esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 4 System Validation
### Simulated In-Motion Results
Therefore, the obtained total cover time (about 250 ms, see Figure 7) may be still enough to correctly read each tag even in the case of several collisions. Similar curves are presented in Figure 8, illustrating the case when the vehicle increases its speed up to 120 Km/h. Compared to Figure 7, it is evident that the curves are time-compressed, meaning that the period during which the link is reliable may decrease to around 100 ms. The analysis in function on the different heights is similar to the case of Figure 7. Also, from a similar analysis as described for the previous case, this time of 100 ms would allow tag identification as well. Figure 7: Simulation results: \(v=50\) Km/h, \(d=2\) m, \(f=866.5\) MHz. Cases illustrated in Figures 9 and 10 are very similar to those shown in Figures 7 and 8, respectively. The difference consists in an increase in the distance between the tag and the antenna to \(d=4\) m, which leads to reducing the time with a \(P_{\textit{coverage}}\approx 1\) to 200 ms (at 50 Km/h) and \(<\)100 ms (at 120 Km/h). Figure 8: Simulation results: \(v=120\) Km/h, \(d=2\) m,\(f=866.5\) MHz. Figure 9: Simulation results: \(v=50\) Km/h, \(d=4\) m,\(f=866.5\) MHz. Figure 11 shows the results for a severe multi-path scenario (the parameter \(\sigma\) is set to \(\sigma=r_{rms}\)), setting a speed of 50 Km/h, as an approximation of an urban environment with a high density of reflections. Even for this case, \(P_{coverage}\) reaches a value close enough to one during at least 100 ms for the different studied heights, so that a correct detection is feasible even for this very harsh scenario. Finally, Figure 12 shows the simulation results by using a carrier frequency of 2450 MHz and no significant multipath interference, apart from the ray reflected on the road. We observe that \(P_{coverage}\) is maximum for a short time interval, in which the antenna is very close to the t  
Figure 11: Simulation results: \(v=50\) Km/h, \(d=2\) m,\(f=866.5\) MHz in a severe multi-path environment. Figure 10: Simulation results: \(v=120\) Km/h, \(d=4\) m,\(f=866.5\) MHz. lower than the time of 25 ms required by the protocol. Therefore, the 866.5 MHz band is a better option by using passive technologies, whereas the 2.45 GHz band would only result feasible when active technologies are employed, whose transmission power is much higher than the passive case. Thereby, these results support the selection of the passive RFID band proposed in Section 4.1.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 2 Primary and Secondary Sources of Gallium and Indium
In the hydrometallurgical process, after sphalerite has been roasted and subjected to a calcination step, deposits of zinc-related elements are precipitated in the elemental ore. Among them is indium. The precipitate, which is one of these residues, is then subjected to a series of technological operations, including numerous washings with acids and bases, manipulation of the pH of the solution, precipitation, filtration, and cementation, during which the individual components are separated. The finally obtained indium cement is burned in a kiln in the presence of chlorides, which allows for the production of ingots of this metal [18]. Secondary sources of indium are used electronic devices, especially those equipped with screens: TV sets, monitors, telephones, electronic billboards, interactive boards, tablets, and laptops. Other important sources are photovoltaic cells and car windows. Economically viable secondary sources of indium include those that can be easily extracted; these are primarily screens and monitors as well as lamps and light bulbs, which can be amounted to a total of about 7.6 megatons (only in 2019) of potential secondary raw material for recycling processes.
