esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Mobile edge computing (MEC), specifically wireless powered mobile edge computing (WP-MEC), can achieve superior real-time data analysis and intelligent processing. In WPMEC, different user nodes (UNs) harvest significantly different amounts of energy, which results in longer delays for lower-energy UNs when data are offloaded to MEC servers. This study involves quantifying the delays in energy harvesting and task offloading to edge servers in WPMEC via user cooperation. In this paper, a method for transferring the tasks that need to be offloaded to edge servers as quickly as possible is investigated. The problem is formulated as an optimization model to minimize the delay, including the time required for the energy harvesting and offloading tasks. Because the problem was non-deterministic polynomial hard (NP-hard), a delay-optimal approximation algorithm (DOPA) is proposed. Finally, with the training data generated based on the DOPA, a deep learning-based online offloading (DLOO) framework is designed for predicting the transmission power of each UN. After each UN's transmission power is obtained, the original model is converted to a linear programming problem, which substantially reduces the computational complexity of the DOPA for solving the mixed-integer linear programming problem, especially in large-scale networks. The numerical results show that compared with the non-cooperation methods for WPMEC, the proposed algorithm significantly reduces the total delay. Additionally, in the delay optimization process for a scale of six UNs, the average computation time of the DLOO is only 0.2% that of the DOPA.  
WPMEC; user cooperation; deep learning; approximation algorithm; delay optimization
# Acoustic Improvement of Stator-Rotor Interaction with Nonuniform Trailing Edge Blowing
## 4 Simulation Results  
In the current paper, we focus on the influence of NTEB on the acoustic characteristics of stator-rotor interaction. On the basis of the compressor geometry and the actual working parameters introduced in Section 2, numerical simulations were conducted to illustrate steady and unsteady conditions. We adopt the NUMECA software to obtain trends and characteristic curves in the range of the working parameters. The axial thrust of the blade surface in the calculation is analyzed in the NUMECA_Fine_Turbo, where the axial thrust is a statistical result of the static pressure of the blade surface and the friction force of the fluid.  
The turbulent working variable obeys the transport equation:  
\[\frac{\partial v}{\partial t}+\overrightarrow{V}\cdot\
abla\vec{v}=\frac{1}{ \sigma}\{\
abla\cdot[(v+(1+c_{b2})\vec{v})\
abla\vec{v}]-c_{b2}\vec{v}\Delta \vec{v}\}+Q \tag{11}\]  
where \(\overrightarrow{V}\) is the velocity vector, \(Q\) is the source term, and \(\sigma\), \(c_{b2}\) are constants.  
The source term includes a production term \(P\) and a destruction term:  
\[Q=\vec{v}P(\vec{v})-\vec{v}D(\vec{v}) \tag{12}\]  
The axial thrusts of the steady and unsteady time average are shown in Figure 5. The axial thrust in the flow path fluctuated around the time-averaged value. The maximum amplitude fluctuation with and without TEB is about 6.56 N and 2.52 N, respectively. From the two plots in Figure 5, the axial thrust of the downstream rotor with NTEB is 2.3 N lower than without NTEB.  
The rotors speed was measured to be 4472.7 rpm when the rotor speed was set at a nominal speed of 4500 rpm. Meanwhile, since the rotor number is approximated as 12 in the simulations, the first-order BPF should be 4472.7/60 \(\times\) 12 = 894.54 Hz, and some of the BPFs are illustrated in Figure 6. The axial thrust of the rotor blades with NTEB is compared with results without NTEB at the first five BPFs in Figure 6. The figure clearly reveals that the axial thrust of the downstream rotor is significantly reduced, and that the vibration amplitudes have also decreased considerably.  
The axial thrusts on the downstream rotor, with and without NTEB and at different blade-passing frequencies, are illustrated in Table 1. Since the periodic flow interaction with the rotor in rotation induces the axial thrust on the rotor blade, the unsteady loads at blade-passing frequencies can be obviously reduced with NTEB. The most significant reduction of the unsteady load resulting from NTEB is 63.83% at the first BPF.  
