esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Purification of Antioxidant Peptides from MGCH
#### 2.2.2. Anion-Exchange Chromatography of MGCH-I  
Ion exchange chromatography is the most popular method for purification of ions and polar molecules, such as proteins, peptides, and other charged biomolecules [27]. Separation using ion exchange chromatography depends upon the reversible adsorption of charged solute molecules to immobilize ion exchange groups of opposite charge, and the adsorption capacity was positively correlated with the charge number of isolated molecules. As shown in Figure 1A, four fractions (Frac.1 to Frac.4) were separated from MGCH-I using a DEAE-52 cellulose column. Amongst them, Frac.1 was eluted using deionized water, Frac.2 and Frac.3 were eluted using 0.1 M NaCl, and Frac.4 was eluted using 0.5 M NaCl. DPPH\(\bullet\) and HO\(\bullet\) scavenging activities of four prepared fractions were showed in Figure 1B, and the result indicated that Frac.4 has stronger DPPH\(\bullet\) (72.2% \(\pm\) 1.8%) and HO\(\bullet\) (81.4% \(\pm\) 3.2%) scavenging abilities than MGCH-I and other subfractions at the concentration of 15 mg protein/mL.  
DEAE-52 cellulose resin is based on the diethylaminoethyl tertiary amine functional group; therefore it can interact with the molecules with negative charges and is widely used to separate proteins and peptides. Furthermore, peptides with acidic and/or hydrophobic amino acid residues such as glutamic acid (Glu), tyrosine (Tyr), methionine (Met), and leucine (Leu) are considered to have strong antioxidant activities [18; 24]. Therefore, anion exchange resins including DEAE-52 cellulose and Q Sepharose FF have been used to isolate antioxidant peptides from protein hydrolysates [8; 28; 29]. The results indicated that the highest antioxidant activity of the peptides obtained in Frac.4 might be due to the acidic amino acid residues in their peptide sequences.
## 3 Results
### Automated Immunofluorescence Image Evaluation  
For the purpose of generating evaluation and comparison of the effect by xylene and isopropanol in a more objective and quantified manner minimizing risks of human-associated bias, automated immunofluorescence image evaluation on Lycopersicon esculent lectin (LEA) DyLight488 and 4',6-diamidino-2-phenylindole (DAPI) stained slides were consequently conducted. Type I alveolar epithelial cells were depicted with green color (produced by LEA) (Figure 3a). Gross assessments in accordance with protocols previously described identified no significant difference between slides undergoing the two methods in terms of alveolar circularity and alveolar wall thickness (% of total alveolar surface area), suggesting statistically identical gross histological morphology structure features in fluorescent staining regardless of whether xylene or isopropanol was used as the clearing reagent during tissue processing (Figure 3b, \(p\) = 0.916; Figure 3c, \(p\) = 0.535). An increment in the density of lectin in recipient lung tissue could be clearly observed no matter whether processed via xylene or isopropanol (Figure 3d,e). Statistically speaking, a significant elevation in lectin intensity was identified in the recipient tissues regardless of tissue processing reagents applied (Figure 3h, \(p\) = 0.0015, Figure 3i, \(p\) < 0.0001), and such significance was seemingly maintained even when tissues processed via the two methods were combined to form pooled donor and recipient groups for comparison (Figure 3j, \(p\) < 0.0001). Moreover, no significant differences were identified regarding lectin intensity between donor and recipient slides processed via xylene and isopropanol (Figure 3f, \(p\) = 0.744; Figure 3g, \(p\) = 0.780), providing favorable evidence supporting a comparable presentation power for image readouts yielded by xylene and isopropanol.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Xylene** & **Isopropanol** & **Z-Value** & \(p\)**Value** \\ \hline Quality of tissue & 1.94 \(\pm\) 0.83 & 1.89 \(\pm\) 0.89 & 0.213 & 0.832 \\ \hline Quality of staining & 2.44 \(\pm\) 0.33 & 2.39 \(\pm\) 0.37 & 0.086 & 0.931 \\ \hline Tissue architecture & 2.06 \(\pm\) 0.80 & 1.83 \(\pm\) 0.92 & 1.109 & 0.268 \\ \hline Clarity of nucleus-cytoplasmic & 1.92 \(\pm\) 0.54 & 2.03 \(\pm\) 0.58 & 0.711 & 0.477 \\ \hline Vascular tissue & 1.33 \(\pm\) 0.86 & 1.14 \(\pm\) 1.11 & 0.841 & 0.400 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of the scoring results of H&E staining characteristic in lung tissue sections processed via xylene and isopropanol methods.  
