esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 5 Simulation Analyses and Results
### Results
When the RSU information transmission distance is 100 m, and the vehicle density is low, with the help of t-RSU, the point-to-point vehicle connectivity ratio of the proposed hybrid layered RSU deployment scheme still reaches more than 50%, which shows excellent performance in the three deployment algorithms. Taking the RSU information transmission distance of 300 m and the vehicle density of 112.5 vehicles/km\({}^{2}\) as an example, compared with the MVCD and RandDeploy deployment algorithms, the proposed algorithm improves the vehicle point-to-point connectivity ratio by 30.85% and 33.27%, respectively. However, when the RSU information transmission distance is 300 m, and the vehicle density is 187.5 vehicles/km\({}^{2}\), the difference in the vehicle point-to-point connectivity ratio of the three algorithms is very small. Especially when the RSU information transmission range is 500 m, there is almost no difference in the vehicle point-to-point connectivity ratio of the three algorithms. At this time, the connectivity ratio of the three algorithms is close to 1, indicating that the network in this area is basically in full coverage. In addition, another phenomenon that is inconsistent with the intuitive view is that when the RSU information transmission distance is 100 m and 300 m, and the vehicle density is low density and medium density, as the vehicle density increases, the connectivity ratio even decreases slightly. This may be because the vehicle density of the entire road network is unsaturated, which makes the vehicle distribution sparse so that the connectivity rate is very small or even lower.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Results Analysis
#### 5.5.3 Visualization Performance  
To compare the advantages and disadvantages of various schemes more intuitively, we reconstruct high-definition images on the DVI2K dataset in a visual way.  
Figure 6 illustrates the visualization results obtained from various image transmission schemes under different SNR conditions. Under high-SNR conditions, both our proposed scheme and the WITT scheme effectively restore the original image. In contrast, the JSCC scheme exhibits an obvious color cast issue, attributed to the limitations of the CNN model in high-precision image processing. Under low-SNR conditions, despite the presence of numerous noise points, our proposed scheme outperforms other schemes in terms of image restoration accuracy due to its superior anti-noise capabilities. These results demonstrate that our proposed CCB transmission scheme provides better visual quality compared to other schemes.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 3 Simulation Scenarios
### Linear UWA Chain Full-Duplex Network
#### 3.1.3 Large Scale Scenarios  
The large scale scenarios represented as Large_L_H have values of L and H between 200 and 1000 km, and 100 to 1000 hops, respectively.  
The transmission range is important for the deployment of underwater acoustic networks. It influences energy efficiency, network connectivity and network reliability. The transmission range is determined by the acoustic modem assumed [27]. For practical applications such as underwater pipeline monitoring, regular sensing is required.  
Typically, there may be a need to communicate over longer ranges, nano-modems [25] could be used to provide this capability. Although, other acoustic modems with higher ranges (300 m-10 km), data rates (up to 62,500 bps) and transmit power (up to 80 W), such as Evologics, DiveNET, LinkQuest [28; 29; 30], etc., are alternatives, however, there is need to consider a trade-off between performance and cost effectiveness in terms of scalability for large scale deployment. In other words, low powered modems exhibit lower power consumption which improves energy efficiency with the appropriate protocols to extend network lifetime.  
