esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of a Closed Cycle Power Takeoff for a Shore-Based Wave Energy Device
## 2 Materials and Methods
### Grid Convergence Analysis  
To assess how dependent the results are on the mesh fineness used, the same problem was solved using six different grids consisting of a growing number of cells. The six meshes are presented in Table 4, with the time needed to perform each simulation. Eight different simulations were performed on each grid with a rotational speed \(N=2000\) rpm and a pressure drop \(P_{3}-P_{01}\) ranging from 600 to 3400 Pa in increments of 400 Pa.  
As the mass flow rates obtained on the different meshes are quite close to each other, Figure 9 shows the relative difference in mass flow rate on each mesh with the one obtained on the very fine mesh. All of the results diverge by a few percentage points. The finer the mesh, the closer the resulting mass flow rate is to the very fine mesh result. The results on the medium mesh are 1% or less away from the results on the very fine mesh. They also need about 20 min per design point to compute, four times less than the very fine mesh simulations, which is deemed reasonable in practice.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Mesh Type**} & **Number of Nodes** & **Number of Nodes** & **Computational Time for** \\  & **for the Rotor Blade** & **for the Stator Blade** & **One Design Point** \\ \hline Extremely Coarse & 97,592 & 65,417 & 4 min \\ Very Coarse & 160,628 & 110,699 & 6 min \\ Coarse & 330,132 & 225,591 & 12 min \\ Medium & 620,330 & 441,336 & 23 min \\ Fine & 1,321,400 & 904,186 & 47 min \\ Very Fine & 1,884,290 & 2,711,420 & 99 min \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mesh characteristics.  
Figure 8: Numerical domains and boundary conditions.  
Figure 10 shows the torque efficiency as a function of pressure drop obtained for the different meshes. The torque efficiency is defined as  
\[\eta = \frac{\tau\Omega}{\mu\Gamma_{p}T_{\Omega 1}(1-\frac{P_{\Omega 1}}{P_{ \Omega 1}}\frac{\gamma-1}{\gamma})} \tag{30}\]  
where \(\tau\) is the torque in N-m and \(\Omega\) is the rotational velocity in rad/s. Discrepancies can be observed in the results on different meshes. Close to the nominal design point, the simulations on a coarser mesh slightly underestimate the efficiency, while, at lower pressure drop, they overestimate it compared to the very fine mesh. The largest differences are observed for the lowest pressure drops where all results are quite scattered. For pressure drops over 1000 Pa, the medium mesh leads to results 1% or less away from the very fine mesh. It is thus the mesh refinement that will be used for every following simulation.  
Figure 10: Torque efficiency at \(N=2000\) rpm for different meshes.  
Figure 9: Relative difference in mass flow rate at \(N=2000\) rpm for different meshes compared to the very fine mesh.
# Coupled Dual-Loop Optoelectronic Oscillator Based on Stimulated Brillouin Scattering
## 3 Experiment and Results
The mode spacing of coupled dual-loop SBS-based TOEO was significantly different from that of ordinary dual-loop OEO. The mode interval of the ordinary dual-loop OEO in [11; 12; 13; 14] was determined by two loops. But the mode interval of the coupled dual-loop TOEO was only determined by Loop1. The mode interval of coupled dual-loop TOEO was independent of the delay of Loop2. Because Loop1 and Loop2 were non-linearly coupled in the coupled dual-loop OEO, this asymmetric loop design allows the two loops to function differently. Loop1 acts as a main oscillation loop to determine the mode interval, while Loop2 acts as a slave oscillating loop to suppress spurious caused by multiplicative phase noise and link delay [23; 24]. Therefore, compared with the ordinary dual-loop SBS-based TOEO and polarization-multiplexed dual-loop SBS-based TOEO proposed in [15; 16], the coupled dual-loop SBS-based TOEO can improve the side-mode and stability, and the phase noise can also be further improved by using the high performance active devices in the link. Figure 8: (**a**) The frequency drift of the coupled dual-loop SBS-based TOEO at 10 GHz; (**b**) the power drift of the coupled dual-loop SBS-based TOEO at 10 GHz. Figure 7: The 5, 10 and 15 GHz microwave signals phase noise of the coupled dual-loop SBS-based TOEO.
