esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual-Band Monopole Antenna for RFID Applications
## 1 Introduction  
RFID is a recent outstanding technology which uses radio frequency (RF) signals for the identification of objects and has been employed in many applications, such as service industries and moving vehicle identification [1]. In general, RFID is a paste and tag antenna for the purpose of identification and tracking by radio. An RFID system contains three major components: a transponder, a reader, and a computer for data processing. The reader (Interrogator) includes an antenna, which communicates with the TAG [2]. The antenna should be inexpensive, light, simple, and easy to fabricate [3].  
Different frequency bands of the electromagnetic spectrum, such as 130 kHz (low-frequency), 13.5 MHz (high-frequency), 900 MHz (ultra-high-frequency), 2.4 GHz, 5.8 GHz, and 24 GHz (microwave frequency) are allocated for RFID applications [4]. Recently, several antenna designs with single, dual, and multi-band characteristics were reported in the literature for RFID applications [5; 6; 7; 8]. A closely-spaced loop antenna array with directional radiation patterns is proposed in [5] to operate at 900 MHz. In [6], an active RFID tag antenna operating at 2.4 GHz has been introduced for RFID applications. In [7], a dual-band circularly-polarized antenna is designed for covering both 900 MHz and 2.4 GHz frequency bands. Another design with multi-band function is proposed in [8], which can cover 0.9/2.4/5.8 GHz RFID frequency bands. In this study, 2.4 GHz and 5.8 GHz are selected as the desired dual operation bands for RFID applications [9; 10; 11; 12].  
Printed monopole antennas are very attractive and suitable for dual-band or multi-band applications owing to their simple structures, compact size, good impedance matching, and omnidirectional radiation patterns [13]. Multi-band monopole antennas can be realized by employing parasitic structures, slots, or slits in the antenna configuration or using various radiating elements with different shapes [14]. Configuration of the presented design consists of a modified F-shaped radiation patch, a rectangular microstrip feed-line, and a ground plane. The antenna with a planar structure is designed on an FR-4 substrate. Its frequency operation covers the frequency bands from 2.2-2.6 GHz and from 5.3-6.8 GHz. The antenna provides omnidirectional radiation patterns at both of the desired frequency bands (2.4 and 5.8 GHz).  
In contrast to the reported RFID antenna designs [5; 6; 7; 8; 9; 10; 11; 12], the presented design exhibits wider impedance bandwidth at lower and upper operation bands with higher gain values, especially at the upper band. The antenna operation bandwidth is highly sensitive to small changes in the fabrication process. There must be very strict guidelines in order to avoid shifting of the resonance to lower or higher frequencies outside the required narrow band of interest. This drawback could be avoided if a wideband antenna is designed instead [15]. The proposed RFID antenna provides more than 85% total efficiency characteristic at the resonant frequencies (2.4/5.8 GHz).
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 7 Conclusions  
In this paper, we addressed the problem of the efficient processing of range monitoring queries over the objects moving along the road network. Given a set of geographically distributed moving objects on the road, the primary goal of our study is to keep the results of queries up to date, while incurring the minimum communication cost and server computation cost by letting the moving objects evaluate several queries that are relevant to them. To achieve this, we introduced the concept of vicinity region and proposed a new spatial index structure, namely the SSP-tree. By assigning each moving object (i) a vicinity region and (ii) a set of query segments inside this region, the moving object can locally monitor whether it may affect the results of nearby queries. The SSP-tree is used to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities. We also described the details of how each moving object and the server communicate each other to cooperatively process range monitoring queries. Through a series of simulations, we showed the effectiveness of proposed method for processing static range monitoring queries in the road network.  
This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright Commission in 2017.  
[PERSON] initiated the idea, developed the research concept, and wrote the manuscript.  
[PERSON] oversaw all of the work and revised the manuscript.  
