esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Fatty Acid Composition  
The fatty acid composition profiles of the KNC breeds are presented in Table 5. Based on comparison of the mean value, the individual fatty acid composition of chicken thigh meat was influenced by both the chicken breed and the fresh or Frozen-Thawed state. The saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), and polyunsaturated fatty acid (PUFA) profiles strongly differed according to the chicken breed. Under fresh conditions, the percentage of PUFAs was the highest in HH3 and WRMD 1, followed by WRMD 2. In contrast, all KNC breeds in this study showed higher (\(p<0.05\)) PUFA proportion than CB. This finding is in agreement with the results of [PERSON] et al. [7] and [PERSON] et al. [24], in which the PUFA content in KNC meat was notably higher than that in CB meat. In contrast, the concentrations of MUFAs were higher (\(p<0.05\)) in CB than in the KNC breeds. Furthermore, under fresh conditions, WRMD 2 and CB showed the highest (\(p<0.05\)) SFA composition, followed by HH3 and WRMD 1. The SFAs myristic acid and stearic acid were the main contributors to the high SFA percentage in WRMD 2.  
In this study, the predominant fatty acids were oleic, palmitic, linoleic, and stearic acids. Moreover, the proportions of the essential fatty acids linoleic and arachidonic acid were higher (\(p<0.05\)) in all KNC breeds than in CB under both fresh and Frozen-Thawed conditions. This indicated that the newly developed KNC breeds, WRMD 2, provided similar advantages to previously recognized KNC breeds (HH3 and WRMD 1). In addition, the higher content of essential fatty acids in KNC is in agreement with previous reports [7; 24; 36]. Furthermore, certain fatty acids are closely related to taste perception. Docosahexaenoic acid is characterized by its sweet and bitter taste; arachidonic acid is closely correlated with an umami taste; and oleic and linoleic acids contribute to a salty and sour taste, respectively [6; 25; 42]. Under fresh and Frozen-Thawed conditions, DHA concentration was the highest in WRMD 2, followed by HH3, WRMD 1, and CB (\(p<0.05\)). The percentage of DHA was the lowest in both fresh and Frozen-Thawed CB meat. Regardless of the KNC breed and fresh or Frozen-Thawed conditions, the content of arachidonic acid, an umami fatty acid, was higher (\(p<0.05\)) in KNC than in CB. However, the percentage of oleic acid in thigh meat from all KNC breeds was lower than that in CB. These findingson fatty acid composition suggest that the newly developed KNC breed, WRMD 2, can provide both essential nutrients and a favorable taste.
###### Abstract  
Ground-based synthetic aperture radar (GBSAR) technology has been widely used for bridge dynamic deflection measurements due to its advantages of non-contact measurements, high frequency, and high accuracy. To reduce the influence of noise in dynamic deflection measurements of bridges using GBSAR--especially for noise of the instantaneous vibrations of the instrument itself caused by passing vehicles--an improved second-order blind identification (SOBI) signal de-noising method is proposed to obtain the de-noised time-series displacement of bridges. First, the obtained time-series displacements of three adjacent monitoring points in the same time domain are selected as observation signals, and the second-order correlations among the three time-series displacements are removed using a whitening process. Second, a mixing matrix is calculated using the joint approximation diagonalization technique for covariance matrices and to further obtain three separate signal components. Finally, the three separate signal components are converted in the frequency domain using the fast Fourier transform (FFT) algorithm, and the noise signal components are identified using a spectrum analysis. A new, independent, separated signal component matrix is generated using a zeroing process for the noise signal components. This process is inversely reconstructed using a mixing matrix to recover the original amplitude of the de-noised time-series displacement of the middle monitoring point among three adjacent monitoring points. The results of both simulated and on-site experiments show that the improved SOBI method has a powerful signal de-noising ability.  
