esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, four novel antenna farms based on reflectarrays for broadband communication satellites in Ka-band have been proposed and evaluated. The exclusive characteristics of reflectarrays, which are able to operate independently at different frequencies or polarizations, have been applied in different ways to improve the SFPB operation compared to conventional reflectors. The impact of using flat or curved surfaces, single or dual-band operation, and single or dual antenna configurations for large reflectarrays in Ka-band has been assessed to achieve the best antenna performance. Table 2 summarizes the main characteristics of the proposed antenna solutions. The preliminary simulations of single band reflectarrays to generate four beams per feed permits operation with a single antenna for Tx and an independent antenna for Rx, which simplifies the simultaneous operation in both bands as well as the design of the feed-chains. However, this solution involves a major difficulty in providing a stable performance in band due to the independent operation at close frequencies, requiring complex reflectarray cells to which intense optimizations must be applied. On the other hand, the two proposed dual-band reflectarrays show promising results. The simulations of the dual reflectarray system would require a further optimization of the subreflectarray to slightly reduce the cross-polar radiation, although the use of a dual configuration with two reflectarrays provides greater flexibility of operation than single aperture systems, which can be used to correct the shaping of the beams in the edge of the coverage. Finally, the dual-band parabolic reflectarray achieves an excellent antenna performance with a simple antenna configuration similar to that of the currently used reflectors. Moreover, the reflectarray elements can be optimized to properly shape the beams in Tx and Rx without the need to shape the antenna surface. The core technologies of the proposed reflectarrays have been experimentally validated, which confirms the capabilities of reflectarrays to provide novel efficient multibeam antenna farms for broadband communications satellites in Ka-band. All the proposed antenna solutions make it possible to halve the number of antennas and feed-chains required onboard the satellite, from four reflectors to two reflectarray antennas, and from a total of N feed-chains (to produce a coverage of N spots) to N/2 feeds, allowing an important reduction in cost, as well as in mass and volume of the payload, which are severely limited in the satellite. Conceptualization, J.A.E. and A.P. ; methodology, J.A.E. and A.P. ; software, D.M.-d.-R., E.M.-d.-R. and Y.R.-V.; validation, D.M.-d.-R., E.M.-d.-R. and J.A.E. ; formal analysis, D.M.-d.-R., E.M.-d.-R. and Y.R.-V.; investigation, D.M.-d.-R., E.M.-d.-R., Y.-R.-V., J.A.E. and A.P. ; data curation, Y.R.-V. and A.P. ; writing--original draft preparation, D.M.-d.-R.; writing--review and editing, E.M.-d.-R., Y.R.-V., J.A.E. and A.P. ; visualization, D.M.-d.-R., E.M.-d.-R. and Y.R.-V.;
## 6 Performance Evaluation
### Latency  
_Latency_ is the time delay between the initiation of the transaction and its commitment to the blockchain.  
In the case of the \"assets\" parameter, we have experimented in instances of 5, 10, 25, 50, 75, 100, and 150 assets in each performance evaluation iteration. We observe that, as we increase the number of assets, the latency in each evaluation iteration remains constant. Notably, in the case of _create_asset_ function (Figure 15a), the average latency remains below 25 s. Similarly, for the of _read_asset_ function (Figure 16a), the average latency remains stable, below 0.05 s, while we observe the higher value of the maximum latency of 0.3 s. This stable latency performance, while asset numbers are increasing, underscores the network's resilience and its capacity to efficiently handle a growing asset load.  
Figure 14: Asset template on the _create_asset_ function.  
Figure 15: Network Latency (s), (a) per “asset” (b) per “worker”, for _create_asset_.  
In the case of the \"workers\" parameter, we have experimented in instances of 1, 2, 5, 10, 15, and 20 \"workers\" in each performance evaluation iteration. The results demonstrate a steady increase in the maximum latency that is proportional to the increase in the value of the \"workers\" parameter in the case of _read_asset_ (Figure 16b). However, the maximum latency does not surpass the 0.6 s and the average latency remains constantly below 0.1 s. On the other hand, as there will be more parallel processes available to process transactions and respond to requests, latency is kept at a constant value and less than 25 s in the case of the _create_asset_ (Figure 15b). It is worthwhile to note that the average latency in the case of _create_asset_ (Figure 15b) sees an increase up to the number of 5 \"workers\" and then remains stable below 20 s for the rest of the iterations. This observation reaffirms the adaptability and stability of the network under varied worker loads.  
