esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanoparticles--Plant Interaction: What We Know, Where We Are?
## 3. Symplast
### Possible Explanation of NP Movement through PD  
How to explain the studies presenting that NP that is much larger in diameter than the cytoplasmic channels travel through PD? Literature data showing the involvement of PD in symplasmic transport of NP indicate only the diameter of PD, ignoring the aspect of the diameter of the cytoplasmic sleeve. Therefore, information about the movement of NP through the PD must be considered with caution. Certainly, in this aspect, the changes in PD conformation that may appear under the influence of NP should be analyzed. Such research has not been conducted so far.  
The question arises what factors influence the size exclusion limit (SEL; size of a molecule that can move freely between neighboring cells) of PD. Many studies have shown that a number of biotic and abiotic factors influence SEL of PD (for review see [81,82]). For example, changes in the redox status of cells modulate PD permeability leading to increase of SEL. Some reports indicate the correlation between intracellular redox status and PD structure and permeability [82]. The mechanism that may allow NP to travel through PD may be related to mitochondria oxidative stress. NP, at least metal and metal oxide, may cause such stress [34]. Moreover, it was documented that the response to osmotic stress can cause PD to dilate [82].  
Since NP have been found in plant organs other than those to which they were applied, there must be a pathway for them to travel through a plant. It has been postulated that after entering a plant, the NP are transported to various tissues through the xylem, which means that at some point in time, the NP must cross the cell membrane barrier in order to enter the cytoplasm and then translocate through the symplast via the PD. This means that both the apoplast and symplast systems are involved in the movement of NP. It is also possible that NP can translocate using other mechanisms such as by binding to the carrier proteins, ion channels or via endocytosis [83, 84, 85, 86, 87]. However, the exact mechanism that is associated with the uptake, translocation and accumulation of NP has not yet been fully confirmed. Whether the NP symplasmic transport occurs requires further, detailed research. At the moment, insufficient data does not enable the participation of PD in the movement of NP with the diameter larger than cytoplasmic sleeve dimensions to be confirmed or denied.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Received Information  
One of the most important parameters to evaluate the efficiency of the system used is the received information. The amount of information that can be received within a specific service area (range), **SA**, is expressed as [18]  
\[\textbf{RI}\left[\textbf{bit}\right]=\frac{\textbf{T}_{\textbf{d}}[\textbf{bit }/\textbf{s}]\cdot\textbf{SA}[\textbf{m}]}{\textbf{v}_{\textbf{s}}\left[ \textbf{km}/\textbf{h}\right]} \tag{25}\]  
where \(\textbf{T}_{\textbf{d}}\) is the transmission rate, while \(\textbf{v}_{\textbf{s}}\) is the vehicle speed.
###### Abstract  
To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.  
impedance function; electric vehicles; mixed traffic flow; travel time; travel cost; queueing system +
Footnote †: journal: Applied Sciences
# Augmented Reality: Mapping Methods and Tools for Enhancing the Human Role in Healthcare HMI
## 3 Results
### Rehabilitation  
AR technology is becoming more popular in rehabilitation due to its ability to create controlled, user-specific environmental, and perceptual stimuli while retaining the ability to interact with the real environment allowing the patient to remain engaged and motivated. [PERSON] et al. [62] presented the first wearable AR system to provide the patient with the prescribed therapy to improve the shoulder range of motion (ROM) by obtaining real-time performance feedback and providing a stimulating, intense, and engaging activity. The potentiality of HoloLens to track users' hands is here used to develop a system for indoor use, with no need for any adjunctive markers, sensors for arms, wires, and cables. However,results suggest that integration of more technical features is still needed to achieve optimal usability of the application.
