}}{3}r\cos\theta\\ \sigma_{2}=\frac{\sqrt{6}}{3}r\cos\big{(}\frac{2\pi}{3}-\theta\big{)}\\ \sigma_{3}=\frac{\sqrt{6}}{3}r\cos\big{(}\frac{2\pi}{3}+\theta\big{)}\end{array}\right. \tag{17}\]  
where \(S_{8}\) denotes the octahedral normal stress, \(J_{2}\) denotes the second bias stress invariant, and \(T_{8}\) denotes the octahedral shear stress.
## 4 Experimental Results and Analysis  
In this section, the results of the proposed algorithm are discussed in both graphic and tabular form. For validation, out of the total 135 images used, 45 images were of sooty mold, 45 images were of powdery mildew, and 45 were of healthy mango plants. As a primary step, the proposed segmentation technique was used. Second, the classification results for this segmentation technique were tabulated by implementing different standardized classifiers. The detailed results, with their descriptions, are discussed in the following section. The identification of each disease is analyzed with the images of the healthy leaves of the mango plant. Then, the classification accuracy of all diseased leaves is compared with the classification of the healthy leaves of the mango plant. The proposed technique was tested with 10 of the most ideal classifiers with 1- fold cross-validation.
# Ohmic Baking of Gluten-Free Bread: Role of Starch and Flour on Batter Properties
## 3 Results and Discussions  
### Starch and Flour Properties  
The physical and chemical properties of starches and flours are shown in Table 1. Starch and flour properties significantly influence the pasting behavior of the raw material, which may have a strong correlation with bread quality. Amylose is important for bread baking, as it influences gelatinization and retrogradation of starch [16], as well as WHC and swelling power, and thus affects final bread properties. [PERSON] et al. [17] reported that low amylose content led to a lower water absorption, higher gas formation in the dough, reduced staling and improved bread crumb texture. The amylose content significantly varied within the studied samples. Cassava starch exhibited lowest amylose content (15.70%), resulting in lowest WHC (1.78 g/g) and highest swelling power (21.17%). Lower amylose content indicates a more crystalline structure of the starch granule, resulting in low water absorption [18]. Additionally, amylose has a suppressing effect on starch swelling power, therefore samples with lower amylose content exhibited higher swelling power. According to [PERSON] [19], the swelling properties of starch granule is mainly the function of amylopectin. In this study, wheat starch showed the highest amylose, thus lowest amylopectin content and the lowest swelling power at 8.42% (see Table 1). Apart from amylopectin content, B-type starches (tuber starch) exhibited higher swelling power than A-type starches (cereal starch). B-type starches have a more open packing of the helical structures, while A-type starches have a firmly packed arrangement of the double helices. The more open B-type starch structure is more accessible by water than the closed package structure [18]. Swelling power and solubility index were positively correlated (\(r\) = 0.94, correlations among starch/flour properties and ohmic-heated bread characteristics are presented in Table 2). According to [PERSON] et al. [17], the solubility of the starch granules reflects the amount of amylose leaching out from the granule into the water after heating. Analog to swelling power, B-type starches demonstrated a higher solubility index than A-type starches as well. In cereal starch, amylose is incorporated into a more compact starch granule, resulting in a reduced amylose mobility to the outer starch granule [17]. Similar results were obtained in this study; wheat starch possessed the highest amylose content (26.55%) and a perceptibly low solubility index at 4.34%.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### Custom ICMPv6 Messages
#### 3.2.6 Flow Example  
Figures 3 and 4 illustrate the modified control flow for the two proposed approaches. In this example, user plane transmission happens between DTN Node A and DTN Node D, with the packet being routed through an intermediate node that operates as a standard, unmodified IP node (Node B). If using custody transfer, before forwarding the packet, Node A writes its own address into the hop-by-hop option header. As discussed, this ensures that the packet contains a custodian address that can later be used for custody transfer purposes. The intermediate IP node (Node B), which does not recognize the custom hop-by-hop option header, simply forwards the packet to the next hop (Node C) without  
processing or modifying it. Node C, being equipped with DTN capabilities, recognizes the hop-by-hop option header of the inbound packet and, if the network is operating with custody transfer, reports custody acceptance to the previous custodian, stores a copy of the packet, replaces the custodian address with its own, and forwards the packet to the next hop (Node D), when possible. Otherwise, if the network relies only on status reporting, the node will temporarily store the packet and forward it without modifying its header. Upon successful receipt of the user plane packet, Node D, as a DTN-capable node, generates a custom ICMPv6 message DTN-PCK-RECEIVED and sends it to the address of the packet sender. When comparing the two methods, the most immediate difference is that, in the custody transfer case, a retransmission responsibility is assigned to the custodian node, and is iteratively moved closer to the destination as the transmission progresses. This feature is not present in the status reporting mode, as it breaks IP compliance by implementing retransmission functionalities at L3. As a result, when the nodes operate in status reporting mode, the communication follows a standard end-to-end flow, whereas in custody transfer mode, communication occurs on a \"DTN_Hop-by-DTN_Hop\" basis.  
Figure 4: Detailed representation of the data and DTN signaling flow. The one above refers to the standard mode based on status reporting and end-to-end reliability, while the one below represents the custody transfer fallback solution.  
Figure 3: Examples of signaling generated by a user plane transmission traversing DTN and non-DTN nodes. The one above refers to the standard mode based on status reporting and end-to-end reliability, while the one below represents the custody transfer fallback solution.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 2 Related Work
Our article introduces a unique framework for conducting IoT-NDN experiments with ndnSIM, making research more straightforward and effective. This framework allows for easy setup through a declarative approach to defining experiment parameters, identifies the most energy-efficient network configurations using methods like grid search optimization, identifies the optimal parameter combinations, and enhances result interpretation through automated visualizations and graph generation, showcasing a comprehensive suite of features not found in previous studies.
## 4 Discussion and Conclusions
With the proposed system in this study, the use of drones in transportation has been emphasized, and it has been seen that it is a method that provides great advantages. If the relevant infrastructure is provided, the delivery time of the packages expected to be delivered to the end user is significantly reduced, and it is predicted that these times will get shorter as drone technology continues to develop day by day. Creating an autonomous structure for drones is of great importance in terms of literature and practical applications. Therefore, the aim of future work is to deliver cargo in a fully autonomous manner without giving any input or even route information to the drone. In the simulation map to be created, the drone is planned to move autonomously and deliver cargo to the specified address. It is being considered to train the drone using reinforcement learning in order to be able to move forward without hitting an obstacle, to continue the mission without being affected by weather conditions, to find the cargo vehicle, and to return the cargo to its owners. Additionally, it is desired to test the algorithms by integrating RGB and depth cameras into the drone. This study was supported by the Scientific Research Projects Coordination Unit of Firat University. Project number SHY.24.01. Not applicable. The data used in the current study are available from the corresponding author upon reasonable request. The author declares no conflict of interest.