second-order blind identification; bridge monitoring; microwave interferometry; ground-based synthetic aperture radar; signal de-noising +
Footnote †: journal: Applied Soft. 2019, 9, 3561; doi:10.3390/APP9173561
## 4 Discussion
On the basis of these findings, it seems that higher doses (e.g., 5 uM) of MSHs may be better than lower doses (e.g., 0.05 uM), especially when it comes to wheat genetic transformation studies, since high doses of MSHs cause hypomethyllation in wheat. Epigenetic modifications at the level of DNA cytosine methylation/demethylation have a significant impact on various stress inducers in plants [24]. Although DNA sequence variation is considered a major evolutionary process leading to phenotypic changes, it is worth noting that DNA methylation modifications can also affect gene expression, potentially contributing to the inheritance of trait differences between generations [37]. Several studies have focused on the molecular effects of MSH therapy on plants. [PERSON] et al. [13] conducted a study to investigate the effect of MSHs on polymorphism and genomic instability in beans. Their study involved treatment with different concentrations of MSH to evaluate their effects on genetic or epigenetic levels, and their results indicated that iPBS polymorphism profiles showed a significant proportion (52.2%) at a concentration of 10\({}^{-4}\) mM, which was the maximum dose of estrogen hormone administered. In our study, the iPBS profile of wheat showed the highest polymorphism rate treated with 0.5 uM estrogen. However, the achieved polymorphism rate was 29.3%. Furthermore, analogous to our study, their results revealed that the presence of estrogen, 17-\(\beta\)-estradiol, testosterone, and progesterone in bean plants affects genomic instability and induces epigenetic changes that serve as a key regulatory mechanism in gene expression. [PERSON] [25] conducted a study to investigate the effects of MSHs on retrotranson polymorphism and genomic instability in wheat in vitro. The researcher administered different concentrations of MSHs to Murashige and Skoog (MS) medium. It was found that treatments with 10\({}^{-8}\) mM+ progesterone resulted in the highest level of genomic template stability (GTS) (80.52%), while treatments with 10\({}^{-4}\) mM+ 17-\(\beta\)-estradiol resulted in the lowest value (68.83%). In addition, the study showed that treatment with 17-\(\beta\)-estradiol resulted in a higher percentage of polymorphism in CRED-iPBS assays, which were digested with _Mspl_ and _H pall_, respectively. According to our results, treatment consisting of the 0.05 uM estrogen experimental group had the highest GTS value, while treatment consisting of the 5 uM progesterone experimental group and the 0.5 uM estrogen experimental group had the lowest GTS. Our iPBS and CRED-iPBS results differed from those published by [PERSON] [25], which may be due to fact that the plant was grown in a different germination environment or because different concentrations of MSHs were used.
# Three-Dimensional Obstacle Avoidance Strategy for Fixed-Wing UAVs Based on Quaternion Method
## 4 Multiple Task Implementation Using a Hierarchical Architecture
### Task Quaternion 2: Collision Avoidance  
What remains is establishing the avoidance coordinate system denoted as an \(a\)-frame. Ideally, the \(a\)-frame should be aligned with \(\mathbf{v}_{axo}\); however, this cannot be achieved since the acceleration of obstacles is difficult to measure in practice. Therefore, we build the \(a\)-frame based on the avoidance points mentioned in Section 2. As shown in Figure 7, \(a_{aa}^{a}\) denotes the error between \(\mathbf{p}_{UAV}^{e}\) and \(\mathbf{p}_{axo}^{n}\), and the positive \(x\)-axis direction of the \(a\)-frame is aligned with \(a_{aa}^{a}\).  
\[\mathbf{a}^{a}=\left[\begin{array}{cc}\left\|\mathbf{a}^{e}\right\|&0&0\end{array} \right]^{\mathrm{T}}=\mathbf{R}_{e}^{a}\mathbf{R}_{n}^{e}(\mathbf{p}_{axo}^{n}-\mathbf{p}_{UAV }^{n})=\mathbf{R}_{e}^{a}\mathbf{R}_{n}^{e}\mathbf{a}^{n} \tag{34}\]  
where \(\mathbf{a}^{n}\) rotates from \(n\)-frame to \(e\)-frame, and then generates the quaternion to rotate from \(\mathbf{a}^{e}\) to \(a^{a}\). That is to say, \(\mathbf{a}^{n}\) first rotates to align with the \(e\)-frame, and then to align with the \(a\)-frame. The obstacle avoidance task quaternion, the rotation matrix between the \(e\)-frame and the \(a\)-frame, and the obstacle avoidance angular velocity are expressed by the following Equations (35)-(38).  
