esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The setup uses a simple approach of fabricating carbon electrodes to polycarbonate shells with additional support to hold electrical circuitry [31]. The setup is designed to fit standard multi-well culture dishes used in in vitro works. It has been successfully used in various research works for stimulating cell lines [32; 33]. Although, the C-Dish\({}^{\text{TM}}\) only provides a bipolar configuration of electrodes. Experiments involving impedance tomography require tetrapolar configurations [34]. This limits the applicability of the C-Dish\({}^{\text{TM}}\) to only the in vitro stimulation of cells. Another recent setup used by [PERSON] et al. uses a PCB mounted with carbon electrodes (designed for a 24-well culture dish setup) to electrically stimulate human dermal fibroblasts [35]. Moreover, in experiments involving cell lines, the cells are grown in the bottom plane of the wells, which are topped up with growth media. The growth media come in the form of liquids or gels that provide the necessary nutrients and conditions for cells to grow in in vitro setups [36]. The formation and type of growth media depend on the cells under consideration. However, most growth media contains glucose, amino acids, inorganic salts, vitamins, growth factors, hormones, and a pH buffer solution [36; 37]. Fundamentally,growth media are highly conductive ionic solutions. Any electrode placed in a culture well containing cells and growth media will conduct electricity through the entirety of the interfacing surface; a part of the stimulus will flow through the cells at the bottom, and the rest will conduct through the growth media, as shown in Figure 1. As only a fraction of the stimulus flows through the targeted cells at the bottom of the well, it is difficult to correlate the external current or voltage to the effective modulating stimulus through the cells. The setup of an in vitro experiment plays a decisive part in the outcomes of the experiment. It is evident that the setups used in research works encompass a wide variety of designs and materials. The commercially available setups are designed for specific experiment protocols. Hence, a versatile, easy-to-fabricate, and configurable electrode setup is highly needed. This work proposes an inexpensive method of building standard biocompatible electrodes for multi-well in vitro experiments. The setup is made of inexpensive, printed circuit board (PCB) technology and is designed for a standard 24-well culture dish. The electrodes are designed to provide effective stimulation at the bottom of the wells. The electric field distribution of the electrodes in the culture medium is analyzed in COMSOL Multiphysics. The electrodes are plated with gold to provide an interface that is compatible with the target biological cells. It also provides an option for setting the electrodes in bipolar or tetrapolar configurations, as required by the experiment protocol. Moreover, there is the flexibility of connecting any number of wells in a series to provide the same modulation to different sets of experiments.
# System Proposal for Mass Transit Service Quality Control Based on GPS Data
## 2 Related Studies
[18], the data used to check and improve public passenger transport planning may be obtained through three different data sources: surveys of public transport users, and automatic passenger counter (APC) and automatic vehicle location (AVL) systems. The AVL systems provide the data source for vehicle location and times of arrival and departure at stops. Several positioning technologies are used by AVL systems. According to [PERSON] et al. [19], they can be broken down into three categories: radiolocation systems that establish position with radio signal triangulation techniques, estimation of the direction and distance travelled (dead reckoning) or proximity detection systems. Currently, radiolocation systems are the most used, particularly the Global Positioning System (GPS) [20], although proximity detection systems are also used, such as the system proposed by [PERSON] et al. [21], who used a Radio Frequency Identification (RFID) system to detect the presence of the vehicle at a stop in order to assist travellers with special needs. Examples of AVL system proposals using GPS have been put forward by [PERSON] et al. [22], who used GPS data to analyse public transport travel time variability; [PERSON] et al. [23], who used GPS data to determine optimal slack time for planning schedule-based services; [PERSON] et al. [24] studied traffic conditions in the transport network using GPS data; [PERSON] et al. [25] used the same data to estimate bus speed; [PERSON] et al. [26] used GPS data from private vehicles to calibrate the input data used in different techniques for modelling road traffic and the effects that it causes. GPS is a case of a global navigation satellite system (GNSS), which has become popular due to its features and the low cost of GPS receivers. In recent years navigation systems have been used that are able to use the signal from several different constellations of satellites to obtain more precise position measurements than those obtained using only signals from GPS satellites; along these lines, [PERSON] et al. [27] studied the use of the GLONASS constellation. The system described in this article evaluates adherence to scheduled passing times on a public road passenger transport network. The system autonomously and continuously records all the events that describe the activity carried out by the transport vehicle (bus) by referencing them in space and time with a level of granularity that enables reliability evaluations using the various indicators mentioned above. The purpose of this may be to adapt to different modes of transport, such as long-distance transport routes on which timetable adherence at stops is a key element in the quality of service, or urban transport, where quality is associated with vehicle frequency. Our proposed system uses an AVL system that periodically records the vehicle position data every time the GPS reading is taken, integrating these data into the model used to represent the transport network, the planning of services and the activities carried out by the transport company.