Figure 2: Scoring results of H&E staining characteristics in lung tissue sections processed with xylene and isopropanol. Histological analysis of H&E staining was performed on tissue sections obtained from donor and recipient lung biopsies. The scoring results were assessed based on the following criteria: overall quality of tissue (**a**); quality of staining (**b**); tissue architecture (**c**); clarity of nucleus–cytoplasmic differentiation/integrity (**d**); vascular tissue (**e**). \(n\) = 12. ns: nonsignificant (\(p\) > 0.05).
## 4 The Sixth Generation of Cellular Communications  
In terms of communications, 5G comprises a modification of a paradigm, focusing on the initial requirements of the Fourth Industrial Revolution and its implications in organizations and society. Nevertheless, new services and requirements continue to develop and, consequently, the demands from communications are always increasing [35; 36; 37]. The future digital society, in the scope of increasing  
Figure 7: Results for two NOMA users with powers of (0.5, 1), with 4 \(\times\) 32 MIMO.  
automation, namely the digital society of 2030 and beyond, comprises more and more connected devices (IoT), including sensors, vehicles, aerial drones, and data. While 5G supports autonomous vehicles, the increasing number of sensors per vehicle requires higher speeds of communications and lower latencies. Society and organizations demand new services to be included in 6G, such as (see Figure 8) the following:  
* Augmented reality (AR) and extended reality (XR);
* Artificial intelligence (AI)-infused applications;
* Wireless brain-computer interactions (BCI);
* Holographic services;
* The integration of communications with localization, mapping, and remote control;
* Emerging eHealth applications;
* Improved autonomous vehicles;
* More efficient support of IoT, namely smart cities and smart houses, supporting an extremely high number of low-power devices;
* Support of flying vehicles and increased mobility speed.  
In addition, 6G aims to have higher energy efficiency and more efficient strategies of energy-harvesting, so that the autonomy of user equipment can be increased, despite its demanding applications.  
These new services and capabilities to be supported by 6G continue to require more efficient networks with increased data rates, lower latencies, more efficient spectral efficiencies, increased energy efficiencies, and improved network capacities. Some of the foreseen requirements for 6G include:  
* Nomadic peak data rates of at least 1 Tbps (100 times higher than 5G);
* Mobile data rates of 1 Gbps (10 times higher than 5G);
* An energy efficiency 10 to 100 times better than 5G;
* A spectral efficiency 5 to 10 times better than 5G.  
While 5G requirements are achieved based on mm-wave and m-MIMO techniques, 6G must incorporate new concepts and frequency bands not yet considered for cellular communications.  
Figure 8: Evolution of cellular generations.  
This includes visible light communications (VLC) and terahertz bands (100 GHz-10 THz) [38], enabling data rates in the order of hundreds of Gbps. VLC is a mature communication technique well suited for short-range coverage, though it is susceptible to interference, such as that from the sun. On the other hand, flying vehicles, such as drones, alongside a communications paradigm based on heterogenous networks (conventional cells, vehicle-to-everything communications (V2X), IoT, drones, balloons, satellites, etc.), will require a three-dimensional (3D) network architecture with 3D coverage, instead of two-dimensional, as considered by 5G. Mobility speeds of up to 1000 km/h are also expected to be a requirement of 6G.
