esa-sceva/satcom-chunk-collection huggingface dataset:
# A Microfluidic Approach for Biosensing DNA within Forensics
## 4 Biocompatibility of Materials for Forensic Microfluidic Devices
By using hydrophilic channel walls it is easier to introduce these kind of solutions into the chip without bubble formation [17]. Since the fabrication of microfluidic channels in glass and silicon often requires cleanroom facilities, due to which the production costs for such chips are relatively high, chips made of these materials are not very suitable as disposables (i.e., for single use only) [17]. Plastic devices can be made using e.g., hot embossing, micromilling, casting or injection molding, for which a cleanroom environment is not necessary, which makes such devices cheaper. Due to their relatively low price plastic chips are (most) appropriate for single-use (i.e., disposable), which in particular circumvents cross-contamination issues [68; 69]. Whereas plastic chips are attractive because of their bio(chemical)compatibility, another advantage of this material is its low thermal conductivity [70]. Materials with a low thermal conductivity are desirable for continuous-flow devices for PCR (which requires different temperature zones in the chip), for which reason plastic as well as glass are attractive [68]. As such, the majority of chips for analysis of biological fluids is fabricated from PDMS or PMMA [64; 71; 72; 73; 74; 75; 76], although glass and silicon are also occasionally utilized [77; 78; 79]. The next subsections contain detailed information about various aspects, such as transparency, thermal conductivity and bio(chemical)compatibility of glass, silicon, COC and PMDS as chip material for forensic microfluidic devices.
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 2 Materials and Methods
### Control Electronics  
The control electronics is powered by the satellite bus. The major task is to control and operate the tunable filters on the satellite by supplying the bias voltage to the tunable resonators of each filter. In order to set the filter to the desired frequency, it must accept telecommands from the ground--a telecommand value represents a filter center frequency. The electronics converts this information into the control voltages supplied to the filter. Further, it takes into account the actual temperature of the filter (by means of measurement) and adjusts the biasing voltages such that the desired frequency is reached, no matter at which temperature the filter actually is.  
In the present configuration, it is foreseen to be able to set the filter to certain discrete center frequencies. For these center frequencies, the biasing voltages at certain discrete temperatures are stored in a memory of the control electronics. Then, the actual temperature of the filter is taken into account by interpolating between stored temperature support points.
## Experimental Results
### The Correlation between Link Quality and Distance  
To show the correlation between the link quality and the distance separating communicating sensors, we linearly deployed a set of five sensors at ground level. By varying the distance between two consecutive sensors, we performed many experiments. In all experiments, sensors were configured to use channel number 26 and 0 dBm as transmission power. Figure 12 shows the PRR and RSSI as a function of the distance. The red points show the mean PRR (Figure 12a) or RSSI (Figure 12b). The red line in the box represents the median, the bottom edge of the box represents the 25 th percentile, and the top edge represents the 75 th percentile.  
Intuitively, one might think that, the lower is the distance between two sensors, the better is the radio link quality between those sensors. The theoretical model described in Section 3, as well as many others available in the literature [30], implicitly make this theoretical result, particularly for the received power level. In addition, many hardware-free distance estimation solutions proposed in the literature consider this theoretical result [41; 42; 43]. Simulation tools for WSN also widely use similar models to measure the received power between two sensors.  
However, the results presented in Figure 12a for PRR, and particularly those presented in Figure 12b for RSSI, show that such assumptions do not hold in practice. Indeed, Figure 12a shows that the measured PRR was better at 12 m than for shorter distances. This may be due to the road shape or other environment factor. In addition, Figure 12b shows that the RSSI was not a strictly decreasing function of the distance. Therefore, such considerations must be taken into account by protocols designers during theoretical or experimental performance evaluation.  