###### Abstract  
Due to the scale effect, tip vortex cavitation (TVC) is the earliest type of cavitation that occurs on real ship propellers. As a result, experts in the ship field have been paying close attention to the accurate prediction of propeller TVC inception for a long time. The motion and growth of the microscopic nuclei in the water have a significant influence on TVC inception. However, the minimum pressure coefficient method--a common method at present--based on the traditional Eulerian framework, neglects the influence of microscopic nuclei and therefore cannot accurately predict the cavitation inception. Moreover, the numerical prediction method for cavitation inception, which is based on bubble dynamics models and considers the influence of nuclei, has not established a set of unified and specific discrimination criteria applicable to propeller cavitation inception. In order to make up for the shortcomings of traditional prediction models and the existing methods based on bubble dynamics in the prediction of TVC inception, we propose a new discrimination method for propeller TVC inception based on bubble dynamics in this paper. The comparison with experimental results demonstrates that our proposed method allows us to predict propeller TVC inception more accurately. In addition, the effect mechanism of tip vortex flow characteristics on nuclei evolution is further investigated, and it is found that when approaching the low-pressure region at a vortex core under the influence of tip vortex suction, nuclei grow explosively under the continuous action of the low pressure at the vortex core until they reach their maximum sizes and then collapse rapidly.  
bubble dynamics; cavitation inception; tip vortex cavitation; propeller
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 2 Delivery Systems for Hydrophilic Phytochemicals
### Liposomes  
Liposomes (Figure 2) are the well-explored representatives of the carrier ensemble. They are defined as sphere-shaped vesicles, consisting of one or more phospholipid bilayers, which are self-formed in aqueous solution. Liposome's structure is biphasic composed of hydrophilic core and hydrophobic bilayer, enabling both hydrophilic and hydrophobic compounds to be delivered. Liposomes were first discovered in 1960s by the British scientist [PERSON]. Since that time, they have started gaining popularity as a prospective delivery system. The Dior \"Capture\" was the first liposomal cosmetic product introduced to the market in 1986. Since that time, the list of liposomal products are increasing [36].  
Based on the physical characteristics (e.g., structure, particle size, lamellarity), liposomes can be divided into different categories: small unilamellar vesicles (SUVs), large unilamellar vesicles (LUVs), giant unilamellar vesicles (GUVs), oligolamellar vesicles,  
Figure 2: The structure of the selected carriers of hydrophilic compounds.  
(OLVs), multilamellarvesicles (MLVs), and multivesicular vesicles (MVVs) [37] (Table 1). There is a wide list of liposome preparation methods, which includes, for instance, film hydration also known as \"Bangham method\", reverse phase evaporation or solvent injection (ethanol, diethyl ether). The application of the specific method is determined by the desired liposome characteristics [38; 39]. All of techniques are presented in Table 1.
## 3 Platform for Real-Life Testing of Communication Protocols
### Testbed Architecture
#### 3.2.2 Testbed Support Services  
Support services are a set of processes that are launched alongside the GNU radio application under test. These processes help generate traffic, monitor the traffic passing through the application, and collect, store, and visualize the monitoring data. One can easily use any network tool like ping or iperf3 to send traffic to the tested application. Alternatively, you can develop custom traffic generators/analyzers using Python and the Scapy library [45], a powerful packet manipulation library.  
In cases where you need to analyze one-way performance or simplex transmission, traffic can be captured and analyzed in a separate process. The Scapy library provides a convenient API to send and sniff packets on L2 and L3. However, using Scapy may introduce significant delays due to the overhead it adds to each packet. For example, the L3 send API selects the interface according to the L3 header for each packet, which is not needed in the testbed since the traffic is injected in the GNU radio through a single interface. This can be overcome by selecting the interface only once and using the socket API. An essential support service in the testbed is the database service, which stores the monitoring data collected during the testing for subsequent analysis. The current implementation employs MongoDB [46] for storage, mainly due to its versatility, which better suits the purpose of the testbed. Several papers have suggested that MongoDB outperforms structured databases, such as MySQL [47]. As MongoDB is schemeless, there is no requirement for preparation for different monitoring data structures. In the testbed environment, the database service is deployed on-premises. The broker process plays a crucial role in collecting monitoring data. It parses the data and writes them to the database. Due to its strong interdependence with the messaging system, the broker will be presented in the next section.  
Apart from the broker, another important support service is the data visualization service. This service helps to visualize the data collected from the GNU radio application under test and monitor the application in almost real-time. Currently, the data visualization service employs Grafana [48] to query and visualize the MongoDB database. Grafana, like the database service, is deployed on-premises.
# Body Area Networks in Healthcare: A Brief State of the Art
## 3 Physical Layer Requirements
### Wearable Sensor Types  
#### 3.3.1 Mechanical Sensors  
Mechanical sensors detect the amount of deformation when they are strained or compressed. The changes in the shape of the sensor are converted into electrical signals. Mechanical sensors are divided into four groups: (1) piezoelectric, (2) piezoresistive, (3) capacitive and (4) iontronic [19].  
