esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
* **Archonyms** **Full Forms**MIMO **Multi-Input Multi-Output RF Radio Frequency JAS-OUS **Joint Antenna Selection and Orthogonality-based User Scheduling PFUS Proportional Fairness User Scheduling IoT Internet of Things V2X Vehicle-to-Everything SNR Signal-to-Noise Ratio UAV Unmanned Aerial Vehicle MGEM Matrix Gaussian Elimination Method CSI Channel State Information POMDP Partially Observable Markov Decision Processes AMCMC Adaptive Markov Chain Monte Carlo NOMA Non-Orthogonal Multiple Access OFDMA Orthogonal Frequency Division Multiple Access JASUS Joint Antenna Selection and User Scheduling QoS Quality of Service JAS-CPUS Joint Antenna Selection and Channel Power based User Scheduling JAS-SUS Joint Antenna Selection and Semi-orthogonal User Scheduling
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
###### Abstract  
An intrabody nanonetwork (IBNN) is composed of nanoscale (NS) devices, implanted inside the human body for collecting diverse physiological information for diagnostic and treatment purposes. The unique constraints of these NS devices in terms of energy, storage and computational resources are the primary challenges in the effective designing of routing protocols in IBNNs. Our proposed work explicitly considers these limitations and introduces a novel energy-efficient routing scheme based on a fuzzy logic and bio-inspired firefly algorithm. Our proposed fuzzy logic-based correlation region selection and bio-inspired firefly algorithm based nano biosensors (NBSs) nomination jointly contribute to energy conservation by minimizing transmission of correlated spatial data. Our proposed fuzzy logic-based correlation region selection mechanism aims at selecting those correlated regions for data aggregation that are enriched in terms of energy and detected information. While, for the selection of NBSs, we proposed a new bio-inspired firefly algorithm fitness function. The fitness function considers the transmission history and residual energy of NBSs to avoid exhaustion of NBSs in transmitting invaluable information. We conduct extensive simulations using the Nano-SIM tool to validate the in-depth impact of our proposed scheme in saving energy resources, reducing end-to-end delay and improving packet delivery ratio. The detailed comparison of our proposed scheme with different scenarios and flooding scheme confirms the significance of the optimized selection of correlated regions and NBSs in improving network lifetime and packet delivery ratio while reducing the average end-to-end delay.  
Keywords:intrabody nanonetworks; fuzzy logic; firefly algorithm; energy-efficient routing protocol; electromagnetic communication in the terahertz band +
Footnote †: journal: Sensors
## 6 Simulations and Discussion
### The Number of Clusterhead/Gateway Changes  
Figure 12 (left) shows the number of clusterhead changes. In order to utilize the hierarchical network structure, it is preferable that the number of changes of clusterheads becomes less. For a 1-hop cluster-based topology, due to the node movement, the cluster structure may not be stable and the ripple effect may be generated under this circumstances. On the other hand, in SAMCA, as the node moving speed becomes faster, the number of clusterhead changes increases only slowly. This is attributed to the fact that the clusters are reconfigured autonomously as the topology changes.  
With parameters \(c_{1}=1\) and \(c_{2}=1\) as described in (2), Figure 12 (right) illustrates the number of gateway changes. Note that the proposed method provides a way to extract the behaviors of the border sensors with modest mobility during the process of gateway selection. However, the changes in gateways increase for the sensor nodes with high mobility, which may adversely affect the performance of the clustering algorithm. Therefore, an alternative approach is that distributed gateways can be composed  
Figure 11: The impact of cluster parameter selection on cluster formation over a time period of 200 seconds.  
by a group of the border sensors and local flooding may be applied to achieve data dissemination and provide link availability between adjacent clusters in a high mobility ad-hoc sensor networks.
## 2 Overview of Fake News and Fake Profiles
### Detection Methods
#### 2.2.2 Human Fact-Checking and Moderation  
Human fact-checking and moderation approaches play a crucial role in the fight against fake news by leveraging the expertise of individuals and organizations to verify information. These approaches can be broadly categorized into _Manual Verification Processes_ and _Fact-Checking Organizations_ methods.  