## 4 Simulation Results
### System Cost Analysis  
An examination of the breakdown of system costs gives further indication as to why HOMER favours the use of the VRB over diesel generators. Table 5 summarises the system costs as calculated by HOMER.  
The figures presented in Table 5 are calculated by HOMER assuming a 25-year project lifetime. The total net present cost (NPC) at the end of the 25-year project is $21,832,144 giving a levelized cost of energy of 29.6 \(\upvarepsilon\)/kWh. Although the 1 MW/18 MWh VRB has a high initial capital cost of $6.05 million, the total annualized cost is comparable to the two diesel generators, despite this high initial capital cost.  
It is in the cost of diesel and the operating and maintenance costs that the VRB distinguishes it from the diesel generators. The VRB has relatively low annual operating and maintenance (O&M) costs with the added benefits of a negative annualized replacement (owing to the long life of the electrolyte) and the lifetime fuel source effectively covered in the initial capital cost. In contrast, the diesel generators have higher O&M costs with the disadvantage of being dependent on an increasingly expensive fuel source. It is for these reasons that HOMER favours the use of a VRB to diesel  
Figure 11: 600 kW diesel generator use in wind/diesel system without VRB storage.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Component**} & **Initial** & **Annualized** & **Annualized** & **Annual** & **Annual** & **Total** \\  & **Capital** & **Capital** & **Replacement** & **O\&M** & **Fuel** & **Annualized** \\ \cline{2-7}  & (S) & (S/year) & (S/year) & (S/year) & (S/year) & (S/year) \\ \hline Fuhrlander 250 & 5,950,000 & 465,449 & 158,066 & 153,300 & 0 & 776,815 \\ Diesel \#1 & 108,000 & 8,448 & 638 & 61,290 & 69,240 & 139,616 \\ Diesel \#2 & 216,000 & 16,897 & 2,094 & 132,300 & 122,387 & 273,677 \\ Battery & 6,050,000 & 473,272 & –42,243 & 11,270 & 0 & 442,298 \\ Converter & 720,000 & 56,323 & 19,127 & 0 & 0 & 75,451 \\ Totals & 13,044,000 & 1,020,389 & 137,682 & 358,160 & 191,626 & 1,707,857 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cost breakdown.  
generators. To further illustrate this point, cost data from the comparison between identical wind/diesel systems with and without a VRB installed is included in Table 6.  
As Table 6 indicates, considerable financial and environmental benefits are to be gained in installing a VRB in this system. An improvement of over 10% renewable penetration is achieved with the use of a VRB. This equates to $11.9 million in savings over the 25-year life of the project and over 1,000 tonnes per year reduction in carbon emissions. The consumer is afforded a 16.1 \(\upvarepsilon\)/kWh break in the cost of electricity. Furthermore, the wind/diesel system has far more financial exposure to the rising price of diesel. With the inevitable increase in the cost of diesel fuel, the cost differences between the two systems are realistically expected to be even greater.
# Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry
## 4 Swing and the Separation Bubble
This issue is addressed experimentally for the first time. Figure 11 shows the flow field around the separation point for a range of spin rates, \(0<\alpha<0.12\), at a fixed Reynolds number (\(\text{Re}=1.69\times 10^{5}\)), which corresponds to a bowling speed of 78 mph. The typical spin rate for a bowler is \(\alpha\)-0.06 (assuming a backspin of 10 rev/s). It can be seen that the separation bubble exists with rotation, even at the maximum spin rate of \(\alpha=0.12\). The laminar and turbulent separation points are broadly unaffected by \(\alpha\). Figure 11: Time-averaged velocity magnitude and streamlines at \(\text{Re}=1.69\times 10^{5}\) on the NSS for different spin rates, \(\alpha\) (equivalent bowling speed of 78 mph). Separation and reattachment angles shown to nearest \(5^{\circ}\). Figure 10: Instantaneous velocity magnitude and streamlines at \(\text{Re}=1.69\times 10^{5}\), showing the intermittency of the separation bubble (equivalent bowling speed of 78 mph). In the context of a cricket match, Figure 3 (supported by the velocity and streamline plots presented in Figures 9 and 10) can be used to explain the bowling tactics used by the fielding side. Consider a bowler releasing a ball at a speed of between 80 mph (\(\mathrm{Re}=1.7\times 10^{5}\)) and 90 mph (\(\mathrm{Re}=2.0\times 10^{5}\)). The ball will decelerate during flight due to the drag force acting against it. If the ball is new, then it will experience CS throughout its trajectory, from the release point to the batsman. RS does not occur because of a high critical Re, owing to the low surface roughness on the NSS. Hypothetically, RS would occur if the bowler could achieve release speeds in excess of 100 mph (\(\mathrm{Re}>2.2\times 10^{5}\))--this is exceptionally rare in cricket. Consider now a ball which has aged 25 overs, again bowled at a speed between 80 mph and 90 mph. Figure 3 shows that RS will occur from the release point, by virtue of the increased surface roughness of the NSS. Figure 9 reveals that this is because of the formation of an LSB forming on the NSS. The sideways deviation caused by RS will be less than that in the CS regime because the non-dimensional side force (\(\mathrm{C_{y}}\)) is smaller in magnitude. As the ball decelerates to around 78 mph (\(\mathrm{Re_{crit}\text{$\sim$}}1.7\times 10^{5}\)), the LSB will become intermittent (Figure 10) before CS occurs. The combination of CS and RS may suppress any lateral deviation in the trajectory of the ball. Faster bowlers (those near 90 mph) will avoid this nulling effect, as the ball will experience more RS before it decelerates to below \(\mathrm{Re_{crit}}\), where it switches to CS. The experimental data (including that associated with the Magnus effect, which is explained in more detail below), can be incorporated into a computational model. This may be a useful tool to professional cricketers and coaches (to understand the implications of different bowling scenarios), and to sports technologists who predict cricket ball trajectories (e.g., Hawkeye).
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 2 Background on Architectures for Remote Sensing Data Integration
### Spatial Organization of Remote Sensing Data  
The main two models for spatial organization of multi-source remote sensing data are: (1) the satellite orbit stripe or scene organization based on the spatio-temporal recording system; and (2) data tiling organization based on the globally meshed grid [13; 14].  
In the first model, the original orbit data are organized according to reception time, and they are stored in a scene unit. Each scene unit is identified by upper, lower, left and right four-point latitude and longitude coordinates. This simple organization method has been adopted by remote sensing data centers around the world, such as NASA's Earth Observing System (EOS) [15] and the CCRSDA [8]. However, due to the four-point index queries in database systems, this model has obvious shortcomings for massive data retrieval and quick access. Therefore, in this paper, we have proposed the LSI model to reduce the dimension of the query index, and this will be described in Section 4.1.  
In the second model, remote sensing images are subdivided into buckets of grid shape, and each bucket is labeled by a unique geocode according to certain coding rules. This is especially useful in database systems where queries on a single index are much easier or faster than multiple-index queries. Furthermore, this index structure can be used for a quick-and-dirty proximity search: the closed points are often among the closest geocodes. The longer a shared prefix is, the closer the two buckets are [16]. This model is generally applicable to the image cache systems and map publishing systems typically used by Google Earth, Bing Maps, and Tiandi Maps of China, for example [17]. However, due to image segmentation and pyramid construction, this model means the amount of data increases by approximately one-third, so that a larger data storage space is required; it also generates a large number of small tiles, which can easily cause a single point of failure, and are not conducive to data storage and management using the distributed file system [18]. Hence, in this paper, we proposed a logical partition index and virtual mapping construction strategy for scene-based remote sensing data, and this will be also described in Section 4.1.