## 3 Results and Discussion
### Effect of Dissociation and Evaporation Rates
The condensation of water that has not yet evaporated and trickles down the hydrate particle leads to local extinction. Thus, a range of factors, in addition to the water evaporation rate, contributes to the ignition delay times at this stage: the thickness of the newly-formed water film, the dissociation rate, and the amount of condensed water. Figure 6: The temperature fields of the gas hydrate ignition area under the conditions of convective heating when varying the mass dissociation rate of the gas hydrate at a heating source temperature of 1273 K and \(R_{\rm g}\) = 0.5 mm: (**a**) \(w_{\rm d}\) = 0.005 kg/(m\({}^{2}\cdot\)s), (**b**) \(w_{\rm d}\) = 0.01 kg/(m\({}^{2}\cdot\)s), (**c**) \(w_{\rm d}\) = 0.1 kg/(m\({}^{2}\cdot\)s). Figure 8a,b presents the temperature fields in the gas hydrate ignition area under the conditions of radiant heating at a mass rate of water evaporation from the gas hydrate sample surface \(w_{\rm evap}\) = 0.01 kg/m\({}^{2}\cdot\)s and 0.1 kg/m\({}^{2}\cdot\)s at a heating source temperature of 1273 K. It is clear from Figure 8a,b that the higher the rate of water evaporation from the hydrate particle surface (Figure 8b), the more the gas hydrate particles and the area around it are cooled due to the water vapor generation. In contrast, at a lower evaporation rate of water (Figure 8a), the temperatures around the gas hydrate particle were higher. Figure 7: The gas hydrate ignition delay times (\(R_{\rm g}\) = 0.5 mm) when varying the mass rate of water evaporation from the fuel sample surface and heating source temperature under the conditions of heat exchange: (**a**) radiant, (**b**) convective, (**c**) conductive.
## 2 Experiment on Panel Unit
### Experimental Results  
Table 1 shows the first six natural frequency results identified by the ERA method for the two samples, and Figure 4 shows the first six vibration mode results identified from sample A. The modes of sample B are the same as those of sample A and will not be listed repeatedly. The first six identified vibration modes are agreed with the theoretical modes, which means those modes are not spurious. Usually, the errors of measured natural frequencies are less than 1% [20]; therefore, these measured natural frequencies are used as the basis for comparison of the calculated results of the model. In terms of the identified natural frequencies, the natural frequency of sample A is smaller than that of sample B, which is the inevitable result of sample A's larger size compared to sample B, and the identified modes have the same rule as the theoretical modes of a rectangular plate [21], which reflects the good modal test results. The maximum difference in natural frequency between two samples is 16.241%, which reflects the influence of panel size on natural frequency. The numerical model of panel units is studied based on the test results.
## 4 Evaluation
Next, Figure 10 shows the results for the same queries, but this time the ranking is based on network traffic. The overhead factor is generally much lower than the previous figure showing the intermediate results. This reduction is achieved by the dynamic operator relocation, which first executes the join. Then, the bytes of the input are compared with those of the output. Finally, the option with fewer bytes is chosen and sent over the network. Consequently, the only overhead is the operator graph size, sent twice for every device on the path. Since only the network traffic counts in this comparison, and the smaller number is always chosen, there is no advantage for any specific optimizer. Without this optimization, the bad join orders would be much worse than they are now. It is also possible that the ranking between excellent and bad join orders changes by turning off this feature. However, in that case, the absolute number of bytes sent through the network will increase, so those join orders can not be called optimal anymore. Figure 9: All optimizers were evaluated based on the produced intermediate results. The X-axis shows which evaluation was performed on how many triple patterns. \(I\) means intermediate results, and \(T\) means network traffic. The Y-axis shows the average factor between the best join order and the chosen join order of a given join order optimizer. A factor of 1 is achieved if the optimizer always chooses the best case. The models are trained on joins with the number of triple patterns specified in their labels. The exciting part of this figure is that the trained model almost always achieves less network traffic than the state-of-the-art static optimizers. When the models are trained on network traffic, their result is almost optimal. They are also slightly better than those models trained on intermediate results. The best possible results were achieved with the model, which trained on all possible mixed triple pattern sizes. Consequently, the figure also shows that overfitting occurs if the models are trained on small query sets, slightly decreasing the quality.
## 3 Characteristic Analysis of the Flux-Controllable NI HTS FSM  
### Field Distribution of the Proposed FSM  
In this study, the operating characteristics of the designed NI HTS FSM were analyzed by a combination of the finite element method (FEM) and an electrical circuit analysis. The electrical circuit model was devised based on the simplified circuit model shown in Figure 2. The \(R_{C}\) value in the electrical circuit model was calculated from the current charging test of the NI HTS coil. Simulations were performed using FEM software, and the electrical circuit was modeled using a circuit modeler, and was simulated by interworking with the electromagnetic analysis, as shown in Figure 10. In this model, the azimuthal resistance of the HTS coil is assumed to be zero. Figure 11 presents the simulation results of the current charging characteristics of the NI HTS field coil under a 100 A field current with a ramping rate of 1 A s\({}^{-1}\). The charging time constant, \(\tau\), can be calculated from the time interval between I\({}_{1}\) and I\({}_{2}\) in Figure 11, as in NI HTS field coil test, because the magnetic field density is proportional to the coil current. Here, I\({}_{1}\) is the coil current at the moment when the power supply current reaches the target, and I\({}_{2}\) (66.48 A) represents the sum of I\({}_{1}\) and I, which is 63% (57.08 A) between I\({}_{1}\) (9.40 A) and a fully charged coil current (100 A). The value of \(\tau\) is 608 s. The coil current is fully charged approximately 9500 s after the excitation current reaches the target. Therefore, the initial time for the simulations of the designed NI HTS FSM in the steady-state condition was set to 10,000 s, considering a full charge of the field current. Figure 12 shows the magnetic flux vector at the rotor position of 0 degrees and 15.75 degrees 10,000 s after the field current reaches the target of 100 A.  