_Manual Verification Processes_ involve human experts who assess the credibility of information through thorough investigation and cross-referencing with reliable sources. One of the seminal works on the automatic detection of fake news was conducted by [PERSON] and [PERSON] [28]. The authors defined the task of fact-checking, collected a dataset from two popular fact-checking websites, and used k-Nearest Neighbors classifiers to handle fact-checking as a classification task.  
_Fact-Checking Organizations_ are dedicated entities that specialize in verifying the validity of information across various domains. Several well-known organizations include:  
* PolitiFact [29]: A well-known non-profit fact-checking website in the United States [30]. It mainly rates the accuracy of claims made by US political news and politicians' statements using classifications such as true, mostly true, half true, mostly false, false, and \"pants on fire\" (i.e., clearly fake news) [31].
* Snopes.com [32]: One of the first online fact-checking sites, launched in 1994 [31]. The site addresses political, social, and other current issues, primarily confirming news stories spread on social media.
* Suggest (also known as GossipCop) [33]: This site investigates fake stories about US celebrities and entertainment published in magazines and web portals. Each story is rated on an authenticity scale from 0 to 10, where 0 denotes a completely false rumor and 10 indicates a report that is entirely true.
* FactCheck.org [34]: A non-profit site that describes itself as a \"consumer advocate for voters\" aiming to reduce the level of deception and confusion in US politics [31]. It primarily assesses assertions made by politicians in speeches, debates, and TV commercials.
* FullFact [35]: An online fact-checking site supported by a British charity that verifies and corrects news and assertions circulating on UK social media.
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
###### Abstract  
We propose an effective, low complexity and multifaceted scheme for peak-to-average power ratio (PAPR) reduction in the orthogonal frequency division multiplexing (OFDM) system for underwater acoustic (UWA) channels. In UWA OFDM systems, PAPR reduction is a challenging task due to low bandwidth availability along with computational and power limitations. The proposed scheme takes advantage of XOR ciphering and generates ciphered Bose-Chaudhuri-Hocquenghem (BCH) codes that have low PAPR. This scheme is based upon an algorithm that computes several keys offline, such that when the BCH codes are XOR-ciphered with these keys, it lowers the PAPR of BCH-encoded signals. The subsequent low PAPR modified BCH codes produced using the chosen keys are used in transmission. This technique is ideal for UWA systems as it does not require additional computational power at the transceiver during live transmission. The advantage of the proposed scheme is threefold. First, it reduces the PAPR; second, since it uses BCH codes, the bit error rate (BER) of the system improves; and third, a level of encryption is introduced via XOR ciphering, enabling secure communication. Simulations were performed in a realistic UWA channel, and the results demonstrated that the proposed scheme could indeed achieve all three objectives with minimum computational power.  
underwater acoustic; PAPR; OFDM; BCH codes; XOR cipher 1  
[PERSON], [PERSON], [PERSON], P. Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels. _J. Mar. Sci. Eng._**2022**, _10_, 91. [[https://doi.org/10.3390/jmse10010091](https://doi.org/10.3390/jmse10010091)]([https://doi.org/10.3390/jmse10010091](https://doi.org/10.3390/jmse10010091))  
Academic Editor: [PERSON]
# _remote sensing_
## 2 Materials and Methods
### Retrieval of Biophysical and Agoronomical Variables  
The collaborative use of SINO-EU EO data aims to advance scientific knowledge on the quantitative use of remote sensing information in agriculture and develop solutions targeted at maximizing the advantage, in this context, of combining multi-temporal and multi-sensor data. The activity aims to respond to the needs for developing remote sensing data assimilation approaches that specifically address issues originating from the multi-scale and multivariate nature of the data involved in the agricultural domain. In this context, some methodological issues concerning the assessment of errors and uncertainties in the remote sensing observations, in the variables that are retrieved as well as crop models' formulation errors and parameter uncertainties, have been analyzed.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 2 Materials and Methods
### Chromaticity Evaluation Method for Color Difference Calculations  
Color evaluation methods are used for quantitative measurement and analysis of color attributes. The color quality evaluation of digital silk printing products involves assessing the chromaticity of a sample by comparing its color difference with that of a standard color block. A color difference formula is used to measure the difference between two different colors to assess the similarity of the colors. The result of the color difference formula is a numerical value, typically denoted as \(\Delta\)E. The smaller the value, the more similar the two colors.  
