esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Lipid Membrane-Based Biosensing Platforms
## 6 Challenges and Advances  
Regardless of the scope of research, lipid membrane platforms have become indispensable tools in biosensing and cell studies. Electrochemistry combined with photonics will most certainly provide an optimal for guiding lipid formation and monitoring membrane interactions [10]. Many challenges lie ahead, mostly referring to the minimization of matrix effects, membrane reproducibility, and sensor reliability [64].  
Micro- and nanofabrication technology are expected to bring membrane biosensors in the market [92]. A critical issue that arises is the addition of a competitive advantage that could ensure a successful commercialization. Standardization of procedures will be a serious step towards the diffusion of membrane technology [93]. These many forms and formats of the bilayer should have a comparable basis that would easily match one membrane system with one application to facilitate technology transfer. Fabrication, validation, and long-term stability protocols should be developed, along with operation and storage specifications, energy demands, and autonomy capabilities.  
The development of lipid-based platforms into nature-mimicking gustatory or olfactory arrays can rely on current chip integration techniques, but the extremely low detection limits required can be only achieved with ligand-gated ion channels, provided that technology advances in controlling protein orientation will become available in the near future [62]. Synthetic or engineered channels, with built-in guidance systems has been recently proposed [65] and might prove to be quite useful for mass production.  
The possibility to construct interdigitated interfaces is expected to open the way for neuronal adhesion [94], with tremendous opportunities for 3D multi-microelectrode platforms [95]. Competitive transduction technologies and systems should become smaller and portable without compromising resolution and accuracy. Further, in situ imaging analysis should become available for mapping the transducer surface in order to measure the concentration and distribution of bioelements on the bilayer [96].  
The biosensing concept of the bilayer lies on the interaction of the target analyte with a biological element or system. This pairing might not always be known or attainable; this is true for many environmental pollutants such as pesticides, herbicides, and insecticides [1; 12]. More insight is required on the interaction of these species with the lipid membranes in order to devise bioelement-free sensing strategies. In order to grasp, in full, membrane functionality, the freely-suspended original platforms have been revisited and partly re-engineered with cushioned floating bilayers [49]. The concept that is expected to be realized in the near future is a free-standing, freely-suspended bilayer that is close to the transducer surface without interacting with it.
# Farm Monitoring System with Drones and Optical Camera Communication
## 3 Proposed Scheme
### System Model
#### 3.3.3. Altitude Limit  
As the altitude of the drone-mounted camera increases, the size of a sensor node in the image plane decreases, and the accuracy of signal demodulation decreases. The altitude of the receiver camera is limited to establish a stable link with a ground sensor node. The constraint is described as  
\[p_{i}\geq p_{th}, \tag{6}\]  
where \(p_{th}\) denotes the threshold for the size of a sensor node in the image plane. By satisfying (6), the optical signal transmitted from the sensor node can be correctly demodulated. In other words, a light source must be sufficiently large in the image to correctly demodulate the optical signals. From (3) and (6), the altitude limit of a receiver camera is determined as  
\[h\leq\frac{fr_{i}}{\rho p_{th}}\cos\theta. \tag{7}\]
## 2 Materials and Methods
### SCOPE (Soil Canopy Observation of Photochemistry and Energy Fluxes)
#### 2.1.2 SCOPE Model Inputs
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Definition** & **Unit** & **Value/Source** \\ \hline Cw & Leaf equivalent water thickness & cm & Measurement \\ Cdm & Dry matter content & g/cm\({}^{2}\) & Measurement \\ N & Leaf thickness parameters & — & 1.4 \\ \hline _Canopy_ & & & \\ LAI & Leaf area index & m\({}^{2}\)/m\({}^{2}\) & Measurement \\ LIDFa & LIDF parameter a, which controls the average leaf scope & — & Inversion \\ LIDFb & LIDF parameter b, which controls the & — & \(-0.15\) \\ \hline _Fluorescence_ & & & \\ foe2 & Fluorescence efficiency for PSII in dark-adapted condition & — & 0.01 or adjusted \\ foe1/fqe2 & Ratio of foe1 to foe2 & — & 0.2 or adjusted \\ \hline _Meteorology_ & & & \\ R\({}_{\text{in}}\) & Broadband incoming shortwave radiation (0.4–2.5 \(\upmu\)m) & W/m\({}^{2}\) & Measurement \\ Ta & Air temperature & T & Measurement \\ P & Air pressure & hPa & Measurement \\ ea & Atmospheric vapor pressure & hPa & Measurement \\ u & Wind speed at measurement height & m/s & Measurement \\ Ca & Atmospheric CO\({}_{2}\) concentration & ppm & Measurement \\ \hline _Geometry_ & & & \\ LAT & Latitude & degree & Measurement \\ ION & Longitude & degree & Measurement \\ VZA & Observation zenith angle & degree & 0 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._SCOPE model, the fqe2 and fqe1 values proportionally impacted the intensity of the PSII and PSI fluorescence spectra. The literature suggested fqe2 and fqe1/fqe2 values are approximately 0.01 and 0.2, respectively, which were determined from the active fluorescence measurements with PAM in the laboratory [25]. However, whether the fixed FQE values are suitable for vegetation in the field at different growth stages has not yet been sufficiently validated. In this study, we inspected the accuracy of SCOPE-simulated SIF with both literature-fixed FQE values and with variable FQE values. The variable FQE values were obtained by fitting the SIF simulations to the observed SIF data (as described in Section 2.4). Other parameters required by the SCOPE model were set to their default values (Table 1).
