esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 2 Preliminaries and Related Works
### Federated Learning  
Without data, any learning in artificial intelligence cannot be done. In most cases, these data are distributed, so the most general solution is to centralize these data and then perform learning on the central server. However, it is not easy to gather much of the data needed for learning in one place, as it requires much computing and communication resources. Unlike conventional centralized learning approaches, federated learning (FL) is a model in which devices in different spaces perform their own learning and work with other devices to form the global learning model. The concept of FL was introduced by Google in 2016 and FL has been applied to Google keyboard, Gboard [29]. FL can be applied to all edge devices, so FL has great potential to be applied in a variety of fields [30]. In the beginning of FL process, the server shares a common model with participants. After that, each participant trains the model they received from the server based on their own data and sends local model parameters back to the server. The server integrates the collected local model parameters to build a global model, and shares it with participants again. The above operations are repeated to get the well-trained global model.  
It is not easy for UAVs to have stable and reliable connectivity and abundant communication resources due to high mobility of UAVs and wireless communications, Thus, in UAV systems, it is difficult to reliably transfer the data used for learning to a central server every time, so conventional learning methods do not work well with a limited amount of data in a real time. A UAV has a computational ability and resources to perform learning independently. By utilizing FL, each UAV trains its own model based on its own data and exchanges only model parameters, so FL is highly suitable for UAV systems. Furthermore, as different data from many UAVs are utilized together to build a global model, a much better trained model can be obtained than when a single UAV performs independent learning with only its data. Consequently, FL can be well applied to and effective in UAV systems [31].
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 6 Conclusions  
We propose a method to utilize dimensionality reduction before k-means++ centroid initialization and kernel k-means clustering. To justify the clustering performance of the proposed method using random projection for both k-means++ and kernel k-means, the JL lemma and the improved bound for kernel methods from [2] are considered. Experimental results indicate that the performance with our approach is close to that of kernel k-means with k-means++ initialization without significant degradation. On a high dimensional dataset in our experiment (\(n=187\), \(D=19,993\)), it is shown that the speed-up of the proposed algorithms is 26 times compared to that of vanilla kernel k-means. The result demonstrates that the proposed method speeds up the kernel k-means, by means of dimensionality reduction using random projection, without significant performance degradation.
# An Overview of Machine Learning and 5G for People with Disabilities
## 3 5G for People with Disabilities
### Improved Connectivity and Reliability  
Instantaneous connectivity, as well as robust and reliable connectivity solutions, is required in 5G to offer the best experience to highly mobile users. High reliability, as well as connectivity, are critical and should always be guaranteed for the successful development of navigation systems for the visually impaired and sound recognition systems for the hearing impaired.
# Online Uniformly Inserting Points on the Sphere +
## 1 Introduction  
Uniformly distributing points over a specific domain has many applications in digital halftoning, numerical integration, computer graphics, etc. For the offline version, the number of points is known in advance, which is to configure \(N\) points as evenly as possible on a metric space, and attention is paid to the final uniformity of this \(N\)-point configuration rather than the uniformity in the process. Different from the offline version, the online version of this problem considers the situation of point insertion requests arriving one by one. Because the total number of points is unknown, points are required to be inserted as uniformly as possible in each insertion step.  
According to known online algorithms, there are several ways to estimate the uniformity of the distribution of the points. To measure the uniformity of points, there are several metrics. Some studies use the minimum pairwise distance [1, 2] to measure the uniformity. Some other studies take the uniformity problem as minimal energy problems [3]. In discrepancy theory [4, 5], uniformity can be defined by the spherical cap discrepancy, which is the supremum of the local discrepancy over all spherical caps. Ref. [6] showed that the spherical cap discrepancy of random point sets, of spherical digital nets and of spherical Fibonacci lattices converges with order \(N^{-1/2}\). Ref. [7] established connections to spherical cap discrepancy and showed some general discrepancy bounds. Uniformity can also be defined by the ratio between the maximal and minimal number of points in a fixed shape within the area.
