esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Integrating Machine Learning into Edge Sliced Networks
### Machine Learning Applied to Edge Computing
Mainly, they formulated an optimization problem to minimize the maximal computational and transmission delay among all users. To solve this problem, they utilized an RL algorithm. Compared to the conventional RL algorithms, their algorithm is able to record historical resource allocation schemes and users' states to avoid learning the same information, thus improving the convergence speed and the learning efficiency. Thus, they are able to reduce the delay among all users up to 18% compared to the standard Q-learning algorithms. There are also some studies that handle the ML-based edge computing systems from the point of view of security. As an example, trying to secure communications and actively detecting unknown attacks, [PERSON]. [58] proposed a combined deep and unsupervised learning model for the MEC environment. Basically, their algorithm consists of a pretraining phase that relies on unsupervised learning to detect hidden units and a fine tuning phase using deep learning to fine tune the parameters that are already trained in the pretraining phase. They compared their model with four other ML-based algorithms, where their algorithm stood out from others based on its ability to capture the nonlinear relations between attacks and corresponding features, and its multi-stacked modules that calculate the nonlinear mapping between input and output. The results showed that their model is able to improve the overall accuracy by 6%. Trying to reduce data traffic and latency for IoT devices, [PERSON]. [59] explored merging ML and edge and cloud computing. They stated that the reason they used deep learning is that it is able to transform data into hierarchical abstract representations that are beneficial for IoT data analytics. They deployed the encoder part of the trained model on the edge to reduce dimensionality, whereas the decoder was employed on the cloud to reconstruct the original signal. As a result, they showed that their method outperforms other deep learning solutions for IoT devices. In addition to that, they grouped the sensors according to location and made comparisons with non-grouped cases, showing their method can reduce network traffic up to about 80%. In short, ML assisted edge computing reduces latency as well as avoids high computing costs by processing the data at the edges rather than in centralized clouds. In addition, it also empowers new technologies such as autonomous vehicles and medical devices by enabling real-time feedback and results that are critical for these services.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 3 Methods
### Parallel Ant Colony Optimisation
#### 3.3.1 Scheduling Jobs  
Given permutation \(\pi\) of all jobs, a resource and precedence feasible solution specifying the start times for every job can be obtained efficiently [23]. This procedure is also called the serial scheduling heuristic.  
Jobs are considered in the order in which they appear in the permutation \(\pi\). A job is selected and examined to see if its preceding jobs have been completed. If so, the job is scheduled as early as possible, respecting the resource constraints. If not, the jobs are placed on a waiting list. If it is possible to schedule a job, the waiting list is examined to see if any waiting job can be scheduled. If yes, the waiting job is immediately scheduled (after its preceding job(s)) and the waiting list is re-examined. This repeats until the waiting list is empty or no other job on the waiting list can be scheduled. At this point the algorithm returns to consider the next job from \(\pi\).
# 3D DNA Nanostructures: The Nanoscale Architect
## 5 Applications
### Encapsulation  
We refer to _encapsulation_ as the concept of designing DNA nanostructures that form dense, continuous walls to produce a functional cavity. In addition, it should also be possible to selectively control the permeation of specific molecules into and out of, or through the cavity. Compartmentalization is enforced in nature by membranes or protein shells to consolidate functions and also defend sensitive operations or molecules from interruptions or damage, and it is of great scientific interest to create artificial nanostructures mimicking this capability. Technologies such as polymer or lipid vesicle technologies [89; 90; 91] fulfill this goal, but in comparison to DNA origami, neither are as programmable and addressable. On the other hand, DNA nanostructures are not continuous surface structures like a membrane is, and their parallel helices also have some fluctuation [48] that can reveal gaps in the structure. This can be improved using multiple layer DNA origami nanostructures [24; 25] such that the maximum gaps through a thick layer of helices are selectively impermeable to chosen molecules of the application. While not all applications of encapsulation are shape-dependent, DNA nanostructures are uniquely capable of modulating the size of a cavity, the position or quantity of contained molecules, and access to the space as determined by DNA-based circuit logic.
## 3 Results and Discussion
### Transmittance  
Plasma-etched samples also showed a decrease of transmittance in the 200-400 nm range, which correlated with the etching depth/time (Figure 2).  