The simulation results show that the polarity of the magnetic flux applied to the armature winding changes as the rotor moves. Figure 13 shows the air-gap flux density distributions due only to the excitation of the NI HTS field coils. The local maximum value of the air-gap flux density appears at the position where the stator tooth is fully overlapped with the rotor pole. The local maximum value directly results in the maximum flux of the armature winding A\({}_{1}\), and thus is more representative than the absolute maximum value [43]. Therefore, flux regulation can be achieved effectively by adjusting the local maximum value.  
Figure 10: Field-circuit coupled finite element method (FEM) model of the designed NI HTS FSM.  
Figure 11: Current charging characteristics of the NI HTS field coil installed in the designed FSM.  
Figure 12: Magnetic flux density vector plots according to the rotor position: (**a**) 0\({}^{\circ}\), and (**b**) 15.75\({}^{\circ}\).  
Figure 13: Air-gap flux density distribution due only to the excitation of the NI HTS field coils when the rotor position is on the d-axis.
## 4 Performance Evaluations and Results Analysis
### Results and Analysis
#### 4.2.2 Zone Traffic  
The zone-specific total transmission predicted in Section 3.5 was also tested as shown in Figure 11. The total traffic that occurred in a zone over 100 min was represented as a graph of the derivative of the average value over 100 experiments. The error rate of the proposed prediction technique and the ns-2 model was shown to be approximately 7%.  
Figure 11: Total network traffic—zone.  
Figure 10: Traffic comparison (**a**) Traffic comparison in Constant Bit Rate (CBR); and (**b**) Traffic comparison in Variable Bit Rate (VBR).
## 2 A Rapid Output Matching Optimization Method with Dual-Objective Orientation
### Selection of the Optimal Load Impedance  
The intended operating bandwidth (\(BW_{\text{o}}\)) is 24-30 GHz. To account for possible performance deviations due to process variations and inaccuracies in device modeling, the design bandwidth (\(BW_{\text{d}}\)) is defined by adding a 2 GHz margin to both the lower and upper boundaries of \(BW_{\text{o}}\), thus providing sufficient frequency redundancy.  
To guarantee the linear output capability of the PA, load-pull simulations were conducted on a stabilized 46 \(\times\) 8 \(\upmu\)m power cell at the 1 dB compression point at six frequencies \(f_{\text{i}}\) (i = 1 to 6) spaced 2 GHz apart in the \(BW_{\text{d}}\) of 22-32 GHz. The obtained \(Z_{\text{opt}}(f_{\text{i}})\) for the maximum \(\text{P}_{\text{1 dB}}\) and PAE are denoted as \(U_{1}(f_{\text{i}})\) and \(U_{2}(f_{\text{i}})\), respectively, both of which are expressed in the complex impedance form, as given by Equations (1) and (2). Varying degrees of deviation between \(U_{1}(f_{\text{i}})\) and \(U_{2}(f_{\text{i}})\) exist at different frequencies. Selecting a target matching impedance \(Z_{\text{opt},\text{s}}(f_{\text{i}})\) closer to \(U_{1}(f_{\text{i}})\) favors a higher \(\text{P}_{\text{1 dB}}\) but moves further away from \(U_{2}(f_{\text{i}})\), resulting in a reduced PAE. Consequently, \(Z_{\text{opt},\text{s}}(f_{\text{i}})\) is generally chosen at an intermediate position between \(U_{1}(f_{\text{i}})\) and \(U_{2}(f_{\text{i}})\) to strike a balance between \(\text{P}_{\text{1 dB}}\) and PAE. To determine \(Z_{\text{opt},\text{s}}(f_{\text{i}})\), weighting coefficients \(m\) and \(n\) are assigned to the impedance points \(U_{1}(f_{\text{i}})\) and \(U_{2}\left(f_{\text{i}}\right)\), respectively, with \(m+n=1\), as described by Equation (3). Since PAE exhibits greater fluctuations in response to mismatch than \(\text{P}_{\text{1 dB}}\), \(m\) and \(n\) are set to \(1/3\) and \(2/3\), respectively. The calculated values are listed in Table 1 and plotted in Figure 1a.  