Figure 12: Correlation between the PRR/RSSI and the distance (please not that the \(x\)-axis is not linear).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**\#Channel** & **11** & **14** & **18** & **22** & **26** \\ \hline \(AVG_{[PRR_{AB}-PRR_{BA}]}\) & 0.09 & 0.01625 & **0.0925** & 0.078 & 0.0505 \\ \hline \(MAX_{[PRR_{AB}-PRR_{BA}]}\) & 0.9 & 0.1 & **1.0** & 0.75 & 0.55 \\ \hline \(AVG_{[\overline{RSSI}_{AB}-\overline{RSSI}_{BA}]}\) & **2.35** & 1.652 & 1.3 & 1.81 & 1.67 \\ \hline \(MAX_{[\overline{RSSI}_{AB}-\overline{RSSI}_{BA}]}\) & **11.67** & 5.0 & 3.9 & 10.43 & 4.7 \\ \hline \%_Unidirectional_ & **40.83** & 13.33 & 9.11 & 9 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Link asymmetry for different communication channels.
# Quality of Service Based NOMA Group D2D Communications
## Appendix B Proof of Theorem 1  
In order to obtain \(\mathbb{P}_{m}\), we require \(F_{h}\). Since, all wireless links exhibit Rayleigh fading and the DRs are uniformly distributed inside disc \(D\) centered at the location of test GT, the CDF \(F_{h}\) can be expressed as [31]:  
\[F_{h}(y)=\frac{2}{R_{D}^{2}}\int_{0}^{R_{D}}\left(1-e^{-x^{2}y}\right)zdz.\] (A11)  
It is challenging to solve the above integral. As such, we approximate it by applying Gaussian-Chebyshev quadrature as:  
\[F_{h}(y)=\sum_{l=1}^{L}b_{l}e^{-c_{l}y}.\] (A12)Based on Equation (A12), \(\mathbb{P}_{m}\) in Equation (5) can be expressed as:  
\[\mathbb{P}_{m} =\int_{0}^{\infty}F_{h}\left(\frac{\varphi_{m}^{\max}(\rho x+1)}{ \rho_{t}}\right)f_{I}(x)dx\] \[=\sum_{l=1}^{L}\int_{0}^{\infty}e^{-\frac{\zeta_{l}\varphi_{m}^{ \max}(\rho x+1)}{\rho_{t}}}f_{I}(x)dx\] \[=\sum_{l=1}^{L}b_{l}e^{-\frac{\zeta_{l}\varphi_{m}^{\max}}{\rho_{ t}}}\int_{0}^{\infty}e^{-\frac{\zeta_{l}\varphi_{m}^{\max}\rho x}{\rho_{t}}}dx\] \[=\sum_{l=1}^{L}b_{l}e^{-\frac{\zeta_{l}\varphi_{m}^{\max}}{\rho_{ t}}}\mathcal{L}_{I}\left(\frac{\zeta_{l}\varphi_{m}^{\max}\rho}{\rho_{t}} \right),\] (A13)  
where \(f_{I}(x)\) is the PDF of interference \(I\) and the last step follows from the definition of Laplace transform. This proves the result in Theorem 1.
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 2 Materials and Methods
### Tower-Based Spectral Measurements  
An automated long term in-situ SIF observation system, SIFSpec, was installed on a tower-based platform about 17 m from the ground for continuous SIF spectral measure  
Figure 1: AR’s observation tower and pictures of the alpine meadow in spring, summer, and autumn captured by the camera mounted above the tower.  
ments [44; 46]. The main components of the SIF observation system include (1) a customized Ocean Optics QE65 Pro spectrometer (Ocean Optics, Dunedin, FL, USA) with a spectral range of 649-805 nm, a spectral resolution of 0.34 nm, a sampling interval of 0.155 nm, and a signal-to-noise ratio (SNR) of \(>\)1000 [46]; (2) a computer storing the spectral data and information about the instrument operation status and a software control system controlling the switching of instrument switches and electronic switches [46]; (3) a Y-shaped bifurcated optical fiber (CPATCH, Ocean Optics, Inc., Dunedin, FL, USA), two five meters long optical fibers (core diameter 1000 \(\upmu\)m); (4) two inline TTL-driven electronic switches (Ocean Optics, Inc., Dunedin, FL, USA) [46]; (5) a bare optic fiber probe (field of view (FOV) of 20\({}^{\circ}\)) and a fiber optic probe (FOV of 180\({}^{\circ}\)) connected to the cosine corrector (CC3-3-UV-S, Ocean Optics, Inc., Dunedin, FL, USA); and (6) an automatic radiator to maintain the temperature inside the instrument case [46]. The fiber optic probe with cosine corrector and the bare fiber optic probe are used to collect the downlink solar incidence spectrum and the uplink feature canopy reflection spectrum, respectively, and the collected spectra are sequentially passed through the long fiber and electronic switch connected to the corresponding probes to form two spectral channels, which reach the bifurcated end of the Y-shaped bifurcated fiber and finally arrive inside the QE65 Pro spectrometer.  