# A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks
## 5 Conclusions  
We proposed an improved clustering scheme for Virtual MIMO-based topology construction (ICV-MIMO) to determine adaptively both the inter-cluster transmission modes and the clustering ranges. The ICV-MIMO scheme partitions a WSN into a number of clusters that have at most three CHs per cluster. The cluster head selection criterions consider both nodes' RBLs and nodes' approximate maximum transmission capability. Each cluster member's transmission power is optimized according to the distance between it and CHs. The added FCHs amortize the cooperation overhead of MCHs while they do not involve more clustering overhead. Also, the problem of unreasonable clustering range setting can be avoided. The simulation results indicate that the ICV-MIMO scheme achieves significant reduction in energy consumption and thus has longer network lifetime, compared with the existing typical virtual MIMO-based scheme.  
Our work considered only SISO mode for the cluster members regardless of the distances from them to CHs. In order to further improve the energy efficiency, a possible future extension is to incorporate SIMO mode in intra-cluster communications, i.e., allows the cluster members to dynamically switch their transmission modes between SISO and SIMO, depending on energy consumption consideration. In addition, in order to stimulate the cooperation of individual nodes, introducing the game-theoretic-based incentive methods [35; 36] into our scheme is also an interesting future extension. Also, the research of event detection methods (e.g., failure detection [37], bursty workload detection [38], target detection [39], attack detection [40]) for distributed systems has been a concern of researchers, which can guarantee continuous, safe, secure, and dependable operation in distributed systems. Thus, in order to achieve the goal for continuous, safe, secure, and dependable operation, it is worthwhile to consider an event detection mechanism for our scheme in future works.  
**Acknowledgments: This work was supported in part by the National Natural Science Foundation of China under Grant (No. 61272494, 61379058, 61379057, 61402542).**  
**Author Contributions: The individual contributions of authors are as follows. [PERSON] proposed the scheme and conceived the experiments. [PERSON] performed the experiments, analyzed the data, and gave the interpretation of simulation results. [PERSON] directed the research and contributed to the refinement of the scheme. The paper was drafted by [PERSON] and subsequently revised and approved by [PERSON] and [PERSON].**  
**Conflicts of Interest: The authors declare no conflict of interest.**
## 4 Proposed System Model
### RES Model  
Photovoltaic (PV) cells and wind turbines can be used as local power generators, also known as distributed RESs, on consumer premises. These RESs can be used for the local energy generation, as well as for charging the batteries in BSUs. The RESs' generated energy, denoted by \(E_{RES}\), can be calculated as in [32], by approximating a local Gaussian function (Figure 2) as follows:  
\[E_{RES}(t,\mu,\sigma)=\frac{1}{\sigma\sqrt{2\pi}}e^{(-\frac{(t-\mu)^{2}}{2\sigma ^{2}})} \tag{4}\]  
where \(t\) denotes the prospection variable (time), \(\mu\) is the mean or central value and \(\sigma\) is the standard deviation.  
The total daily energy generated from RESs must be positive, i.e., greater than zero and on a daily basis, it is given by:  
\[0\leq E_{RES}\leq E_{RES_{(max)}} \tag{5}\]  
where \(E_{RES_{(max)}}\) is the maximum available RESs' generated energy capacity. If in any time interval, the RESs' generated energy exceeds the end-user energy demand \(E_{T}\), i.e.,  
\[E_{RES}>E_{T} \tag{6}\]  
then it is sold back to the grid as per their prior agreement or can be used for charging batteries in BSUs for later use, particularly during peak hours.