\[\theta_{ea}=\cos^{-1}\left(\frac{\mathbf{a}^{a}\mathbf{\cdot}\mathbf{a}^{e}}{\left\|\mathbf{a }^{e}\right\|^{2}}\right)\cdot\mathbf{k}_{ea}=\frac{\mathbf{a}^{a}\times\mathbf{a}^{e}}{ \left\|\mathbf{a}^{a}\times\mathbf{a}^{e}\right\|} \tag{35}\]  
\[\mathbf{q}_{ea}=\left[\begin{array}{cc}\eta_{ea}&\mathbf{e}_{ea}^{\mathrm{T}}\\ \end{array}\right]^{\mathrm{T}}=\left[\begin{array}{cc}\cos\left(\frac{\theta _{ea}}{2}\right)&\mathbf{k}_{ea}^{\mathrm{T}}\sin\left(\frac{\theta_{ea}}{2} \right)\end{array}\right]^{\mathrm{T}} \tag{36}\]  
\[\mathbf{R}_{e}^{a}=I+2\eta_{ea}S(\mathbf{e}_{ea})+2S^{2}(\mathbf{e}_{ea}) \tag{37}\]  
\[\mathbf{\omega}_{ea}^{a}=S^{\dagger}(a^{a})(\mathbf{R}_{e}^{a}S(\mathbf{\omega}_{ea}^{e}) \mathbf{R}_{n}^{a}n^{a}-\mathbf{R}_{n}^{e}\mathbf{R}_{e}^{a}(\mathbf{v}_{OBS}^{n}-\mathbf{v}_{UAV}^ {n})) \tag{38}\]  
Finally, two subtask quaternions are combined into a total task quaternion, which is defined as the desired quaternion. The desired angular velocity is also derived:  
\[\left\{\begin{array}{c}\mathbf{q}_{nd}\mathbf{:=}\mathbf{q}_{na}\mathbf{=}\mathbf{q}_{ne}\otimes \mathbf{q}_{ea}\\ \mathbf{w}_{nd}^{d}\mathbf{:=}\mathbf{w}_{na}^{d}\mathbf{=}\mathbf{R}_{e}^{d}\mathbf{w}_{ne}^{e}\mathbf{+} \mathbf{w}_{ea}^{e}\end{array}\right. \tag{39}\]
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 2 Wireless Sensors Networks (WSN) in the IEEE 802.15.4: Zigbee and OCARI Protocols
### OCARI Protocol Stack
#### 2.2.1 General Information  
The OCARI was initially a French research project that was funded by the French National Research Agency (ANR) [20]. This project started in December 2006, and it ended in November 2009 [21]. The project gathered industrial partners (DCNS, EDF, and Telit) and academic partners (INRIA, LATTIS, LIMOS, and LRI). The main objective of the project was to develop WSN technologies that can operate in strongly constrained environments and specifically in industrial environments. Specifically, OCARI is currently implemented on a platform that is based on the Atmel IEEE 802.15.4 transceiver and the ARM Cortex M3 microcontroller. This type of a platform is typically commercialised by Dresden Electronik GmbH, Adwave, and HiKoB. Figure 2 shows where OCARI technologies are situated when compared to ZigBee technologies, from which they were largely inspired. When Zigbee is an upper layer of the IEEE 802.15.4 layer, OCARI technologies optimally stand out from the ZigBee ones: In particular, they introduce a new medium access control layer protocol called MaCARI, which is in charge of enhancing medium use, and a specific scheduling algorithm, which allows to improvenetwork activity, called OSERENA, which is used in its updated version. These are succinctly described in Appendix A for the sake of readability.  
The OCARI protocol stack thus competes with the IEEE 802.15.4 technologies dedicated to WSNs, and in particular with ZigBee technologies. The OCARI protocol stack offers an opportunity to easily deploy meshed networks at a large scale in an industrial environment or at a field scale for experimental measurements. OCARI offers significantly greater performances in energy consumption than Zigbee. The theoretical limit of the operable modules is approximately 400 nodes. It is necessary for an OCARI network to deploy two types of nodes, namely a network controller (also termed as CPAN) and PAN modules (which are fully functional devices as stated in IEEE 802.15.4 terminology) that form the network nodes equipped with sensors or actuators. The working principle of an OCARI network is illustrated in Figure 3.