## 3 Tree Compatibility and Incomplete Directed Perfect Phylogeny  
### Tree Compatibility  
A _profile on \(X\)_ is a set \(\mathcal{P}=\{\mathcal{T}_{1},\mathcal{T}_{2},\ldots,\mathcal{T}_{k}\}\) where, for each \(i\in[k]\), \(\mathcal{T}_{i}=(T_{i},\phi_{i})\) is a phylogenetic \(Y_{i}\)-tree on some set \(Y_{i}\subseteq X\), and \(\bigcup_{i\in[k]}Y_{i}=X\) (Figure 1). \(V(\mathcal{P})\) denotes \(\bigcup_{i\in[k]}V(T_{i})\), and \(E(\mathcal{P})\) denotes \(\bigcup_{i\in[k]}E(T_{i})\). The _size_ of \(\mathcal{P}\) is \(M_{\mathcal{P}}=|V(\mathcal{P})|+|E(\mathcal{P})|\).  
\(\mathcal{P}\) is _compatible_ if there exists a phylogenetic \(X\)-tree \(\mathcal{T}\) such that \(\mathcal{T}\) displays \(\mathcal{T}_{i}\), for each \(i\in[k]\). Such a tree \(\mathcal{T}\), if it exists, is a _compatible supertree for \(\mathcal{P}\)_ (Figure 2). The problem of finding a compatible supertree for a profile, or reporting that no such supertree exists, is called the _tree compatibility problem_.  
Profiles consisting of binary trees (and, in particular, rooted triples) are common in practice. As Figures 1 and 2 show, a compatible supertree for a profile of binary trees need not itself be binary.  
Suppose \(\mathcal{T}\) is a compatible supertree for profile \(\mathcal{P}\). It follows from the definition of the notion of \"displays\" that, for any cluster present in some tree in \(\mathcal{P}\), there is a corresponding cluster in \(\mathcal{T}\). More formally, for each \(i\in[k]\), there exists a mapping \(\sigma_{i}\) from \(V(T_{i})\) to \(V(T)\) with the following property. For each \(v\in V(T_{i})\), \(Y_{i}(v)=X(\sigma_{i}(v))\cap Y_{i}\) (here, we use the cluster notation of Section 2.1). We say that \(v\)_maps to \(\sigma_{i}(v)\)_; see Figure 2. Note that the mapping \(\sigma_{i}\) need not be unique.  
Figure 1: A profile. Leaves are labeled with species; internal nodes are numbered for later reference.
# An Effective Beam Width Model of Slab in Thick Wall-Thick Slab Joints Subjected to Lateral Forces
## 4 Results
### Basic Assumptions  
* The inflection points of the vertical and horizontal components (the bearing wall and the flat slab) are both assumed to occur at the mid-height and the mid-span, respectively.
* The bearing wall and the connection portion between the wall and slab are assumed to be rigid bodies because the rotational stiffness of the bearing wall is much larger than that of the slab.
* The shear behavior of the slab was not considered to be significant as the thickness of the slab was relatively small compared to the plane dimensions.  
Figure 6: Calculation flow chart for the effective beam width \(b_{e}\).  
* There is no vertical load applied on the top of the wall nor gravity load applied on the slab; the self-weight of the slab is not considered therefore the influence that might be brought from the vertical load is ignored.
* Considering the initial stiffness is defined as the stiffness at a small deformation (0.05% drift ratio or 1/2000 rad deformation angle), the effects of initial crackings, concrete shrinkage, creep, and other nonlinear behaviors are not taken into account.
* The effect of reinforcing rebars in the concrete slabs is neglected since it does not play any role in the stiffness prediction.