# Abnormal Ship Behavior Detection Based on AIS Data
## 2 Related Work
From the review of the research on abnormal behavior detection for ships, we can observe the following problems: (1) most of the existing studies have defined the \"abnormal behavior of ships\" in their respective fields without elaborating on the reasons for these definitions; (2) while previous studies have usually been based on using historical data to extract relevant knowledge about the behavior of ships, existing studies have focused more narrowly on the behavior of ships on specific routes; and (3) the existing research on detection is not comprehensive, with the research methods only considering the single attribute characteristics of the ship and not a fusion of characteristics. Some studies still only identify normal or abnormal conditions in a binary fashion, not considering the degree of abnormality for the impacting factors. In this study, the existing research was combined with the actual geographical environmental factors for a single ship's motion behavior to create a relatively comprehensive framework for detecting abnormal behavior. Based on the graph structure learning method and the tree linking strategy, we detected the structure of maritime routes to represent the ship's travel route. The extracted network structure characteristics maintained the real geographic mapping between nodes and could reduce the storage requirements for the maritime routes. Then, using each segment as a unit, the probability characteristics of the trajectory points were detected from the perspectives of spatial attributes and thematic attributes, and the score value for the degree of abnormality of the trajectory points was obtained to evaluate the ship's motion mode.
# A Genomic Survey of Signalling in the Myxococcaceae
###### Abstract  
As prokaryotes diverge by evolution, essential 'core' genes required for conserved phenotypes are preferentially retained, while inessential 'accessory' genes are lost or diversify. We used the recently expanded number of myxobacterial genome sequences to investigate the conservation of their signalling proteins, focusing on two sister genera (_Myxococcus_ and _Coralococcus_), and on a species within each genus (_Myxococcus xanthus_ and _Coralococcus exigus_). Four new _C. exigus_ genome sequences are also described here. Despite accessory genes accounting for substantial proportions of each myxobacterial genome, signalling proteins were found to be enriched in the core genome, with two-component system genes almost exclusively so. We also investigated the conservation of signalling proteins in three myxobacterial behaviours. The linear carotenogenesis pathway was entirely conserved, with no gene gain/loss observed. However, the modular fruiting body formation network was found to be evolutionarily plastic, with dispensable components in all modules (including components required for fruiting in the model myxobacterium _M. xanthus_ DK1622). Quorum signalling (QS) is thought to be absent from most myxobacteria, however, they generally appear to be able to produce CAI-I (cholerae autoinducer-1), to sense other QS molecules, and to disrupt the QS of other organisms, potentially important abilities during predation of other prokaryotes.  
caroteonoids; comparative genomics; development; fruiting body formation; one-component systems; quorum signalling; two-component systems; myxobacteria; Myxococcus
## 1 Introduction
However, such an active mechanism is hardly employed for small satellites due to deployment costs and complexity. In addition, the friction effects cannot be neglected as they generate a difference between deployment tests and simulations. The goal of this work is to propose a passive truss-link mechanism for large deployable structures of small satellites. In contrast to the many truss-link mechanisms [11; 12; 13; 14; 15; 16; 17], we do not accommodate any active driving mechanisms because they are not simple and not sufficiently reliable. As driving mechanisms, only conventional torsion spring hinges with proper latching mechanisms were employed in this work. To achieve successful deployment, an inverse identification technique for equivalent friction torque (EFT) was proposed based on the results of the response surface method combined with the central composite design technique. Finally, a torque margin analysis was conducted to predict whether the deployment would be successful. This works consisted of three sections: first, we discussed the configuration design from conceptual design to detailed modeling considering deployment kinematics. Second, deployment dynamics simulations and tests were conducted. To minimize the discrepancy between the test and simulation, the friction-compensation technique was introduced, and results were verified by a torque margin analysis. Finally, we provided concluding remarks.
# Photogrammetry-Based 3D Textured Point Cloud Models Building and Rock Structure Estimation
###### Abstract
[17] established an online shared repository of photogrammetry data and light detection and ranging (LiDAR) for researchers. [PERSON] et al. [18] investigated the applicability of photogrammetry and LiDAR to the derivation of joint intensity, mean trace length, and block size. Compared with laser scanning, photogrammetry has the advantages of lower equipment cost and better portability [19; 20]. However, the process of generating point cloud models is conducted by commercial software, in most studies, after rock mass photographs are collected; thus, not every researcher has easy access to the method of digital photogrammetry. This study aims to provide researchers with a low-cost method of building a photogrammetry-based textured 3D point cloud model (FMBPM) of the exposures of rock masses and to illustrate the applicability of the method to estimating the rock structure of rock masses. The paper is organized as follows: (1) the primary geological conditions of a rock slope at the study site are introduced, a section of which is selected to display the processes and to verify the applicability of the FMBPM; (2) the processes of collecting photographs and generating textured 3D point cloud models are proposed; (3) a coordinate transformation means of converting model coordinates to physical coordinates is supplied; (4) the accuracy of the textured 3D point cloud model is verified, and trace lines of the model are extracted; (5) a joint disk model is built for estimating the rock structure of the section of the rock slope.
