esa-sceva/satcom-chunk-collection huggingface dataset:
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 2 Sensor Integration into Observation Platforms
### Protocols and Standards
#### 2.2.5 Efficient XML Interchange  
The Efficient XML Interchange (EXI) is a World Wide Web Consortium's (W3C) format that enables the compression of large ASCII XML files into efficient binary files, significantly reducing its size. This format has been designed to allow information sharing between devices with constrained resources, meeting requirement 7 [17].
## 3 Results
### Forest Canopy Height  
Due to the volumetric decorrelation term indicated in Section 2.2, forest canopy height can be interpreted from the InSAR data. Based on the forest canopy height measurements provided by the GEDI instrument, this section assesses the behavior of \(|\gamma|\) with the variation of height in both the Corrientes and the Neuquen sites, see Figure 11. The analysis is divided into two parts to account for the temporal decorrelation term by selecting two long temporal baselines, i.e., 48-day, SAOCOM-1 interferograms for each site with HH polarization and short temporal baseline SAOCOM-1 interferograms, i.e., 8 and 16-day. As a complement, Sentinel-1 VV interferograms are also considered since VV polarization is more similar in terms of \(|\gamma|\) behavior to the HH one, see Section 4.2.  
Figure 11: \(|\gamma|\) vs. GEDI canopy height for forests in Corrientes and Neuquen sites.  
Figure 10: Visual comparison of \(|\gamma_{VV}|\) of SAOCOM-1: (**A**) 8-day pair, 3 in Table 2, and (**B**) 16-day pair, 4 in Table 2.
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Packet Collision Sensing  
In cellular V2X, every OBU continuously monitors the channel. The only time that it cannot monitor it is the time slot it transmits in. This is due to the half-duplex communication. Each time slot is composed of multiple frequency resources, each of which can be utilized by different vehicles for their safety beacon transmissions. In the monitored time slot, the transmissions from other vehicles are decoded in two steps. First, the SCI from the physical sidelink control channel (PSCCH) is decoded. Second, based on the information decoded from the SCI, the transport block (TB) in the physical sidelink shared channel (PSSCH) is found and the payload is decoded.  
When a transmitted packet in a resource is not decoded, there are three possibilities. First, the absolute received power is below the reception threshold. This will most likely happen when the transmitter is far from the receiver. Second, the interference and noise power are relatively strong compared to the signal power. Third, a packet collision has occurred in the resource. From the perspective of the collision feedback provider, the first case means that it is probably not in the region of interest (ROI) of the colliding transmitters because the most safety-critical distance is typically much shorter than the C-V2X communication range [16]. The latter two possibilities, however, need to be closely examined. In fact, the primary responsibility of a candidate feedback provider is to distinguish between the two. Figure 2 shows the described logic.  
Figure 1: Vehicles X and Y decide to reselect a frequency resource R_Kn’, leading to a persistent collision event.  
To implement the logical flow in Figure 2, three conditions should be considered, which are marked as (1)-(3) in the flowchart. They are respectively related to the RSSI, SINR, and IBE. The SINR condition is used to distinguish collisions from successful transmissions. The RSSI condition is necessary to identify feedback provider (s) in the ROI of both the colliding transmitters. Finally, the IBE condition ensures that the suspected collision is not caused by an in-band emission.