Piezoelectric sensors are based on the piezoelectric effect generated by materials under external force like strain, force or pressure. The most used inorganic materials for piezoelectric sensors are zinc oxide (ZnO), gallium nitrite (GaN) and lead zirconate titanate (PZT), while ZnO nanomaterials are used to develop nanosensors. The use of inorganic materials implies a high processing cost and the use of lead, which is highly toxic for living beings. A good candidate for replacing actual inorganic piezoelectric materials is polyvinylidene fluoride (PVDF). This material has mechanical flexibility, biocompatibility, low processing cost and chemical inertness.  
Figure 1: Model-based engineering methodology.  
A classical investigation shows the use of piezoelectric pressure sensors to develop a reliable non-invasive method of determining the peripheral arterial pressure wave velocity in humans [20]. The aim of this article is to achieve a reliable and non-intrusive way to monitor the arterial pressure in patients with arterial disease, pre- and post-surgery patients, and pre- and post-treatment patients. The measurement of elasticity of peripheral arterial blood vessels is called arterial pulse wave velocity. They measure two pulse waves at different points, one at the wrist and the second one at the brachial artery near the elbow. The pulse wave velocity is calculated by determining the pulse transit time between the points described above and the distance measured between each other. The sensors used in this application are piezoelectric devices that generate a potential difference when they are under a mechanical load. This study uses three different ways to determine the arterial pulse wave velocity: (1) foot-to-foot, which is based on the velocity of the foot of the pressure wave, (2) peak-to-peak, which is similar to the first one, the only difference being that the points of observation are two peaks of the pulse wave (proximal and distal), and (3) the coefficient of the arterial pulse at the proximal measurement position and the distal position, called cross-correlation.  
In conclusion, piezoelectric sensors have high frequency response, accuracy and sensibility making them a good choice for body movement or vital sign sensors. However, some drawbacks for these kinds of sensors are poor spatial resolution and they suffer charge leaks.
## 2 Purpose and Concept  
On this basis, this paper proposes a method of obtaining the displacement information of an whole bridge structure from different views. According to the field of view of a camera, the bridge structure is divided into several areas, and image data are collected under different working conditions to form the image database of the bridge facade under the time-space sequence. The entire field displacement information of the bridge is obtained by establishing the connection between the time and space series. For this purpose, noncontact remote sensing (NRS) is designed to obtain the time-space sequence image data of the test bridge under different test conditions.  
In order to obtain the full field displacement deformation of the whole structure, it is no longer used to track the artificial marked and corner points as the target points, but to track the displacement information by extracting every pixel of the lower edge contour line of the main beam as the virtual marked points. Modelling correction and damage identification is more conducive than measuring of finite points. Abundant data can be accumulated to provide more details data for machine learning and life-cycle maintenance.  
The paper is organized as follows. Section 3 covers the theoretical background of the intelligent NRS system and the layout of lab. Section 4 proposes the algorithm to connect the fields of view and track the deformation, presents the analysis with the theoretical model, and discusses the validation of the proposed sensor and algorithms in full-field noncontact displacement and vibration measurement. The results are summarized in Section 5.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
###### Abstract  
The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor.  
PM2.5; temperature inversion; classification; regression; forecast; machine learning Article
## 2 Fundamental Principles
### Measurement Range
#### 2.4.1 Maximum Range--Nyquist Theory  
Since the analogue signal is being digitised through its sampling via the optical detector pixels the maximum sample distance from the fibre that the sensor can resolve is limited by the sampling rate of the signal by the detector. The signal is digitised through the sampling of its wavelength components intensities across an optical detectors pixel array and thus is victim to [PERSON] theory of requiring a sampling rate of at least twice that of the measured signal frequency. This limitation due to hardware can be calculated through the use of Equation (13):\[z_{max}=\left(\frac{\lambda_{0}^{2}}{4}\right)\left(\frac{N_{s}}{\Delta\lambda} \right). \tag{13}\]
## 3 Named Data Networking Architecture
### Packet Forwarding and Routing  
The forwarding of interest and data packets is managed by three NDN components included in all nodes: content store (CS), pending interest table (PIT), and forwarding information base (FIB), as presented by [1; 14].  
As mentioned previously, NDN's content store is a cache storage for the data packets, similar to today's Internet buffer router memory. FIB is used to route the interest packets which are identified by name prefix(es) table and outgoing interfaces.  
Figure 3 illustrates the NDN forwarding design. In this model, when an interest packet is received by the first node, a lookup (search for the named data) is initiated by CS for the previously cached contents. In the case that the intended data has a matching at CS, it is replied to by a named content packet to the consumer. Otherwise, the router (or node) looks up the requested data (content) name in the PIT component. If a matching content name is in the PIT table, the NDN router (node) registers a consumer interface for the requested interest. Thus, the router can satisfy future incoming interest requests. Otherwise, an NDN router adds a new PIT entry and FIB can inform the sourcing interface if there are no routes to satisfy the request.  