The authors declare no conflict of interest.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor Type** & **Detectable Gases** & **Linearity** & **Cross Sensitivity** & **Power Consumption** & **Maintenance** & **Response Time (TWO)** & **Life Expectancy** \\ \hline Electro-chemical [78] & Gases which are electrochemically active, about 20 gases & Linear at room temperature & Can be eliminated by using chemical filter & Lowest, very little power consumption & Low & \textless{}50 s & 1–2 years \\ \hline Catalytic [79] & Combustble gases & Linear at 400 °C to 600 °C & No meaning when measuring mixed gases & Large, need to about 400 °C to 600 °C & Low sensitivity with time due to poisoning and burning out & \textless{}15 s & Up to 3 years \\ \hline Solid-state [80] & About 150 different gases & Linear at operational temperature & Can be minimized by using appropriate filter & Large, need heating element to regulate temperature & Low & 20 s to 90 s & 10+ years \\ \hline Non-dispersive Infrared [81] & Hydrocarbon gases and carbon dioxide & Nonlinear, need linear procedure & All hydrocarbons share a similar absorption band, make them all cross sensitive & Small, mainly consume by the infrared source & The least & \textless{}20 s & 3–5 years \\ \hline Photo-ionization [82] & Volatile organic compounds (VOCs) & Relatively linear & Any VOCs with ionization potentials less than the ionizing potential of the lamp used will be measured & Medium, mainly consume by the ultraviolet source & The lamp requires frequent cleaning & \textless{}3 s & Depend on the Ultraviolet lamp, normally 6000 h \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the five types of gas sensors.
## 3 Application Domain
### Data Preprocessing  
In this study, three participants were invited for data collection. All three collected data on general roads, while two also collected data on highways. Taking the first participant as an example, using the sliding window method (with a sliding speed of five time series points), all of the collected time series data were divided into 567 driving segments. The driving duration varied among participants, resulting in a different number of final driving segments. Each clip has 100 timing points (approximately 50 s of driving time). The clustering method used in this study is described below. The first step is to set a threshold. The dynamic timing warping (DTW) method is then used to compare two driving clips. When the difference between two driving clips is within the threshold, they belong to the same cluster. A new data cluster will be created when the newly-added clip does not belong to any cluster. This study followed the above method and organized the 567 driving clips into 73 clusters. The setting of this threshold is arbitrary and has no substantive significance. When this value is set too high, the number of clusters generated will be relatively small (relatively, it means that the difference within the cluster is more significant). On the contrary, relatively more clusters will be generated when set too low. When there is relatively enough data on subjects in the future, the choice of this value will have more substantial significance.
## 1 Introduction  
Among the seventeen Sustainable Development Goals (SDGs) of the United Nations, there are \"good health and well-being\" (SDG three) and \"sustainable cities and communities\" (SDG eleven). To support these goals, new technologies related to data survey can provide important information for decision makers. According to the 2014 UN-ESA report, the percentage of people living in urban areas is expected to increase from 54% up to 66% by 2050. Urban gardens play a key role in improving the living quality of citizens. Not only do urban gardens mitigate the temperatures of heat islands, but they also provide green spaces to visitors who can experience being close to a natural environment [1]. In this work, we assess the feasibility of using internet of things (IoT) sensors and a detailed and accurate three-dimensional (3D) model of a historical urban garden to map comfort indicators related to climatic variables. We foresee that these technologies will, in the near future, be replicated for better planning of parks, gardens, and visits, as well as for a better understanding of human behavior in green areas [1].
# Fake Base Station Detection and Link Routing Defense +
## 4 Fake Base Station Threat
### Faulty but Legitimate Base Station  
Our blacklisting identification focuses on the attacker (fake base station) and not on accidental/unintentional failures (faulty base station). We therefore distinguish between fake and faulty but unintentional, the latter of which can be caused by the lack of connectivity services at the time of the user equipment access. The base station cannot provide connectivity at some times, which is often temporary, i.e., the faulty base station operates correctly at other times. The 3 GPP 5G NR supports such temporary faulty cases not having connectivity, and our work builds on the protocol for such faulty cases. For an accidentally _faulty base station_, we continue with the NAS process for the connectivity setup, but if it fails, the base station sends an RRC release message to the user equipment along with the rejection cause, e.g., authentication reject. The accidentally faulty base station thus releases the RRC connection with the user equipment, while the adversarial fake base station continues to hold on to the RRC connection, depriving its availability further. The faulty base station differs from the fake base station in that it explicitly releases and ends the connectivity setup. The faulty (and legitimate) base station operation is described in Figure 2c (in contrast, the legitimate and working-well base station operation is depicted in Figure 2a).