Due to the fact that there are no similar architectures to the proposed one in the literature, we cannot compare the evaluation results of this research work directly to evaluation results from other research works. However, we compared our performance evaluation results to results from works on IoT networks for real-time healthcare applications [45; 46] and from a work on the performance evaluation of HF for use in IoT [38]. We observed similar values (i.e., minimum latency of 0.1 s [45] and 4 s [38]) and, thus, our performance evaluation of the proposed architecture has demonstrated satisfactory results in terms of latency in the cases of _create_asset_ and _read_asset_ functions. The values of latency are considered adequately low and within an acceptable range, indicating that the proposed HF architecture responds to requests in a timely manner.
## 4 System Model  
Some researchers have tried to model the UORA procedure using a Markov chain model with some assumptions for simplified analysis in the early stage of IEEE 802.11 ax standardization. As the IEEE 802.11 ax standard has been developed, the specific procedure of UORA has been defined. To modify and correct the Markov model and the analysis method, [PERSON] et al. tried to generate a more generalized model and analysis method [37]. Many researchers have tried to evaluate its performance, especially its efficiency and throughput [38; 39]. One of the purposes of this paper is similar to that of those studies on system modeling. Because the research is almost close to the actual IEEE 802.11 ax UORA procedure, a small disagreement still exists between the Markov chain model and the actual UORA procedure. Therefore, we tried to enhance the Markov model and the analysis method.
## 4 Results  
### System Implementation  
Three distinct modules are implemented, namely the IoT nodes, the Edge System and the Cloud system. For reasons of efficiency, the Edge System application logic is allocated into two distinct nodes, the Gateway Super node that serves as an aggregator and the IOTA powered Edge node that is hosted in the Cloud. The data from the Gateway Super node are eventually pushed to the Edge node through an email carrying along a log file with the sensors' data as shown in Figure 16. The implementation is mainly focused on the Cloud system as the IOTA powered Edge node simulates  
Figure 14: Number of Transactions vs sensor data logs (SDLs)/MAM message.  
Figure 15: System scalability in terms of Proof of Work (PoW) cost. X-axis is the number of transactions in units of thousands.  
most of the activities and pushes the data that it receives to the database, so in essence the Edge and Cloud systems communicate through the common database.  
The Edge logic is ported in C to be able to run natively on the hardware, while the MAM sub-module is simulated since there is no stable MAM implementation.  
**IoT Nodes Module**  
The physical parts of the IoT node constitute the following blocks, as depicted in Figure 17  
With respect to its performance the most notable aspects are:  
* **Coverage Distance:** The maximum, transmission distance is a function of antenna power, frequency and transmission speed. For 868 MHz radio frequency, 1 Kbps transmission speed and 10 dbm antenna power, a 12.5 km line of sight has been achieved. For a 433 MHz frequency the distance is doubled. The transmitter and receiver use antennas of half wave 1.2 dBi peak gain. In case the topology requires it, IoT nodes are used as repeaters (with minor modifications to the firmware).
* **Energy Consumption** Hibernate mode is the dominant mode for the IoT node since the total consumption in this state is 6 \(\upmu\)A. During broadcast, for output power of 10 dbm in the antenna, the consumption is 32 mA. Fifteen (15) levels of output power are available, while the transmission speed can be selected from a range of 1 Kbps to 300 Kbps. Therefore, for a 20 bytes transmission packet every ten (10) min with maximum antenna power and 1 Kbps transmission rate, the average power consumption of the IoT node is approximately 22 \(\upmu\)A and its 650 mAh battery could endure for approximately 3.5 years. The node's battery will be recharged through the photovoltaic panel which allows the battery to fully charge within 20 h of exposure to solar radiation.  
Regarding data aggregation, the sensors depicted in Figure 1 acquire the corresponding parameters shown in Table 1.  
Figure 16: Log File Instance.  