If lookup data are not found in the network, an interest is routed to the content producer with its FIB information. The _longest prefix match_ information in FIB determines where to send it for each of the interest packets. In addition, each of the outgoing interfaces at the FIB entry is stated as a reference for the packet routing. In the end, if the name is not even found by the content producer, the issued interest becomes unsatisfied. Additionally, these packets can be used for denial of service attacks in NDN. To mitigate this, when all corresponding interest packet requests are missed and NACK (negative acknowledgments) packets occur, the forwarder can limit the requests of the NDN router, as described by [18].  
Lastly, the PIT checks to match entry for arrived data packets downstream of an NDN router. When a paired entry is obtained, the named content (data) is forwarded (transmitted) to face (interface), cached, and PIT discards the entry. Additionally, PIT has the capability of discarding/removing the data packet when signature verification is failed.
## 4 Simulation and Performance Evaluation
### Performance Analysis under Different Parameters
#### 4.2.4 The Impact of Velocity of Source Data  
In this section, we study the impact of velocity of source data on the performance of our algorithm. The parameters we set are shown as follows: (1) the number of all nodes is 500; (2) \(r_{e}\) is 30 m; (3) \(Th\) is 70%; (4) the event area changes by period, and the period is 800 s; (5) the velocity of sink is 5 m/s. The number of data packets source nodes produce varies from 0.2 to 1.8 in one second.  
In Figure 17, the black line represents network's lifetime. The network's lifetime decreases with the increasing of velocity of source data. When velocity is less than or equal to 0.8, network's lifetime decreases fast. When velocity is greater than 0.8, network's lifetime decreases very slowly. The blue line represents the number of data packets collected. The number is decreased in the mass. However, the number gets the peak value when velocity is 1.0. That means sink gets the most data although the lifetime of this situation is short. The red line represents the variance of residual energy. The red line is increased in the mass, so the network could be more balanced if the velocity is smaller.  
According to the experiment results and analysis above, network can achieve a better performance if velocity is smaller in the mass. There is a special case in it. When velocity adopts 1.0, sink can collect the most data although the lifetime is short.  
Figure 16: The impact of period of changing event area on the network performance.
## 4 Influence of Space-Arrangement and Exterior Wall Thermal Resistance on Indoor Air Temperature  
It can be seen from the previous analysis that, in the design of passive solar buildings, the main rooms such as the bedroom and the living room are usually arranged in the south orientation, and the auxiliary rooms in the north orientation. The indoor space layout combined with the directional characteristics of solar radiation is conducive to improving the thermal environment of the main room. However, it is worth noting that in multi-space passive solar buildings, there is heat transfer between indoor and outdoor, and between adjacent rooms. Therefore, the indoor air temperature of the multi-space solar buildings is affected by space-arrangement and thermal resistance of exterior walls.  
In order to study the coupling influence of the space-arrangement mode and the thermal resistance of envelope on the indoor air temperature, an available computation program needs to be selected and used to implement numerical simulations. Design Builder is the first comprehensive interface simulation software developed for EnergyPlus. Owing to these powerful features, it was chosen as the software for this study. The weather data for the Lassa area was downloaded from the official website of the U.S. Department of Energy [35].  
According to the parameters of space-arrangement modes in Figure 8 and Table 2, the building numerical models (Figure 9) for the three space-arrangement modes were established by the software of Design Builder and the indoor air temperature change in each room was analyzed. In addition, since the indoor air temperature is the most important parameter affecting the indoor thermal environment, in order to simplify the analysis and evaluation, the indoor air temperature is adopted as the indoor thermal environment evaluation parameter.  
The thermal parameters and envelope configuration parameters of the building numerical models are determined based on previous literatures [36; 37] and the typical local passive solar building features. The windows are a plastic single-frame and double glazed with the thermal resistance of 0.4 m\({}^{2}\)-K/W. The area ratio of the south-facing window to wall is set to 0.5. The thermal resistance of the floor is assigned to 3.3 m\({}^{2}\)-K/W. The component and thermal parameters of the building envelope are listed in Table 3.  
Figure 9: Building numerical models.  
In the numerical simulation analysis, the non-stationary calculation was performed, and the calculation period was determined to three months (1 December to 1 March of the following year). The influence of space-arrangement and wall thermal resistance on the indoor air thermal environment was investigated by analyzing the change of the average indoor air temperature values over the coldest month (January). In this study, the purpose of determining the three-month period of the numerical calculation is to eliminate the influence of the initial conditions in the non-stationary calculation process on the results.