# Social Internet of Vehicles for Smart Cities
## 2 SIoV Technologies and Components: Next Generation of Vehicles  
The design, development and deployment of vehicular networks is boosted by recent advances in context-aware technology and wireless vehicular communication techniques, such as dedicated short-range communications (DSRC), Long-Term Evolution (LTE), IEEE 802.11p and Worldwide Interoperability for Microwave Access (WiMax) [29]. An increasing number of social network applications is being proposed for vehicular networks, which leads to a shift from traditional vehicular networks toward SloV. In this section, we briefly introduce the key aspects that enable SloV in current vehicular networks. Similar to [30], we focus on three main components: (1) next-generation vehicles; (2) vehicle context-awareness; and (3) SloV context-aware applications.  
Vehicular _ad hoc_ networks (VANETs) are a kind of mobile _ad hoc_ network that has been proposed to enhance traffic safety and provide comfort applications to drivers. The unique features of VANETs include fast-moving vehicles that follow pre-determined paths (_i.e._, roads) and messages with different priority levels. For example, messages for comfort and infotainment applications have low priority, while messages for traffic safety applications require timely and reliable message delivery [31]. Using the on-board unit, vehicles can communicate among themselves (vehicle-to-vehicle, V2V) and with roadside units (vehicle-to-infrastructure, V2I). This enables several other forms of communication, such as vehicle-to-broadband cloud (V2B), where the vehicle communicates with a monitoring data centre, vehicle-to-human (V2H) to communicate with vulnerable road users, for example pedestrians or bicycles, or vehicle-to-sensor (V2S), where the vehicles communicate with sensors embedded in the environment [31].  
[PERSON] _et al_. [32] introduced next generation vehicles as smart vehicles that represent the convergence of communications, infrastructure, computers and autonomy, resulting in new capabilities, such as sensing, navigation, sociability, context-awareness and communication. Based on these aspects, smart vehicles exhibit five features: self-driving, safety driving, social driving, electric vehicles and mobile applications.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
###### Abstract
The effectiveness of ECL-INS-LMOA in solving large-scale regional mapping task planning problems is experimentally verified. The proposed method provides a reference for imaging satellite task planning for surveying and mapping product production in the future. The rest of this paper is organized as follows: Section 2 briefly introduces some work related to the proposed method, including the multi-objective task planning model, ECL-INS-LMOA, to solve and the particle update strategy in LSOCSO; Section 3 introduces the principles and procedures of ECLUS and ECL-INS-LMOA in detail; Section 4 presents the experiments and analysis; Section 5 discusses the experimental results obtained. Finally, the conclusion is drawn in Section 6.