An increased optical scatter due to a higher surface roughness by the etching sample to a 100 nm depth could not exceed \(\sim\)0.0012% at 300 nm (calculated using the values \(R_{0}\) (one surface, at 300 nm) = 0.075, \(\sigma_{1}\) = 0.73 nm, \(\sigma_{2}\) = 1.4 nm in the \(\Delta R_{d}=R_{d2}-R_{d1}=R_{0}\frac{4\pi}{\lambda^{2}}\left(\sigma_{2}^{2}- \sigma_{1}^{2}\right)\) formula according to the diffuse reflectance and surface roughness relation in [29]). The induced additional absorption in the UV range with an increased exposure time to the plasma might be another reason; however, we cannot provide a more detailed explanation for this. One might think about a likely induced contamination by the plasma source, but multiple previous experiments with a similar etching of fused-silica substrates did not show any decrease of transmittance down to 190 nm. Possible contaminants should also  
Figure 2: Transmittance of unprocessed and different-depth plasma-etched Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystals. Absorption band at \(\sim\)600 nm is due to doped Co\({}^{2+}\) ions.  
reduce the laser damage resistance; however, on the contrary, the LIDT did not decrease as shown below. The decreased UV transmission also should not have any negative impact on the Q-switching properties of the Co\({}^{2+}\):MgAl\({}_{2}\)O\({}_{4}\) crystal, as it was concluded in a study by [PERSON] et al. [30].
## 5 Conclusions  
It is well discussed in the literature that anionic PS has no lethal effect on Gramageative bacteria [24; 28]. However, our study demonstrates that high concentration and more exposure time of CHL to the red light is lethal to _P. aeruginosa_ planktonic cells and combination with PEI potentiates lower concentrations of CHL to exert the same lethal effect. In addition, the synergism between CHL and PEI is obvious against the biofilm and persister cells. Overall, this study gives new insights into the aPDI of CHL against _P. aeruginosa_.  
**Supplementary Materials:** The supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/ijms241512098/sl](https://www.mdpi.com/article/10.3390/ijms241512098/sl)]([https://www.mdpi.com/article/10.3390/ijms241512098/sl](https://www.mdpi.com/article/10.3390/ijms241512098/sl)).  
**Author Contributions:** Conceptualization, M.M., P.R. and A.B.; methodology, M.M. and P.R.; validation, M.M. and P.R.; investigation, M.M.; resources, A.B. and M.L.; writing--original draft preparation, M.M.; writing--review and editing, M.M, A.B., M.L. and P.R.; supervision, A.B.; project administration, A.B. and M.L.; funding acquisition, A.B. and M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by German Egyptian Long-term Scholarship (GERLS), 2019 (57460069) funded by the Ministry of Higher Education and Scientific Research of the Arab Republic of Egypt and the German Academic Exchange Service (DAAD). This study was partially funded by the German Space Agency (DLR, Deutsches Zentrum fur Luft- und Raumfahrt), BMWK projects 50 WB1923 (M.L.).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding authors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene** & **Sequence (5’->3’)** & **Primer Size (b)** & **Product Size (bp)** & **Reference** \\ \hline _relA_ & GAGATCCCTGCTGCTAC & 20 & 171 & This study \\  & CATAGGCACGATCCGATA & 20 & & \\ _spoT_ & CGACAAGGTCGATACCTGCT & 20 & 195 & This study \\  & TIGGCCATCTCTTCCATTC & 20 & & \\ _lon_ & CCCGTGGCGTCCTACATA & 20 & 138 & This study \\  & GAATGCGCCTCTTGACCTCT & 20 & & \\ _rpoS_ & CTCCCCGGCCAACTCCAAAAG & 21 & 200 & [60; 61] \\  & CGATCATCGCTCTCCGACCAG & 21 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primers used in this study.  