Color scientists have developed four commonly used color difference formulas, CIELAB, CMC (l:c), CIE94, and CIEDE2000 for evaluating color differences in different fields. Among them, CIEDE2000 is currently the most theoretically close to human vision [20]. The paper uses CIEDE2000, as in the current textile color control and evaluation, CIEDE2000 is the best choice [21]. The CIEDE2000 formula is as follows:  
\[\Delta E_{2000}=\left[\left(\frac{\Delta L^{\prime}}{k_{L}S_{L}}\right)^{2}+ \left(\frac{\Delta C^{\prime}}{k_{C}S_{C}}\right)^{2}+\left(\frac{\Delta H^{ \prime}}{k_{H}S_{H}}\right)^{2}+R_{T}\left(\frac{\Delta C^{\prime}}{k_{C}S_{ C}}\right)\left(\frac{\Delta H^{\prime}}{k_{H}S_{H}}\right)\right]^{\frac{1}{2}} \tag{5}\]  
where:  
\(K_{L}\), \(K_{C}\), and \(K_{H}\) are the weighting factors for lightness, chroma, and hue, respectively, based on their perceptual relevance in practical applications. S\({}_{L}\), S\({}_{C}\), and S\({}_{H}\) are the weighting functions for lightness, chroma, and hue, respectively. R\({}_{T}\) is the interaction term.  
\(K_{L}=2\), \(K_{C}=K_{H}=1\), as these specific values are recommended for textiles.  
The CIEDE2000 formula was used to calculate color differences between the PANTONE TCX color blocks with printed silk twill and silk crepe satin color blocks, as well as the Pix2 Pix-predicted silk twill and silk crepe satin color blocks with printed silk twill and silk crepe satin color blocks. The lightness differences (\(\Delta\)L\({}_{00}\)), chroma difference (\(\Delta\)C\({}_{00}\)) and hue difference (\(\Delta\)H\({}_{00}\)) contribution to \(\Delta\)E\({}_{00}\) was calculated and expressed as percentage [22].
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 3 Results
### Effect of Myricetin on a-Tubulin  
To validate the results obtained from the oncobigram analysis that myricetin might interact with tubulin, U2 OS cells transfected with GFP \(\alpha\)-tubulin were subjected to myricetin treatment at concentrations of 0.1, 1, and 10 \(\upmu\)M for 24 h. The effect of myricetin on microtubules is depicted in Figure 7. Indeed, myricetin inhibits microtubules. In contrast to the  
Figure 5: **(A)** Cytotoxicityicity of the six selected chamomile derivates with celecoxib (positive control) to 60 NCI tumor cell lines was plotted as mean log\({}_{10}\)IC\({}_{50}\) values for each tumor type. **(B,C)** the inhibitory activity of myricetin and celecoxib (control drugs), respectively, against the NCI 60 cell line panel plotted as mean log\({}_{10}\)IC\({}_{50}\).  
Figure 6: Cross-resistance profiling of myricetin to standard anticancer drugs. **(A)** Percentage of standard anticancer drugs from different pharmacological classes that significantly correlated to the responsiveness of NCI tumor cell lines to myricetin. It shows that 5 of the 10 tubulin inhibitors and 2 of the 14 tyrosine kinase inhibitors significantly correlate to myricetin. **(B)** The tubulin inhibitors are correlated to myricetin.  
well-organized microtubules seen in the untreated control cells, the myricetin-treated cells displayed shortened microtubule fragments that condensed around the nucleus, similar to vincistine-treated cells. Conversely, paclitaxel-treated cells exhibited extended and disorganized tubulin. Additionally, at the higher concentration of 10 uM myricetin, not only was the polymerization of microtubules significantly hindered (as evident from the reduced density of microtubules), but there were also multiple spindle poles observed in the area of the nucleus.