# Overexpression of _PtDXS_ Enhances Stress Resistance in Poplars
## 2 Results
### Expression Levels of MVA-, MEP-, ABA-, and GA-Related Genes in PtDXS-Overexpressing Plants  
Expression levels of the MEP-related genes _DXR_, _HDS_, _HDR_, _MCT_, and _CMK_ were increased significantly in the transgenic plants (Figure 5A). Expression levels of the MVA-related genes _HMGS_, _HMGR_, and _MVD_ revealed significant diferences between the transgenic and WT poplars (Figure 5B). In addition, the expression levels of _DXS_, _DXR_, _HDS_, _HDR_, _MVK_, and _MVD_ were increased significantly by the overexpression of _PtHMGR_ (Figure S8). The expression levels of MVA- and MEP-related genes revealed significant differences between the _PtDXS_- and _PtHMGR_-overexpressing plants. Therefore, we speculated that the MEP pathway might play a leading role in the biosynthesis of terpenoids, and the MVA pathway an auxiliary role. Furthermore, transgenic lines exhibited higher expression levels of the downstream genes _IDI_, _GPS_, _GPPS_, and _GGPPS_ in comparing with WT poplars (Figure 5C).  
Changes in the expression levels of MVA- and MEP-related genes may affect those of ABA- and GA-related genes. The expression levels of _NCED1_, _NCED5_, _NCED6_, _ZEPI_, _ZEP2_, and _ZEP3_ were significantly higher in the transgenic plants, but that of _NCED3_ was lower (Figure 5D). The expression levels of _GA20 OX-1_, _GA20 OX-2_, _GA20 OX-3_, and _GA20 OX-4_ were significantly higher in the transgenic plants (Figure 5F). In addition, while _IAA1_ gene revealed significant lower expression in transgenicpopolars, _IAA2_, _IAA6_, _IAA18_, and _IAA19_ genes revealed no significant differences between transgenic and WT poplars (Figure 5E).
## 4 Simulation Environment
### Energy Model for Simulation  
The energy consumption of data communication for the proposed algorithm is considered to be a popularly adopted energy model in previous related works [24]. In this paper, we assume an  
Figure 8: Network topology for simulation. **(a)** Grid topology with 36 router nodes; **(b)** Random topology with 36 router nodes; **(c)** Random topology with 72 router nodes; **(d)** Linear topology with 6 router nodes.  
energy model where the radio dissipates \(e_{elec}\) to run the transmitter or receiver circuitry, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) for the transmitter amplifier depending on the distance of \(d_{0}\). Here, \(d_{0}\) is a distance consuming energy differently depending on the transmission and reception distance of radio frequency; thus, to transmit an \(m\)-bit message a distance \(d\) using the communication energy consumption model in each time interval of the mobile node. More details are represented in Equation (8).  
\[E(MobileNode_{n})=\begin{cases}2(e_{elec}(m)+\varepsilon_{fs}d^{2}m)+e_{elec} \sum_{i=1}^{n}m_{i}&if\quad d<d_{0}\\ 2(e_{elec}(m)+\varepsilon_{mp}d^{4}m)+e_{elec}\sum_{i=1}^{n}m_{i}&if\quad d\geq d _{0}\end{cases} \tag{8}\]  
Equation (8) is composed of the following two components. The first one is the energy consumption value to transmit an \(m\)-bit message a distance \(d\) (one for DIS message and one for DAO message) between a mobile node and neighbor router nodes. Another component is the energy consumption value for receiving DIO messages between a mobile node and neighbor router nodes (between one and \(n\)). In Equation (8), the transmission range and traffic load are important factors when the energy consumption values are derived in the communication process to select a parent node. We will use the model in Section 5.
## 2 Mathematical Programming Model  
### Problem Definition  
The satellite's imaging actions, data transmission actions, and attitude maneuvers all consume energy. However, during its operation, the satellite cannot always replenish energy by receiving external radiation. Therefore, during scheduling, the satellite has a limited total energy, and the energy consumed in executing tasks must comply with energy constraints. Due to the limitations of the onboard storage capacity of satellites, the imaging and data transmission plans of the satellites need to ensure that the observed data stored in the satellite meets the constraints of the storage capacity.  
For agile satellites, their attitude maneuverability is stronger, and the selectable time window for ground targets is longer. When observing a target, it is necessary to determine the start and end time of the observation task within the feasible time window. Due to user demands for the quality of the target image, it is necessary to limit the side viewing angle of the satellite during target imaging to avoid issues where the image resolution does not meet user requirements.  