Figure 5: Axial thrust with and without nonuniform trailing edge blowing (NTEB).
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## Nomenclature  
CI Computational Intelligence  
CV Constant Voltage  
DC/DC Direct Current to Direct Current  
GMPP Global Maximum Power Point  
IC Incremental Conductance  
MPP Maximum Power Point  
MPT Maximum Power Point Tracking  
OV Open Voltage  
PSO Particle Swarm Optimization  
PV Photovoltaic  
P&O Perturb and Observe  
SC Short Current Pulse
## 4 Discussion
Therefore, it is not yet known whether TRP-activators or other interventions reduce discomfort during or after muscle cramps when ingested immediately following cramp onset. Muscle cramps are typically offset by the passive stretching of the muscle secondary to pain and loss of function [14]. In this study, the participants were asked to relax and to allow the cramp to subside unaided. The severity of discomfort that was reported by the participants varied greatly, so it is not possible to suggest any relationship between the severity or duration of discomfort and PJ. Through assessing PJ mouth rinsing and ingestion, this study was able to measure the effects of exclusively stimulating oropharyngeal receptors with PJ. Although the literature suggests that PJ alleviates skeletal muscle cramping by stimulating the oropharyngeal TRP-channels that trigger an inhibitory supraspinal reflex [17, 21, 32, 38], the current study does not support these findings. The authors found no statistical differences in cramp duration or perceived discomfort between PJ ingestion, PJ mouth rinsing, and water in active cramp-prone adults. Despite expanding the inclusion parameters, the authors were unable to induce cramps in eight cramp-prone participants during the familiarisation session, resulting in exclusion of these participants from the study, thereby reducing the total number of datasets collected. Furthermore, the unsecured foot position of the participants during testing may have also contributed to the variability in the data. By not adequately restraining the foot, there is the potential for adjacent muscles to cause reciprocal inhibition that affects FHB activation [39], possibly impacting the results. Improved methods for inducing and maintaining a cramp for long enough to assess the effects of interventions should be a priority for future research. Given the limitations of the study, recommendations for future research can be suggested. The aetiology of EAMC is still unknown; however, it is likely multifactorial in nature, with altered neuromuscular control [38] and hypohydration contributing to onset [34]. To better understand a mechanism that causes the immediate relief of EAMC, future studies should compare PJ with water by testing mouth rinsing and ingestion for both conditions. Furthermore, the intervention and placebo groups should be considered to enable blinding. However, an appropriate placebo for PJ will be difficult to create and may present challenges with assessment.
## 4 Experiments and Discussion
### Data Description and Preprocessing  
In this paper, we used the WM-811K dataset, which is a real-time semiconductor dataset including 811,457 wafer map images collected from 46,293 lots during the semiconductor fabrication process [3]. This is an open-source dataset that can be downloaded from the Multimedia Information Retrieval (MIR) laboratory website [25]. The dataset includes nine regular wafer map failure pattern types: Center, Donut, Local, Edge-Local, Edge-Ring, Scratch, Random, Near-Full, and None. The description of the dataset is shown in Table 2. There are a total of 172,950 labeled data in the original dataset of 811,457, with data for each failure class being highly imbalanced, especially the 'Near-Full' class, which only contains 149 images. Hence, we removed the 'Near-Full' class from our experimental data and focused on other eight wafer map failure pattern types. Figure 5 shows a typical example of eight wafer map failure types. We used random sampling to extract 500 images  
Figure 4: The configuration of the (**a**) DCNN1, (**b**) DCNN2, (**c**) DCNN3, (**d**) and DCNN4-based prediction models. Note: C denotes the convolutional layer, P the pooling layer, and FC the fully connected layer.  
for the eight classes from the original dataset. For the final experimental dataset, a total of 4000 images belonging to the eight classes were used. Then the experimental dataset was split into training data, validation data, and test data, in proportions of 60%, 20% and 20%, respectively.