# Behavior of Circular CFST Columns Subjected to Different Lateral Impact Energy
## 3 Test Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Specimen**} & \(N_{\mathrm{r}}\) & \(N^{\mathrm{P}}\) & \(f_{\mathrm{y}}\) & \(f_{\mathrm{u}}\) & \(f_{\mathrm{cu}}\) & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(k_{\mathrm{\epsilon}}\)} & \(\Delta_{\mathrm{u}}\) & \(\Delta_{\mathrm{y}}\) & \multirow{2}{*}{\(\mu\)} & \multirow{2}{*}{**Failure**} \\  & **(kN)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & & & & **(mm)** & **(mm)** & **Modes** \\ \hline C20-L0-E0 & 594.10 & 490 & 264 & 352 & 22.13 & 1.00 & 1.00 & 30.00 & 8.49 & 3.53 & - \\ C20-L0.50-E5000 & 500.15 & 490 & 264 & 352 & 22.13 & 0.84 & 0.94 & 16.70 & 7.78 & 2.15 & mode 3 \\ C20-L0.50-E7500 & 510.00 & 490 & 264 & 352 & 22.13 & 0.86 & 0.86 & 16.15 & 6.76 & 2.39 & mode 3 \\ C20-L0.50-E10000 & 500.05 & 490 & 264 & 352 & 22.13 & 0.84 & 0.73 & 19.11 & 7.36 & 2.60 & mode 2 \\ C20-L0.50-E12500 & 550.20 & 490 & 264 & 352 & 22.13 & 0.93 & 0.92 & 16.72 & 5.84 & 2.86 & mode 3 \\ C20-L0.50-E15000 & 490.20 & 490 & 264 & 352 & 22.13 & 0.83 & 0.78 & 15.54 & 6.69 & 2.32 & mode 3 \\ C20-L0.33-E5000 & 523.75 & 490 & 264 & 352 & 22.13 & 0.88 & 0.65 & 17.23 & 7.77 & 2.22 & mode 4 \\ C20-L0.33-E7500 & 500.30 & 490 & 264 & 352 & 22.13 & 0.84 & 0.58 & 19.73 & 7.37 & 2.68 & mode 1 \\ C20-L0.33-E10000 & 550.10 & 490 & 264 & 352 & 22.13 & 0.93 & 1.36 & 19.47 & 5.52 & 3.53 & mode 3 \\ C20-L0.33-E12500 & 475.25 & 490 & 264 & 352 & 22.13 & 0.80 & 0.52 & 19.74 & 7.85 & 2.51 & mode 2 \\ C20-L0.33-E15000 & 500.25 & 490 & 264 & 352 & 22.13 & 0.84 & 0.64 & 13.35 & 6.46 & 2.07 & mode 2 \\ C20-L0.25-E5000 & 500.30 & 490 & 264 & 352 & 22.13 & 0.84 & 0.68 & 15.57 & 7.60 & 2.05 & mode 2 \\ C20-L0.25-E7500 & 460.25 & 490 & 264 & 352 & 22.13 & 0.77 & 0.67 & 17.62 & 7.98 & 2.21 & mode 4 \\ C20-L0.25-E10000 & 468.70 & 490 & 264 & 352 & 22.13 & 0.79 & 0.65 & 18.15 & 6.53 & 2.78 & mode 1 \\ C20-L0.25-E12500 & 385.20 & 490 & 264 & 352 & 22.13 & 0.65 & 0.47 & 17.37 & 7.77 & 2.34 & mode 1 \\ C20-L0.25-E15000 & 337.85 & 490 & 264 & 352 & 22.13 & 0.57 & 0.90 & 7.47 & 3.73 & 2.00 & mode 1 \\ C30-L0-E0 & 580.00 & 544 & 264 & 352 & 32.70 & 1.00 & 1.00 & 29.73 & 6.37 & 4.67 & - \\ C30-L0.50-E5000 & 563.10 & 544 & 264 & 352 & 32.70 & 0.97 & 1.19 & 20.51 & 5.92 & 3.46 & mode 4 \\ C30-L0.50-E7500 & 640.00 & 544 & 264 & 352 & 32.70 & 1.10 & 1.26 & 22.78 & 6.68 & 3.41 & mode 2 \\ \hline \end{tabular}
\end{table} 
Table 5: Test results of all specimens.
# Low Power Blockchained E-Vote Platform for University Environment
## 2 Ease of Use
### Low Power Platform  
An application can have various performances/consumption on a given target by varying the algorithmic or architectural target [51; 52]. There is a lot of representative research in measure-based estimation techniques [53; 54], which collect energy consumption data in a cycle-by-cycle. In the context of blockchain, the majority of work analyzes performance without any energy analysis [55; 56].  
Raspberry Pi and FPGA are popular platforms oriented to low power and low-cost computing applications [20] especially for developing countries [57; 58; 59; 60].  
[PERSON] et al. [20; 58] analyze time and power consumption of an HW/SW hashing algorithm: Keccak in a Zedboard FPGA.  
[PERSON] et al. [61] use a Raspberry Pi to study the time and energy performance of miners in an Ethereum network.  
For mobile devices with a limited storage, the Jupiter blockchain [62] aims at solving the problem of storing account wallets. However, this blockchain does not support energy performance evaluation.  
In a data-center environment based on ARM cores, the energy efficiency is studied by [PERSON] et al. [63]. They analyze the CPU and the memory access performance [57].  
Some power consumption measures of the Raspberry Pi with an external power meter are presented in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  
Based on the literature [53; 57; 64], the power consumption of the quad core raspberry 3 depends on two factors: the power consumption in the idle state and the power consumption relative to CPU utilization \"\(u\)\" in the range 0 to 1. So, the consumption (W) can be written in this mathematical form:  
\[Consumption=a*u+b \tag{1}\]  
For example, the consumption of the board in the idle state will only consume approximately 1.5 W. According to [53; 64], using the quad core board in Keccak algorithm will consume approximately 2.4 W  
\[\text{Power (W) = Idle\_power + Running\_power = 1.57 W + 0.181 * CPU\_utilization\%} \tag{2}\]  
\[\text{Power\_Keccak256 = 2.4 W}. \tag{3}\]  
Thus, Raspberry achieves a reasonable performance with significant low energy especially for non-real time applications.
