esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this work, we developed and applied several neural controller structures to a nonlinear and unstable real magnetic levitation system.  
First the system was linearized and obtained its transfer function and system parameters around an equilibrium point. Next we designed classical lead and lead-lag compensators that provided the training data for the neural networks used in the control. These training data were generated by applying a random reference signal in order to stimulate the system and obtain a response with rich information. The training data consist of samples of the reference signal, the control signal, and output of the system. These input and output samples were used to train an identification network that learned to emulate the dynamics of the system. This network was used to simulate the system response for different input references.  
The first neural controller designed was the inverse neural network that learns to model the inverse dynamics of the system. In this case, a three layered network gave the best performance for the different reference signals. This network was tested on the experimental levitation system with and without adaptive block. The addition of the adaptive block proved to eliminate the steady-state error.  
An internal model controller was also analyzed by using the inverse model controller network and the identification network designed in previous steps. We verified that this structure successfully reduce the noise and perturbations on the system.  
The last neural controller trained was the model reference controller, using, as training data, the response of the system controlled by the inverse model controller with the adaptive block. When comparing both the model reference and internal model controllers, we observed that the responses were very similar with slight differences. In fact, the model reference controller gives a response with zero steady-state error without using an adaptive block, unlike all the other tested neural controllers.  
In this work, we proved that neural networks can be used as robust controllers for nonlinear and unstable systems, with performance levels better than classical controllers.  
As a future investigation, we could implement and test the internal model controller and the model reference controller into the experimental levitation system, and compare the responses and robustness of the control structures against the results presented in this study. For that, using higher clock frequency, faster ADC, and more memory would allow us to experiment with deeper neural networks and smaller sampling periods.  
[PERSON] performed the simulation and experimental results. [PERSON] and [PERSON] participated in the analysis of the results and writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 30: Control signal applied by the reference model control for the sinusoidal reference.
## 2 Materials and Methods
### Methods
#### 2.2.1 A Process-Based Farquhar GPP Model (FGM)
The FGM estimates the mean carboxylation capacity of a unit sunlit leaf (V\({}_{\rm cmax25\_sunlit}\)) and shaded leaf (V\({}_{\rm cmax25\_shaded}\)) area with the following models [64; 65; 53]:  
\[\rm V_{cmax25\_sunlit}=\frac{\rm\Omega LV_{cmax25}\ (1.0-exp(-k_{n}-K_{b}(\theta_{ z})\rm\Omega L))}{(k_{n}+K_{b}(\theta_{ z})\rm\Omega L)}/L_{sunlit} \tag{5}\]  
\[\rm V_{cmax25\_shaded}=\frac{\rm\Omega LV_{cmax25}}{L_{shaded}}\bigg{[}\frac {1.0-exp(-k_{n})}{k_{n}}-\frac{1.0-exp(-k_{n}-(\theta_{z})\rm\Omega L)}{(k_{n }+K_{b}(\theta_{z})\rm\Omega L)}\bigg{]} \tag{6}\]  
where \(\rm\Omega\) is the clumping index; L is the total LAI; L\({}_{sunlit}\) and L\({}_{shaded}\) are the LAIs for sunlit leaves and shaded leaves, respectively; k\({}_{n}\) is the coefficient of leaf nitrogen allocation; \(\rm\theta_{z}\) is the solar zenith angle; K\({}_{b}(\theta_{z})\) is the light extinction coefficient; and V\({}_{\rm cmax25}\) is the maximum carboxylation rate standardized to 25 \({}^{\circ}\)C for sunlit leaves. L\({}_{sunlit}\) and L\({}_{shaded}\) are estimated using [PERSON]'s law as follows:  
\[\rm L_{sunlit}=\frac{-e^{-K_{b}(\theta_{z})\rm\Omega L}}{K_{b}(\theta_{z})} \tag{7}\]  
\[\rm L_{shaded}\rm=L-L_{sunlit} \tag{8}\]  
In this study, V\({}_{\rm cmax}\) represents the maximum carboxylation rates, standardized to 25 \({}^{\circ}\)C hereafter (i.e., V\({}_{\rm cmax25}\)). In the FGM, the temperature effect on V\({}_{\rm cmax}\) is modeled as follows:  
\[\rm V_{cmax}=\frac{V_{cmax25}\ exp(a1(T\ 25))}{(1+exp(a2(T\ -41))} \tag{9}\]  
where T is air temperature and a1 (0.051) and a2 (0.205) are empirical parameters based on measurements [65; 66]. The canopy total GPP is the sum of the GPP for both the sunlit and the shaded leaves:  
\[\rm GPP=A_{n_{sunlit}}L_{sunlit}+A_{n_{shade}}L_{shade} \tag{10}\]  
where A\({}_{n_{sunlit}}\) and A\({}_{n_{shade}}\) are the net photosynthesis rate for a unit sunlit leaf area index and a unit shaded leaf area index, respectively. Additional details of the theoretical framework, default parameter values for the FGM, and model calibration method can be found in our previous study [35].