###### Abstract  
The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB's three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component's error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network.  
ultra-wideband; robotic total station; neural network; indoor positioning
## 3 Control Design
### Sliding Mode Control with a Backstepping Technique
 \tag{20}\]  
The control vector is chosen  
\[u=F_{2}^{-1}(x)\big{(}\dot{x}_{3d}-F_{1}(x-K_{2}s_{2}-\lambda_{2}e_{3}-s_{1}- \eta_{2}\text{tanh}\,\frac{s_{2}}{\psi_{2}}\bigg{)}\bigg{)} \tag{21}\]  
where \(K_{2}=diag(k_{21},k_{22},k_{23})\in R^{3\times 3}\) is an arbitrary positive matrix, \(\eta_{2}=diag(\eta_{21},\eta_{22},\eta_{23})\in R^{3\times 3}\) is a robust gain positive diagonal matrix of the sliding mode control \(s_{2}\), it is chosen how to \(\eta_{2i}>|\Delta_{2i}|t||(i=1,2,3)\), \(\psi_{2}=\left[ \begin{array}{cc}\psi_{21}&\psi_{22}&\psi_{23}\end{array}  \right]^{T}\in R^{3\times 1}\) is a width vector, and \(\text{tanh}\bigg{(}\frac{s_{2}}{\psi_{2}}\bigg{)}\) is defined in Appendix C.  
**Assumption 1**. : _The perturbation, \(\Delta_{2}(t)\), varies with respect to time, and it is bounded \(\|\Delta_{2i}\|t\|\|\leq\eta_{2i}(i=1,\ldots,3)\)._  
Consider the Lyapunov function candidate  
\[V_{2}(s_{1},s_{2})=V_{1}(s_{1})+\frac{1}{2}s_{2}^{T}s_{2} \tag{22}\]  
The derivative of the Lyapunov function (22) is  
\[\dot{V}_{2}(s_{1},s_{2})=\dot{V}_{1}(s_{1})+s_{2}^{T}\dot{s}_{2}(t) \tag{23}\]Replacing (21), and (18) into (23), the derivative of the Lyapunov function can be rewritten as follows:  
\[\dot{V}_{2}(s_{1},s_{2})=\dot{V}_{1}(s_{1})+s_{2}^{T}\dot{s}_{2}=-\sum_{i=1}^{2} \left(s_{i}^{T}K_{i}s_{i}+s_{i}^{T}(\Delta_{i}(t)-\eta_{i}\tanh(s_{2}))\right)- z_{3}^{T}\lambda_{2}s_{1}\leq-\zeta^{T}\Xi\zeta+\delta \tag{24}\]  
where \(\zeta=\left[ \begin{array}{c}s_{1}\\ z_{3}\\ e_{3}\end{array} \right]\in R^{9\times 1}\) and  
\[\Xi=\left[ \begin{array}{ccc}K_{1}&-\frac{1}{2}\lambda_{2}&0\\ -\frac{1}{2}\lambda_{2}&\lambda_{2}^{T}K_{2}\lambda_{2}&K_{2}\lambda_{2}\\ 0&\lambda_{2}^{T}K_{2}&K_{2}\end{array} \right]\in R^{9\times 9}.\]  
To guarantee the stability and robustness of the controlled system, the \(\dot{V}_{2}(\zeta(t))\) is a negative-definite function. The parameters \(K_{1}\), \(K_{2}\), and \(c_{2}\) are chosen how the matrix \(\Xi\) is a positive definite matrix.
