esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems
## 2 Materials and Methods  
### Structure and working principle of the CPS  
The CPS mainly includes a pumping mechanism and a swing mechanism, as shown in Figures 1 and 2. The pumping mechanism consists of a main pump, a main valve, signal valves, two pumping cylinders and two concrete cylinders.  
The swing mechanism consists of a swing pump, two swing valves, two swing cylinders and a distribution valve. The main pump is an electrically proportional controlled variable piston pump with constant power control, and the swing pump is a variable pump with constant pressure control [20; 21]. The pump and concrete cylinders are fixed to each other. The two pumping cylinders alternately extend and retract to drive the concrete cylinders to suck and deliver concrete. The swing cylinder drives the distribution valve to swing and connects it to the concrete cylinder that pumps concrete [22]. The signal valves are used to sense the displacement of the pumping cylinders and provide a control signal to reverse the swing cylinder, so the signal valve is the unit that connects the pumping mechanism to the swing mechanism. Through the coordinated action of the pumping mechanism and the swing mechanism, the continuous delivery of high-pressure concrete is realized.  
The detailed working principle is as follows. The hydraulic oil from the main pump acts on the nonrod cavity of the pumping cylinder through the main valve, causing the main cylinder to extend. When the piston of the main cylinder passes the upper pressure-detection point, a differential pressure signal is generated in the two chambers of the signal valves, and the signal valve is switched to the working position. The pressure oil from the swing pump then acts on the swing valve to change direction through the signal valve, and then the swing valve simultaneously drives the main valve and the swing cylinder to reverse. When the main valve is switched to the working position, the pumping cylinder is reversed and the concrete cylinder changes from pushing to sucking, completing a pumping work cycle.
## 2 Materials and Methods
### Deterministic Approximate EM Algorithm and Its Convergence for the Exponential Family
#### 2.1.1 Context and Motivation
In the following, we consider a deterministic approximation of \(p_{\theta}(z)\) noted \(\hat{p}_{\theta,n}(z)\) which depends on the current step \(n\) and on which we make no assumption at the moment. The resulting steps, defining the \"Approximate EM\", can be written under the same form as (1):  
\[\theta_{n+1}:=F_{n}(\theta_{n}):=\operatorname*{argmax}_{\theta\in\Theta} \mathbb{E}_{z\sim p_{\theta_{n},n}(z)}[\ln h(z;\theta)]\,, \tag{2}\]  
where \(\{F_{n}\}_{n\in\mathbb{N}}\) is the sequence of point to point maps in \(\Theta\) associated with the sequence of approximations \(\{\hat{p}_{\theta,n}(z)\}_{\theta\in\Theta;\,n\in\mathbb{N}}\). In order to ensure the desired convergence guarantees, we add a slight modification to this EM sequence: _re-initialisation of the EM sequence onto increasing compact sets_. This modification was introduced by [PERSON] et al. [33] and adapted by [PERSON] [7]. Assume that you dispose of an increasing sequence of compacts \(\{K_{n}\}_{n\in\mathbb{N}}\) such that \(\cup_{n\in\mathbb{N}}K_{n}=\Theta\) and \(\theta_{0}\in K_{0}\). Define \(j_{0}:=0\). Then, the transition \(\theta_{n+1}=F_{n}(\theta_{n})\) is accepted only if \(F_{n}(\theta_{n})\) belongs to the current compact \(K_{j_{n}}\), otherwise the sequence is reinitialised at \(\theta_{0}\). The steps of the resulting algorithm, called _Stable Approximate EM_, can be written as:  
\[ \begin{cases}ifF_{n}(\theta_{n})\in K_{j_{n}},\text{ then }\theta_{n+1}=F_{n}( \theta_{n}),\text{ and }j_{n+1}:=j_{n}\\ ifF_{n}(\theta_{n})\
otin K_{j_{n}},\text{ then }\theta_{n+1}=\theta_{0}, \text{ and }j_{n+1}:=j_{n}+1\,.\end{cases}  \tag{3}\]  
This re-initialisation of the EM sequence may seem like a hurdle, however, this truncation is only a theoretical requirement. In practice, the first compact \(K_{0}\) is taken so large that it covers the most probable areas of \(\Theta\) and the algorithms (2) and (3) are identical as long as the sequence \(\{\theta_{n}\}_{n}\) does not diverges towards the border of \(\Theta\).