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
## 2 Proposed Framework
### Simulator Structure and Setup
#### 2.1.1 Network Emulator Environment  
As can be seen in Figure 4, the control signal is captured through the controller on the master side and forwarded to the network domain in a specifically encoded format. SDN, network function virtualization (NFV), and mobile edge computing (MEC) are employed with 5G technology in the core network of the proposed testbed to overcome the 1 ms latency challenge and to provide support for the next-generation industrial applications [19]. The network domain receives these packets and forwards them to the slave domain to perform the required task. The feedback in form of the haptic data is sent from the slave teleoperator to the master side human operator. The proposed IoTactileSim utilizes the Mininet emulator for the network design, resembling a real-world network operations and hardware in a virtual environment [20]. Mininet employs process-level virtualization to develop a virtual communication network with virtual hosts and connects them via virtual Ethernet pairs.  
The proposed IoTactileSim enables the evaluation of large custom topologies with actual application traffic traces by deploying them into the physical network. It also enables the utilization of emerging technologies such as SDN, NFV, and MEC. In the SDN framework, control planes are separated from the forward plane in the network. The emulator is written in python language and freely available at the Mininet official website ([[http://mininet.org/](http://mininet.org/)]([http://mininet.org/](http://mininet.org/)), accessed on 10 November 2021). An overview of the basic architecture of the Mininet with open-source virtual switches Open vSwitch (OVS) and SDN standard protocol OpenFlow is depicted in Figure 5a. Mininet by itself is a network emulator that allows users to mimic real network topologies. It also enables users to build such network topologies in SDN architecture. This is what Mininet is capable of in a nutshell. It does not provide any support for integrating tactile Input/Output (I/O) modules or any other modules for that matter. It only provides us with a virtual environment where all network nodes are present (virtually) on a single physical device. The contribution of the IoTactileSim over Mininet is defined as:  
* IoTactileSim allows users to integrate several tactile I/O modules with the Mininet environment.  
Figure 4: Indepth overview of the proposed virtual testbed IoTactileSim.  
* IoTactileSim enables users to implement each network module on a separate physical device.
* IoTactileSim also has an embedded tactile support engine which is not present in Mininet. This support engine can be modified by the user based on their use cases.