## 2 Results and Discussion
### Optimization of HS-SPME Parameters  
The scent of _Chrysanthemum_ flower is derived from a complex mixture of volatile organic compounds with different polarities, volatilities, functional groups, molecular masses, and concentrations [17]. HS-SPME is an equilibrium method that must be optimized to recover volatiles efficiently and to produce high-quality results. Several organic coatings such as PDMS, DVB and CAR are alternative choices [25]. Temperature also affects the collection of volatiles; as the temperature increases, the recovery of volatile compounds improves, because heat helps to release compounds from the solid sample into the headspace and facilitates the SPME process [26]. In the present study, we obtained fiber equilibrium at room temperature. Thus the floral aroma compounds were extracted under almost \"natural\" conditions. The other important aspects of SPME sampling are fiber exposure time, amount of sample, handle graduation, and desorption temperature; all of these factors affect the binding of the analyte to the finite sites available on the fiber coating [27].  
Previous studies have shown that HS-SPME is a simple, rapid, and efficient technique for quantitative determination of the volatile compounds in _Chrysanthemum_ flowers [28], coconut water [27], _Syringa oblata_[23] and grapes [29]. However, the optimum extraction conditions vary according to the plant species. We designed an orthogonal test L\({}_{16}\) (4\({}^{4}\)) to determine the optimum extraction conditions for _Chrysanthemum_. The importance of the four factors, from most important to least, was A (sample weight) \(>\) D (desorb temperature) \(>\) B (extraction time) \(>\) C (handle scale), when peak area was used as the evaluation index (Table 1). Thus, sample weight had the greatest effect on the test results, followed by desorption temperature, extraction time, and then handle scale. We concluded that the optimal parameter combination was A\({}_{2}\)B\({}_{2}\)C\({}_{3}\)D\({}_{4}\), according to the mean values of each factor. Therefore, the conditions to achieve optimal results were as follows: sample fresh weight 2.0 g; exposure time of SPME fiber to sample headspace, 30 min at room temperature (25 \({}^{\circ}\)C); scale of fiber holder, 3 cm; fiber desorption temperature 250 \({}^{\circ}\)C.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 4 Experiments and Analysis
### Simulation Results
#### 4.2.1 Energy Consumption  
The vertical ordinate represents energy consumption and the horizontal ordinate represents time interval. The time interval changed from 1 s to 10 s and one point was taken every second. The results of energy consumption are shown in Figure 5.  
With the increment of time interval, both curves decrease in different degrees. With the increase of time interval, the transmission interval of data packets in the network becomes larger and the competition of the channel is not as intense as before. This indicates that the probability of data collision becomes small, and nodes rarely retransmit data because of data collision, so the energy consumption becomes smaller. In the backoff algorithm of the S-MAC protocol, the contention window is a fixed value, which means that it will not be adjusted adaptively with the change of the network load. On the contrary, the IS-MAC protocol is able to dynamically change the contention window according to the channel condition. When the channel is in an extraordinarily intense condition, the current contention window is increased. When the channel is not intense, the current contention window is reduced. This leads to the result that data collision is easier to happen in the S-MAC protocol, so the S-MAC protocol requires more energy to maintain packet retransmission. However, the IS-MAC protocol adopts the method of dynamically changing contention window and obviously consumes less energy. When the time interval is bigger than 4 s, the collision of data packets becomes small. The energy consumption of the nodes mainly come from transmitting data packets. The total amounts of data packets are almost the same in both protocols. Meanwhile, nodes using the IS-MAC or S-MAC protocol can successfully transmit the vast majority of data packets. Therefor, the energy consumption of the IS-MAC and S-MAC protocols is the same. It can also be concluded from the figure that the energy consumption of the IEEE 802.11 MAC protocol is high all the time. Although it has the same backoff mechanism as the S-MAC protocol, it does not follow the periodic sleep/listening mechanism and the nodes are always active. The amount of energy when the medium is idle is almost consumed as much as when the medium is busy. Therefore, large amount of energy will be consumed during the whole work.
