esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 6 Conclusions  
In this paper, an MGBD precoder is proposed by optimization with modified MMSE criterion and simultaneously performing partial nulling. Because the noise component is additionally considered, the simulation results show that the proposed scheme provides improved BER and throughput performances compared to GBD. The proposed scheme obtains diversity gain by reducing the number of data streams unlike RBD. These advantages provide the possibility that the MGBD precoder obtains maximum throughput in all SNRs by adaptively changing the number of data streams according to channel state. In exchange for this performance improvement, the proposed scheme has higher complexity than RBD and GBD. Although the number of multiplications for the MGBD precoder is higher than that of the RBD precoder when BS transmits full streams, the MGBD precoder can reduce system complexity by setting the postcoding matrix to a unit matrix. Also, an adaptive MGBD precoder for which throughput performance is maximized at all SNRs is proposed. The conventional RBD requires high SNR in order to obtain appropriate throughput and the throughput performance for conventional GBD is lower than for the proposed scheme. Additionally, the gap for throughput performance between adaptive MGBD and the adaptive GBD precoder increases as the number of data streams grows. Thus, the proposed scheme reduces the required SNR in order to obtain the desired throughput. These advantages are valuable in wireless communication systems, because high throughput performance is achieved at unfavorable channel conditions.  
Conceptualization, W.-S.L.; methodology, W.-S.L. and J.-H.R.; software, W.-S.L.; validation, W.-S.L., J.-H.R., Y.-H.Y., D.H. and H.-K.S.; formal analysis, W.-S.L.; investigation, J.-H.R. and Y.-H.Y.; writing-original draft preparation, W.-S.L.; writing-review and editing, W.-S.L.; supervision, Y.-H.Y., D.H. and H.-K.S.; project administration, D.H. and H.-K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers) and was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2018-0-01423) supervised by the IITP(Institute for Information & communications Technology Promotion). Corresponding author: [PERSON].  
The authors declare no conflict of interest.
## 2 Measured Parameters
### Ionospheric Parameters  
The VIS installed at the ASJI runs only when the station is attended, usually from December through early March, recording vertical incidence ionograms at 10 min sampling (see Figure 2). The ionograms are graphs representing the time-of-flight against transmitted radio frequency reflected in the ionosphere from which one extracts the ionospheric characteristics [18]. The measured characteristics are the lowest frequency observed in the ionogram (fmin), the critical frequencies of the ionospheric layers E, Es, F1 and F2 (foE, foEs, foF1 and foF2, respectively), the virtual (or group) heights of the ionospheric layers E, Es, F1 and F2 (h'E, h'Es, h'F1 and h'F2, respectively), and the maximum usable frequency for a single hop transmission at 3000 km reflected at the F2 layer (MUF(3000)F2). Note that the virtual heights (h') give an estimation of the height at the bottom of the respective ionospheric layer and the critical frequencies (\(f_{0}\)) are related to the maximum electron density (\(N_{m}\)) of the particular ionospheric layers by the following well known expression: \(N_{m}=(1/80.6)f_{0}^{2}\), where density is expressed in m\({}^{-3}\) and frequency in Hz. We refer the reader to [15] for more details.
# MapReduce Algorithm for Variants of Skyline Queries: Skyband and Dominating Queries
## 4 Skyband and Dominating Query Processing
### Worst Rank Computation  
The coordinator computes the worst rank and corresponding worst rank attribute for each object. Figure 5 illustrates the \"worst rank computation\" by the coordinator. In the example, \(O_{1,1}\) has rank values of two and six for attributes \(a_{1}\) and \(a_{2}\), respectively. In the object, \(a_{2}\)'s rank is the worst among all attribute ranks. Therefore, the worst rank of \(O_{1,1}\) is six and the corresponding worst rank attribute of \(O_{1,1}\) is \(a_{2}\). Therefore, the coordinator generates (\(O_{1,1},<a_{2},6>\)) as a key-value pair for \(O_{1,1}\).