This problem can be described as maximizing scheduling benefits by appropriately arranging which satellites will execute the targets observation tasks, the execution times of these observation tasks, which ground stations will receive the satellites' data downlink tasks, and the execution times of the downlink tasks, all while satisfying the constraints of satellite energy and storage capacity.
## 3 Results
### Upregulation of NRF2 by Herb-Derived Essential Oils in DPCs  
To examine the antioxidant properties of essential oils, their ability to modulate NRF2 in DPCs was evaluated. In RT-PCR experiments followed by densitometric analysis, all essential oils dose-dependently increased _NRF2_ mRNA levels (_p_ < 0.01) (Figure 2a,b). Essential oils increased NRF2 protein expression in the cytoplasm and nucleus of DPCs, as determined by immunocytochemical staining (Figure 3). In particular, _NRF2_ immunoreactivity was detected in the nuclei of DPCs treated with essential oils, indicating that the essential oils tested had the potential to activate NRF2 in DPCs (Figure 3, arrow).  
Figure 3: Confocal microscopic detection of Nrf-2-positive fluorescence in plant essential oils-treated DPC. (**a**) Nrf2-positive fluorescence signals from non-treated DPCs (control). (**b**) Nrf2-positive fluorescence signals from lavender-treated DPCs. (**c**) Nrf2-positive fluorescence signals from lemongrass-treated DPCs. (**d**) Nrf2-positive fluorescence signals from rosmemory-treated DPCs. (**e**) Nrf2-positive fluorescence signals from chamomile-treated DPCs. The arrow represents nuclear translocation of Nrf2. Scale bar: 20 \(\upmu\)m.  
Figure 2: Dose-dependent induction of NRF-2 mRNA expression in essential oils-treated DPC. (**a**) The DPCs were treated with lavender oil (0.025–0.01%), lemongrass oil (0.0012–0.005%), rosmemory oil (0.005–0.02%), or chamomile oil (0.005–0.02%). GAPDH was used as an internal standard to quantify the loading dose of mRNA. The figure shows representative results of triplicate RT-PCR experiments. (**b**) NRF-2 mRNA level is quantified by densitometric analysis * \(p\) < 0.05.
## 3 General Classification of Modern Haptic Interfaces
### Hydraulic Haptic Interfaces  
The creation of wearable hydraulic haptic feedback interfaces is limited by the frequent use of massive pipelines with external fluid conveyance in traditional hydraulic equipment. For soft robotics and wearable technology, on the other hand, novel flexible and stretchy pumping technologies have recently been developed. These include electrostatic, stimuli-sensitive gels, and thermally responsive materials. In this case, liquid dielectrics could be a functional implementation rather than an elastomeric polymer [28]. To understand why they are a valuable solution, the most common use case of liquid dielectrics is dielectric elastomers [29]. A dielectric elastomer membrane stretches over 100% when a voltage is applied across its thickness, causing it to narrow out and expand. Dielectric elastomers have thus been created as transducers for a wide range of applications, such as soft robotics and adaptive optics. Additionally, liquid dielectrics more effectively solve problems of catastrophic failure in dielectric elastomer actuators, given their self-healing nature, which is not present in solid dielectrics.  
On the other hand, as reported in [30], the polydimethylsiloxane (PDMS) stretchable polymer was chosen to realize pumps and channels due to its chemical stability and easy processing, allowing excellent stretchability and a long lifetime. Figure 4 describes various typical structures of hydraulic activating haptic feedback interfaces (Figure 4b,c).  
In conclusion, it should be noted that hydraulic haptic interfaces may efficiently produce straining forces, out-of-plane deformations, pressures, and vibrations, as a function of the volume and liquid's particular characteristics. The hydraulic haptic interface often applies more force than other technologies because of the high density of the fluid employed. The reaction time difficulties of hydraulic haptic devices, on the other hand, approaches hundreds of Hz due to the low liquid flow speed, and the deformation range would decrease, reaching zero at a high-frequency stage. A voltage of several hundred volts, or even more than 1 kV, is required to activate electrorheological fluid and dielectric liquid. Integrating a high-voltage amplifier on the body raises safety issues, restricting the future wearable applications of hydraulic systems.  