Figure 4: FD-based linear UASN network scenario.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 2 Related Work
### Depth based Routing using Multiple Paths  
Multipath power-control transmission (MPT) protocol for time critical applications is proposed in [12]. Every source node directs multiple copies of data packet towards the surface gateways with the purpose of avoiding retransmissions. These copies are then combined at sink to get original data packet. MPT performs well in terms of delay and energy consumption in sparse regions. However, as the node density increases both delay and energy consumption increase because of data packets collision in the network. [PERSON] et al. proposed an efficient data-gathering routing (AEDG) protocol for UWSNs in [13]. This protocol uses autonomous underwater vehicle (AUV) to collect data from randomly deployed gateway nodes. During attachment to gateways, every sensor node follows shortest path tree algorithm. Gateway node collects data from member nodes and makes sure that less number of nodes are attached to it, because the involvement of more number of nodes resulting in high energy consumption. AEDG rotates gateway nodes in order to balance energy consumption. New gateway node is selected on the basis of high residual energy and it must be in the range of AUV. AEDG prolongs network lifetime and increases throughput. However, end-to-end delay is compromised. A power-efficient routing (PER) protocol for UWSNs which uses forwarding node selector mechanism to find appropriate forwarder is proposed in [14]. Every sensor node generates binary tree to direct multiple copies of a data packet to sink. In order to control duplication, every node uses forwarding tree trimming mechanism. If the number of duplicate packets forwarded by a sensor node exceeds threshold, the sensor node drops it and trims the tree. This protocol uses fuzzy logic inference system and decision tree which help to select appropriate forwarder and generate the tree. PER increases PDR at the cost of delay and energy. Duplicate data packets information maintenance at every node consumes a lot of energy. [PERSON] et al. proposed reliable and energy-efficient multipath communications in UWSNs [15]. In this protocol, multiple path forward error correction (M-FEC) based on hamming code technique is used. This technique encodes data packet at source node which is decoded at every intermediate node and the destination in order to reliably transmit data packet in the network. To calculate packet error rate (_PER_), authors use markovian model. Multiple copies of a data packet are sent through different paths by source node to avoid retransmissions. The source node adds error correction code in each data packet before transmission which allows the receiver to detect and correct errors. When a data packet reaches the destination, all the copies are combined to generate original data packet. M-FEC achieves reliability and saves energy by eliminating retransmissions. However, data packet correction at each node increases delay.
# Multi-Backup Beams for Instantaneous Link Recovery in mmWave Communications
###### Abstract  
In this paper, a novel link recover scheme is proposed for standalone (SA) millimeter wave communications. Once the main beam between the base station (BS) and the mobile station (MS) is blocked, then a bundle-beam is radiated that covers the spatial direction of the blocked beam. These beams are generated from an analog beamformer design that is composed of parallel adjacent antenna arrays to radiate multiple simultaneous beams, thus creating an analog beamformer of multiple beams. The proposed recovery scheme features instantaneous recovery times, without the need for beam scanning to search for alternative beam directions. Hence, the scheme features reduced recovery times and latencies, as opposed to existing methods.  
millieter wave; analog beamforming; beam recovery; link blockage; multi-beam; recovery times +
Footnote †: journal: Accepted: 6 October 2019; Published: 10 October 2019
## 1 Introduction  
Shell construction is one of the most efficient load-bearing structures, with the advantage of economizing on materials and reducing the weight of the structure. Cylindrical shells are used in many fields due to their special geometric configuration, including silos and tanks (see Figure 1) in civil engineering, where they are subjected to loads such as axial pressure, radial pressure, torsion, and combinations of these three types of loads. The dominant type of damage to cylindrical shells is buckling failure, which is generally sudden and often leads to the failure of the structure without visible symptoms or even catastrophic accidents, see Figure 2. Therefore, the buckling of cylindrical shells has long been a longstanding issue.  
Typical cylindrical shell structures are made of (stainless) steel and achieve high economic efficiency through the optimal utilization of materials. Currently, the increasing cost of materials and the need for greater sustainability are prompting the manufacturers of cylindrical shells to rethink the previous concept and to constantly search for new types of shell structure construction in civil engineering. The application of orthotropic rotation shells, composed of thin shells with strong ring stiffeners, allows optimal matching to typical loads, such as wind. Ring stiffeners can contribute to a significant increase in resistance to wind and negative pressure. Research in the 1960s and 1970s showed that stiffeners also increased axial compression.  
Recently, researchers [3] have re-evaluated published experiments on particularly thin-walled cylindrical shells. The results of parametric studies on closed-ring stiffened cylindrical shells confirm the high load-bearing capacity expected from published experiments. It has been proven that current standards are too conservative in some areas. In general, it is important to review the research history and development of ring-stiffenedcylindrical shell structures and their application in steel structures for their further development and promotion, as well as for their wider application in practice.
# A Multi-Leak Identification Scheme Using Multi-Classification for Water Distribution Infrastructure
## 3 Performance Evaluation of Different Classifier Scenarios
### Performance Evaluation of Different Classifier Scenarios  
The proposed MC-MLI customizes every classifier for the assigned pipe sections. The hydraulic behavior of each pipe section can be different. It depends on various factors, such as pipe locations, user behaviors, pipe properties, network structure, etc. The classifier customization will improve not only detection performance but also operating efficiency by selecting a proper feature combination. In addition, the classifier customization facilitates meeting certain performance requirements. For example, the classifier scenario with short detection time and high sensitivity will be chosen for the important pipe sections. The classifier scenario with high specificity will be chosen for the pipes with a lower importance level. This reduces the probability of false alarms as well as unnecessary inspection costs.  