# A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm
###### Abstract  
Internet of Things (IoT) is a new concept in the information and communication technology studies which indicates that any creature (human, animal, or object) can send and receive data through communication networks, such as the internet or intranet platform. Wireless sensors have limited energy resources due to the use of batteries to supply energy, and since it is usually not possible to replace the batteries of these sensors. In addition, the lifespan of the wireless sensor network is limited and short. Therefore, reducing the energy consumption of sensors in IoT networks for increasing network lifespan is one of the fundamental challenges and issues in these networks. In this paper, a routing protocol is proposed and simulated based on an ant colony optimization algorithm's performance. The clustering is performed with a routing method based on energy level criteria, collision reduction, distance from the cluster-head to the destination, and neighborhood energy in the proposed method. The cluster head is selected based on the maximum residual energy, minimum distance with other clusters, and consumed energy. This energy is minimized to reach the base station. The node with more energy than the threshold is selected as the new cluster head. Then, four conditions are applied for routing: the shortest path, the leading path, the shortest distance to the source node and the destination node, and routing. Results show that after about 50 cycles of transferring information, only the average of 19.4% of the initial energy is consumed in the network nodes. Therefore, obtained results illustrate that the proposed method helps to retain the energy more than 40% comparing the available methods.  
energy: optimization; IoT; ant colony algorithm +
Footnote †: journal: Journal of Computer and Communication  
**2021**, 14, 1709. [[https://doi.org/10.3390/en14061709](https://doi.org/10.3390/en14061709)]([https://doi.org/10.3390/en14061709](https://doi.org/10.3390/en14061709))
## 3 Results and Discussion
### Results of Cleaning Tests on Mock-Ups
This outcome suggests that the cleaning method used in Test 14 achieved a more controlled and selective action, preserving the integrity of the underlying layers while removing the varnish film. These findings highlight the effectiveness of nanostructured gels in certain conditions and emphasize the need for precise control over solvent interaction to avoid unintended alterations to underlying materials. Another aspect observed during the tests is the tendency of Polar Coating B and Polar Coating G to cause bleaching or matting of the surface. This issue was particularly evident when these solvents were used coupled with the nanostructured gels. The bleaching effect suggests that the solvents may have interacted with the surface altering its appearance, possibly by damaging the surface finish or by affecting the optical properties of the paint. This observation highlights a potential limitation in the application of the two nanostructured fluids, which could impact the overall aesthetic quality of the treated surfaces. Although all the tested gels loaded with solvents were effective in varnish removal, they all showed a tendency to leave on the surface some swollen varnish residues, which required the interposition of Japanese paper or fabric layer and an additional cleaning step with the use of sponges. Figure 6: In this figure, Dry and Peggy gels were compared under the same conditions: (**a**) Mock-ups during the application of the rigid gels; (**b**) Paint surface after the cleaning treatment in visible light; (**c**) in UV light. Indeed, the tests proved that the interposed layer of paper or fabric between the gel and the surface enhanced the migration of softened ketone varnish within the cleaning system. Nonetheless, it is worth noting that the use of highly volatile solvents can be challenging, as their rapid evaporation leads to the quick drying of the varnish, thus causing the paper or fabric to adhere to the mock-ups surface. Focusing on the Blitz-Fix(r) sponges, they effectively permitted the capture of softened varnish without leaving behind residues. The use of small sponge pieces allows for a controlled and gradual removal process, enhancing precision and minimizing the risk of damaging the underlying surface.
# The Role of Powered Surgical Instruments in Ear Surgery: An Acoustical Blessing or a Curse?
## 5 Generation of Noise and Vibrations by Otological Surgical Instruments and Hearing Loss
### MES Procedures
#### 5.2.3 Hearing Loss after MES Procedures  
A commonly accepted range for the incidence of sensorineural hearing loss in the ipsilateral ear after ear surgery is from 1.2% to 4.5% of patients. The higher figure of 4.5% is from a study of 1680 ear surgeries reported by [PERSON] et al. [46] in 1973, and the lower figure of 1.2% is from a study of 2,305 ear surgeries performed from 1965 to 1980, reported by [PERSON] et al. [47] in 1984. What is notable about these papers is that they are among the first large scale studies published in the literature and that they are from more than close to 35 years ago. Both authors attributed some of these hearing losses to the surgeon being too aggressive in the area of the ossicular chain, and [PERSON] et al. [47] in particular, noted that the incidence of sensorineural hearing loss was lower in patients treated from 1974 to 1980. They attributed this drop to better technique and better drills. In 1989, [PERSON] et al. [48] reported what they characterize as an important sensorineural hearing loss after tympanoplasty in a larger percentage of patients at 16.7% than previously reported in literature, but the hearing loss was restricted to over 8000 Hz, which is typically not measured. [PERSON] et al. (2017) reported in 1992 that they found no evidence of an even temporary threshold shift after ear surgery in a patient group of 40; however, they only tested up to 4000 Hz. A recent study by [PERSON] et al. (2017) in 2017 looked at factors of the experience of the surgeon and the use of a powered drill in hearing outcomes for patients undergoing a tympanoplasty which required drilling in the ear canal, a less invasive procedure than a mastoidectomy, and they found that neither factor exhibited a correlation with high-frequency hearing loss. In contrast, [PERSON] et al. (2016) found in 2016 that the experience of the surgeon was a significant factor, but the use of a drill was not significant in the incidence of postoperative sensorineural hearing loss between 500 Hz to 4000 Hz after tympanoplasty which was 7% for residents, but only 1% for more experienced surgeons. However, they could not identify any obvious errors on the part of the residents.  