## 2 Operating Principle of the Active Balancing System  
### Balancing System  
The liquid transfer balancing system introduced in this paper is driven by compressed air. The system comprises a control subsystem, a balance actuator built in the hollow rotor system, and an air subsystem. A flowchart of the active balancing system is presented in Figure 1. The operating principle can be indicated as follows: several vibration sensors and a data collection system are used to measure the vibration of the hollow rotor system. From these vibration data, the unbalance vibration amplitude and phase are continuously obtained by the control unit, and, when the vibration amplitude exceeds its set value, the control unit will start the balancing operation. Based on the detected unbalance vibration, the control unit determines the compensation mass and transmits control commands to the solenoid valves through the input/output (I/O) card. Compressed air from the air compressor is then led to the balance actuator that is pre-built in the hollow rotor system. Through the gas channels in the actuator, compressed air flows into the required chamber, and then the liquid in the relevant chamber is impressed into the contrary chamber. The mass distribution of the balance disc is altered through the liquid transmission between two contra-positioned chambers, and a compensation mass is generated to decrease the unbalance vibration of the rotor system. The balance actuator stops working and the solenoid valves are closed until the unbalance vibration amplitude increases to its set value again.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Centralized Context Cloud Architecture
#### 3.1.6 Hop Limit  
The hop limit is an essential feature when there are exchanges between vehicles. It permits us to avoid propagating data too far from its relevance area or after they expire. Indeed, a vehicle far from an RSU is not aware of the validity state of the data being propagated. Natively, the CAM is a single-hop message. Its routing header does not have any information about the hop limit. Thus, it is necessary to add it to the ICN payload. Let us note that if the hop limit is 1, we can use a default value instead of adding the information.
## 2 Analysis Methods
### CCHP Model  
For the compressor and turbine, isentropic efficiency is provided to represent their actual performance [24]:  
\[\eta_{\text{C}}=\frac{(h_{\text{is},e,\text{C}}-h_{\text{i},\text{C}})}{(h_{ \text{e},\text{C}}-h_{\text{i},\text{C}})} \tag{1}\]  
\[\eta_{T}=\frac{(h_{\text{i},\text{T}}-h_{\text{e},\text{T}})}{(h_{\text{i}, \text{T}}-h_{\text{is},e,\text{T}})} \tag{2}\]  
Enthalpy at the exit state is acquired through the property association \(f\) by using the REFPROP software [39]:  
\[h_{\text{is},e}=f(s_{i,e,e},p_{e}) \tag{3}\]  
where \(s_{\text{is},e}\) equals to the inlet entropy during isentropic compression/expansion processes.  
The power is calculated by:  
\[\dot{W}_{\text{C}}=\dot{m}_{\text{CO}_{2}}(h_{\text{e},\text{C}}-h_{\text{i}, \text{C}}) \tag{4}\]  
\[\dot{W}_{T}=\dot{m}_{\text{CO}_{2}}(h_{\text{i},\text{T}}-h_{\text{e},\text{T }}) \tag{5}\]  
For heat exchangers, the properties of supercritical CO2 can largely change in a small temperature band. Therefore, the heat exchanger can be discretized many small sections that each of them can be considered with constant properties [26]. The discretization is completed by splitting the total enthalpy variation of hot fluid into several equal parts. The heat transfer and mass flow rate of water or air for each section n are respectively calculated by:  
\[\dot{Q}_{n}=\dot{m}_{\text{CO}_{2}}\big{(}h_{\text{CO}_{2},n+1}-h_{\text{CO}_{ 2},n}\big{)} \tag{6}\]  
\[\dot{Q}_{n}=\dot{m}_{\text{water or air}}(h_{\text{water or air},n+1}-h_{\text{water or air},n}) \tag{7}\]  
\[\dot{m}_{\text{water or air}}=\frac{\sum\limits_{n=1}^{N}\dot{Q}_{n}}{(h_{ \text{water or air},N+1}-h_{\text{water or air},1})} \tag{8}\]  
The HST and LST are presumed to be completely insulated and the inlet and outlet conditions have no difference [36]:  
\[h_{\text{e},\text{HST}}=h_{\text{i},\text{HST}},h_{\text{e},\text{LST}}=h_{ \text{i},\text{LST}} \tag{9}\]  
Isenthalpic process is assumed through the throttle valve:  
\[h_{\text{e},\text{TV}}=h_{\text{i},\text{TV}} \tag{10}\]  
The heat transfer rate of CS is written as [24]:  
\[\dot{Q}_{\text{CS}}=\dot{m}_{\text{CO}_{2}}(h_{\text{hot},\text{i}}-h_{\text{ hot},\text{e}})=\dot{m}_{\text{CO}_{2}}(h_{\text{cold},\text{e}}-h_{\text{ cold},\text{i}}) \tag{11}\]