The performance of different classifier scenarios of MC-MLI is shown in Figure 5. The y-axis denotes average accuracy, average sensitivity, average specificity, and average detection, respectively. The x-axis denotes the classifier scenarios. Each classifier scenario refers to a feature combination. The number of feature combinations is calculated as:  
\[NC=\sum_{m=1}^{M}C_{m}^{M} \tag{11}\]  
Note that there are 10 features for classifier development, and thus \(M\) equals 10. The total number of classifier scenarios is 1023. The 1 st to 10 th classifier scenarios refer to the 1 st to 10 th feature combinations comprising one feature. The 11 th to 55 th classifier scenarios refer to the 11 th to 55 th feature combinations comprising two features, and so on. The result reveals that the accuracy, sensitivity, and specificity are generally improved with the increasing number of features. Meanwhile, the detection time becomes longer. The result is consistent with the previous discussion that increasing feature dimensions (i.e., the number of feature elements) usually improves the detection performance, but it leads to longer detection time. In brief, the classifier scenarios with the feature vectors made of >7 features (i.e., >967 th classifier scenarios) achieve over 85% accuracy, over 80% sensitivity and over 80% specificity. The detection time is about 5 s. The classifier performances can  
Figure 5: Performance evaluation of various classifier scenarios of MC-MLI.  
be converted to overall scoring using the weighted sum of the performance indicators. To summarize, the customization in MC-MLI provides high adaptiveness to meet various system requirements. The best classifier for each pipe section can be evaluated using overall scoring.
## 4 Conclusions  
The investigations demonstrated that the degree of roasting and the type of device used did not alter the individual aromatic properties of the analyzed coffees. The volatile compound profiles and the content of their main groups were specific to each coffee type and were associated with the conditions of growth, harvesting, and storage. The study showed a dominant role of alcohols, acids, aldehydes, azines, and hydrazides in the aromas of all coffees. As shown by the chemometric analysis, the high content of pyridine from the group of azines differentiated the types of coffee, which was especially evident in the case of the Brazilian coffee. The analysis of volatile compounds carried out with the use of the Agrinose yielded consistent results to those obtained with GC-MS. It should be concluded that the quick and low-cost analysis of volatile substances in coffee using an electronic nose with a matrix of MOS sensors is a reliable tool for assessment and classification of coffee types.  
Conceptualization, methodology and data analysis, G.M., R.R.; research support, B.D.J.; manuscript review, T.O. and M.C.; preparation of research material and manuscript review, D.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The tests were partially carried out using a device made in the project of the National Centre for Research and Development (NCBR) (Grant No. PBS2/A8/22/2013).  
The authors declare no conflict of interest.  
Figure 5: Loading plot (**a**) and score plot (**b**) of the principal components analysis carried out for sixteen sensor readings and types of coffee from Brazil, Ethiopia, Guatema, Costa Rica, and Peru.
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### Power Management
#### 2.2.1 Shunt Regulator  
Figure 5 illustrates the implemented shunt regulator. This line regulator creates a current discharge path to ground \((I_{1}+I_{2}+I_{3}+I_{4})\) to keep a constant \(V_{DD}\) below a certain level. It also serves as over-voltage circuit protection. The mechanism starts once the \(V_{REC}\) voltage provided by the HF stage reaches a certain \(V_{DD_{TH}}\) voltage threshold. The operation of this circuit can be described in four regions: \(V_{REC}\ll V_{DD_{TH}}\), \(V_{REC}=V_{DD_{TH}}\), \(V_{DD_{TH}}<V_{REC}<2V_{DD_{TH}}\), and \(V_{REC}>2V_{DD_{TH}}\).  
At \(V_{REC}\ll V_{DD_{TH}}\) all transistors of the shunt regulator are in the cut-off region and the discharge currents \(I_{1}-I_{4}\) must be in the order of pico-amperes because the reservoir capacitor of the power harvester system needs to store charge as fast as possible to supply the whole sensor. When \(V_{REC}=V_{DD_{TH}}\) a discharge path is created by adding \(I_{1}+I_{3}+I_{4}\). Under this scenario, all transistors that comprise the \(I_{1}\), \(I_{3}\) and \(I_{4}\) branches must operate in the subthreshold region, allowing a current flow of just a few tens of nano-amperes, and transferring most of the charge collected by the power harvester to the next subsystems.  