# Programmable Organic Chipless RFID Tags Inkjet Printed on Paper Substrates
CimtTEC, Departament d'Enginyeria Electronica, Universitat Autonoma de Barcelona,
the development of the active and passive devices on flexible polymeric substrates, e.g., polyethylene terephthalate, polyethylene naphthalate (PEN), and polyimide films, among others. Some of the examples are inkjet-printed antennas, TFT arrays, capacitors and logical circuits among others [31; 32; 33; 34; 35; 36; 37; 38]. However, as regards printed chipless RFID tags manufactured on paper substrate, only a few examples have been reported [39; 40]. Chipless Radio Frequency Identification (RFID) is a wireless technology used for identification (ID), tracking, sensing, and authentication/security applications [41; 42]. In the field of authentication and security, one promising scenario for chipless RFID is secure paper. Within this particular application, equipping documents (e.g., banknotes, certificates, exams, ballots, official documents, etc.) with a planar ID code is envisaged as a means to fight against counterfeiting. The main general advantage of chipless RFID over chipped RFID systems is the absence of silicon integrated circuits, or chips, which in the case of chipless RFID tags are replaced with printed encoders. Such encoders can be fabricated by means of printing techniques, such as screen printing, rotograv, flexography, or inkjet, and represent a low-cost solution as compared with conventional chip-based RFID tags [43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54]. However, chipless RFID tags present three main limitations: (1) data capacity, (2) tag size, and (3) shorter read ranges. These negative aspects and the fact that the materials (inks) and manufacturing processes (such as substrate functionalization and printing) necessary for tag fabrication are still not significantly cheaper than passive chipped RFID tags have limited the market penetration of chipless RFID technology. However, if only tag manufacture is considered, tag cost can be dramatically reduced by replacing ICs with encoders and the cost of mass-produced chipless RFID tags is predicated to fall below USD0.01 [43]. In our chipless RFID system described in a previous study [55] the tags are formed by chains of identical resonant elements printed or etched in one side of the substrate. These tags are read by proximity (through the near field), by displacing them over the sensitive part of the reader. The tag encoding is achieved by the presence or absence of certain resonant elements at predefined positions and is carried out by means of an interrogation harmonic signal tuned to the resonance frequency of the resonant elements. The presence or absence (resonator detuned or inoperative) of a resonant element will produce a variation of the amplitude of the interrogation signal at the output of the reader line. As a result, an amplitude modulated (AM) signal that contains the tag ID code is obtained. The tag ID code is inferred bit to bit, sequentially, by displacing the tag over the reader--a method of reading based on time division multiplexing [56].
# Intelligent Measurement of Coal Moisture Based on Microwave Spectrum via Distance-Weighted kNN
## 3 Results and Discussion
### Further Evaluation on Additional Data  
To better verify the effectiveness of the proposed measurement method of coal MC, on-site testing was carried out using the designed measurement system. The coal samples (validation samples) collected from the site were sieved through a 13-mm sieve and then packed into the rectangular container for microwave signal collections. The true MCs of the validation samples were obtained by standard laboratory methods [6]. A total of 27 verification experiments were carried out, and the corresponding 27 groups of coal validation samples were independent of the previous 281 normal data. The scatter results of the true and the predicted MCs are shown in Figure 7a. The \(R^{2}\), MAE, and RMSE values of the verification experiments are 0.8583, 0.2903, and 0.3541, respectively. Verification experiments based on different methods and based on different frequency bands were also carried out on the verification samples, and the results are shown in Figure 7b,c, respectively. It should be noted that the same calculation process was repeated 6 times to reduce accidental errors. Although the results of the validation experiment were reduced compared with those of the laboratory, the DW-kNN method still achieved the best results, indicating that the proposed method can effectively measure the MC of coal.
# Fully Superconducting Josephson Bolometers for Gigahertz Astronomy
## 4 Experimental Realization of JES and Nano-TES
### Density of States and Thermal Properties of the Active Region
Furthermore, the electronic temperature in \(A\) varies for distances of the order of the electron-phonon coherence length \(l_{e-ph}=\sqrt{D_{A}\tau_{e-ph}}\simeq 180\)\(\mu\)m [44], where \(D_{A}=(t_{Al}D_{Al}+t_{Cu}D_{Cu})/(t_{Al}+t_{Cu})\simeq 5.6\times 10^{-3}\) m\({}^{2}\)/s is the diffusion constant of the active region and \(\tau_{e-ph}=C/G\simeq 6\)\(\mu\)s is the electron-phonon scattering time. Since the length of the active region is \(l=1.5\)\(\mu\)m \(\ll\)\(l_{e-ph}\), the electronic temperature in \(A\) can be assumed homogeneous.