The SIF observation system uses a \"sandwich\" mode to collect spectral data [47]. A dataset consists of three spectral acquisitions, the downlinked solar incidence spectrum, the uplinked feature canopy reflection spectrum, and the downwelling solar incidence spectrum, which are acquired by controlling the alternate opening and closing of two electronic switches [46]. Since the mismatch of time between measurements of downwelling and upwelling introduces errors due to weather variations, the downwelling solar incidence spectrum is represented by the average of the two measured downwelling solar incidence spectra. Before acquisition of each spectrum, the spectrometer optimally determines the integration time based on the received spectral signal and records the dark current [46]. Under normal conditions, a complete set of data acquisition takes about 15 seconds during midday, and about two minutes during sunrise and sunset.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 5 Case Studies
### Traffic Scenario Definition
#### 5.3.1 Scenarios  
The real datasets contain 322, 421, and 233 Malpensa flights. They were manually prepared and then read with RouGe Software V1.11 from DLR (Braunschweig, Germany). All military flights, helicopters, departures, and parachutist transports were filtered. To validate the data visually, the datasets were converted to the Google Earth KML data format. Figure 14 illustrates the flown trajectories on 11 May 2019 that occurred under adverse weather conditions. One can recognize that multiple holdings were flown due to thunderstorms at and around the TMA area. Figure 15 shows the flown trajectories on 6 August 2019 under normal undisturbed weather conditions.  
The FlightRadar24 data prepared as simulation scenarios contain the following information about callsign, aircraft type, weight category, position data plus time, speed, flight altitude, and heading.  
The constructed simulation scenarios were each planned using an AMAN with no adverse weather as a baseline and weather forecasts from different forecast models for two different severe weather events. Flight paths, times, the number of clearances, and fuel consumption were used as key performance indicators (KPI) to compare these models. The considered traffic scenario is based on the dataset from 11 May 2019. Seven scenarios were planned using an AMAN and simulated:  
* Without MET information (baseline);
* With MET data from the WRF-RUC, PhaSt, and RaNDeVIL models for the weather of 11 May 2019;
* With MET data from the WRF-RUC, PhaSt, and RaNDeVIL models for the weather of 6 August 2019.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Airport** & \multicolumn{3}{c}{**MXP**} & \multicolumn{3}{c}{**BGY**} \\ \hline
**Date** & **11 May 2019** & **7 July 2019** & **6 August 2019** & **11 May 2019** & **7 July 2019** & **6 August 2019** \\ \hline number of arrivals & 233 & 322 & 421 & 122 & 147 & 144 \\ \hline time until landing in extended TMA (sec) & 719–4693 & 693–2913 & 715–2436 & 782–2985 & 817–4836 & 703–5679 \\ \hline average time in extended TMA (sec) & 1196 & 1246 & 1161 & 1197 & 1186 & 1332 \\ \hline maximal flown distance in extended TMA (km) & 676 & 302 & 352 & 415 & 718 & 739 \\ \hline average flown distance in extended TMA (km) & 165 & 168 & 161 & 109 & 160 & 185 \\ \hline average flown distance in extended TMA at peak period (km) & 349 & 225 & n/a & 215 & 247 (194 without outliers) & 310 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data summary of available historical traffic.
# Exhaust Gas Characteristics According to the Injection Conditions in Diesel and DME Engines
## 2 Experimental Apparatus and Methodology
### Experimental Methodology  
Fuel injection conditions were changed by controlling the injection timing, injection duration, injection pressure, pilot injection rate, and injector needle lift using the ECU program.  