# Olive Oil with Ozone-Modified Properties and Its Application
## 2 Results and Discussion
### Characteristic Parameters of Oils before and after Ozonation  
The characteristics of the oil significantly change with the ozonation process. The acid and peroxide values had a statistically significant increase after ozonation (\(p\) = 0.0338, \(p\) = 0.0218 respectively; Kruskal-Wallis test) (Table 4). The increase in the ozonated oil peroxide value indicates the presence of compounds with an oxidising potential. According to the literature data [2,3] and our results, ozonation increases the peroxide value with the amount of ozone introduced to the matrix. The iodine value decreased in the oil after the ozonation process (\(p\) = 0.0218, Kruskal-Wallis test), which is the effect of a decrease in the number of double bonds due to ozonolysis, resulting in the formation of organic compounds of a different structure [2,3]. In general, in comparison to the untreated oil, deeper changes in acid, iodine, and peroxide values were observed for oil after ozonation with 0.10 mole O\({}_{3}\)/100 g (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Refined Olive Oil**} & \multicolumn{3}{c}{**Ozonated Olive Oil**} \\ \cline{3-5}  & & **0.04 Mole O\({}_{\textbf{3}}\)/100 g** & **0.10 Mole O\({}_{\textbf{3}}\)/100 g** & **\(p\) Value \({}^{\star}\)** \\ \hline \hline Hexanal & nd & 36.14 \(\pm\) 0.18 & 20.63 \(\pm\) 0.07 & 0.0241 \\ Octanal & nd & nd & 0.36 \(\pm\) 0.06 & \textgreater{}0.05 \\ Oct-1-ene & nd & nd & 0.08 \(\pm\) 0.03 & \textgreater{}0.05 \\ Non-4(\(E\))-enal & nd & 7.09 \(\pm\) 0.11 & 10.81 \(\pm\) 0.10 & 0.0429 \\ Nonanal & nd & 56.77 \(\pm\) 0.18 & 68.12 \(\pm\) 0.17 & 0.0429 \\ \hline \hline \end{tabular}  
* nd—not detected; \({}^{\star}\) Kruskal-Wallis test, followed by multiple comparison test; each value represents the mean of three individual replications \(\pm\) SD.  
\end{table}
Table 2: The composition of the surface phase of olive oil depending on the ozone concentration used.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Temperature**} & \multicolumn{3}{c}{**Density (g cm\({}^{-3}\))**} \\ \cline{2-5}  & \multirow{2}{*}{**Refined Olive Oil**} & \multicolumn{3}{c}{**Ozonated Olive Oil**} & \multirow{2}{*}{\(p\)**-Value \({}^{\star}\)**} \\ \cline{3-5}  & & **0.04 Mole O\({}_{\textbf{3}}\)/100 g** & & **0.10 Mole O\({}_{\textbf{3}}\)/100 g** \\ \hline
20 \({}^{\circ}\)C & 0.9168 \(\pm\) 0.0001 & 0.9343 \(\pm\) 0.0000 & 0.9647 \(\pm\) 0.0000 & 0.0218 \\
40 \({}^{\circ}\)C & 0.9041 \(\pm\) 0.0001 & 0.9204 \(\pm\) 0.0000 & 0.9500 \(\pm\) 0.0000 & 0.0218 \\ \hline \hline \end{tabular}  
* Kruskal–Wallis test, followed by multiple comparison test; each value represents the mean of three individual replications \(\pm\) SD.  