## 2 Materials and Methods
### Volatile Organic Compounds (VOCs) Analysis  
VOCs analysis was carried out as previously reported [27] and recently applied by [PERSON] et al. [28] and [PERSON] et al. [29] on both white and red wines.  
VOCs extraction was carried out by Liquid-Liquid Extraction (LLE). Briefly, 100 mL of each sample was added to 5 mL of CH\({}_{2}\)Cl\({}_{2}\) as a solvent and 250 \(\mu\)L of an alcoholic solution of 2-octanol as an internal standard (258 ppm/ethanol) (Sigma Aldrich, St. Louis, MO, USA). The mixture was magnetically stirred for 1 h at room temperature (21 \(\pm\) 1 \({}^{\circ}\)C). The resulting emulsion was transferred to a separating funnel with an emery top and kept at 4 \({}^{\circ}\)C for 12 h to help the two phases separate more easily. Finally, the obtained organic aromatic extract was recovered, dehydrated using Na\({}_{2}\)SO\({}_{4}\), and then kept at \(-\)20 \({}^{\circ}\)C until GC/MS analysis.  
For High Resolution Gas-Chromatography/Mass-Spectrometry (HRGC/MS) analysis, chromatographic conditions and the identification procedure were the same as that already reported in [PERSON] et al. [30]. One \(\mu\)L of organic extract was injected in splitless mode, while the injection port of a GC/MS-QP2010 quadrupole mass spectrometer (Shimadzu, Shimadzu corp., Kyoto, Japan) was maintained at 250 \({}^{\circ}\)C. The GC/MS was equipped with a DB-WAX UI column (60 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness, J&W Scientific Inc., Folsom,CA 95360, USA). The carrier gas was helium (1.3 mL/min) and the temperature program used was the following: 40 \({}^{\circ}\)C for 5 min, raised up to 220 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min, and held for 20 min at the maximum temperature. Electron impact mass spectra were recorded with ion source energy of 70 eV, while the temperature was kept at 230 \({}^{\circ}\)C. The peak areas were measured using a GC/MS solution program Shimadzu version 2.30 (Shimadzu corp., Kyoto, Japan). The compounds content (semiquantitative) was estimated as a ratio of each compound's response (peak area) to that of the internal standard and reported in mg/L. The VOCs were identified by comparing their retention times with those of pure reference standards under the same chromatographic conditions, as well as by the comparison of experimental mass spectra with those found in the NIST library. When the pure chemical standard was not available, the detected substances were given the designation \"tentative\" (t).
## 3 Results
### Time-Kill Kinetic Study Results of the Pesto Sauces
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Time (Hour)** & **0** & **2** & **4** & **6** & **24** \\ \hline