# A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Pulsar Data
## 3 Datasets  
### The Ipsala Test Site and Ground Data  
The selected test area, Ipsala, is located in the North-West part of Turkey with its center at 37\({}^{\prime\prime}\)53\({}^{\prime\prime}\) N and 6\({}^{\circ}\)19\({}^{\prime}\)32\({}^{\prime\prime}\) N coordinates. Ipsala is one of the biggest rice cultivation sites in Turkey with approximate average of 190 square kilometers. Based on the knowledge gathered from the Trakya Agriculture Research Institute (TARI), rice cultivation is done in the area by local farmers between May and September.  
As shown in Figure 4, field campaigns were conducted \(\pm\)5 days of SAR acquisitions to have representative rice morphology parameters. In each year, the test fields were selected by the expertise of the TARI researchers. To monitor the evolution of the morphology parameters, at each field following parameters were measured: above water stalk height, stalk diameter, leaf length, leaf width, the number of plants per m\({}^{2}\), the number of fillers per plant, and the number of leaves per filler. In Figure 3 the evolution of some morphology parameters is depicted for each IRRI phase using  
Figure 3: Temporal variations of the rice crop biophysical collected between 2013–2015 from the regularly conducted ground campaigns in Ipsala (Turkey). Due to technical limitations, stalk diameter data was not measured in 2015. The measurements are grouped according to the IRRI growth phases as a Box-and-Whisker plot. Box presents the information for the quartiles while the whiskers present minimum and maximum values for IRRI growth stages IRRI-1 [Early vegetative], IRRI-2 [Late vegetative], IRRI-3 [Early reproductive], IRRI-4 [Late reproductive], IRRI-5 [Maturative].  
a box-whisker plot. For each parameter, a quasi-linear increase is observed until IRRI-3. From IRRI-3 on, a decrease is noticed in the stalk diameter due to reduced water content.
###### Abstract  
Bi-metallic nanoparticles (NPs) have appeared to be more efficient as antimicrobials than mono-metallic NPs. The fungus _Aspecgillus terreus_-mediated synthesis of bi-metallic Ag-Cu NPs was optimized using response surface methodology (RSM) to reach the maximum yield of NPs. The optimal conditions were validated using ANOVA. The optimal conditions were 1.5 mM total metal (Ag + Cu) concentration, 1.25 mg fungal biomass, 350 W microwave power, and 15 min reaction time. The structure and shape of the synthesized NPs (mostly 20-30 nm) were characterized using several analytical tools. The biological activities of the synthesized NPs were assessed by studying their antioxidant, antibacterial, and cytotoxic activity in different NP concentrations. A dose-dependent response was observed in each test. Bi-metallic Ag-Cu NPs inhibited three clinically relevant human pathogens: _Klebsiella pneumoniae, Enterobacter cloane,_ and _Pseudomonas aeruginosa_. _Escherichia coli, Enterococcus faecalis,_ and _Staphylococcus aureus_ were inhibited less. The DPPH and hydrogen peroxide scavenging activities of the NPs were high, reaching 90% scavenging. Ag-Cu NPs could be studied as antimicrobials in different applications. The optimization procedure using statistical analyses was successful in improving the yield of nanoparticles.  
bi-metallic nanoparticles; RSM; optimization; Ag-Cu; microwave power +
Footnote †: journal: Applied Physics Letters B
# Payment Maintenance Decision Making Based on Optimization Models
## 5 Discussion
### Validation of Results Obtained from the Models  
The proposed MMQLB model is a single-objective linear optimization model that can provide an exact result. Its result is a maintenance scheme that maximizes the maintenance quality under a limited maintenance budget, such as some key schemes shown in Table 4. Meanwhile, the MBMMQ model, which is a biobjective optimization model, was solved by NSGA-II, a heuristic algorithm. In Figure 3, each point represents an optimal maintenance scheme solved by the biobjective optimization model for 149 roads. Among the optimal maintenance schemes suggested by the MBMMQ model, three schemes were highlighted along with their objective function values, and the costs exactly matched the results obtained by the MMQLB model, satisfying the corresponding cost constraints. The best PCI improvement was 1724.29, with a cost of 10,199,000 CNY; the maximum PCI improvement was 1025.2, with a cost of 4,967,400 CNY; and the necessary PCI improvement was 154.51, with a cost of 1,567,200 CNY. Some solutions of MMQLB were included in the solution set of MBMMQ. This suggests that the results of the MMQLB model verified the part results of the MBMMQ model in this case. For this reason, we believe that the solutions found by the NSGA-II were indeed optimal and that they formed the Pareto front for our biobjective maintenance decision-making problem. The overall shape of the Pareto front indicated the same trend of increasing sum of minimal maintenance cost with the total improved quality. The Pareto front for our problem was discrete, but more importantly, it had a nonconvex shape. This was clearly shown when sum of PCI improvement reached around 1000, where the front took an obvious turn. This suggests that it is often difficult to use a heuristic algorithm to obtain an exact convex Pareto front for a multiobjective decision-making problem.