## 2 Literature Review
### Hybrid Flow Shop Lot Streaming (HFS-LS)  
Early and some recent publications in HFS-LS (e.g., [15; 16; 17; 18; 19; 20]) are limited to a special case where there are only two stages. To the best of our knowledge, the first major research effort in lot streaming in the general hybrid flexible flow shop with more than two stages was reported in [21]. The authors developed a parallel genetic algorithm with makespan criterion, sequence-dependent setup time, and machine release date. The authors also demonstrated that lot streaming could bring greater makespan reduction in hybrid flows shop than in pure flow shop as the former allows the overlapping of operations not only across stages but also within the parallel machines of a given stage. [PERSON] et al. [22] developed a genetic algorithm for HFS-LS in the presence of work-shift constraint. The authors assumed that the processing of a sublot cannot be started if the remaining time of the work-shift does not allow the sublot to be completed, in which case, the sublot has to wait for the next work-shift.  
Techniques based on migrant birds optimization were developed in [23; 24] to minimize the total flow time and makespan, respectively. [PERSON] et al. [25] proposed a genetic algorithm to minimize the makespan and energy utilization. The minimization of energy utilization is achieved via machine selection, where each stage may have unrelated parallel machines with different power consumption and processing speed. Energy-aware multi-objective HFS-LS was presented in [26] to minimize the average sojourn time, energy consumption, earliness, and tardiness. [PERSON] et al. [27] developed an evolutionary algorithm (with consistent sublots) to minimize the makespan and number of sublots in the presence of setup and transportation.
## 5 Validation of the Proposed Method
### The Improvement Effect of BOA-GNG  
In this section, we demonstrate the detailed effect of improvement for BOA-GNG. As described in Section 3.3, BOA-GNG adopts the adaptive learning rate of neurons, which is related to the Euclidean distance of the input data and the winner neuron, the maximum Euclidean distance between the winner neuron and its neighbors, and the winning times of each neuron. Figure 3a indicates the changing curve of the learning rate and neuron winning times of one neuron on the Payload dataset. Although the adaptive learning rate fluctuates due to the calculated Euclidean distance of the input data and winning neuron during the learning process, we can see that the improved learning rate adjustment strategy can obtain a faster convergence speed.  
Figure 2: The current curve before and after fault.  
Here, we discuss the detailed hyperparameter optimization of BOA-GNG based on the Payload dataset. The hyperparameters of the proposed method described in line 1 of Table 3 are the maximum edge's age \(a_{max}\), the maximum of the neurons \(N_{m}\), and the threshold of isolated neuron deletion \(N_{w}\). The parameter set \(p=(a_{max},N_{m},N_{w})\), and the objective function is defined in Equation (5). We selected samples from the training set at a ratio of 4:1 as the validation set, randomly selected 320 sets of \(p\), and then calculated the value of the objective function. In order to determine the appropriate search scope of the parameter set \(p\), the control variable method was used to analyze the relations of optimization loss and hyperparameters. As shown in Figure 3b, as the value of \(a_{max}\) is greater than 120, the value of the objective function decreases. If the value of \(N_{m}\) is greater than 260, the value of the objective function remains almost unchanged. The value of the objective function decreases when the value of \(N_{w}\) is greater than 150. Therefore, initialize the search scope of the parameter set \(p\) is as follows:  
\[p=\{a_{max}\in[20,120];N_{m}\in[20,260];N_{w}\in[20,150]\} \tag{12}\]  
On the basis of the range setting of hyperparameters, the Bayesian optimization steps in Algorithm 3 are conducted to search the optimal parameter setting of BOA-GNG, and the best parameter combination \(p\) is obtained, where \(p=(32,160,88)\). Figure 4 shows the iteration loss of BOA-GNG under different hyperparameter settings via grid search and Bayesian optimization, which demonstrates that the latter can obtain a better convergence value with a faster convergence speed.  