## 4 Discussion
### Comparison with other GPP Products  
The increasing interannual trend in the GPP predicted by the FGM (+0.55% yr\({}^{-1}\)) was in the range of that estimated using other GPP products (+0.47% yr\({}^{-1}\) to +0.92% yr\({}^{-1}\)) (Figure 6). From 2001 to 2016, terrestrial ecosystem productivity showed a significant increasing trend (\(p\) < 0.01) in Europe according to five previous GPP products (i.e., BEPS, MODIS, GLASS, GOSIF, and VPM) (Figure 6). The annual total GPP across Europe from 2001 to 2016 predicted by the VPM [116] showed an increasing trend of +0.92% yr\({}^{-1}\), which is almost double the predictions of the FGM (Figure 6). Other studies reported that the increasing GPP trend detected by the VPM may be an overestimate [2], since the VPM is not strictly calibrated using field observations at FLUXNET sites [116]. In contrast, the CCW GPP products failed to detect the increasing GPP trend across Europe while successfully capturing the increasing trend of GPP on the global scale [2]. In the case of LUE models, the model parameters, especially those related to the fraction of photosynthetically active radiation (FPAR) and LUE, may have uncertainties and lead to errors in model estimations. Previous studies that estimated GPP dynamics were based mainly on LUE models and process-based models and, in most cases, did not include the spatial and temporal dynamics of V\({}_{\text{cmax}}\)[51]. These models are unlikely to produce reliable simulations of photosynthesis-climate interactions at a fine temporal resolution or on a large scale.  
Prior studies highlighted continuous increases in global terrestrial production during the last two to three decades based on remote sensing data [2; 133]. In particular, enhanced GPP mainly occurs in the boreal and temperate regions, where widespread greening and climate warming occur [130]. In this study, we also found that European vegetation showed a significant 'greening' trend (i.e., increases in the LAI) from 2001 to 2016 (+0.62% yr\({}^{-1}\)). In addition, we further found that the GPP increasing trend throughout the European terrestrial ecosystem estimated by the FGM (+0.55% yr\({}^{-1}\)) was proportional to the greening rate detected by the LAI and other GPP products (0.47-0.67% yr\({}^{-1}\)) (Figure 11). With the help of spatially and temporally continuous V\({}_{\text{cmax}}\) maps, we can expect that process-based models will help us to better understand the driving forces of enhanced carbon assimilation in Europe. However, determining how land surface greening, climate change, and other factors contribute to the increase in GPP observed across Europe is beyond the scope of this study and warrants further investigation.
## 2 Materials and Methods
### DAS Test Data Acquisition  
We recorded long-term DAS data with a Fotech Helios DAS interrogator device in two different railway test sites. At one site, the track was located in a tunnel, with the single-mode fiber optic cable installed in a cable trench; in the other site, there was an open track with the single-mode fiber cable directly attached to one rails' foot with clips. For all experiments, already existing telecommunication fiber cables were used, and no special installation of cables was done for this work. At these two sites, we recorded a total of approximately 1000 h of data with 3000 trains passing the infrastructure. The total rail track length monitored during the recordings was approximately 20 km. Refer to Section 3.3 for details on the sites. Apart from these long-term recordings, two shorter recordings were available, of around one hour each, from an open track with the cable in a cable trench. The standard telecommunication fibers were used for the recordings, and the effect of fiber darkening over time was not evaluated in this study.  
The Helios DAS is a coherent (C-OTDR) phase sensitive interrogator device that delivers the optical detector voltage, encoding the laser light phase change, as a 16 bit resolution signal, sampled with 150 MSamples/s. This results in a physical spacing of the DAS segments of 0.68 m, given by the light speed in the fiber. The effective spatial resolution of the measurement is limited by the laser pulse length of 100 ns, resulting in a size of the laser pulse of 20.2 m, over which the phase measurement is averaged. Table 1 summarizes the relevant measurement parameters. The cable used for test site 1 is a single-mode, stranded mini cable 60 \(\times\) 5 \(\times\) 12 E9/126, the cable at the second test site is a single-mode stranded fiber optic cable 5 \(\times\) 12 E9/125 A-DF(ZN)2Y(BN)2 Yv 5 \(\times\) 12 E9/125-G652D.  