**Treatment** & \multicolumn{4}{c}{**L. _monocytogenes_** **Counts (log CFU/mL)**} \\ \hline Control & 4.82 \(\pm\) 0.12 \({}^{\rm{Ad}}\) & 6.14 \(\pm\) 0.29 \({}^{\rm{Ac}}\) & 7.62 \(\pm\) 0.12 \({}^{\rm{Ab}}\) & 8.18 \(\pm\) 0.34 \({}^{\rm{Aa}}\) & 8.28 \(\pm\) 0.25 \({}^{\rm{Aa}}\) \\ NPO-34 & 4.93 \(\pm\) 0.17 \({}^{\rm{Ad}}\) & 6.05 \(\pm\) 0.31 \({}^{\rm{Ac}}\) & 6.66 \(\pm\) 0.23 \({}^{\rm{Bb}}\) & 7.77 \(\pm\) 0.27 \({}^{\rm{Aa}}\) & 8.02 \(\pm\) 0.14 \({}^{\rm{Aa}}\) \\ NPO-54 & 4.94 \(\pm\) 0.35 \({}^{\rm{Ad}}\) & 6.07 \(\pm\) 0.16 \({}^{\rm{Ac}}\) & 7.49 \(\pm\) 0.19 \({}^{\rm{Ab}}\) & 8.07 \(\pm\) 0.19 \({}^{\rm{Aa}}\) & 8.20 \(\pm\) 0.33 \({}^{\rm{Aa}}\) \\ \hline Control & 4.89 \(\pm\) 0.27 \({}^{\rm{Ae}}\) & 6.25 \(\pm\) 0.11 \({}^{\rm{Ad}}\) & 7.51 \(\pm\) 0.36 \({}^{\rm{Ac}}\) & 8.10 \(\pm\) 0.18 \({}^{\rm{Ab}}\) & 8.73 \(\pm\) 0.24 \({}^{\rm{Aa}}\) \\ TPO-34 & 4.85 \(\pm\) 0.08 \({}^{\rm{Ae}}\) & 6.10 \(\pm\) 0.29 \({}^{\rm{Ad}}\) & 6.73 \(\pm\) 0.24 \({}^{\rm{Bc}}\) & 7.82 \(\pm\) 0.36 \({}^{\rm{Ab}}\) & 8.50 \(\pm\) 0.26 \({}^{\rm{Aa}}\) \\ TPO-54 & 4.90 \(\pm\) 0.19 \({}^{\rm{Ad}}\) & 6.13 \(\pm\) 0.38 \({}^{\rm{Ac}}\) & 7.39 \(\pm\) 0.15 \({}^{\rm{Ab}}\) & 8.12 \(\pm\) 0.31 \({}^{\rm{Aa}}\) & 8.64 \(\pm\) 0.21 \({}^{\rm{Aa}}\) \\ \hline Control & 4.85 \(\pm\) 0.26 \({}^{\rm{Ad}}\) & 6.11 \(\pm\) 0.42 \({}^{\rm{Ac}}\) & 7.63 \(\pm\) 0.17 \({}^{\rm{Ab}}\) & 8.21 \(\pm\) 0.26 \({}^{\rm{Aa}}\) & 8.73 \(\pm\) 0.21 \({}^{\rm{Aa}}\) \\ HPO-34 & 4.92 \(\pm\) 0.17 \({}^{\rm{Ae}}\) & 6.05 \(\pm\) 0.14 \({}^{\rm{Ad}}\) & 6.65 \(\pm\) 0.31 \({}^{\rm{Bc}}\) & 7.75 \(\pm\) 0.45 \({}^{\rm{Ab}}\) & 8.50 \(\pm\) 0.09 \({}^{\rm{Aa}}\) \\ HPO-54 & 4.93 \(\pm\) 0.21 \({}^{\rm{Ad}}\) & 6.08 \(\pm\) 0.28 \({}^{\rm{Ac}}\) & 7.52 \(\pm\) 0.20 \({}^{\rm{Ab}}\) & 8.09 \(\pm\) 0.19 \({}^{\rm{Aa}}\) & 8.64 \(\pm\) 0.38 \({}^{\rm{Aa}}\) \\ \hline \end{tabular}  
* At the same time of storage, different capital letters indicate significant (_p_ < 0.05) differences between different samples. For the same sample, different lowercase letters indicate significant (_p_ < 0.05) differences over time. Control: bacteria without oil, NPO-34: bacteria with oil from 34% oil content non-process sample, NPO-54: bacteria with oil from 54% oil-content non-processed sample, TPO-34: bacteria with oil from 34% oil-content thermal-processed sample, TPO-54: bacteria with oil from 54% oil-content thermal-processed sample, HPO-34: bacteria with oil from 34% oil-content HPP sample, HPO-54: bacteria with oil from 34% oil-content HPP sample, HPO-54: bacteria with oil from 54% oil-content HPP sample.  