**Acknowledgments:** [PERSON] received a German Egyptian Long-term Scholarship (GERLS), 2019 (57460069), funded by the Ministry of Higher Education and Scientific Research of the Arab Republic of Egypt and the German Academic Exchange Service (DAAD). This study was partially funded by the German Space Agency (DLR, Deutsches Zentrum fur Luft- und Raumfahrt), BMWK projects 50 WB1923 (M.L.). The authors are grateful to [PERSON] and [PERSON] for their help with the R program.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Effect of the Light Intensity on the Growth of Scenedesmus dimorphous  
In these experiments, the cultures C4, C5, and C6 with intensities of light of 27, 60.75, and 76.27 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) respectively were evaluated. These intensities were used with a constant light/dark cycle of 14/10 h. In the culture C4 at a light intensity of 27 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\), it was observed that there was a low growth of biomass, reaching a maximum biomass value of 1.55 g\(\cdot\)L\({}^{-1}\) (Table 4). It was also noted that the exponential growth phase was low and extended until 12 days of cultivation (Figure 7). The Productivity and _Fc_ were 0.09 g\({}_{\text{ccl}}\cdot\)L\({}^{-1}\cdot\)d\({}^{-1}\) and 0.23 g\({}_{\text{CO2}}\cdot\)L\({}^{-1}\cdot\)d\({}^{-1}\), respectively (Table 4). This behavior was probably due to the low intensity of light to which the microalga was exposed, so that, the growth was photo-limited [48].  
\begin{table}
\begin{tabular}{c c c} \hline
**Airlift Reactor** & _KLa_ **(h\({}^{-1}\))** & **Ref.** \\ \hline External loop (inclined) & 9 to 26 & [42] \\ External loop & 14.4 & [43] \\ Flat panel & 31.27 & [44] \\ Concentric tube & 28.8 & [45] \\ Concentric tube & 1.54 to 3.71 & Present work \\ Flat panel & 1.9 & [46] \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of volumetric mass coefficients among different Airlift configurations.  
Figure 7: Effect of the light intensity on growth of _S. dimorphus_.  
In the culture C5 at a light intensity of 60.75 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\), the microalga had a better growth compared to the culture carried out at 27 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\), reaching a maximum biomass value of 1.85 g\(\cdot\)L\({}^{-1}\) (Table 4). Besides, the exponential phase was reduced to 9 days (Figure 7), the Productivity and _Fc_ were 0.16 g cel\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\) and 0.28 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\), respectively (Table 4). In the culture C6, the light intensity was increased to 76.27 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\), and _S. dimorphus_ reached a biomass of 3.25 g\(\cdot\)L\({}^{-1}\); the productivity and _Fc_ were 0.25 g cel\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\) and 0.46 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\), respectively. [PERSON] et al., 2013, 22 cultured _S. dimorphus_ using a simulated fuse gas that had 15% (_v/v_) of CO\({}_{2}\) with a light intensity of 100 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\). They obtained a maximum biomass of 2.77 g\(\cdot\)L\({}^{-1}\); the Productivity and _Fc_ were 0.362 g cel\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\) and 0.664 g CO\({}_{2}\)\(\cdot\)L\({}^{-1}\)\(\cdot\)d\({}^{-1}\), respectively.
## 2 Materials and Methods  
### Bioprocess  
The bioprocess for this study was gold bioleaching operated in two stages: in the first stage, submerged fermentation was carried out to produce the acidic fermented broth (bio-leaching substance) from _A. niger_, where the variables evaluated were controlled; in the second stage, the gold bioleaching process was undergone using the broth fermented from the first stage, to evaluate the response variable, as shown in Figure 1.  
For the fermentation stage, the operational conditions were evaluated using a \(2^{5-2}\) fractional factorial design. The independent variables were carbon source (C), nitrogen (N), potassium (K), the addition trace elements (T), and agitation (A), in terms of mechanical or aeration mixing, as shown in Table 1. The response variables in this stage were: pH (Mettler-Toledo pH meter), presence of protein by Biuret methodology [19], and organic acids quantification (citric, oxalic, malic, and acetic acids) by high-performance liquid chromatography in an HPLC (Shimadzu Corporation, USA) equipped with a UV/VIS diode array detector--DAD at 210 nm.  
Figure 1: Bioprocess system.  
The fermentation experiments were carried out in duplicate in 300 mL Erlenmeyer flasks, inoculated with 10% of a fungal spore solution of _A. niger_, and aerated with lab scale compressors. The fermentation time was 10 days. Subsequently, samples were filtered to obtain the fermented broth.  
After obtaining the fermented broth, the bioleaching process was carried out using a gold mineral from the mine \"La Victoria\" in Girardota (Antioquia). The particle diameter was 75 \(\upmu\)m, and samples were put in contact with each of the fermented broth resulting from the design of experiments using a ratio of 80g of mineral per 170 mL of filtered fermented broth, for 6 h, with constant stirring at 150 rpm.  