# A Multiple-Input Neural Network Model for Predicting Cotton Production Quantity: A Case Study
## 4 Proposed Multiple-Input Neural Network Prediction Model
In summary, the following equations describe the operations performed by an LSTM unit: \[i_{t} = \sigma(U_{i}x_{t}+W_{i}h_{t-1}+b_{i}),\] \[f_{t} = \sigma(U_{g}x_{t}+W_{g}h_{t-1}+b_{g}),\] \[c_{t}^{*} = \tanh(U_{c}x_{t}+W_{c}h_{t-1}+b_{c}),\] \[c_{t} = g_{t}\odot c_{t-1}+i_{t}\odot c_{t}^{*},\] \[o_{t} = \sigma(U_{o}x_{t}+W_{o}h_{t-1}+b_{o}),\] \[h_{t} = o_{t}\odot\tanh(c_{t}),\] where \(x_{t}\) denotes the input of each unit, \(W_{*}\) and \(U_{*}\) are matrices of weights, and \(b_{*}\) are the bias vectors with \(i\in\{i,g,c\}\), the operator \(\odot\) denotes the Hadamard product (component-wise multiplication), \(\sigma\) is the sigmoid function, and \(h_{t}\) is the output of the memory cell which denotes the hidden state. * **GRU layer**: Gated Recurrent Units (GRU) were originally proposed by [PERSON] et al. [22] and were inspired by the LSTM units, but with simpler implementation and calculations. Its main difference is that a GRU unit only has two gates (update and reset) which modulate the flow of information, without the utilization of memory gates, since it exposes the full hidden content without any control. The update gate \(z_{t}\) controls the level the unit updates its content, by taking into consideration a linear sum between the previous state \(h_{t-1}\) and the input \(x_{t}\). The reset gate \(r_{t}\) is computed in a similar manner with the update gate \(z_{t}\). Finally, the activation \(h_{t}\) of a GRU unit constitutes the linear combination between the previous \(h_{t-1}\) and the candidate activation \(h_{z}\), which is computed similarly to the traditional recurrent unit. The operations performed by an GRU unit are briefly described by \[z_{t} = \sigma(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}),\] Figure 1:0 Figure 1:1 Figure 1:2  
where \(x_{t}\) denotes the input vector and \(W_{*}\) and \(U_{*}\) are matrices of weights with \(i\in\{z,r,h\}\) and \(b_{*}\) are the bias vectors. In our numerical experiments, the performance of the proposed MNN model was evaluated using three different architectures (Figure 2), namely:  
* MNN\({}_{1}\) utilizes three dense layers of 16, 24, and 10 neurons in Hidden Layer -1-, Hidden Layer -2-, and Hidden Layer -3-, respectively, and a dense layer of 10 neurons after the concatenate layer. * MNN\({}_{2}\) utilizes three LSTM layers of 30, 50, and 20 units in Hidden Layer -1-, Hidden Layer -2-, and Hidden Layer -3-, respectively, and a dense layer of 12 neurons after the concatenate layer. * MNN\({}_{3}\) utilizes three GRU layers of 16, 24, and 10 units in Hidden Layer -1-, Hidden Layer -2-, and Hidden Layer -3-, respectively, and a dense layer of 12 neurons after the concatenate layer. All neurons in the hidden layers used a Rectifier Linear Unit (ReLU) activation function while the neuron in the output layer used sigmoid activation. The kernel and bias initializer in all layers were set as the default as well as the recurrent initializer in the recurrent layers. Figure 1:3 _Cont._
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