###### Abstract  
Magnetic nanoparticles coated with zwitterionic poly(ionic liquid)s were applied for dispersive solid-phase extraction of oligonucleotides. The materials were synthesized by miniemulsion copolymerization of ionic liquids and divinylbenzene on magnetic nanoparticles. The functional monomers contain a positively charged imidazolium ring and one of the anionic groups: derivatives of acetate, malonate, or butyl sulfonate ions. Adsorption of unmodified DNA oligonucleotide on obtained materials was possible in ion-exchange (IE) and hydrophilic interactions (HI) mode. The adsorption in IE was possible at low pH and was almost complete. The adsorption in HI mode required the usage of appropriate addition of organic solvent but did not provide full adsorption. Studies on the desorption of the analytes included determining the impact of ammonium acetate concentration and pH and organic solvents addition on the recovery. The material containing acetic fragments as an anionic group was selected for the final procedure with the use of 10 mM ammonium acetate (pH = 9.5)/methanol (50/50, \(\sigma/v\)) as an elution solution. The magnetic dispersive solid-phase extraction procedure was tested for the oligonucleotides with various modifications and lengths. Moreover, it was applied to extract DNA oligonucleotide and its synthetic metabolites from enriched human plasma without any pre-purification, with recoveries greater than 80%.  
Keywords:antisense oligonucleotides; magnetic nanoparticles; poly(ionic liquid)s; magnetic dispersive solid-phase extraction; serum samples +
Footnote †: journal: Article
## 3 Materials and Methods  
### Methodology  
The system workflow is shown in Figure 4 and includes the following:  
1. Data collection from the software and hardware complex (SHC) with pH/ORP sensors [18] by indirect measurement sensor channels. At the command of the SHC controller, the self-priming pump ensures liquid renewal in the flow chamber, where it is analyzed by a set of signal sensors and measuring analyzers. The values from the sensor channels are processed by the controller and transmitted via the TCP/IP protocol to the application server for further processing. Frequency of receiving data from sensors--1 measurement per minute (1440 per day; 43,200 per month; 525,600 per year), representing the total number of measurements used in the analysis -3,679,200).
2. Saving the obtained data to the database.
3. Loading time series data for analysis. The task is to detect time series anomalies.
4. Data analysis.
5. Making decisions about automatic water sample collection for detection of the instances of exceeding the MPC. The anomalies found are indicators for subsequent laboratory chemical water analysis by direct measurements.  
Figure 3: Anomaly detection metrics.  
Automatic water sample collection.
* Analysis of water's chemical composition. Direct measurement analyzers measure chemical composition in the laboratory. The value of the tested indicator (for example, Manganese) is found and compared with the standards of the maximum permissible concentration of Manganese in water. Exceeding the MPC of contamination is recorded in the report.
* Saving the results of the completed analysis.
* Using the results for subsequent data analysis.
* Comparison of the results of indirect measurement analysis and the analysis of the water composition (direct measurement).
## 5 Comparison of CRPDC with Other Driving Circuit  
Finally, a characteristic comparison is carried out between the proposed CRPDC and one traditional driving circuit. Figure 14a depicts the schematic diagram of the traditional reverse discharging power driving circuit (RDPDC), which is made up of one optocoupler, two MOSFET components, and two diodes [19; 31; 32; 33]. For the RDPDC, while the voltage of the input PWM signal is at a high level, the proportional valve coil is driven by the Vcc power source directly. While the voltage of the input PWM signal is at a low level, the holding current of the proportional valve coil will flow through the two diodes and back to the Vcc power source [34].  