## 3 Model Formulation
### User Cost (\(C_{u}\))
#### 3.1.3 User In-Vehicle Cost (\(C_{ui}\))  
Similarly, the product of demand, in-vehicle time and the value of time can define the user in-vehicle cost (\(C_{ui}\)). The \(C_{ui}\) is formulated based on the average journey time and is calculated in two main parts: the run time and the dwell time. Running costs for all passengers (\(C_{nii}\)) are equal to the link travel distance from stop \(i\) to station \(j\) in route \(k\) (\(L_{ijk}\)) divided by the average bus real speed (\(V_{k}\)).  
The dwell time is the boarding and alighting time at the feeder bus stops (\(t_{aF}\)) and rail stations (\(t_{dT}\)). The observation of feeder bus stops and rail stations revealed that the dwell time is an important part of in-vehicle travel time. This time will increase the user, operation and social costs for both feeder bus and train travel and consequently has a significant effect on the total cost of the transit network.  
Dwell time will increase user costs by increasing the in-vehicle time for a boarding passenger. In addition, this time cost will increase operation costs by increasing fuel consumption, maintenance and personnel costs. Accordingly, with the increase in pollution, noise, greenhouse gases, _etc._, the social costs will also be higher.  
Since the time spent on boarding and alighting has an important role in user in-vehicle time, we tried to present a new concept for determining such costs. Moreover, because of the variation in the time spent on boarding and alighting, which is dependent on the dwell time at each of the bus stops, the geometric series equation was adopted to develop a more accurate model for distributing the dwell cost of the bus stops along the routes.  
The average cost of dwell time is determined by demand multiplied by the rate of passenger boarding and alighting. The derivation of the dwell cost for feeder buses and trains is discussed in detail in Appendix A. Therefore, the in-vehicle cost, including in-bus and in-train cost, for route \(k\), is given as follows:  
\[C_{ui}\ =\ C_{rui}\ +\ C_{dui} \tag{6}\]  
where \(C_{rui}\) is the running cost for all passengers, given as follows:  
\[C_{rui}=\mu_{I}\left[\frac{1}{V_{k}}\sum_{i=1}^{I}\left[q_{i}\times\left(\sum _{j=1}^{J}L_{ijk}\right)\right]+\left(Q_{k}\times t_{Tj}\right)\right] \tag{7}\]  
and \(C_{dui}\) is the average cost of dwell time as described in Appendix A, given in the following equation:  
\[C_{dui}=\mu_{I}\left[\left(\frac{1}{2}(n_{k}+1)\times Q_{k}\times t_{dF} \right)+\left(Q_{k}\times t_{dT}\right)\right] \tag{8}\]  
The first and second terms in Equations (7) and (8), respectively, denote the feeder bus and train user costs. In Equation (6), \(C_{rui}\) represents the running cost for all passengers, which is equal to the link travel distance from stop \(i\) to station \(j\) in route \(k\) (\(L_{ijk}\)) divided by the average bus speed on route \(k\) (\(V_{k}\)). \(t_{Tj}\) denotes the riding time between station \(j\) and the destination of the train regardless of boarding and alighting times.
## 4 Conclusions  
In this study, we integrated a satellite and SGCMGs and designed a model with the gimbal angular velocity as input. We showed the proposed SISM and BSDW can generate optimal gimbal angular velocity input without solving inverse kinematics. As the target gimbal angle can be explicitly designed by the proposed control system, it can guide the initial gimbal angle, which is advantageous for the next mission. Numerical simulation showed that the singularity avoidance performance up to the target gimbal angle, and the guidance performance of the gimbal angle, are improved with respect to the conventional method owing to the optimality of the gimbal angular velocity input. Moreover, we showed that it is possible to perform agile attitude maneuvers considering the next mission.  
Although this paper assumes that the satellite is a rigid body, robustness against external disturbances such as solar array paddles and fuel sloshing should also be evaluated. This is a task to be verified in the future. However, we demonstrated that the vibration of solar array paddles, which is not considered in the SDRE controller, can be stabilized by the state-dependent weight matrix as shown in [21]. Therefore, even with the proposed method, it can be stabilized in the same way, and it is considered that it also has the robustness against the above disturbances.  