## 3 Results and Discussions
### Tocopherols Composition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tocopherol Contents** & \multirow{2}{*}{**Alijó**} & \multirow{2}{*}{**Moncorvo**} & **Vila Nova de** & **One-Way ANOVA** \\  & & & & **Cóa** & **(\(p\)-Value) 1** \\ \hline \(\alpha\)-Tocopherol & 379.9 \(\pm\) 78.8 & 360.2 \(\pm\) 60.1 & 385.4 \(\pm\) 71.9 & 0.5738 \\ \(\beta\)-Tocopherol & 6.5 \(\pm\) 3.8 & 5.51 \(\pm\) 1.66 & 6.26 \(\pm\) 1.12 & 0.5062 \\ \(\gamma\)-Tocopherol & 53.0 \(\pm\) 20.8 \({}^{\mathrm{b}}\) & 26.84 \(\pm\) 13.90 \({}^{\mathrm{a}}\) & 75.95 \(\pm\) 33.30 \({}^{\mathrm{b}}\) & \(<\)0.0001 \\ \(\Sigma\) Tocopherol & 439.4 \(\pm\) 84.1 \({}^{\mathrm{a}}\) & 392.5 \(\pm\) 59.8 \({}^{\mathrm{b}}\) & 467.6 \(\pm\) 99.6 \({}^{\mathrm{a}}\) & 0.0438 \\ \hline \hline \end{tabular}  
* Different lower case letters mean significant statistical differences between the geographical locations, at a 5% significance level (one-way ANOVA followed by the [PERSON]’s multi-comparison test).  
\end{table} 
Table 3: Tocopherol contents (mg/kg of oil) of \(\alpha\)-, \(\beta\)- and \(\gamma\)-tocopherol as well as Vitamin E (total tocopherol content) found in oils extracted from oleaster (mean \(\pm\) standard deviation) collected from different geographical locations (Alijó. Moncorvo and Vila Nova de Foz Cóa). Thetocopherol profile was similarly to that of olive oil, with \(\alpha\)-tocopherol representing more than 90% of the totaltocopherol content [33]. The amounts quantified in the present work were much higher than those observed for oleaster oil from Algeria, which varied from 87 to 182 mg/kg of oil [7] and Turkey, with values lower than 40 mg/kg of oil [34] but were of the same order of magnitude of those reported by [PERSON] [27], for seven populations from Tunisia (309.5-781.8 mg/kg of oil); and by [PERSON] [12] that analysed two Tunisian samples and reported \(\alpha\)-tocopherol contents of 313-390 mg/kg of oil. When comparing the results obtained in Table 3, for oleaster oils, with similar studies with olive oils obtained from cultivated varieties, it can be concluded that the oils from the studied oleaster samples presented, in general, higher values of \(\alpha\)-, \(\beta\)- and \(\gamma\)-tocopherol contents, than the traditional ones (12.2-630 mg/k of olive oil) [33; 35]. Additionally, the amounts of \(\gamma\)-tocopherol observed in the present work were much higher, in some cases of five orders of magnitude, compared to those reported for varietal olive oils and also for oleaster oils from other regions. These differences could be attributed to genetic factors. In fact, [PERSON] [33] and [PERSON] [28] concluded that tocopherols are genetically regulated and are highly cultivar-dependent. Tocopherols play important roles, acting as antioxidants, and therefore protecting lipids in human body and stored oils from oxidation and, in this sense, oleaster oils could be foreseen as important sources of these bioactive compounds, with high oxidative stability.
# Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics
## 2 Development of the Fumigation Robot
### Setting Up the Navigation Stack  
In the proposed system, fumigation is performed autonomously around an area, and those hotspots are mapped in real time in the robot's map database. For autonomous navigation, there are different levels of execution. The first step is the mapping of the environment. Various sensors for mapping the environment are depth cameras, 3D Light Detection and Ranging (LiDAR), and 2D-LiDAR. In this research, a 3D-LiDAR is used instead of other sensors to map the environment. Three-dimensional-LiDAR is better for mapping than the passive mapping method using depth cameras [36]. Moreover, depth cameras are affected by illumination, and many details are lost depending on how well-lit the environment is. Two-dimensional-LiDARs have a very limited field of view, as any object below or above its scan area is undetected. Hence, considering all these points, a 3D-LiDAR is used for this application. There are different mapping algorithms used in most 3D-LiDAR integrated systems, namely Lidar Odometry and Mapping (LOAM), Cartographer by Google, High-Definition LiDAR (HDL) graph simultaneous localization and mapping (SLAM), and Lidar Inertial Odometry via Smoothing and Mapping (LiOSAM). Compared to the existing mapping algorithms, LOAM was the best until a few years ago. It was even the top-ranked LiDAR-based method in the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset benchmark site. However, drifting in large-scale tests came with a significant drawback. Hence, LIO-SAM [37] is used contrary to the LOAM in the proposed system. In the experimental results, the LIO-SAM showed a translation error of 0.96 compared to LOAM, which had a translation error of 47.31%. Following the creation of the detailed map, navigation must be carried out. The navigation history goes back to the Devish robot, designed to carry out navigation inside office ecosystems [38]. These navigation stacks used finite state machine (FSM) for less complex environments; hence, a behavior tree was used inside navigation stacks to make decisions on complex tasks. For example, in [39], shooting tactics in soccer games were decided using a decision tree, which would never have been possible with FSM. These decision trees are used in navigation. The navigation stack by the robot operating system (ROS) is the most widely used, and its successor is Navigation2. It was built for ROS2, which uses Data Distribution Service (DDS) for communication. DDS is an industrial communication protocol that offers secure data transmission between robots. Navigation2 stack has been released for different motions of robots, namely differential, holonomic, legged, and Ackermann, for a wide spectrum of environments. In the proposed system, the Navigation2 stack is used for navigation.
# Deep Feature Learning with Manifold Embedding for Robust Image Retrieval
## 6 Experiments
### Evaluation and Comparison
#### 6.3.1 Effectiveness of Manifold Learning-Based Training Example Selection  
To validate the effectiveness of our proposed method, a set of comparison experiments is conducted. In the comparison group, training image pairs are directly generated by selecting \(k\)-nearest  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta\) & **N-S Score** & **Epochs** \\ \hline
0.125 & 3.86 & 8 \\
0.25 & 3.87 & 10 \\
0.5 & 3.88 & 12 \\
1 & 3.84 & 16 \\
2 & 3.82 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of balance factor \(\beta\) on retrieval accuracy and convergence speed. The experiment is conducted on UKBench with VGGNet-16, and retrieval accuracy is measured by N-S Score. Convergence speed is measured by the number of epochs to reach the best retrieval accuracy.  
Figure 3: Impact of the number of nearest neighbor \(k\) on the number of mined image pairs (no. of pairs) and its corresponding precision. The experiment is conducted on UKBench (**a**) and INSTRE (**b**) with ResNet-50. Only the first mining iteration is recorded. Precision denotes the ratio of true positive pairs over all mined image pairs. Note that anchor image is counted, so \(k\) starts from 2.  
neighbors of the original similarity relationship instead of refined similarity. Retrieval performance is compared on the UKBench under the same experimental setting except for the above difference.  
As demonstrated in Table 2, the manifold learning-based training pairs selection method outperforms the original one. Specifically, with the mining method based on the manifold-refined similarity, it reaches an N-S Score of 3.88, while, with the mining method based on the original similarity, the N-S Score is 3.84 on VGGNet-16. A similar situation happens on ResNet-50 with a performance boost from 3.93 to 3.96.  
This improvement comes from two aspects. On the one hand, refined similarity contains extra local geometry information of the intrinsic data manifold compared to original similarity. The extra information helps to mine out some hard pairs, while it can not be done with the original one. On the other hand, the refined similarity is more accurate than the original one. Thus, some false pairs are filtered out. Fine-tuning with a cleaner training set naturally brings us better retrieval performance.