The response variable to determine the action of the fermented broth on the mineral was estimated with the infrared spectroscopy technique (FTIR, Shimadzu), obtaining the value of the absorbance of the infrared spectrum at wavenumbers between 400 to 4000 cm\({}^{-1}\).
## 2 Materials and Methods
### Methods
#### 2.3.2 A Brief Overview of PRYM-Maize  
A process-based RS crop yield model, PRYM-Maize, which was developed in our previous study and running on a daily step, was used to simulate maize yield in conjunction with MODIS data in this study [35]. PRYM-Maize consists of two basic modules: water balance and productivity modules. The water balance module (WBM) follows the RS and water balance-based Penman-Monteith model version 2 (RS-WBPM2) [38], which was modified from [PERSON], et al. [40], to simulate water stress based on MODIS VIs and meteorological data. The productivity module consists of multiple sub-processes, photosynthesis, grain conversion, and respirations. The ecosystem-level photosynthesis rate was scaled from the leaf-level value based on a two-leaf canopy model and the MODIS-based LAI (Equation (1))). A full description of the model can be found in [PERSON], [PERSON], [PERSON] and [PERSON] [35] or Supplementary Text S1.
## 3 Airborne Segment
### Spectral Efficiency Improvement
Specifically, an HD system characterized by a separate resource (frequency band or time), highlighted by different arrow colors, between the backhaul link (black arrows) and the access link (green arrows) is shown in Figure 5. Conversely, Figure 5 emphasizes an FD scenario where both the backhaul link and the access link share the same frequency band or time resource. In the case of an FD scenario (Figure 5), it can be seen that the two interference topologies due to the collaboration among different devices should be accurately addressed in order to reduce the performance degradation of the overall system, precisely, the interference within the same transceiver, also known as Self-Interference (SI) as well as the interference coming from neighboring users, identified as access or backhaul interference. FD communications can be successfully implemented if each FD device is capable of guaranteeing a sufficient SI cancellation (SIC), namely a satisfactory transmitted signal attenuation below a certain threshold in order that it does not create a problem for its receiver. An extensive overview about hardware and software SIC is reported in [128]. Concerning the interference coming from the simultaneous communications of other users, it can be accurately reduced by minimizing the radiation pattern lateral lobes in the direction of other users through advanced beamforming techniques [12; 13; 14]. Although FD wireless commun  
Figure 5: Example of (**a**) Half-Duplex (HD) and (**b**) Full-Duplex (FD) wireless communication through ground and airborne segment. nication has attracted much attention in UAV-assisted wireless communication [130; 131; 132; 133], recently, Hybrid-Duplex (HBD) communication has triggered enormous interest [134; 135; 136; 137; 138]. It consists of a wireless network where both FD and HD devices are involved, as depicted in Figure 6. More in detail, in Figure 6, FD technology is implemented only at the ground segment base stations (FD-GS), whereas the airborne segment operates in HD mode (HD-AS). A separate resource (time or frequency) is dedicated for the uplink and downlink signal related to the UAVs (highlighted by different arrows color), whereas for the ground segment BS, they share the same resource. This choice seems to be plausible since the SIC turns out to be easier to tackle at ground level rather than at an airborne one as well as from the energy point of view.
## 3 Materials and Methods
### Methodology
#### 3.3.1 Vegetation Indices  
The detection of crop species was performed based on 12 vegetation indices calculated from 14 Sentinel-2 images. Calculations were carried out in SNAP (version.8.0.2) and involved resampling in Band 2 (B2) with a 10 m resolution. Vegetation indices were calculated for multitemporal images between 30 May 2020 and 24 September 2020. PCA was carried out for all 12 spectral indices. Table 1 shows an overview of the used indices and a short description of each index used.