The injection timing (10\({}^{\circ}\), 14\({}^{\circ}\), 18\({}^{\circ}\), and 22\({}^{\circ}\) before top dead center (BTDC)) was adjusted using the common rail injection device manual controller based on the injection timing (22\({}^{\circ}\) BTDC) of the mechanical injection pump in accordance with the specifications of the single-cylinder horizontal diesel engine that was used as the test engine. In this instance, the injection lift (0.14, 0.21, 0.24, and 0.36 mm) was changed based on the injection nozzle (\(\psi\) = 0.22 mm \(\times\) 5 ea), and the injection characteristics, engine performance, and exhaust gas characteristics were investigated according to the pilot and main injections. The engine revolutions were 1800 rpm, load ratio was 75%, and common rail injection pressure was 50 MPa. The braking thermal efficiency was focused on maintaining the current common rail engine and reducing NOx among the exhaust gases [10; 11; 12].
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 3 Fuzzy Rule Generation Method According to Input Space Partitioning Method
### Scatter Partitioning Method  
The scatter partitioning method is used to compensate the disadvantage that the number of rules increases exponentially as the input variable of the grid partitioning method described above increases. The scatter partitioning method divides the input space into \(n\) clusters through a clustering algorithm. A specific area is divided by the number of \(n\) clusters, and the number of each specific area is the number of fuzzy rules. Therefore, the number of rules in the scatter partitioning method is the number of clusters, and there is a strong advantage in terms of the number of input variables. The scatter partitioning method is divided into various kinds according to the clustering algorithm. In this paper, we use subtractive clustering (SC), fuzzy C-means (FCM) clustering, and context-based fuzzy C-means (CFCM) clustering.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 4 Reduced Overhead Routing Protocol
### Point to Multi-Point Reduced Overhead Routing
#### 4.2.3 Techniques to Reduce the Impact of False Positive on Routing  
We are using a BF configuration that targets a maximum false positive rate of 1%. In order to reduce the impact of false positives on our P2 MP routing approach, we also present different forwarding rules here.  
**Rule-1:** If the destination node is the direct neighbor of the node that is processing the IP packet header, the node forwards the packet directly to the destination node without examining whether any of its neighbors are present in the BF.  
**Rule-2:** If the source node is the direct neighbor of the node that is processing the IP packet header, the node ignores the source node's address, i.e., it does not apply SAX hash function to the source node address.  
**Rule-3:** Following the above rules may reduce the false positive rate, however routing loops can still occur due to the false positive characteristics of the BF. To mitigate the impact of forwarding a packet in a loop for long, we suggest the use of the IPv6 hop limit field. With the assumption that the root can reach any node in a network in at most \(h\) hops, the hop limit field can be set to the value of \(h\). In the evaluation presented here we have assumed a value of \(h=12\).
## 5 Discussion: A Strategy for Renewal and Densification
### A Radical Scenario of Densification  
The third option proposes a radical impact in quantity and quality: the renewal and densification of all the resettlement communities. The transformation of all the resettlement communities requires a general citywide plan of interventions with priorities and phases. It implies social, environmental, and economic costs, but if all the resettlement villages in Suzhou were densified, for example, by duplicating the floor area ratio, it would accommodate 1 million people, which is a very relevant contribution to the existing stock considering that the growth of the population of the city over the past 5 years was 2.16 million people. In a strategic approach, the new FAR is advisable to be higher than double the existing one, as in the Nankuan New Village case, which was increased by three times. In fact, replacing the existing buildings, which were modern when the first inhabitants arrived, but today are, or soon will be, middle-class, goes along with the challenge of the definition and implementation of a new standard of residential environment promoted by the state. What was good enough for just resettled people was decided when the resettlement operations started, so it should not be repeated when new conditions emerge.  