## 2 Results
### Host Range in X. vesicatoria, X. euresicatoria pv. euresicatoria and X. hortorum pv. garneri  
The host range of the two phages was evaluated with 28 strains, belonging only to three _Xanthomonas_ species, including the 5 _X. vesicatoria_, 21 _X. euresicatoria pv. euresicatoria_ and 2 _X. hortorum pv. garneri_ strains. Our results showed that phage XaC1 was able to lyse three _X. vesicatoria_ strains (XvA, XvB and Xv-17) but no strains belonging to _X. euresicatoria_ or _X. hortorum pv. garneri_. Meanwhile, Xbc2 lysed XvA, XvB and Xv-2 strains from _X. vesicatoria_, but no strains for other species (Table 1). These results indicate that the XaC1 and Xbc2 phages exhibited narrow host ranges specific for the lysis of certain _X. vesicatoria_ strains but not for other species of xanthomands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strains** & **Crop 1** & **XaC1** & **Xbc2** \\ \hline _X. vesicatoria_ & & & \\ XvA & Tomato & + & + \\ XvB & Tomato & + & + \\ Xv-2 & Tomato & \(-\) & + \\ Xv-17 & Tomato & + & \(-\) \\ Xv-44 & Tomato & \(-\) & \(-\) \\ _X. euresicatoria pv. euresicatoria_ a & & & \\ Xe-1 & Tomato & \(-\) & \(-\) \\ Xe-4 & Tomato & \(-\) & \(-\) \\ Xe-5 & Tomato & \(-\) & \(-\) \\ Xe-6 & Tomato & \(-\) & \(-\) \\ Xe-7 & Tomato & \(-\) & \(-\) \\ Xe-8 & Tomato & \(-\) & \(-\) \\ Xe-11 & Tomato & \(-\) & \(-\) \\ Xe-13 & Tomato & \(-\) & \(-\) \\ Xe-15 & Tomato & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Host range of jumbo phages infecting _Xanthomonas._ +, indicate lysis; \(-\), indicate no lysis  
Figure 1: Morphology of _X. vesicatoria_ jumbo phages (**a**) XaC1 and (**b**) Xbc2. Lytic plaques were observed by double agar overlay done with TSB top agarose (0.4%) over TSA medium and incubated at 27 \({}^{\circ}\)C for 18 h.
## 4 Numerical Results
### Performance Comparisons
The stringent timing requirements regarding the channel state and residual energy information for the proper operation of the greedy algorithm give to our method a two-fold advantage: (i) a relaxed time requirement due to the use of past, instead of real-time information; (ii) the process of gathering energy consumption from the sensor nodes is by far less complex than estimating the channel state. Another relevant advantage of our scheme when compared with the greedy algorithm of [21], especially when \(F>1\), is that some sensor nodes with higher priority data can be prioritized to occupy the time or time-frequency slots before those nodes with lower priority data. This is due to the fact that the activity levels are determined to be applied in the subsequent block of \(F\) frames. On the other hand, in the greedy algorithm, a sensor node transmission must be done exactly when the channel is favorable to the enabled node, meaning that it cannot be prioritized.
# Optimization of Multistage Coilgun Based on Neural Network and Intelligent Algorithm
###### Abstract  
The parameter optimization of a multistage synchronous induction coilgun (SICG) is a time-consuming task. Traditional machine learning methods can accelerate the process by building predictive models, but they require separate modeling for an SICG with different stages, which requires numerous datasets and is a cumbersome process. This paper proposes a method for building a predictive model for an SICG with different stages based on a recurrent neural network (RNN). In this method, the feed time of a 2- to 10-stage SICG is selected from the standard orthogonal design table as the training and test datasets, and the current filament method (CFM) is used to calculate the dataset label. The gate recurrent unit (GRU) neural network is used to study the training dataset, and the predictive model has good accuracy with respect to the test dataset, with an average error of 0.022. The predictive model and a particle swarm optimization (PSO) algorithm are applied to optimize the feed time of the SICG with different stages. The results show that the three-stage SICG can achieve a muzzle velocity of 50 m/s for a projectile, while the maximum muzzle velocity of the three-stage SICG in all datasets is 46.87 m/s.  
synchronous induction coilgun (SICG); finite element method (FEM); current filament method (CFM); particle swarm optimization (PSO); neural network Article
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Meaningful Encryption
Since the original secret image is already encrypted and resembles a noise-like image, it adds another layer of security to steganography by preventing attackers from directly obtaining the secret image through steganalysis alone. However, these algorithms have the drawback of high computational requirements due to the implementation of two layers of security: image encryption and steganography.