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
###### Abstract
The results indicate that the proposed method can significantly reduce the mosaicking time while ensuring the accuracy of the mosaic and maintaining high resolution. This method can, thus, be used on the mosaic of multiple large-area spaceborne ScanSAR images. The remainder of this paper is organized as follows: Section 2 introduces the ScanSAR modes of GF-3. Section 3 presents a traditional feature-based image mosaic process. Section 4 shows the details of our proposed method. Section 5 reports the experimental results and discussion. Conclusions are provided in Section 6.
## 2 Structure of Nafion and Its Proton Conduction Mechanism  
The materials for proton exchange membranes are selected on the basis of high ion conductivity, mechanically strength, chemical stability, low gas permeability and effective water management, high protection from fuel crossover and being environmental friendly. Moreover, polymer membrane materials should exhibit both hydrophilic and hydrophobic properties. The hydrophilic part of the polymer membrane absorbs water, while the hydrophobic part blocks the permeability of hydrogen and oxygen [37; 38]. PEM materials must be chemically inert to prevent degradation under exposure to -OH and -OH radicals. Perfluorosulfonic acid-based membranes provide both hydrophobic and hydrophilic properties in membranes. Its hydrophobic perfluorinated backbone supports the membrane in terms of chemical and mechanical stability, while the hydrophilic pendant chain in the membrane is responsible for water intake, which promotes ion cluster formation [39]. Various  
Figure 2: Schematic diagram of an anion exchange membrane fuel cell (AEMFC).  
types of perfluorosulfonic acid (PFSA) membranes, such as Nafion 117 and 212, are commercially available [40]. Thick membranes like Nafion 117 (183 \(\upmu\)m) offer high resistance to fuel crossover, while the thin Nafion 212 membrane provides high ionic conductivity but is more prone to fuel permeability. Due to their thermally sensitive nature, these membranes cannot operate above 80 \({}^{\circ}\)C, making them suitable for low-temperature fuel cells. Moreover, its high proton conductivity, chemical stability, and mechanical strength somehow fulfill the criteria of membranes used in fuel cells [41]. The DuPont company manufactured the first PFSA-based membrane for fuel cells, which consists of Nafion. Nafion consists of a backbone of Tetafluoroethylene (TFE) copolymer and a perfluoro vinyl ether pendant chain. The Nafion membrane delivers high proton conductivity of more than 0.1 S cm\({}^{-1}\) to 0.13 S cm\({}^{-1}\) at a temperature of 80 \({}^{\circ}\)C under 100% relative humidity (RH). The Nafion membrane can sustain stable power for over 60 \(\times\) 10\({}^{3}\) h [42; 43]. The ion conduction in the Nafion membrane is based on Grothuss and vehicular mechanisms [44]. The protons generated by the oxidation and reduction process in the fuel cells combine with water present in the Nafion to make hydronium ions (H\({}_{3}\)O\({}^{+}\)). These hydronium ions combine with additional water molecules to form larger hydrated complexes, such as H\({}_{5}\)O\({}_{2}\)\({}^{+}\) and H\({}_{5}\)O\({}_{3}\)\({}^{+}\). These ions travel through the free water available in the membrane. Protons move as part of larger hydrated complexes (e.g., H\({}_{3}\)O\({}^{+}\), H\({}_{5}\)O\({}_{2}\)\({}^{+}\), H\({}_{5}\)O\({}_{3}\)\({}^{+}\)) through the membrane by diffusion. This movement is called a vehicular mechanism. Moreover, in the Grothuss mechanism, protons hop on sulfonic groups in the membrane and jump from one group to the other group through electrostatic attraction [45].