The characteristics of traditional RDPDC are simulated on the NI Multisim 14.0 software as well, as shown in Figure 14b. While 3 kHz PWM signals (Vcc: 24 V) with various duty cycles are used to drive the proposed CRPDC (energy-storage capacitance value: 20 uF) and the traditional RDPDC, the results are depicted in Figure 15. It is found that the rise time (from 10% to 90% steady-state value) of the traditional RDPDC under the 70% duty cycle is 12.90 ms. In contrast, the rise time of CRPDC under the 70% duty cycle is evaluated to be 2.58 ms, which means a faster and more stable dynamic response. For the traditional RDPDC, the steady-state error defined as the maximum deviation value from the average value of coil current (from 0.03 s to 0.04 s) is calculated as 45.3 mA, 98.6 mA, 141.0 mA, and 133.2 mA, respectively. In contrast, the steady-state error of the proposed CRPDC is evaluated to be 9.7 mA, 15.8 mA, 16.7 mA, and 12.0 mA, which means a high-accuracy current-control capability. The traditional RDPDC results in a large discharging speed of coil current, which further contributes to a poor steady-state current. In contrast, the coil current response of the proposed CRPDC is stable and fast. Therefore, the proposed CRPDC can realize the coil current with high accuracy and a fast response.  
Figure 14: The traditional RDPDC: (**a**) Schematic diagram; (**b**) Multisim model.  
Figure 15: _Cont._
## 4 Results and Analysis
### Vertical Motion  
The rate of ascent of the balloons during all the missions was almost constant, as shown in Figure 14. This is believed to be due to the fact that while the balloon rises, the air becomes increasingly rarefied and its density drops. In the same way, the balloon expands, exposing a larger cross-section. The two effects roughly compensate each other so that the rising speed is almost constant; therefore, the ascent rate depends essentially on the amount of lift initially imposed on departure. If the weather is rainy or very cloudy, the water and the ice that the payload will encounter during ascent may cause an early burst at an altitude of about 20 km instead of the nominal 30-35 km.  
Lower burst altitude means that the total distance travelled will be shorter, as happened with ISM-HAB#1 and ISM-HAB#2. If the burst happens too early, a larger portion of the balloon could remain attached to the payload, affecting the correct deployment of the parachute; in such conditions, the payload will descend more rapidly.  
Figure 14: Altitude profiles as a function of time.  
Figure 13: Interference spectrograms: (a) 2.4 GHz and (b) 862–922 MHz.  
Some difficulties in predicting the ascent rate were encountered. Specifically, as it can be seen in Figure 15, the simulator slightly overpredicted the ascent rate, imposing an initial lift mass. Having set different lifts for each mission, and having achieved different ascent rates, we were eventually able to derive a calibration relationship for ascent rate vs. lift (Figure 16), that will be implemented in a future version of the predictor.
## 1 Introduction
### The Role of VLC in 6G  
Latency performance of RF-based technology in vehicular applications is usually affected by the density of vehicles [18]. In this scenario, Visible Light Communication (VLC), which exploits LED-based lighting systems to encode information in the optical carrier through light intensity modulation schemes [19], recently raised as a prime candidate in the communication technology for the implementation of safety-critical protocols involving fast communication between vehicles [20] in Massive Machine-Type Communications (mMTC), as DSCR in urban scenario [21]. This interest was raised thanks to the large deployment of LED illumination systems in modern cars and motorbikes, and to the high directionality of the optical channel.  
However, whilst most of the initial works in the field of VLC for ITS applications focused on the implementation of unidirectional links [22; 23; 24; 25; 26; 27], also integrating VLC nd 5G technologies [28], only recently a preliminar work has been carried out on bidirectional VLC links [29]. The importance of a detailed analysis of latencies occurring in the message delivery process is highlighted in [30; 31]. Bidirectional communication between vehicles is extremely important for mission-critical services such as assisted or automated driving. Ultra-low latency is a mandatory characteristic for future smart vehicles, and VLC is a promising technology for this scope. For example, if two vehicles continuously exchange data (e.g., the speeds), they can automatically keep a safe distance, allowing a secure braking in case a critical situation occurs.  
In future 6G networks, the two technologies, RF and VLC, could coexist. An hybrid RF-VLC solution could be used to reduce the high density of RF users and to increase the range limitation of VLC technology [32]. While current 5G standard does not include VLC as an integrated technology for, e.g., vehicular communications, 6G is envisioned to include and integrate VLC as an enabling technology for killer applications, in particular in vehicular networks.