\[U_{1}(f_{\text{i}})=R_{1}(f_{\text{i}})+jX_{1}(f_{\text{i}}) \tag{1}\]\[U_{2}(f_{\rm i})=R_{2}(f_{\rm i})+jX_{2}(f_{\rm i}) \tag{2}\] \[Z_{\rm opt,s}(f_{\rm i})=mU_{1}(f_{\rm i})+nU_{2}(f_{\rm i})=mR_{1}(f _{\rm i})+nR_{2}(f_{\rm i})+j[mX_{1}(f_{\rm i})+nX_{2}(f_{\rm i})] \tag{3}\]
## 3 Research Methodology  
Table 4 provides a comprehensive listing of practically-oriented decoy state enabled systems and experiments. With respect to signal and decoy state MPNs, there is relatively little consistency or adherence to [PERSON] and coworkers' 2005 work where they proved the optimal signal state MPN \(\cong\) 0.5 and decoy state MPN \(\cong\) 0.1 [25]. Similarly, there is considerable disparity in the protocol occurrence percentages with signal states ranging from 50% to \(\sim\)99%, decoy states ranging from \(<\)1% to 40%, and vacuum states ranging from 0% to 25%. Note, while these experimental MPN parameters differ from those proposed by [PERSON] et al., such discrepancies do not indicate that the [PERSON] et al. security model is inappropriate. Furthermore, such differences may be due to differences in system architectures such as end-to-end losses (e.g., channel loss, insertion loss, detection efficiencies, etc.), security bounds, and post-processing techniques.  
Despite the decoy state protocol's wide-spread employment, its effectiveness in detecting PNS attacks has not been thoroughly addressed in the literature. For example, in his defining work on the decoy state protocol, [PERSON] states \"Any attack by [PERSON] that will change the value of any one of the \(Y_{n}\)'s and \(e_{n}\)'s substantially will, in principle, be caught with high probability by our decoy state method\" [24]. Likewise, in the most detailed treatment available on detecting PNS attacks, the author merely states \"significant deviation of the measured ratio from this expected value indicates a PNS by [PERSON]\" [45]. Thus, we desire to study the decoy state QKD system's ability to detect PNS attacks through model and simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**Signal MPN**} & \multirow{2}{*}{**Decoy MPN**} & \multicolumn{2}{c}{**Occurrence**} & \multirow{2}{*}{**Propagation**} & \multirow{2}{*}{**Key Rate (bps)**} \\  & & & & **Percentage (\(\mu/v/Y_{0}\))** & & \\ \hline
1 [46] & 0.80 & 0.12 & 90/10/0 & 15 & 165 \\
2 [47] & 0.55 & 0.152 & 63.5/20.3/16.2 & 60 & \(<\)428 * \\
3 [5] & 0.425 & 0.204 & 75/25/0 * & 25 & 5.5 k \\  
1.5/0.5 & 45 & 300 k \\ \hline \hline \end{tabular}  
* Value estimated or assumed from reference; ** Values as reported; *** Multiple systems employed.  
\end{table}
Table 4: Decoy State Enabled QKD System Configurations.
# Management Platforms and Protocols for Internet of Things: A Survey
## 8 IoT Device Management Platforms
### Carriots  
Carriots is an IoT platform that manage data devices provided with cloud services, and that also connects devices to other devices and systems [110]. Therefore, if a system is connected to the platform, it can also be modeled as a device. From its RESTful API, Carriots aim to collect and store any data originated from the most diverse devices. The application engine can guarantee availability to its users no matter the volume of connected devices. These connected devices are associated with services (i.e., physical devices or other resources) and all services belong to a project. As shown in Figure 29, the logical architecture of Carriots consists of the following modules: (i) the REST API; (ii) Big Data; (iii) Project and Device Management; (iv) Business Rules and Event Processing; (v) Security; (vi) Logs and Debug; (vii) Control Panel, and; (viii) External Communication Module.  