## 4 Discussion  
This study demonstrated that the ANOVA-PDR could be a valuable tool for UV-Vis spectroscopy to identify the sources of variations in a complicated sample set from multi-factorial-designed experiments. The ANOVA was combined with PDR for the first time and provided an exact and comprehensive comparison of the differences between classes and offered results with visual plots, which helps interpret the significance for the arrangement and control of factors. The overall degrees of separation are evaluated by calculating the geometric mean of the PDRs. The ANOVA-PDR was proved to be an effective supplement to multivariate modeling such as PLS-DA.  
With respect to the UV-Vis spectroscopy of olive oil adulteration, the study indicated that the EVOO origin and adulteration level are effective sources of variation in the spectra, which may cause potential difficulties in the suitability of the EVOO adulteration detection model. The subsequent PLS-DA and PLSR models for EVOO adulteration detection were also consistent with this conclusion. The results demonstrated that the EVOO adulteration detection model established by the UV-Vis spectroscopy combined with PLS may achieve unbiased results without the aid of a proper model transfer and validation routine. To overcome this problem, further research can also focus on a more sensitive and selective detection methods, as well as to devise a controlled approach to select training samples for the chemometrics model. Additionally, since all the adulterants in this study were collected from China only, the adulterant-type factor raised insignificant variances. When analyzing possible impurities, it is better to examine both adulterant oils (corn, soy, sunflower) from local producers, as well as producers from the countries of origin of olive oil. In addition, other types of widely available oils, such as canola and peanut oils could be included in the model. In this manner, a robust and reliable model can be achieved.  
Conceptualization and funding acquisition, B.G.; methodology and investigation, J.Z.; validation, W.L.; writing--original draft preparation, J.Z.; writing--review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China (Grant No. 32001819, 32272426).  
Not applicable.  
The data presented in this study are openly available upon request.  
The authors declare no conflict of interest.
# A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
## 6 Conclusions  
In this work, a designer-friendly, simplified model of CNTFET was developed and fitted to previously published data. Its simplicity, stemming from the separability property of the functional dependence of the drain current on the gate and drain voltages, allow direct design and optimization of circuit bias points, as was demonstrated by the designing of a CNTFET LNA. That, coupled with a noise model to optimize the overall noise figure of the amplifier, was then simulated using the full-fledged Verilog-A Stanford model augmented with noise sources. The simulation results show good agreement to the predicted performance, proving the correctness and usefulness of the proposed simplifications. A comparison of the circuit performance was also made with reference to several state-of-the-art architectures found in the literature, encompassing both conventional CMOS technologies and CNTFET circuits, confirming the overall validity of the proposed high-gain LNA design.  
Conceptualization, P.C., G.B., C.T., L.F., D.M., G.D. and L.P.; methodology, P.C., G.B. and C.T.; software, P.C. and G.B.; formal analysis, P.C., G.B., C.T. and L.F.; investigation, P.C., G.B. and C.T.; resources, G.D.; data curation, L.F. and D.M.; writing--original draft preparation, P.C. and G.B.; writing--review and editing, P.C., G.B., C.T., L.F., D.M., G.D. and L.P.; visualization, P.C. and G.B.; supervision, C.T.; project administration, L.F.; funding acquisition, G.D. and L.P.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by the European Project \"NANO components for electronic SMART wireless circuits and systems (NANOSMART)\", H2020--ICT-07-2018-RIA, n. 825430.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Reference** & [22] & [23] & [24] & [25] & [26] & [29] & [27] & [28] & **This Work** \\ \hline Node & 180 nm & 180 nm & 450 nm & 32 nm & 65 nm & 130 nm & 65 nm & 130 nm & 90 nm \\ Technology & CMOS & CMOS & CNTFET & CNTFET & CMOS & CMOS & CMOS & CNTFET \\ Data & Sim. & Meas. & Meas. & Sim. & Meas. & Meas. & Sim. & Sim. \\ Bandwidth (GHz) & 3–6 & 1.05–3.05 & 1–1.2 & 3–38 & 1–20 & 0.1–2.7 & 0.03–3 & 0.1–5 & 0.01–2.5 \\ Power Supply (V) & 1.8 & 1.8 & 2.5 & 1.0 & 1.6 & 1.2 & 1.2 & 1.2 & 2.0 \\ Power (mW) & 15.3 & 12.6 & N/A & 16 & 20.3 & 1.32 & 5.7 & 4.4 & 18 \\ Gain (dB) & 20.14–21 & 16.9 & 11 & 13.7–14.7 & 12.8 & 20 & 11.6 & 20 & 25.7 \\ NF (dB) & 3.5–3.6 & 2.6–3.1 & 8 & 0.4–1.3 & 3.3–5.3 & 4.0 & 2.7–3.32 & 3.04–3.97 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the designed LNA with other works.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Methodology  