In the third case, \(V_{DD_{TH}}<V_{REC}<2V_{DD_{TH}}\), the \(m_{1}\), \(m_{5}\) and \(m_{7}\) transistors begin to operate in saturation region allowing a discharge current in the order of micro-amperes. At this point, \(m_{3}\) begins to operate in the linear region. Finally, when \(V_{REC}>2V_{DD_{TH}}\) all \(m_{1}\) transistors operate in saturation mode while \(m_{3}\), \(m_{5}\) and \(m_{7}\) operate in the linear region. This last scenario creates discharge currents in the order of tens to hundreds of micro-amperes, protecting the overall passive tag against over-voltage spikes.
# First Retrieval of Sea Surface Currents Using L-Band SAR in Satellite Formation
## 2 Basic Principles
### Cross-Track Interferometry (XTI)  
Figure 2 is a geometric schematic of the XTI. The sign \(\otimes\) denotes the flight direction towards the paper. Antennas \(A_{1}\) and \(A_{2}\) are mounted perpendicularly to the flight path, and the angle between the baseline \(\vec{B}\) and the horizontal direction is \(\alpha\). The two antennas image the same resolution unit of the sea surface simultaneously. Considering the slight difference in the imaging angles of the two antennas, the range difference of the radar echo signal is \(\triangle R=R_{1}-R_{2}\), where \(R_{1}\) and \(R_{2}\) are the distances from the two antennas to the sea surface scatterer \(P\). The range difference can be transformed into an interference phase as follows:  
\[\phi_{XT}=\frac{4\pi(R_{2}-R_{1})}{\lambda}=\frac{4\pi B_{XT}\sin(\theta- \alpha)}{\lambda} \tag{6}\]  
where \(B_{XT}\) represents the length of the cross-track baseline, \(\theta\) is the incidence angle, and \(\alpha\) denotes the baseline inclination angle. After obtaining the interference phase \(\phi_{XT}\), the height of the target \(P\) relative to the reference plane \(h\) can be calculated based on the simple geometric relationship shown in Figure 2.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 4 PRC Modeling
### PRC Modeling  
The repeat time obtained for each satellite was applied in shifting the PRC data collected before the experiment date. Figure 3 compares the time series of PRCs before (top) and after (bottom) aligning them based on the computed repeat time. Prior to applying a repeat time, although the PRC patterns were similar, a time lag was observed. On the other hand, after alignment, time lags almost completely disappeared, and overlapping and exactly matched shapes showed up. The PRC values that were shifted after applying repeat times were used to calculate modeling coefficients by means of polynomial curve fitting. The mathematical equations used for modeling can be found in [26]. In order to find the optimal degree of the fitting polynomial function, degrees from the 4 th through the 12 th were tried. The effectiveness of the fitting function was evaluated via root mean square error (RMSE) of residuals and data processing time.  
The data processing times from curve fitting according to each polynomial degree were measured, and we found that the computation time slightly increases as the degree increases. However, no significant difference was observed, and the boundary that can help in choosing an optimal degree was not clear either. On the other hand, RMSE values after fitting showed a clear difference, depending on the degree of the polynomials. Figure 4 shows mean RMSE values averaged for all the satellites modelled. In this figure, the vertical bar indicates RMSE in each of the satellite navigation systems. The RMSE of the two systems became gradually smaller with the increasing degrees. In particular, GPS RMSE was larger than BDS RMSE at less than the eighth degree, whereas  
Figure 3: Alignment of PRCs before (top) and after (bottom) application of repeat times: (**a**) PRC of GPS PRN 1 for three days prior to 23 April 2016; (**b**) PRC of BeiDou PRN-C11 (MEO) from 7 and 14 days before 23 April 2016.  
GPS RMSE values were smaller above the eighth degree. As shown in the figure, PRC fitting of the GPS was better than BDS as the degree became higher. A dotted line in Figure 4 denotes an improvement ratio in RMSE, compared to the previous degree. The largest improvement ratio was found at the sixth order in both systems. When we applied even number-degree polynomial functions, the improvement ratio was better than in cases with an odd number. This was because PRC values of all satellites, except for the BDS GEO satellite, have the upside down U shapes, resulting in better matches at even-numbered degrees. In the present study, we selected the sixth degree as optimal, considering the improvement ratio as well as the applicability to both systems.