\end{table} 
Table 1: Time–kill kinetic study results of oils from different pesto sauces against \(L\). _monocytogenes_.
###### Abstract  
One of the ongoing tasks in space structure testing is the vibration test, in which a given structure is mounted onto a shaker and excited by a certain input load on a given frequency range, in order to reproduce the rigor of launch. These vibration tests need to be conducted in order to ensure that the devised structure meets the expected loads of its future application. However, the structure must not be overtested to avoid any risk of damage. For this, the system's response to the testing loads, i.e., stresses and forces in the structure, must be monitored and predicted live during the test. In order to solve the issues associated with existing methods of live monitoring of the structure's response, this paper investigated the use of artificial neural networks (ANNs) to predict the system's responses during the test. Hence, a framework was developed with different use cases to compare various kinds of artificial neural networks and eventually identify the most promising one. Thus, the conducted research accounts for a novel method for live prediction of stresses, allowing failure to be evaluated for different types of material via yield criteria.  
Keywords:mass operator; machine learning; structural stress; artificial neural network; live prediction; vibration test +
Footnote †: journal: Applied Sciences
###### Abstract  
This study assesses the environmental performance in the end-of-life (EoL) of double walls made of carbon-reinforced concrete (CRC) and steel-reinforced concrete (SRC). The most feasible CRC EoL scenarios are evaluated using life cycle assessment and their environmental performances are then compared to those of SRC. The results showed that mechanical recycling is the best CRC EoL scenario, with a global warming potential (GWP) of 7.0 kg CO\({}_{2}\) eq., while the use of renewable energy can save over 50% of GWP. For SRC, the best scenario was obtained using a mobile recycling plant (GWP of 8.8 kg CO\({}_{2}\) eq.). In general, the further life of the reinforcements is hardly comparable. Steel can be recycled nearly without losses or downcycling, while a closed cycle of carbon fibers is not yet possible. Therefore, carbon fiber properties or EoL processes need to be improved for a closed loop with an optimized environmental performance.  
LCA; end of life; recycling; carbon-reinforced concrete; steel-reinforced concrete; circular economy +
Footnote †: journal: Applied  
beginning with the extraction and processing of raw materials (cradle), associated with different production and use stages (gates), and ending with its end-of-life (EoL). The EoL describes the moment a product is no longer used and disposed, recycled, or utilized in another way (down- or upcycling) [8]. Subsequently, an important step is to be able to assess the sustainability of the product's EoL already in the design process of the material. Especially for expensive and energy-intensive carbon fibers, a definition of sustainable concepts for a reutilization or recycling paths is essential [9]. Although a consideration of the entire life cycle is relevant, until now, only few LCA studies focus on the EoL or full life cycle of CRC (see Section 2.4).  
In this study, we analyze how the recycling of CRC compares with the recycling of conventional SRC in terms of environmental impacts. The aim of this study is to identify different CRC EoL scenarios and to assess the environmental impacts of the most feasible ones currently available by means of an LCA. Furthermore, an LCA for the EoL of SRC is made and compared to the environmental impacts of the identified EoL scenarios of CRC.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Routing in VANETs
#### 2.3.1 Classification  
In [5], the routing protocols are categorized as topology-based and geographic protocols. In topology-based routing, a route is established between the source and the destination prior to data transmission. This is achieved by maintaining routing tables comprising of link information shared among vehicles through the exchange of control packets. Topology-based routing protocols are further classified into proactive, reactive and hybrid. In contrast, geographic routing protocols neither maintain routing tables nor establish routes prior to data transmission. Instead, data packet is forwarded to the next best hop, which is selected based on the information available in a vehicle's vicinity. These protocols are further classified into sender-based and receiver-based forwarding. The former approach enables the vehicle that sends the data packet to select the next best forwarding vehicle towards destination, while in the latter approach, the sending vehicle broadcasts the data packet and the receiving vehicle decides whether or not to forward the packet.