This study uses current research case studies as a benchmark to support a real-world dataset model of a smart building with integrated smart IoT devices and the management of data using Cloud- and Fog-based scenarios. The simulation in this study demonstrates and validates the benefits of Fog computing and its optimisation of energy consumption, latency, and network bandwidth usage. This study utilised a dataset collected from heterogeneous smart IoT devices operating on one floor of a smart office building. The IoT device dataset was inputted into the iFogSim2 network simulation toolkit. In addition, the experiment for this study was conducted on a MacBook Pro computer with a 2.3 GHz 8-Core Intel Core i9 Processor with 16 GB RAM. The fractional selectivity of the input-output relationship was 1.0 within each module. At the same time, the parameter numeric infrastructure values, such as the Cloud datacentre, router/proxy server, and gateway, were derived from the latest literature that uses real-world infrastructure values such as [9; 16; 34; 42; 43]. The parameter values for the IoT smart devices, sensors, and systems were derived from real-world manufacturers' specifications and datasheets of the smart devices, sensors, and systems used in the simulation in this study.  
This study further highlights the challenges of high latency, energy consumption, and network bandwidth usage in Cloud computing. Adopting the geographical mobility feature within the Fog computing paradigm was explored to address and optimise Cloud computing challenges during data management in an IoT network within a smart building. One of the knowledge gaps and limitations within this research area is the need for real-world data collected from smart IoT devices and used within the experiment and simulation process. In most of the literature, the use of synthetic data, for example, in the iFogSim2 network simulation toolkit, is commonplace. Therefore, this study bridges this gap by using real-world data generated and captured through IoT smart devices and inputted into the iFogSim2 network simulator toolkit for processing. The results from this study highlightand demonstrate the performance metrics of energy consumption, latency and network bandwidth usage using real-world data in a smart building environment. For example, real-world applications can be simulated to ensure consistency and service availability in a smart building, especially for applications requiring real-time response as part of their core functionality.
## 1 Introduction
This is further demonstrated by [25] who show that lower values of the first baroclinic Rossby radius of deformation are present in the western Mediterranean Sea. These values are approximately between 2 and 16 km, in comparison with a 20-30 km range found at mid-latitudes of the Atlantic Ocean [26]. The Mediterranean values are actually closer to the values found in the Arctic Ocean [27]. The western Mediterranean Sea is one of the areas of the global ocean that will be sampled during the SWOT fast-sampling phase [28]. This phase covers the first 60-90 days after launch, during which the satellite will provide daily high resolution Sea Surface Height (SSH) measurements over a limited repeated orbit for purposes of calibration/validation of the SWOT sensor/instrument. The goal of this study is to assess the capacity of SWOT to resolve the fine scales in the western Mediterranean. Our first objective is to generate pseudo-SWOT data from numerical model outputs in this region in order to understand its temporal and spatial sampling pattern in this area. We then apply a noise-reduction processing technique to pseudo-SWOT data to find out the spatial scales that SWOT may ultimately be able to resolve. Given SWOT's irregular time sampling and consequent variable spatial coverage, in this paper, we focus on the spatial scales resolved within a swath.