**Acknowledgments:** This work was supported by education and research fund from Keio University.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the proposed method and verification simulations; [PERSON] performed the numerical simulations; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(\Omega\) & Wheel speed of the SGCMG  
\(\hat{g}\) & Gimbal axis vector of the SGCMG  
\(\hat{g}_{i}\) & \(i\)-th gimbal axis vector of the SGCMG (\(i\) = 1, 2, 3, 4)  
\(\beta\) & Skew angle of four SGCMGs  
\(\delta_{i}\) & \(i\)-th gimbal angle of the SGCMG (\(i\) = 1, 2, 3, 4)  
\(r\) & Reference of the state vector  
\(x\) & State vector  
\(T_{ref}\) & Reference of the torque vector  
\(T\) & Torque vector produced by the angular momentum exchange  
\(T_{c}\) & Control torque input vector by the controller  
\(\delta\) & Gimbal angular acceleration vector  
\(\delta_{ref}\) & Reference of the gimbal angular velocity vector  
\(\delta\) & Gimbal angular velocity vector  
\(\hat{\delta}_{c}\) & Control gimbal angle input vector by the controller  
\(\delta\) & Gimbal angle vector  
\(\tilde{\delta}_{e}\) & Gimbal angular acceleration error vector  
\(\delta_{e}\) & Gimbal angular velocity error vector  
\(\delta_{e}\) & Gimbal angle error vector  
\(\delta_{r}\) & Target gimbal angle vector  
\(A(\delta)\) & Jacobian matrix of SGCMGs depending on \(\delta\)  
\(A^{\prime}(\delta_{e})\) & Jacobian matrix of SGCMGs depending on \(\delta_{e}\)  
\(m\) & Singularity parameter  
\(q\) & Quaternion vector of the satellite attitude  
\(q_{e}\) & Quaternion error vector of the satellite attitude
## Materials and Methods
### Characterization Methods  
#### 2.3.1 Fourier-Transform Infrared Spectroscopy (FTIR) Investigations  
The structural characterization of obtained MAPLE thin films and corresponding starting materials (dropcast) were performed using a Nicolet iN10 MX FTIR microscope (Thermo Fisher Scientific, Waltham, MA, USA) with a mercury cadmium telluride (MCT) liquid nitrogen-cooled detector, performed in reflection mode (4 cm\({}^{-1}\) resolution), within the range of 4000-600 cm\({}^{-1}\). The registered scans were co-added and transferred to absorbance values using OmnicPicta software (Thermo Scientific, Waltham, MA, USA). IR spectra maps displayed the spectral absorbance of characteristic wavenumbers in color changes starting with blue color (the lowest intensity) and increasing through green, yellow, and red colors (the highest intensity).
# Assessment of DPPC Liposome Disruption by Embedded
## 3 Results
### The ANS Emission in DPPC Liposomes
#### 3.1.1 Kinetics of ANS Adsorption on DPPC  
The time-dependent FI changes of ANS during its interaction with DPPC liposomes in the gel phase without and with TM or Toc at different concentrations are presented on Figure 2. In the case of pure DPPC adding of ANS to the membranes, this causes the rapid increase of FI, which occurs almost instantly and then remains at a constant level. When topcuberols are embedded into DPPC liposomes, the kinetics of ANS fluorescence changes to the biphasic character. In the presence of Toc or TM, we first observe sharp FI raising (fast component) and then slowly increasing component, leading to its plateau. FI of first fast component grows proportionally with increasing topcuberol concentration, whereas the slower component reaches its plateau at longer times with increasing topcuberols concentrations. A similar behavior of ANS kinetics was observed for the liposome systems containing other topcuberol derivatives [21; 42].  
The recorded kinetics were fitted to two models: a double exponential component and bidose response model. In both models, the data were well-fitted to two components, which suggests the existence of two independent binding sites for ANS in DPPC. The first sharp component with obtained times on the order of a tenth of a second is related to the binding of ANS molecules to the available sites on the outer liposome leaflet; it is a diffusion-controlled process. The second, slower component, with the time in the range from 200 to 800 s, describes the motion process of ANS molecules deeper into the inner leaflets of the bilayer connected with the disruption of the membrane structure due to the presence of topcuberols molecules. Similar assumptions regarding the presence of two of the ANS-binding mechanisms to liposomes have also been reported by other authors [33; 43]. In both stages, the observed increasing fluorescence intensity with a rising amount of TM or Toc reflects the formation of more binding sites, which depend on the topcuberolsconcentration. However, the same amount of ANS added to the liposome suspension with TM exhibits lower FI compared to \(\mathrm{Toc}\), indicating that fewer ANS molecules bind to the liposomes with \(\mathrm{Toc}\). It is correlated with limited access of ANS molecules to the DPPC structure because of bigger structural changes in DPPC caused by embedded TM.