Figure 17: Physical Parts of the IoT node.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 5 Application to Predictive Maintenance of LNG Tank Refilling by Means of Trucks
### Sensor-Data-Processing Task  
Raw data stored in the database are analyzed following the data validation and reconstruction procedure described in Section 4.1. This procedure ensures data reliability for further decision-making. In this section, we explain the procedure for deriving one of the spatial models.  
Figure 4 shows the time evolution of gas consumption (measured by flowmeter, QIC) and the level of LNG (NVT) in the storage tank of a satellite plant during several days of March 2018 after resampling with a sampled time of 10 min. There was discrepancy between the QIC flow sensor and the tank level in interval days 78 to 80, while the flow sensor indicated a zero value when tank level decreased. This anomalous behavior could not be detected by using Steps 2 to 4 presented in Section 4.1 because, in summer, gas consumption could be zero and remains in this state for several hours. This figure also shows that the tank level had cyclical behavior; after finishing the truck discharge, the tank level was at its maximum, and it decreased with consumption.  
Using Step 5 (spatial-model test) of the methodology presented in Section 4.1, when correlating the integral of gas-flow consumption between two refilling operations with the LNG tank level, ia relationship was observed given by the constant of liquid-to-gas conversion (\(K=244\)). This is shown in the XY plot of Figure 5, where the blue continuous line corresponds to the correlation between the integral of gas-flow consumption and LNG tank level in 19-20 March 2018, and the red continuous line is calculated by  
\[Q_{a}(t)=\int_{0}^{\Delta t_{r}}q(\tau)d\tau, \tag{8}\] \[\dot{y}(t)=y(0)-(1/K)Q_{a}(t), \tag{9}\]  
Figure 4: LNG(Liquefied Natural Gas) level in storage tank (NVT) in blue, and gas consumption (QIC) in red.  
where \(\hat{y}\) is the estimated level, \(y(0)\) the initial tank value, \(K\) the liquid-to natural gas conversion constant in normal conditions, \(\Delta t_{r}\) is the time between two refilling operations, \(q\) the natural gas consumption, and \(Q_{a}\) the accumulated natural gas consumption. Finally, the black discontinuous bands show the estimated uncertainties. This spatial model is also useful to reconstruct the wrong data provided by the flowmeter sensor. This spatial model could be useful if faulty data appear in the LNG level in the tank. In this case, gas consumption could be used to reconstruct level data.
# A 5G NR FR2 Beamforming System with Integrated Transceiver Module
## 4 Conclusions  
In this paper, we present a 5G NR FR2 beamforming system with an integrated transceiver module. This system facilitates simultaneous data transmission and reception using the same module. The incorporation of an eight-channel custom-made BFIC in the phased-array antenna system significantly contributes to enhanced performance and efficiency. The beamforming antenna exhibits the horizontal scanning ability from \(-50^{\circ}\) to \(+50^{\circ}\) with \(10^{\circ}\) intervals. Furthermore, the integrated transceiver module offers flexibility in adjusting the frequencies of both the local oscillator (LO) and intermediate frequency (IF) with minimal complexity. The transceiver module achieves a transmission and reception conversion gain of approximately 20 dB. In this work, we demonstrate a circuit to the system. The integration of our own eight-channel BFIC, array antenna, and our own measurement setup enables the effective demonstration of a 5G mm-wave system. The feasibility of real-time video transmission in various directions using the beamforming system is successfully showcased, highlighting its applicability in diverse equipment, particularly in radar, mobile, and satellite communications.  
Conceptualization, A.B. and J.-G.K.; methodology, A.B., M.K. and J.-G.K.; investigation, A.B., M.K. and J.-G.K.; writing--original draft preparation, [PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON], M.K., S.S. and [PERSON]-[PERSON]; supervision, [PERSON]-[PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, J.-G.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2018-0-01663, Development of Communication Sensing Converged B5G Millimeter Wave System) and has been conducted in part with the Research Grant of Kwangwoon University in 2022.  
Not applicable.  
Figure 21: A transmitted OFDM measurement spectrum from 28 GHz RF transceiver.  
Figure 22: A received OFDM measurement spectrum from 28 GHz RF transceiver.
# Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
## 2 Quantum Compressive Sensing  
Our Quantum Compressive Sensing protocol is summarized in the following steps:  
1. Training
2. Measurement
3. Projection
4. Sampling  
In the _training_ step, an \(n\)-qubit Born machine \(|\Psi\rangle\) is prepared, representing the distribution of possible signals \(y\). In the context of TNCS, \(y\) may be the message [PERSON] wishes to send to [PERSON]. In the context of LIDAR geomaging, each element of \(y\) may represent the amount of foliage at a particular coordinate in space, so that we might interpret \(y\) as an image with \(n\) pixels. The Born machine maps each possible signal \(y\) to a complex amplitude \(\langle y|\Psi\rangle\) whose square modulus is the probability that \(y\) is observed. We discuss the pixel-qubit mapping in more detail in Section 3, and training procedures in Section 4.  
In the _measurement_ step, \(m\) distinct measurements are made on an unknown \(n\)-dimensional signal \(y\), as described by a classical \(m\times n\) sensing matrix. The measurement outcomes obtained are \(x=Ay\). In the context of TNCS, \(x\) may be the partial message which [PERSON] sends to [PERSON]. In the context of LIDAR geomaging, each element of \(x\) may be the total intensity reflected into a single-pixel camera by a randomly-assigned configuration of micro-mirrors. Both \(x\) and \(A\) are considered as inputs to the next step of our protocol.  
In the _projection_ step, the Born machine \(|\Psi\rangle\) is projected onto a state \(|\Psi_{x}\rangle\) which spans only those states consistent with the measurement outcome \(x\). This step involves applying a non-unitary operator onto a quantum state, which is highly non-trivial. The majority of our paper addresses this problem with several different strategies in Section 5.  
In the _sampling_ step, all qubits are measured, obtaining a binary string representing a probabilistic estimate \(y^{\prime}\) of the original signal \(y\). If the original signal \(y\) is non-binary, the reconstruction \(y^{\prime}\) is at best an approximation, but it is (ideally) qualitatively sufficient to convey the required information (e.g., the geological structure being imaged, or the thesis of [PERSON]'s message). Optionally, the sampling process may be repeated many times to generate a sequence of binary signals \(y\) whose statistics could be reverse-mapped onto a non-binary image. However, because both the projection and sampling steps irrevocably alter the state of the machine, \(|\Psi\rangle\) would have to be re-trained after each sample. Therefore, we focus mainly on \"single-copy\" sampling throughout this work, although we acknowledge that multiple trials are generally required in both the training and projection steps.
# Analysis, Evaluation and Simulation of Railway
## 1 Introduction
### Storage Possibilities
#### 1.3.2 Stationary Energy Storage Systems (SESS)  
The main advantage of Stationary Energy Storage Systems is that there are no space and weight restrictions for the storage system, as they are installed in the trackside. In addition, Stationary Systems allow storing the regenerative energy for various trains at the same time, and the energy recuperated can be used in a different train that initially generates it. This fact helps to reduce the demand power peaks and to increase the number of vehicles that can circulate on the railway path reducing the investments to improve the electrical supply system.  
However, SESS also have some restrictions. In the first place, the capability of the system is a function of the location of the vehicle, increasing the losses, due to transmission with the distance between the train and the storage system. Furthermore, it is impossible to apply these systems in cases of non-electrified railway paths, or in shorts distances without supply (last-mile, tunnels, etc.).  
Some real projects of SESS are currently being used in different railway systems. In Philadelphia, ABB has installed a Li-ion batteries system called ENVILINE ESS [26], providing revenue by generating frequency regulation services on the local energy market. This system has also been installed in Warsaw Metro, improving its energy efficiency. Otherwise, Siemens [22; 27] has provided supercapacitors SESS (Sitras SES) in Madrid, Cologne, Rotterdam, Toronto, Portland and Beijing metro lines.  
In respect to flywheels, VYCON installed a system for the Los Angeles Country Metropolitan Transportation Authority (LA Metro) Red line (MRL) called WESS, in order to recover the braking energy from their trains [28]. There is also a flywheel system installed in London underground's Piccadilly Line [29].