## 4 Conclusions  
In this article, a low-power, high-data-rate, area-efficient BPSK demodulator utilizing a delayed digitized format of the input BPSK signal is presented. The presented circuit is designed and simulated in a standard 0.18 \(\upmu\)m CMOS process using a 1.8 V power supply. Post-layout simulation and Monte Carlo analysis show that the presented circuit (including the clock recovery block) consumes 5.6 \(\upmu\)W, 6.7 \(\upmu\)W, and 8.5 \(\upmu\)W at frequencies of 2 MHz, 10 MHz, and 20 MHz, respectively. The occupied active area of the whole circuit is \(17\times 27\)\(\upmu\)m\({}^{2}\) and besides simplicity and low power consumption, the designed demodulator circuit benefits from a DRCF of 100%.  
Conceptualization and design, M.G.; formal analysis, M.G.; software, M.G.; investigation, M.H.M.; writing--original draft preparation, M.G.; writing--review and editing, M.H.M.; supervision, M.H.M., P.A., and S.H.-H.; funding acquisition, S.H.-H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflict of interest.
## 8 Conclusions  
CNG technology can be advantageous over LNG for transporting by ship small quantities of gas over short distances, up to 3000-5000 km. However, these advantages are connected to the development of advanced technologies for the design and construction of lightweight and safe pressure vessels for CNG carriers. The vessels loading and unloading processes also must be optimized from the points of view of timing and energy consumption, because they have an impact on the economic feasibility of the whole CNG transport project.  
The simulation model developed for this purpose proved to be a useful tool for the study of the loading and unloading system. The application of the model to a CNG carrier with the specifications of the GASVESSEL project made it possible to highlight the following points, depending on the calculation hypotheses on the heat exchange of the tanks with the hold:  
* The time required for the loading process varies between 35 and 51 h, and for the unloading process, it varies between 46 and 55 h;
* The temperature in the pressure vessels varies at the end of the loading process between 78 and 60 \({}^{\circ}\)C, while at the end of the unloading process, it varies between \(-\)60 and \(-\)17 \({}^{\circ}\)C. In the first case, the relatively high temperature reduces the fill efficiency of the vessels, while in the second case, the low temperature is worrying for the proper functioning of the compressor, so that a gas heater is required;
* The energy consumption can vary in the range of 150-180 kJ for a unit mass of CNG;
* The total fuel consumption of the processes varies between 1.14% and 1.45% of the total cargo.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Methane** & **Hydrogen** \\ \hline loading time [h] & 35 & 21 \\ final pressure [bar] & 300 & 300 \\ loaded mass [ton] & 7609 & 724 \\ loaded volume [Nm\({}^{3}\)] & 10,601,922 & 8,157,685 \\ stored energy @20 \({}^{\circ}\)C [MWh] & 130,278 & 31,136 \\ compressors energy [kWh] & 180,575 & 163,636 \\ fuel consumption [\%] & 0.5 & 1.7 \\ specific energy consumption [kJ/kg] & 96 & 945 \\ num. of pressure vessels used & 1 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loading process: methane vs. hydrogen.  
Figure 23: Compressor power (H\({}_{2}\) loading).  
The next research goals are performing some experimental tests on the tanks that will allow the model to be fine-tuned. In the future the model will be integrated in a ship overall energy simulation model. As regards the aspects related to safety, the rules and regulatory requirements will be applied currently being issued by the certification bodies, such as those by the American Bureau of Shipping [22].  
[PERSON] conceived the presented idea and was responsible of the research. [PERSON] and [PERSON] designed the model, and [PERSON] designed the computational framework and performed the data presentation. All authors have read and agreed to the published version of the manuscript.  
The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 723030.  
The authors declare no conflict of interest.
# BOPVis: Bridge Monitoring Data Visualization for Operational Performance Mining
## 5 Case Studies  
### Dataset  
In the case studies, a real-world suspension bridge in China is used. A suspension bridge usually consists of five components: the stiffening girders, main cables, main towers, anchorage blocks, and suspenders, as shown in Figure 4. The self-weight of the girder and the traffic load on it are transmitted through the vertical suspenders to the main cables, which are suspended between the two upright towers and are finally anchored to the anchorage blocks at each end of the bridge. Although suspension bridges have the largest spanning ability among all types of bridges, they are quite flexible systems and thus vulnerable to excessive deformation under various loadings and actions during the operational stages. Therefore, the global deformation of suspension bridges is one of the major concerns of bridge owners.  