## 2 Materials and Methods
### Finite Element Analysis  
#### 2.2.1 Geometry and Analysis Conditions  
On the basis of the above generated geometry, 2D modeling of the sealing part at final shut-off position was conducted using a commercial software, Inventor 2019, as shown in Figure 10. The contact stress rises with the increase of the slope angle based on the Hertzan theory shown in Equation (10) and Figure 11, so simulations were carried out only in the case of minimum (66.1\({}^{\circ}\)) and maximum angle (86.1\({}^{\circ}\)). \[P_{contact}=0.798\sqrt{\frac{F}{l(\frac{1-v_{1}^{2}}{E_{1}})+(\frac{1-v_{2}^{2} }{E_{2}})}},\,F=P_{working}\times\cos\varphi \tag{10}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline Diameter of the disc (mm) & 190.1 & Offset 1, \(E\) (mm) & 2.5 \\ Thickness of disc, T (mm) & 15 & Offset 2, \(H\) (mm) & 23 \\ Outer diameter of the seal (mm) & 218 & Offset 3, \(\gamma\) (\({}^{\circ}\)) & 10 \\ Thickness of seal (mm) & 7 & Half conical angle, \(\alpha\) (\({}^{\circ}\)) & 13.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The value of triple offset butterfly valve (TOBV) parameters. Figure 9: Procedure for generation of slope angle of the contact surface. In order to investigate contact stress behavior according to the seal design, a series of finite element (FE) simulations were performed with the different total number of seal layers by laminating alternately the American Society for Testing and Materials (ASTM) A240 Grade 316 stainless steel (SS) and the graphite (G) (1-layer (SS), 3-layer (SS/G/SS), 5-layer (SS/G/SS/G/SS), 7-layer (SS/G/SS/G/SS/G/SS)) and thickness of graphite (0.6 mm\(\sim\)1.2 mm at the interval of 0.2 mm), as shown in Figure 12. Full-thickness (7 mm) of the seal is fixed, so thickness of 1-layer stainless steel depending on that of the graphite was calculated according to the number of laminated layers, as listed in Table 2. The analysis models were simulated according to the temperatures (the room temperature of 22 \({}^{\circ}\)C and the high temperature of 350 \({}^{\circ}\)C) using commercial finite element software, ANSYS workbench ver. 18. Figure 11: Scheme of normal load (\(F\)) acting on the contact surface. Figure 12: Analysis model with the different number of layer. SS, stainless steel; G, graphite. Figure 10: Two-dimensional (2D) geometry of triple offset butterfly valve (TOBV). The chemical composition and mechanical properties of the ASTM A240 Grade 316 stainless steel are listed in Table 3. Thermal and mechanical properties of A240-316 (retainer and steel layer), A216 Wrought Carbon with Grade B (WCB) (body), and graphite according to the temperatures (25 \({}^{\circ}\)C-350 \({}^{\circ}\)C) are listed in Table 4 [16; 22; 23].
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 2 Conceptual Foundations
### Additional Anthropogenic Impacts on the Environment and Society  
In addition to natural disasters, many locations in the UK are susceptible to anthropogenic risks. One particular activity that poses a risk to safety and the environment is the illegal dumping of waste. Illegal waste sites (IWSs), comprising both unlawful dumping sites and official sites that have exceeded their legal permit limits, are a global issue [21] as they can impact human health due to releases of highly toxic fumes [22] and pollute and degrade air, soil and water quality, resulting in long-term ground contamination [23]. The combustion of IWSs is also a regular issue that proves costly and endangers lives. Dealing with IWSs costs the English economy an estimated GBP 1 billion annually, a figure that has increased by 55% since 2015, and is recognised as a significant factor in preventing the UK from achieving its climate ambitions [24].