The large investment needed for the replacement must increase the number of people in the same area but also:  
* Enhance the open spaces in quantity and quality: given the small and scattered greenhouse spaces in the existing communities and the surrounding areas, the action of densification should re-design these resources, so that they can also play a role in fulfilling the needs of the interaction of the community. The plan will consider the contribution of the open spaces to the thermal and light comfort and to mitigate the heat island effect;
* Realize an integrated transportation system, integrating public transport, pedestrian and cycling lanes, and car parking places connected to the pedestrian and bike network; limit the extension of the road network; and improve the accessibility to the compounds with a more permeable system of gates;
* Diversify the units to match the needs and preferences of the different households and new lifestyles; diversify the housing types: higher density housing does not always require high-rise developments;
* Mix land use to create an attractive street life;
* Implement the \"sponge city\" guidelines for rainwater retention and water purification [100] and include systems for saving and producing renewable energy (Figure 4d).
* Obviously, in addition to these specific improvements, the capacity of infrastructures, utilities, and public services will be increased according to the quantity of residents and users.
## 3 Results
### FC system
#### 3.1.2 FC Net Power and Electrical Efficiency  
FC electrical efficiency, \(\eta_{sys}=P_{FCnet}/P_{FC}\), varied in the range of 82% to 92% for all used strategies analyzed in this paper (Figure 8). The low and high values were obtained for large and light loads, respectively. Higher values can be obtained using \(k_{fuel}=0\) in the optimization function (9) because the objective is the maximization of \(P_{FCnet}\). The air/fuel-PFC strategy uses the power threshold \(P_{ref}\) to switch between the strategies air-PFC and fuel-PFC.
Thus, \(P_{FCNet}\) levels using the air/fuel-PFC strategy for \(P_{Load}\leq P_{ref}\) are close to those of the fuel-PFC strategy and different than those of the air-PFC strategy, especially at light load (see Figure 8). However, the \(P_{FCNet}\) levels using the air/fuel-PFC strategy for \(P_{Load}>P_{ref}\) and the air-PFC strategy are slightly different due to different initial conditions for the FC system operated under the strategies air/fuel-PFC and air-PFC. The \(P_{FCNet}\) levels using the sFF strategy are very close to those using the air/fuel-PFC strategy.
## 4 Conclusions
### Summary of the Results Obtained
The summary of the research presented in this article, i.e., a comparison of methodological similarities and differences between the classical bin packing problem (BPP) and the proposed methodology of stacking pallet load units problem (SPLUP), is shown in Table 5. The main conclusion is that the SPLUP considered by the authors is different from typical BPP, even combined problem of bin packing and vehicle loading. The mathematical model of SPLUP includes typical BPP constraints and additional restrictions such as filling the top layer, fragility, and height homogeneity. Due to the limited number of similar works, a comparison of different approaches with the perspective of computational experiments, which would indicate the effectiveness of the proposed approach in relation to other studies, is not included. However, in principle, this was not the aim of this work at this stage of research. Finally, the verification of the proposed methodology for the SPLUP solution is limited to the experimental computations of analyzed real-world cases. The computational experiments illustrated the correctness of the developed model as they show the potential for reducing the space occupied in logistic processes, including the necessary space on the vehicle load bed or in the racking slot of the distribution warehouse. The research focused neither on a large number of instances analyzed nor on orders in which a large number of PLUs created that need to be stacked. This will constitute the subsequent step of the research. The computation time for the analyzed instances and using evolutionary engines was no more than several seconds, except one instance with computation time of around 1 min., which constitutes the entire procedure promising. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Comparative Parameter**} & \multicolumn{2}{c}{**Literature 2**} & \multicolumn{2}{c}{**Presented Research 2**} \\ \hline \multirow{3}{*}{The subject of decision problem} & Packaging of products & All 1  & + & - \\ \cline{2-4}  & Packaging of pallet load units & - & - & + \\ \cline{2-4}  & Packaging of products and vehicle loading & [3,4,16] & + & - \\ \hline Type of decision problem & NP-hard type & All 1 & + & + \\ \hline \multirow{3}{*}{Dimensions of analysis} & 1D & [15] & + & - \\ \cline{2-4}  & 2D & [2,7] & + & + \\ \cline{2-4}  & 3D & [1, 2, 3, 6, 12, 13, 14] & + & - \\ \hline \multirow{3}{*}{Item shape regularity} & Regular shape & All 1  sec [13] & + & + \\ \cline{2-4}  & Irregular shape & [13] & + & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of methodological similarities and differences between classical bin packing problem and stacking pallet load units problem.