## 1 Introduction
### Background and Related Work
In the receiver of SIM-based systems, the optical signal is firstly converted to an electrical one. After that, the demodulation and detection are performed in the electrical domain. It is necessary to estimate the received signal phase variation in the process of demodulation. This estimation is often performed by the phase-locked loop (PLL). The received signal phase recovery is not perfect. Actually, there is a difference between received signal phase and estimated phase. This difference is a random process, and it is known under the name of phase noise. The effect of phase noise on SIM-based systems was considered in [15; 16; 17; 18; 19; 20]. In [15], we studied the deleterious effect of imperfect reference signal recovery on detection of SIM-based multilevel phase-shift keying (MPSK) signals over strong turbulence conditions. [PERSON] et al. [16] studied the effect of phase noise on detection of SIM-based binary PSK and quaternary PSK signals over the FSO channel with lognormal scintillation, while this analysis is extended to \(M\)-ary PSK signal detection in [17]. In [18], a similar analysis was performed for the case of Gamma-Gamma turbulence induced scintillation together with pointing errors, where the approximate expressions for error probability were derived. In [19], the authors derived approximate formulas for error probability during diversity reception of SIM-based MPSK signals transmitted over an FSO channel in the presence of phase noise. [PERSON] et al. [20] analyzed the effect of phase noise on error performance of the SIM-based PSK system over the FSO channel with Malaga (\(\mathcal{M}\)) turbulence induced scintillation. They derived approximate expressions for SEP for single-hop and multi-hop FSO decode-and-forward relaying configurations. In our previous work [21], we analyzed the performance of the SIM-based \(M\)-ary differential PSK FSO system over Gamma-Gamma turbulence channel and pointing errors. In that case, our aim was to estimate the effect of phase noise generated by the local oscillator used for down-conversion in the differential PSK receiver. In [22], we gave a review of papers considering the effect of phase noise on the performance of SIM-based FSO systems. Based on the results of those contributions, it is evident that phase noise has a strong effect on error performance of SIM-based PSK FSO systems.
# Hybrid Quantum-Classical Deep Neural Networks Based Smart Contract Vulnerability Detection
###### Abstract  
The increasing adoption of blockchain technology has presented significant challenges in maintaining the security and reliability of smart contracts. This study addresses the problem of identifying security flaws in smart contracts, which may result in monetary damages and diminished confidence in blockchain systems. A Hybrid Quantum-Classical Deep Neural Network (HQCDNN) approach was proposed, combining quantum computing principles with classical deep learning methods to identify various vulnerability types, including access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The SmartBugs Wild Dataset was used for training, with TF-IDF employed as a preprocessing technique optimized for hybrid architectures. Experiments were conducted using hybrid architectures with 2-qubit and 4-qubit quantum layers, alongside a classical deep neural network (DNN) model for comparative analysis. The HQCDNN model attained accuracy levels ranging from 96.4% to 78.2% and F1-scores between 96.6% and 80.2%, showcasing enhanced performance compared to the classical and deep learning models referenced in the literature. These results highlight the capability of HQCDNNs to improve the identification of security flaws in smart contracts. Future work could focus on evaluating the model on actual quantum devices and expanding its application to larger datasets for further validation.  