\end{table}
Table 3: The effect of temperature on the density of olive oils.
## 3 Experiments
### Evaluation Metrics  
The results of the proposed models were compared using four performance metrics: precision, recall, F1-score and HTER. The last one is a metric widely used in colorization recognition task. From the confusion matrix, precision (P), recall (R), F1-score and HTER are obtained as shown in Equations (2)-(5).  
\[\mathrm{P}=\mathrm{TP}/(\mathrm{TP}+\mathrm{FP}), \tag{2}\]  
\[\mathrm{R}=\mathrm{TP}/(\mathrm{TP}+\mathrm{FN}), \tag{3}\]  
\[\mathrm{F1}=2*(\mathrm{P}*\mathrm{R})/(\mathrm{P}+\mathrm{R}), \tag{4}\]  
\[\mathrm{HTER}=\frac{\mathrm{\frac{FP}{\mathrm{TN}+FP}+\mathrm{\frac{FN}{ \mathrm{TP}+FN}}}}{2}, \tag{5}\]  
In the current problem, TP corresponds to colorized images correctly classified, TN corresponds to original images correctly classified, FN corresponds to colorized images classified as original images, and FP corresponds to original images classified as colorized images. The ideal value of P, R and F1-score is 1, and their worst performance value is 0. In contrast, the ideal HTER value is 0 and the worst performance is 1. Additionally, the best model is not only the one with the highest F1-score and the lowest HTER, but also the one with a good balance between P and R.
# Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs
## 4 New Generation of Cell Wall Inhibitors
### Target: Arabinogalactan/LAM
#### 4.2.2 BTZ043  
_Discovery._ A sulfur-based chemical library was tested for antibacterial and antifungal activities. A class of compounds, the nitrobenzothiazinones, was found to have specific activity against mycobacteria. The most promising hit BTZ038 was a racemic molecule and synthesis of its \(S\) enantiomer gave BTZ043 [225].  
_Activity._ BTZ043 is lipophilic, bactericidal, and is active against drug-susceptible, MDR and XDR _M. tuberculosis_ strains, with an MIC ranging from 2 to 70 nM. In contrast to the novel TB drugs targeting the mycolic acids, BTZ043 was less effective in non-replicating conditions, suggesting that BTZ043 would have to be used in combination with other drugs to be effective against TB [225]. BTZ043 was tested with first-, second-line and in-development TB drugs and showed no antagonistic effects. BTZ043 was additive with most of the drugs tested and synergistic with BDQ [226]. The compound is as effective in a mouse model of _M. tuberculosis_ infection as INH. BTZ043 was well tolerated in rats, had low interaction with the CYP450 enzymes and showed no mutagenic or genotoxic properties [227].  
_Mode of action._ BTZ043 is a prodrug activated by DprE1 (Rv3790). DprE1 reduces the nitro group in BTZ043, yielding a nitroso metabolite. This metabolite reacts with the thiol group of a cysteine (Cys387) residue in the substrate-binding site of DprE1 to form a covalent bond that irreversibly inhibits DprE1, classifying BTZ043 as a suicide inhibitor [228]. DprE1 catalyzes the first step in the epimerisation of decapenylphosphor! ribose (DPR) to decapenylphosphor! arabinose (DPA), the arabinose donor for the synthesis of arabininogalactan and LAM. Treatment of _M. tuberculosis_ with BTZ043, therefore, results in inhibition of DPA formation and ultimately inhibition of both arabininogalactan and LAM [225].  
Resistance.Mutation at the Cys387 position of _dprE1_ is the main mechanism of resistance to BTZ043. Frequency of mutation is low (<10\({}^{-8}\)); however, the resulting mutants _dprE1_ Cys387 Ser and Cys387 Gly are highly resistant to BTZ043 (250-10,000-fold) [225].  
Clinical trial.As of December 2019, BTZ043 is in Phase 1b/2a clinical trial to assess safety, tolerability, interaction with first-line TB drugs and early bactericidal activity in newly diagnosed patients infected with uncomplicated, smear-positive, drug-susceptible TB.