Figure 4: The iteration loss of Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG) under different hyperparameter settings.  
Figure 3: (**a**) The changing curve of learning rate and neuron winning times. (**b**) Value range setting of Bayes-Optimized Adaptive Growing Neural Gas (BOA-GNG) hyperparameters.
## 2 Materials and Methods  
### System Architecture  
This study uses a HoloLens2 mixed-reality device, a server, a Kinova Gen3 robot arm, a depth camera, and an RGB camera. As shown in Figure 1, the HoloLens2 is used to provide virtual scenes to users after receiving object information from the server. The server is implemented on a notebook computer. The server is used to connect the other devices and send the messages that the other devices need. The Kinova Gen3 robot arm is a 6 DoF robot arm and is used as the execution module to pick up objects. The robot arm receives the target object and target position from the server. The depth camera is the Intel RealSense D415, and the RGB camera is the built-in RGB camera of the RealSense D415. Both cameras are used to acquire images of the work scene.  
Figure 1: Interaction between the hardware models.  
Figure 2 is a flowchart of the method proposed in this paper. The first step of the method is connecting the robot arm to the server and connecting the server to the HoloLens2. Subsequently, the RGB and depth cameras capture RGB and depth images of the objects in the robot arm's workspace. Then, object recognition and localization are performed. In this study, cubes of different colors were used as objects because they were easy to recognize and grasp. Color recognition was employed for object identification. This process uses the RGB image and the depth image to obtain the category of objects and the coordinates of the objects in the coordinate system of the robot arm's base. After object recognition and localization, the category and the position of the objects are sent to HoloLens2 via communication between the Robot Operating System (ROS) and HoloLens2, and a virtual scene is generated. In the virtual scene, users move the digital twins of objects using gesture commands, and HoloLens2 sends the task information to the server. When the server receives the user's task, the server autonomously controls the robot arm to complete the tasks.
## 5 Experiments
### Experimental Testbed  
An experimental testbed was built to assess the performance of the proposed OEC system. The elements of such a testbed were two nodes built on a Raspberry Pi 3B single-board computer (SBC) with a Nordic nRF52840 development kit [44] linked via serial port to enable Bluetooth Mesh functionality. Figure 10 depicts one of such nodes. After the developed software was flashed onto the nodes, the Nordic nRF Mesh app for Android was used to provision and configure the nodes. Figure 11 shows two screenshots of nodes provisioned through the app. Two nodes that have already been provisioned are shown on the left (\"0\(\times\)0027\" and \"0\(\times\)0023\"), and the configuration of one of them, on the right, where a key has been linked and the publish and subscribe settings have been set up. This experiment was carried out by creating the group \"0\(\times\)C000\", where each node is configured to both publish and subscribe, enabling only the nodes that are subscribed to that group to view the content that they publish.  
The Bluetooth Mesh provisioning controller handles provisioning for Nordic development kits. It supports four alternative out-of-band (OOB) authentication methods and uses the Hardware Information driver to generate a deterministic UUID that identifies the device.  
Figure 10: Components of a testbed IoT node.
## 3 Proposed System
### IoT-Fog Computing Supported Multi-Node (FCMN) Architecture  
The proposed system could be very effective in the current outbreak of COVID-19 and also useful in monitoring health on a regional basis, during epidemic disease outbreaks or at a patient level. Almost, the whole world has been in long lockdown conditions to control the spread of the deadly COVID-19 virus. However, it is essential that after the end of the lockdowns, the COVID-19 patients or unknown spreaders are identified, properly tracked and monitored. The multi-node architecture of the proposed system could be able to perform the initial screening at various locations, and store the social facial image and GPS location of the particular subject. The FCMN architecture is the combination of connected 1-to-N FCSN nodes located at various locations.  