Due to the nature of the optical measurement principle, neither the absolute phase nor the fiber strain is directly accessible from the signal output. Furthermore the signal is delivered with an ambiguity of 2\(\pi\) (equal to the \"fringes\" observed in a [PERSON] interferometer), which results in a non-linearity in very strong signals. A detailed description of the signal generation for a similar DAS setup is found in [9]. The laser pulse repetition frequency of the Helios DAS device was set to 2000 Hz for all recordings. This data stream is the input to our signal processing stages.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 3 Challenges and Motivations
### Technological Advancements in IoT and Blockchain for Agriculture
#### 3.2.2 Emergence of Blockchain as a Solution  
This subsection explores how blockchain is applied in the agricultural and animal husbandry IoT ecosystems:  
Resolving Data Management Challenges:
* Immutable data records: Blockchain introduces a distributed ledger system where once records are created, they become unalterable. This aspect guarantees the integrity and trustworthiness of data gathered in the agricultural and animal husbandry IoT, including critical information on crop growth and climatic conditions.
* Enhancing data transparency and traceability: Blockchain enables comprehensive recording and tracking of agricultural products from production to consumption. This level of transparency not only improves supply chain visibility but also augments product traceability, optimizing the overall supply chain management. Boosting Security and Trust:
* Decentralized framework: Blockchain's decentralization diminishes single-point failure risks, storing information across multiple network nodes. This enhances data security, significantly reducing risks of data loss or manipulation.
* Secured transactions: Utilizing blockchain for securely documenting transactions, such as trading and logistics of agricultural products, fortifies transactional security. The encryption of each transaction bolsters the security and transparency within the supply chain. Enhancing Privacy Protection:
* Data encryption: blockchain's capability to encrypt data safeguards sensitive information like production costs, outputs, and trade secrets, preventing unauthorized access and breaches.
* Controlled access: it enforces the protection of sensitive and confidential data by allowing only authorized individuals to access specific datasets.  
Blockchain technology, distinguished by its immutability, decentralization, transparency, and secure encryption, presents potent solutions for data management, security enhancement, trust establishment, and privacy protection within the IoT frameworks of agriculture and animal husbandry. Therefore, a new era of efficiency, reliability, and security in agriculture and animal husbandry is heralded by integrating blockchain with IoT in these sectors.
## 2 Characteristic Signal Selection and Wavelet Denoising Processing
### Wavelet Denoising
#### 2.2.5 Analysis of Wavelet Denoising Simulation Results  
The time domain waveform of the original signal shows too much noise, including machine noise during grinding. These noises will seriously affect the quality and accuracy of the signal, so it is necessary to denoise the signal. The vibration signal collected in the process of tool grinding is taken as an example to improve the wavelet threshold denoising.  
Figure 1 is the comparison figure of signal denoising by wavelet hard threshold, wavelet soft threshold, and improved wavelet threshold function, respectively. Table 2 shows the signal-to-noise ratio of wavelet denoising with different thresholds. As can be seen from Table 1, the signal-to-noise ratio of the proposed method reached 21.815345 dB, which is greater than the hard threshold and soft threshold functions, and the denoising effect is better, while more useful signal components are retained.  
Figure 1: Comparison figure of wavelet denoising with different thresholds.
## 2 Materials and Methods
### Effectiveness Evaluation of Landslides Engineering Remedial Measures  
Thus, the framework proposed in this study encompasses an evaluation of the effectiveness of remedial works in two steps. The first step uses regional scale satellite monitoring data with the objective to provide a general overview of the regional scale remedial programs and to highlight localized problems to be further investigated. The second step involves local scale evaluations on ground-based landslide monitoring data (if available) and field investigations.  
Based on previous InSAR-HCA results, controlled landslides with noticeable ground surface deformations were targeted and received detailed field investigations. The integrity of their major engineering remedial structures, such as anti-slide piles or drainage ditches, were carefully inspected. Other deformation features of these controlled landslides were also closely recorded periodically in case of sudden deforming acceleration, which may cause engineering structure malfunction. Apart from this, other sources of monitoring data, like long-term GPS and borehole inclinometer data, were combined to predict the future deformation trend and the overall stability of landslide.