In the following two cases, the global deformation pattern of suspension bridges and the dominant factor of operational deformation are explored through our BOPVis system. The considered bridge is a two-tower suspension bridge with the main span, i.e., the horizontal distance between both towers, being over 1.5 km. There are more than 200 sensors deployed on the bridge, including GPS rovers to measure the deformation,  
Figure 4: The structural components and deformation pattern of a suspension bridge. The red and blue arrows show the corresponding relationship between the movement of the tower tops and the girder mid-span.  
temperature sensors, humidity sensors, and anemometers to measure wind speeds at different points on the bridge.  
The bridge's global deformation is measured by 13 GPS rovers with a reference station. Each GPS rover records three translational movements in three orthogonal directions, i.e., X-, Y-, and Z-axis. The X-axis is along the bridge axis from west to east (longitudinal direction), the Y-axis is perpendicular to the bridge axis from south to north (lateral direction), and the Z-axis is along the vertical direction from bottom to top. There are a total of 39 data channels related to structural displacements.
## 1 Introduction
### Grid Integration Challenges of Variable Renewable Energy Sources and Storage Systems
In the energy sector nowadays, the electricity market liberalization and distributed generation (DG) have been the key drivers for the expansion of the concept of small-scale energy sources. Regarding the fight against global climate change, the use of variable renewables such as wind and PV energy are important to ensure sustainability and energy conservation. Integrating VRE sources is turning out to be a real challenge for the smooth operation of DGs. Grid operators face immense issues in scheduling the generated power from the DGs, especially due to variable renewable energy sources (VREs), which are difficult to be forecasted. An effective management of the generating resources in the DG network iscurrently very important. It can be said that the energy storage solutions can be incorporated for energy management in many ways, and the conventional usage of energy storage devices was mostly for long-term storage applications. Today, these devices can be used for energy storage and delivery from millisecond to months. These systems can act as spinning reserves for providing short-term power supply to manage instant variability in DG-generated power. Energy storage solutions can compensate for the intermittency and variability of PV and wind energy sources and improve the power reliability and quality. On the other hand, energy storage systems (ESSs) can also provide ancillary services to enable quality power delivery to the end users. The optimized selection in the sizing and siting of ESSs is an important challenge for engineers. The dynamic management of energy generated in a DG system can enhance the performance of the system, thereby enabling reliable and quality energy delivery. National regulations or features such as Feed-in-Tariff (FiT), bond yield interest rates, and other economic dynamics have great impact on the operation of DGs, in which case ESSs can act as added assets to achieve better economic dispatch solutions. The effective usage and implementation of energy storages in the distributed grid requires flexible and intelligent energy management strategies that are capable of handling the dynamics of distributed systems, while ensuring the efficient and effective usage of storage devices. However, the further implementation of hardware-in-the-loop optimization, simulations, weather forecasts, algorithms, and other intelligent techniques and tools have now been attempted to propose advanced energy management scheme strategies to improve storage lifetime and operability [71].
## 2 System Overview
### Target Response Recovery and Demodulation
After alignment, the transition edges of \(s_{in}\) are located by taking its first derivative, and the transition type of low-high or high-low is determined with its second derivative. With these edges, the number of samples in the shortest measured symbol is found, across which in an MLS symbol will span one clock cycle and in PSK will span a half clock cycle. From here, the binary sequence is extracted and \(s_{in}\) is partitioned into \(F_{Samp}/(2\times F_{Clk})\) sections where the sign of the section is determined. After aligning the transition edges in \(s_{in}\), the modulated \(I/Q\) signals are separated into logical low and high portions. Each of the windows obtained from processing \(s_{in}\) are applied to the time and AC coupled \(I/Q\) signals, in which each window is averaged. The logical highs and lows in \(s_{in}\) are used to rectify and merge the corresponding halves of the \(I/Q\) signals. It is important to note that every MLS will have an odd number of symbols, resulting in the sum of the high and low logic states being off by one. Therefore, to correct the DC offset generated by the extra symbol, each of the logical low and high portions of the \(I/Q\) signals are shifted toward each other. Then, the low and high \(I/Q\) signals are interpolated and resampled at a rate of \(F_{Clk}\). This allows them to be aligned with the windowed and averaged time information, and each pair of low and high channel data are averaged to get the target motion \(I/Q\) responses.