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 4 Glider Performance Analysis
### Glider Polar Chart Equations-Velocity Analysis  
According to the principle of Archimedes, the net buoyancy force is defined by \(B=\rho gV_{d}\), where the reference displaced volume \(V_{d}\) was defined in the Section 3.4 with the main operation states of the vehicle. Substituting the buoyancy expression from (8) into (10), the next equation is obtained to calculate the required displaced volume in function of the hydrodynamics:  
\[V_{d}=\frac{\left(K_{D0}+K_{D1}\alpha+K_{D2}\alpha^{2}\right)U^{2}}{\rho g\sin \gamma}. \tag{20}\]  
If the vehicle's max velocity \(U\) during descent and ascent; the hydrodynamic coefficients; the fluid density; and the required glide angle \(\gamma\) are known, then the reference volume \(V_{d3}\) and \(V_{d4}\), as described in Section 3.4, could be obtained, calculating first \(\alpha\) through (16).  
Subtracting the dive velocity \(U\) from Equation (20), the analytical equation to calculate the resultant velocity as a function of the displaced volume \(V_{d}\), the fluid density \(\rho\), and the hydrodynamic parameters and the glide angle are as follows:  
\[U=\sqrt{\frac{\rho gV_{d}}{\left(K_{D0}+K_{D1}\alpha+K_{D2}\alpha^{2}\right)} }\sin^{1/2}\gamma. \tag{21}\]where the projections of horizontal velocity \(u\) and vertical velocity \(w\) are obtained in (22) and (23):  
\[u=\sqrt{\frac{\rho gV_{d}}{(K_{D0}+K_{D1}\alpha+K_{D2}\alpha^{2})}}\sin^{1/2} \gamma\cos\gamma, \tag{22}\]  
\[w=\sqrt{\frac{\rho gV_{d}}{(K_{D0}+K_{D1}\alpha+K_{D2}\alpha^{2})}}\sin^{3/2} \gamma. \tag{23}\]  
Equations (21)-(23) can generate the characteristic performance charts of underwater gliders; generating contour curves with different \(V_{d}\) values and making sweeps with respect to the glide angle \(\gamma\). This in turn calculates the angle of attack \(\alpha\) that varies with respect to angle \(\gamma\). The resultant velocity variation with respect to the glide angle (\(U\) vs \(\gamma\)) is called the \"dive velocity chart\". The \"glider polar chart\" is the horizontal velocity graph \(u\) on the abscissa axis and the velocity \(w\) on the ordinate axis (\(u\) vs \(w\)). Both charts are obtained in the next section in order to discuss the analysis process based on the proposed method.
## 3 Results and Discussion
### Sample Characterization
#### 3.1.2 TG/DTA Analysis  
The thermal behavior of MO, M, LA, and L was recorded from room temperature up to 700 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min in air. The obtained graphs for the four investigated materials are similar, which proves analogous behavior under heating, probably due to their similar chemical composition. All investigated materials contain mainly hemicelluloses, cellulose, and lignin. At the end of the pyrolysis process, solid carbon-containing residues are obtained. The TG/DTA graphs are presented in Figure 2.  
Figure 1: XRD curves of plant materials MO, M, LA, and L.  
Figure 2: TG/DTA curves of plant materials MO (**a**), M (**b**), LA (**c**), and L (**d**).  
As previously reported [12], the first region of the DTA curve (20 to 100 \({}^{\circ}\)C), related to the elimination of moisture and adsorbed water, is not clearly distinguished because the samples were initially dried at 60 \({}^{\circ}\)C in an oven for 48 h.  
Three clearly defined regions are observed by higher temperatures, as follows:  
The region from 100 \({}^{\circ}\)C to 200 \({}^{\circ}\)C is connected with the evaporation of compounds of high volatility. The slope to the next region (between 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C) can be assigned to the thermal decomposition of hemicellulose, cellulose, and some lignin fractions. The first two regions are almost identical for all investigated materials. Above 400 \({}^{\circ}\)C, the peaks for the samples based on the raw plant materials (MO and LA) are broader as compared with those for the waste products (M and L), which are narrower. Anyway, the results confirm those obtained from the XRD analysis.  
The TG curve, which is related to the change in mass with the change in temperature, shows losses of over 80% of the weight when heating up to 700 \({}^{\circ}\)C.