Finally, realistic kinesthetic feedback can be provided using electrorheological (ER) fluids controlled via an electric field [32]. In particular, by applying a time-varying electric field, different haptic feedback can be provided, enabling issues of magnetorheological (MR) fluids to be overcome; in fact, the electrical circuitry for regulating ER fluid is simpler than that for MR fluid.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 2 Electrospinning
### Industrial Electrospinning Techniques  
The needle-based electrospinning technique is not particularly suitable for industrial applications due to its low production rate and its lack of economic and productive potential for scale-up, such as with the needle-free electrospinningning technique. In general, three additional techniques for scaling-up nanofiber manufacturing include multi-jet electrospinningning, multi-needle electrospinningning and needle-free electrospinningning. In the multi-jet electrospinningning technique, a uniform web of nanofiber is not generated due to the repulsion effect between jets. Using an array of syring as spinnerets increases the pace of nanofiber production while also allowing for the mixing of various polymers at the right ratio [26]. However, needle-free electrospinningning can achieve the highest mass production rate of nanofibers. This method is considered to be a novel electrospinningning mode that originates from an open liquid surface. Unlike needle-like nozzles, many jets are produced from the surface of the polymer solution using a metal electrode and without the impact of the capillary effect. Using this needle-free electrospinningning technique, it is possible to produce large quantities of nanofiber mats with consistent quality [27; 28]. Electrospun membranes with interconnected porous morphology and homogenously distributed nanopores are ideal for pressure-operated liquid filtration processes, which may be achieved by using various spineret shapes [29]. A schematic representation of the needle-based (see Figure 2a) and needle-free electrospinningning (see Figure 2b) machines is shown in Figure 2.  
Figure 2: Needle-based (**a**) and needle-free (**b**) electrospinningning setups. Reprinted from [30], originally published under a CC-BY 4.0 license.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 4 Bandwidth Allocation and AP Association Algorithms
### Bandwidth Allocation Algorithm  
Firstly, we propose the FBA-BP algorithm that allocates bandwidth according to the bandwidth demand of the clients and the clients' business priority. Herein, all clients are divided into four classes according to their application business, and then each class determines the priority in accordance with their demand for bandwidth and delay. As mentioned in Section 3, the priorities are classified into four classes according to their application business. The transmission time \(T_{i}\) of client I is calculated by client demand \(S_{i}\), and thus, the total time T for an AP can be achieved by all clients associating with it. Of course, the newly arriving client also needs to be considered. The FBA-BP algorithm can be described in Algorithm 1.  
```
Input: Set of STAs' throughput \(\{S_{1},S_{2},\cdots,S_{n}\}\), Set of STAs' transmission time \(\{T_{1},T_{2},\cdots,T_{n}\}\) Output: Set of APs' allocated transmission time \(\{t_{1},t_{2},\cdots,t_{n}\}\), where \(\sum_{i=1}^{n}t_{i}=T\)
1: Sort the clients by their business priority in the ascending order; With the same priority, sort the clients by their bandwidth demands in the descending order
2: Initialize \(T_{total}=T\), count = n, k = 1, \(t_{ave}=T_{total}/n\)
3: A = \(\varnothing\), B = \(\varnothing\)
4: for each STA k in the sorted clients (k \(\leq\) n) do
5: if T\({}_{k}>\) tave then
6: Add STA k into set B
7: k = k + 1; continue;
8: else add STA k into set A
9: \(t_{k}=T_{k}\), \(T_{total}-=T_{k}\), count = count \(-1\), update \(t_{ave}=T_{total}/\)count
10: k = k + 1
11: end if
12: end for
13: for STA k \(\in\) set B do
14: \(t_{k}=t_{ave}\)
15: end for
```  
**Algorithm 1**The Fair Bandwidth Allocation Based on Clients' Business Priority (FBA-BP).  
The FBA-BP firstly sorts the clients by the client's business priority. If the clients' business priority is the same, the clients are sorted according to their bandwidth demand. Next, if the demanded time \(T_{k}\) of the client k is equal to or less than the current average allocated time \(t_{ave}\), the client k also will be added into set A. Otherwise, client k will be added into set B. Then the current average allocated time is updated with the rest allocable transmission time and the algorithm starts up the next turn until it traverses all the clients associated with the AP. Finally, each client in set B will be allocated with time \(t_{ave}\). Ultimately, Each AP will allocate its transmission time to all its associated clients. Moreover, to meet clients' demands, it also is necessary that the AP should sort the clients by their business priority before allocating its bandwidth.