Data exchanged between devices, connected systems, and the platform can be represented in two different ways: (i) the sensors send data in JSON or XML formats (in a particular platform format) using the REST API, or (ii) through the MQTT message protocol [111].  
The Project and Device Management module contains the projects created by users and provides device and its software management, i.e., device provisioning, enabling and disabling devices, and updating firmware. Storing and executing events in the form of scripts created using the Groovy programming language and using if-then-else rules is the responsibility of the Business Rules and Event Processing module.  
_Pros: Application used to trigger the other functionality are supported_  
_Cons: less friendly user interface._  
Figure 28: Illustration of the Xively architecture.  
Use Case  
Nowadays, cities have the challenge of improving, protecting the environment, decreasing energy use, and reducing CO\({}_{2}\) emissions, as well as having to detect and correct any excesses in light consumption or in water spillage and control expenses.  
Carriots City Life is an IoT platform that works like the city brain. It collects data from different sensors or information reported by the citizens and mixes it all to better manage municipal services [112]. it is a cloud platform that allows people to collect, integrate, store and analyze all the city data with a global vision.
## 2 Experimental
### Flushing of Soils with Oil-in-Water Emulsions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multirow{2}{*}{\begin{tabular}{c} **Emulsion** \\ **[L]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Contained** \\ **Plant Oil** \\ **[L]** \\ \end{tabular} } & \multirow{2}{*}{**Emulsifier**} & \multirow{2}{*}{\begin{tabular}{c} **Unit of** \\ **Emulsifier** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Type of** \\ **Emulsifier** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Total Oil** \\ **Emulsifier** \\ **[L]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Contained** \\ **Mineral** \\ **Oil [\%]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Plant** \\ **Oil [L]** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\% of Oil** \\ **Captured** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mineral** \\ **Oil [L]** \\ \end{tabular} } \\ \hline \multirow{2}{*}{27 May 2021} & \multirow{2}{*}{190} & \multirow{2}{*}{10} & \multirow{2}{*}{10} & \multirow{2}{*}{5} & \multirow{2}{*}{kg} & \multirow{2}{*}{
\begin{tabular}{c} Soy \\ techinine \\ powder \\ \end{tabular} } & \multirow{2}{*}{9} & \multirow{2}{*}{65} & \multirow{2}{*}{3.15} & \multirow{2}{*}{63\%} & \multirow{2}{*}{5.85} \\  & & & & & & & & \\  & & & & & Soy & & & & \\  & & & & & & & & \\  & & & & & & & & \\
8 June 2021 & 190 & 10 & 5 & kg & lectinine & 8 & 60 & 3.2 & 64\% & 4.8 \\  & & & & & powder & & & & \\
9 June 2021 & 190 & 10 & 6 & L & Giralee HE & 8.5 & 60 & 3.4 & 57\% & 5.1 \\
17 June 2021 & 190 & 10 & 6 & L & Giralee HE & 8 & 55 & 3.6 & 60\% & 4.4 \\
18 June 2021 & 190 & 10 & 6 & L & Giralee HE & 7 & 50 & 3.5 & 58\% & 3.5 \\
29 June 2021 & 190 & 10 & 6 & L & Giralee HE & 7.5 & 50 & 3.75 & 63\% & 3.75 \\
30 June 2021 & 190 & 10 & 6 & L & Giralee HE & 8 & 45 & 4.4 & 73\% & 3.6 \\  & & Total & 70 & & & & & & & \\ \hline \hline \end{tabular}  
* * Amount of oil was estimated from the level of the oil in the collection IBC. The oil phase was pumped off after max. 36 h.  
\end{table} 
Table 6: Flushing and pumping trials in the non-saturated zone. Figure 6: Separation of the emulsion after passage through the non-saturated zone and pumping off to the ground: The top layer is a mix of plant oil and mineral oil washed out from the soil. The main experiments carried out in the saturated zone during the field trials are tabulated in Table 7:  
As Table 7 shows, a total of 120 L of plant oil were infiltrated into the saturated zone, of which 92-95% were later collected back from the top of the groundwater after max. 1 h. Along with that plant oil, 7.58 L of mineral hydrocarbons were extracted from the ground.