Thus, the literature as it relates to the role of powered surgical instruments, specifically electric drills in postoperative sensorineural hearing loss, is inconclusive at best, and depends on the study design and definition of postoperative sensorineural hearing loss.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 2 Related Works
[27] proposed the 3 Anchor DV-Hop localization algorithm, which groups all anchor nodes into triplets and selects the best three from each group to locate the unknown node. [PERSON] et al. [28] argued that the closer an anchor node is to an unknown node, the more reliable the estimated distance between them becomes, and proposed using only the five closest anchor nodes for localization. However, different network parameters and topologies affect the number of anchor nodes involved in localization. In recent years, the rise of intelligent optimization algorithms has provided new approaches to optimizing DV-Hop localization. Scholars have applied various optimization algorithms to globally optimize the position of unknown nodes in the third stage of the original DV-Hop multilateration process. For example, improvements to DV-Hop based on Particle Swarm Optimization [29], Grey Wolf Optimization [30], Genetic Algorithm [31], and Artificial Bee Colony Optimization [32] have been proposed. Although existing research strives to enhance the localization accuracy of the DV-Hop algorithm, it still faces challenges such as accuracy improvement bottlenecks and limitations in adaptability. This study aims to address critical shortcomings in current DV-Hop-based localization methods for IoT networks: First, the original DV-Hop algorithm is mainly applicable to scenarios with uniformly distributed nodes. In non-uniform networks, its performance deteriorates significantly, and the localization accuracy drops sharply. This study will conduct an in-depth analysis of the impact of non-uniform node distribution on the hop count between nodes and the average hop distance, pointing out that it disrupts the inherent relationship between the hop count and the physical distance. Second, the BGWO algorithm is introduced to establish an anchor node selection mechanism. By performing binary encoding on the participation states of anchor nodes and defining an optimization objective function based on localization error, BGWO can screen out a subset of dominant anchor nodes suitable for localizing unknown nodes in non-uniform networks. Only this subset is used for localization, effectively eliminating the interference from anchor nodes with unreliable estimated distances. Third, the GWO algorithm is adopted to directly minimize the distance error. This not only reduces the sensitivity to distance estimation errors but also avoids the dependence of the least squares algorithm on both estimation accuracy and initial iteration values, thus significantly improving the final localization accuracy. The collaborative innovation in the above three aspects provides a more effective localization method for non-uniform IoT networks.
# Characteristics of Magnetic Fields Induced by the Wake of an Underwater Vehicle
###### Abstract  
Underwater vehicles generate hydrodynamic wakes within a large area that last for a longtime during navigation, thus generating induced magnetic fields, and these are of great significance for detecting and tracking underwater vehicles. In combination with the wakefield and magnetic field simulations, this study adopts the dynamic overlapping mesh technology to conduct a numerical simulation of the wake magnetic field during the movement of an underwater vehicle. This paper introduces the causes of formation and laws of evolution of the wake magnetic field, analyzes its spatial distribution and time-domain changes, and discusses the time-frequency domain characteristics at different monitoring points as well as the effects of navigation speed and acceleration on wake magnetic fields. Our results indicate that the wake magnetic field of an underwater vehicle belongs to a low-frequency weak signal of 0-5 Hz; as the navigation speed increases, the barycenter frequency of the wake magnetic field decreases and the half-energy bandwidth increases. The increase in acceleration of the underwater vehicle will cause a higher growth rate of the wake magnetic field. This paper provides a theoretical reference for the detection of underwater vehicles based on wake magnetic fields.  
magnetic fields; wake; underwater vehicle; CFD +
Footnote †: journal: Applied Sciences