## 4 Case Study
\begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Region** & \(\mathbf{S_{i}^{\text{TOPSIS}}}\) & **Rank** \\ \hline A1 & ANTOFAGSTA & 0.11018 & 11 \\ A2 & ARICA Y PARINACOTA & 0.028086 & 16 \\ A3 & ATACAMA & 0.052145 & 13 \\ A4 & AYSEN DEL GENERAL CARLOS IBANEZ DEL & 0.048315 & 14 \\ A5 & BIOBIO & 0.26128 & 5 \\ A6 & COQUIMBO & 0.16179 & 8 \\ A7 & LA AraUCANIA & 0.27901 & 4 \\ A8 & LIBERTADOR GENERAL BERNARDO & 0.24404 & 6 \\ A9 & LOS LAGOS & 0.22853 & 7 \\ A10 & LOS RiOS & 0.11135 & 10 \\ A11 & MAGALLANES Y DE LA ANTATRICA & 0.08478 & 12 \\ A12 & MAULE & 0.28241 & 3 \\ A13 & METROPOLITANA DE SANTIAGO & 0.96042 & 1 \\ A14 & NUBLE & 0.15 & 9 \\ A15 & TRAPACA & 0.047757 & 15 \\ A16 & VALPARAISO & 0.28511 & 2 \\ \hline \end{tabular}
\end{table} 
Table 7: Results of WASPAS scores for each region. Figure 4 shows the rank comparison of the alternatives using WASPAS, TOPSIS, and combined rank methods. The horizontal axis represents the 16 alternatives labeled from 1 to 16, and the vertical axis represents the ranks assigned to them, with \"1\" being the best rank (most preferred) and 16 being the worst rank (least preferred). The blue circle line represents the WASPAS ranks, and the orange square line shows the rank results from the TOPSIS method. Finally, the yellow diamond line shows the combined ranks from WASPAS and TOPSIS. Figure 4 shows the scores for the 16 political regions of Chile in the horizontal axis as alternatives in this study (see Table 6 or Table 7 for the assignment of each label). The graph reflects the combined WASPAS-TOPSIS results, normalized between 0 and 1, where a higher score indicates a better-performing alternative. The evaluation of the proposed hybrid MCDM model strength was achieved through a sensitivity analysis that modified essential parameters. We examined the effect of varying \(\lambda\) values in WASPAS from 0.1 to 0.9 and CRITIC-derived weights on the results. The model demonstrated stability through consistent ranking results for the highest- and lowest-ranked areas across various scenarios. Middle-ranked options experienced minor variations, although they did not significantly affect the overall selection priorities. This study demonstrates that the methodology produces reliable and meaningful results for emergency preparedness area prioritization. \begin{table}
\begin{tabular}{c c c c} \hline
**ID** & **Region** & \(S_{c}\) & **Rank** \\ \hline A1 & ANTOFAGASTA & 0.21571 & 10 \\ A2 & ARICA Y PARINACOTA & 0.11608 & 16 \\ A3 & ATACAMA & 0.14675 & 13 \\ A4 & AYSEN DEL GENERAL CARLOS IBANEZ DEL & 0.13825 & 15 \\ A5 & BIOBIO & 0.37475 & 6 \\ A6 & COQUIMBO & 0.27033 & 8 \\ A7 & LA ARAUCANIA & 0.4138 & 3 \\ A8 & LIBERTADOR GENERAL BERNARDO & 0.3768 & 5 \\ A9 & LOS LAGOS & 0.35708 & 7 \\ A10 & LOS RIOS & 0.21417 & 11 \\ A11 & MAGALLANES Y DE LA ANTARTICA & 0.17654 & 12 \\ A12 & MAULE & 0.41982 & 2 \\ A13 & METROPOLITANA DE SANTIAGO & 1 & 1 \\ A14 & NUBLE & 0.25133 & 9 \\ A15 & TARAPACA & 0.13836 & 14 \\ A16 & VALPARAISO & 0.39655 & 4 \\ \hline \end{tabular}
\end{table} 
Table 9: Results of combined scores for each region.