# Microwave-Assisted Extraction of Fatty Acids from Cultured and Commercial Phytoplankton Species  
[PERSON]  
1 Center of Nuclear Energy in Agriculture (CENA), University of Sao Paulo (USP), Piracicaba 13416-000, Brazil; [EMAIL_ADDRESS] (J.A.M.); [EMAIL_ADDRESS] (A.O.d.S.); [EMAIL_ADDRESS] (E.P)  
[PERSON]  
1 Center of Nuclear Energy in Agriculture (CENA), University of Sao Paulo (USP), Piracicaba 13416-000, Brazil; [EMAIL_ADDRESS] (J.A.M.); [EMAIL_ADDRESS] (A.O.d.S.); [EMAIL_ADDRESS] (E.P)  
[PERSON]  
2 Lipidomic and Bioorganic Laboratory, Center of Chemical, Food and Pharmaceutical Sciences, Forensic Chemistry, Federal University of Pelotas, Pelotas 96010-610, Brazil; [EMAIL_ADDRESS] (L.M.B.); [EMAIL_ADDRESS] (C.M.P.d.P.)  
[PERSON]  
3 Department of Clinical and Toxicological Analysis, Faculty of Pharmaceutical Sciences, University of Sao Paulo (USP), Av. [PERSON] 580, Bl. 17, Sao Paulo 05508-900, Brazil; [EMAIL_ADDRESS]  
[PERSON]  
in freshwater, seawater, or wastewater. Moreover, their cultivation contributes to reducing the presence of greenhouse gases in the atmosphere as they convert approximately 2 tons of inorganic CO\({}_{2}\) into 1 ton of biomass [5; 6; 7]. Therefore, microalgae and cyanobacteria can be a promising alternative for biodiesel production as well as conventional raw materials [1; 8; 9; 10].  
Current methods regarding biodiesel synthesis from microalgae and cyanobacteria biomass are time-consuming and require large amounts of organic solvents as procedures rely on an initial step of oil extraction followed by transesterification to synthesize fatty acid alkyl esters [2; 3]. In this sense, \"green\" techniques such as ultrasound-assisted transesterification and microwave irradiation have been studied to overcome the disadvantages associated with conventional biofuel synthesis [7; 11].  
Among ecofriendly methodologies, the application of microwave irradiation has a great potential for biodiesel production, as the extraction and transesterification can occur in a single step in situ. This combination not only reduces the synthesis time, solvent amount, and production cost eliminating complex and unneeded steps of the procedure [1; 12], but also assists the biodiesel production from microbial biomass through cell wall disruption, rapid and homogeneous heat generation, and pressure, thereby enabling an efficient oil extraction [8; 10; 13].  
Despite the numerous advantages in the use of microorganisms to produce biodiesel, the literature lacks studies involving common freshwater cyanobacteria for this purpose. Therefore, the aim of this study was to investigate the potential of MAE to extract fatty acids from a _Chlorella_ sp. microalga and two cyanobacteria, namely, _Arthospira_ sp. and _Spharospermopsis torques-regime_, for biodiesel obtention. Furthermore, two different fatty acid and total lipid extraction methods, i.e., traditional extraction and microwave-assisted extraction, were compared.
## 6 Design of Magnetic Signal Processor
### Digital Board Design  
The digital processing section utilizes the Zynq XC7Z020 processor chip, manufactured by Xilinx, Inc., headquartered in San Jose, CA, USA [24]. This chip includes an FPGA and a central processing unit (CPU), and it is equipped with 1 GB of memory and 32 GB of storage capacity. The CPU core processing module acts as the control center for the carrier magnetic interference compensation system. It performs system power control and monitoring, self-diagnostics, parameter configuration, raw data acquisition, compensation parameter calculation, real-time compensation computation, and data storage. The peripheral interface of the core board is shown in Figure 19. An industrial-grade TransFlash (TF) card is used to store system files and acquisition data. An Ethernet interface is provided for data communication, and a USB interface is used for system debugging and necessary power modules.  
Figure 18: A block diagram of the triaxial flux gate acquisition board.