# Autonomous Intersection Management by Using Reinforcement Learning
## 3 Methodology
### Deep Reinforcement Learning-Based Autonomous Intersection Management  
This subsection will give a detailed introduction to our method called deep reinforcement learning-based autonomous intersection management (DRLAIM). Figure 2 shows the basic framework of our proposed method. The description of our proposed system is as  
Figure 1: Combination of traffic phases at intersection.  
follows. The intersection controller and the vehicle are the two components of DRLAIM. The brake-safe control model, which is explained in Section 3.3 and is used in the vehicle portion, ensures that the car always endures in the brake-safe condition, allowing it to brake safely without hitting other vehicles with higher priority. The deep reinforcement learning for intersection control module is discussed in Section 3.4. The priority assignment model is discussed in detail in Section 3.5. It sends a request to the intersection controller to ask for a priority to pass the intersection. A vehicle must wait in front of the stop line until it receives a priority from the confirmation message sent by the intersection controller. Then it can cross the intersection according to its priority value.  
The intersection controller contains the components (i) queue, (ii) environment, (iii) priority assignment model, and (iv) agent. The queue architecture is illustrated in Figure 3, where each sub-queue stores the vehicle ID of vehicles and the time the queue received this request in the corresponding lane. Time indicate the time at which vehicles enter the queues. Initially, the vehicles ID start with one, and initially time value will be dependent upon the time at which vehicles enter. Once a request is confirmed, the corresponding request consisting of vehicle ID and timestamp will be removed from the queue.  
The environment portion keeps track of the number of cars in the junction, the size of the line, and the average amount of time they spend waiting. The agent must decide which course of action will let cars pass through a junction. At each time step \(t\), the agent examines the environment's state \(s_{t}\) and selects an action \(a_{t}\) that permits cars to pass. The agent then transmits the priority assignment model, the action \(at\), and the relevant requests from the queue to act \(a_{t}\). The environment's state changes to \(s_{t}+1\) when the action is completed, and the agent is rewarded with \(r_{t}+1\). An agent's objective is to create the best possible policy to maximize the overall reward \(\sum_{t}r_{t+1}\). The connected cars are added to the priority queue, which holds the priority relations among the vehicles one by one, once the priority assignment model gets an action \(a_{t}\) and the concurrent requests. Confirmation messages and a priority list are then sent to the appropriate cars by using the priority assignment methodology. Next, we will introduce the methodologies for the brake-safe control model, deep reinforcement learning for intersection control, and priority assignment model in detail.
## 4 Conclusions  
In this investigation, Al/CNT nanocomposites were produced by powder metallurgy route with 1.00 vol.% of the CNTs. The CNTs were dispersed by ultrasonication with the Al powder in isopropanol for 15 min. The tensile test results revealed an increase in the yield strength of 185% for nanocomposites than the Al matrix. However, a decrease in ductility  
Figure 15: (**a,b**) IPF maps, with high-angle (black) and low-angle (red) boundaries, (**c,d**) IPFs, (**e,f**) GNDs density and (**g,h**) Al and Al/CNT samples at the head of the tensile samples.  
is observed, which can be explained by the presence of some CNTs clusters at the grain boundaries of the matrix.  
The nanoindentation test exhibits the nanocomposite's high hardness values and corresponding high values of reduced Young's modulus, which corresponds to the presence of CNTs.  
The increase in the yield strength of the nanocomposites can be attributed to the load transfer mechanism due to the CNTs elongated observed in the fracture surface of the tensile samples, which can have acted as a bridge, hindering the crack propagation. However, CNTs clusters are observed, which confirms that these are present at the grain boundaries that can significantly decrease the ductility of the nanocomposites.  
Microstructural characterization and Vickers hardness evolution of the tensile specimen at different regions explain the different behavior of the nanocomposite samples. The Al samples revealed higher hardness values close to the surface fracture than the nanocomposites. The presence of a higher dislocation density can justify this behavior. In addition, different crystalline orientations and Taylor factors are observed for the samples, corroborating the results observed in the tensile tests. In the non-tested regions, the nanocomposites exhibit a higher density of dislocations and a higher hardness value that can be attributed to CNTs in the matrix.  
Conceptualization, I.C.; investigation, I.C. and S.S.; writing-original draft preparation, I.C. and S.S; supervision, S.S.; formal analysis, S.S.; validation, S.S.; writing-review and editing, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data can be available upon request from the authors.  
The authors are grateful to CEMUP--Centro de Materiais da Universidade do Porto for the expert assistance with SEM.  
The authors declare no conflict of interest.