The conceptual workflow of the FCMN architecture of the proposed system is presented in Figure 5. The initial screening data, such as body temperature, facial image and GPS location is encrypted and transmitted by individual FCSN nodes in real-time to an internet-connected cloud database server. The remote monitoring system for the assigned health-worker or government authorities could also be implemented for real time monitoring of health record trends of particular geographical regions, epidemic diseases or particular patients. The travel track of a specific subject or patient could also be monitored through the facial image match algorithm and the health data of the patient together with GPS coordinates could be stored in a cloud database with the date and time of the data records, while maintaining appropriate privacy protection of the data. The hardware connectivity and detailed architecture of FCMN is presented in Figure 6. In the present work, the student and research access version of the software named \"Tableau Software\" [33] was utilized.  
Figure 4: Hardware architecture of fog computing supported single node (FCSN).
## 5 Conclusions  
This review summarized the studies that assessed FTIRs performance on the measurement of vehicle exhaust emissions. The mean differences compared to regulated or other methods were around \(\pm\)2.5% for CO\({}_{2}\), CO, NOx, and NH\({}_{3}\) with a variability (one standard deviation) of 5% for CO\({}_{2}\) and 10% for CO, NOx, and NH\({}_{3}\). For CH\({}_{4}\), acetaldehyde, and formaldehyde, the mean differences were \(\pm\)10% (variability 10-20%), but for total hydrocarbons, much higher differences were noticed. The differences were similar regardless of the sampling location of the FTIR (dilution tunnel or tailpipe). Assessment of prototype portable FTIRs on the road confirmed these findings also on-board, but for a narrow range of environmental and driving conditions. Based on these results, FTIRs may be an alternative for on-road testing. However, more studies with commercial portable systems are necessary to cover a wider range of environmental and driving conditions. The introduction of FTIRs in the regulation will require strict technical and performance requirements and procedures based on recently developed standards.  
Conceptualization, B.G.; formal analysis, B.G.; writing--original draft preparation, B.G.; writing--review and editing, M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data are summarized in the tables.  
The authors declare no conflict of interest. The opinions expressed in this manuscript are those of the authors and should in no way be considered to represent an official opinion of the European Commission. Mention of trade names or commercial products does not constitute endorsement or recommendation by the European Commission or the authors.
_Applied_  
_sciences_  
Wire Electrochemical Machining with Pulsating Radial Electrolyte Supply and Preparation of Its Tube Electrode with Micro-Holes  
**[PERSON], [PERSON] \({}^{*}\), [PERSON] and [PERSON]**  
National Key Laboratory of Science and Technology on Helicopter Transmission, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; [EMAIL_ADDRESS] (C.X.);  
[EMAIL_ADDRESS] (Z.H.); [EMAIL_ADDRESS] (D.Z.)  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
Received: 6 December 2019; Accepted: 22 December 2019; Published: 2 January 2020  
**Featured Application: Low-cost manufacture of turbine slots with well surface integrity.**  
**Abstract:** Wire electrochemical machining (WECM) has great advantages and potential for fabricating parts with ruled surfaces made of difficult-to-machine materials. Characterized by a relatively short flow path, a pulsating radial electrolyte supply in WECM is proposed to improve the machining capability for thick workpieces. The tool is a tube electrode with a line of micro-holes on cylindrical surface. This paper introduces research into the processing of micro-holes in the tube electrode using a rotating helical electrode. The quantitative relationship among the feed rate, the applied voltage, and the diameter of the outlet holes was determined experimentally. A tube electrode with holes of varying diameters was fabricated by adjusting the applied voltage. Using it as a tool electrode, kerfs with a length of 10 mm and an averaged width of 0.903 mm were machined at a feed rate of 6 \(\upmu\)m/s in a 30 mm-thick block, and there was no short circuit during processing. It was shown experimentally that using a tube electrode with holes of varying diameters as a tool electrode provides better process capacity for pulsating radial electrolyte supply in WECM.  
**Keywords:** wire electrochemical machining; pulsating radial electrolyte supply; tube electrode; micro-hole