## 2 Problem Formulations
### System Description
The state space form of the dual arms is presented as follows:  
\[\dot{\mathbf{x}}_{i1}=\mathbf{x}_{i2} \tag{2}\] \[\dot{\mathbf{x}}_{i2}=M_{i}^{-1}(\mathbf{x}_{i1})(\mathbf{\tau}_{ i}-\mathbf{C}_{i}(\mathbf{x}_{i1},\mathbf{x}_{i2})\mathbf{x}_{i2}-\mathbf{G}( \mathbf{x}_{i1})-\dot{\mathbf{d}}_{i})\]  
Let is define the position tracking error as follows:  
\[\mathbf{e}=\mathbf{x}-\mathbf{x}_{d}\in R^{6\times 1} \tag{3}\]  
where \(\mathbf{x}=\big{[}\mathbf{x}_{11}^{T},\mathbf{x}_{21}^{T}\big{]}^{T}\in R^{6 \times 1}\) and \(\mathbf{x}_{d}=\big{[}\mathbf{x}_{11d}^{T},\mathbf{x}_{21d}^{T}\big{]}^{T}\in R ^{6\times 1}\) present a position vector and the desired position vector, respectively. Figure 1: A dual 3-DOF manipulator. The position synchronization between two robotic arms: synchronization errors are evaluated by the synchronous errors, \(\mathbf{e}_{is}(i=1,2)\), which are defined as  
\[ \begin{array}{l}\mathbf{e}_{1s}=\mathbf{e}_{1}-\mathbf{e}_{2}\in R^{3\times 1 }\\ \mathbf{e}_{2s}=\mathbf{e}_{2}-\mathbf{e}_{1}\in R^{3\times 1}\end{array}  \tag{4}\]  
The relationship between the tracking error and synchronous error in (4) can be rewritten as  
\[\mathbf{e}_{s}=\mathbf{Te} \tag{5}\]  
where \(\mathbf{T}= \begin{bmatrix}\mathbf{I}_{3\times 3}&-\mathbf{I}_{3\times 3}\\ -\mathbf{I}_{3\times 3}&\mathbf{I}_{3\times 3}\end{bmatrix} \) is the cross-coupling matrix, \(\mathbf{e}_{s}= \begin{bmatrix}\mathbf{e}_{1s}^{T},\mathbf{e}_{2s}^{T}\end{bmatrix}  ^{T}\in R^{6\times 1}\) is the synchronization error vector, and \(I\in R^{3\times 3}\) is an identity matrix. A cross-coupling error is defined in (6) to guarantee that both the position synchronization between two robotic arms and the tracking objective in the proposed control are obtained. \[\mathbf{e}_{c}=\mathbf{e}+\boldsymbol{\alpha}\mathbf{e}_{s}=(\mathbf{I}+ \boldsymbol{\alpha}\mathbf{T})\mathbf{e}\in R^{6\times 1} \tag{6}\]  
where \(\boldsymbol{\alpha}=diag[\alpha_{1},\ldots,\alpha_{6}]\in R^{6\times 6}\) is a positive-definite diagonal matrix. When \(\boldsymbol{\alpha}\) is chosen properly, \((\mathbf{I}+\boldsymbol{\alpha}\mathbf{T})\) can become positive definite with full rank. As a result, when the cross-coupling error, \(\mathbf{e}_{c}\), is driven to zero, the synchronization error, \(\mathbf{e}_{s}\), and the tracking error, \(\mathbf{e}\), will reach zero instantaneously. Then, the control goals will be achieved.
## 2 Materials and Methods
### Testing Procedures
#### 2.4.3. Y-Shaped Reactive Agility (Y-SRA) Test  
A diagram illustrating the setup of the agility protocol is shown in Figure 3. The participants initiated all the sprints from a starting position located 30 cm behind the first timing gate. Sprint times were measured using photoelectric timing gates (Microgate, Bolzano, Italy). The timing equipment in the middle of the course was positioned outside foam barriers, spaced 1 m apart (Figure 1). Exit gates were placed at the left and right ends of the test course, with the center of each gate visually aligned, perpendicular to the intended running direction. Sprint times were recorded telemetrically, and all the data were transmitted to a personal digital assistant for analysis. The timing gate system determined the direction the participants would take after completing the initial 5 m sprint. Upon breaking the beam at the midpoint of the course, a light on either the left or right exit gate would flash, signaling the direction to proceed. The participants were required to react to this stimulus and sprint as quickly as possible through the illuminated gate (Figure 3). The delay between breaking the beam at the midpoint and the light stimulus appearing was approximately 40 to 45 milliseconds. To prevent participants from anticipating the exit direction, they were explicitly instructed not to predict the gate. The investigator ensured compliance by visually monitoring their technique and comparing their reactive performance times to their planned sprint times [23].  
Figure 2: Illustrated movement patterns for the Modified Agility T-Test (MAT). The arrows represent the athlete’s movement directions throughout the test, including forward sprinting, lateral shuffling, and backward running along the T-shaped path.