Keywords:quantum machine learning; quantum neural networks; hybrid quantum-classical neural networks; smart contract; smart contract; vulnerability detection +
Footnote †: journal: Applied sciences
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 1 Introduction
There were several reasons for building failures and severe damage, including poor material quality, inadequate reinforcement, and framing problems related to their lateral load-carrying systems, deficiencies in construction materials and reinforcement configuration [14], inadequate development length, violations of bending stirrup ends at 135 degrees, violations of confinement zones, violations of the strong beam-stronger column analogy, and issues with building inspections [15]. Many models have no consideration for the surveyor's subjectivity, which causes observational vagueness and uncertainty [16]. It is also possible that the assessment would be affected by a lack of sufficient experience or knowledge, as well as many other factors, engineering errors, and inconspicuous parameters [17]. Therefore, a novel consensus-based Likert-LMBP model has been developed using post-earthquake visual data of 194 buildings in the Kahramanmaras earthquake region for rapidly assessing the seismic performance of buildings before an earthquake. Building vulnerability variables are measured utilizing a five-point Likert scale with consensus values derived from linguistic data based on visual images from expert construction engineers. After structural analyses, each building is classified as no damage, slight damage, moderate damage, or severe damage/collapse. Similarly to studies published in the literature, this model was designed for reinforced concrete buildings of 1-7 stories; thus, it cannot be used for RC buildings higher than 7 stories. Research gaps will be filled in the following ways:  
* Seismic vulnerability can be assessed quickly before an earthquake occurs. * Pre-earthquake visual images can be used to assess existing buildings. * Experts use linguistic variables in their assessments. * Consensus values combine differing opinions from experts. * LMBP provides a high-performance solution to the proposed model.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results
### Simulation Results
#### 5.2.2 The Number of Handovers  
In wireless communications, the ping-pong effect is one of the crucial problems that leads to packet loss and high computation cost. This effect is clearly expressed through the number of handovers. Therefore, to perform an evaluation of our proposed algorithm in this respect in comparison with the approach without CCN and RSS-VHD, we considered a scenario simulation as given in Section 5.2.1; however, the running application is always a normal traffic type during the simulation time.  
Figure 4 shows the number of handovers that were experienced with our proposed approach with and without CCN, as well as the RSS-VHD during a patient movement. The figure demonstrates that the MADM with CCN (10% handover) significantly outperforms the MADM without CCN (22% handover) and the RSS-VHD approach (40% handover) and strongly reduces the ping-pong effect. This circumstance is caused by the fact that MADM is based on a combination of attributes, unlike RSS-VHD, which depends on a single metric. The current network will be maintained if it still satisfies the QoS of the running application, even if better networks co-exist. Moreover, MADM with the CCN  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Normal Traffic**} & \multicolumn{2}{c}{**Emergency Traffic**} \\ \hline Network & Ranking Value \(C_{2}\) & Network & Ranking Value \(C_{1}\) \\ WiFi (802.11b) & 0.93114 & LTE & 0.9614 \\
4G (LTE) & 0.14878 & WiFi (802.11b) & 0.1257 \\ UMTS & 0.06263 & UMTS & 0.0660 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Network ranking order with respect to the ranking values.  
approach helps to reduce end-to-end delay, packet loss and utilization of the network by leveraging the benefits of CCN caching and therefore helps to increase QoS and avoid unnecessary handover.
## 5 Conclusions  
Since maintaining the accuracy of VDS is crucial to support smart transportation systems, a governmental office regularly manages the deployed VDS. However, the conventional VDS evaluation systems based on video-frame analysis, piezoelectric cables, or lasers have the drawbacks of high installation and maintenance costs, long analysis time, lack of scalability (_i.e._, difficulty to detect multiple lanes), and lack of objectivity of evaluation. To alleviate the aforementioned drawbacks of the conventional systems, this paper proposed an overall system architecture for a Portable Vehicle Detector Evaluation System (PES) based on wireless sensor networks. The paper describes the customized two-tiered network architecture including Light Weight MAC (LW-MAC), Level-based Static Routing (LSR), and time synchronization scheme, as well as the vehicle detection algorithm. Based on a field implementation of PES, it was demonstrated by extensive evaluations to produce accurate traffic information such as volume count, speed, and occupancy. Especially, PES was evaluated to measure volume count and speed at the accuracy of 99% and 98%, respectively. Future work includes the improvement of accuracy of the measured traffic information as well as the  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{2}{|c|}{**11-19 September 2006**} & \multicolumn{6}{c|}{**PES, 18 April 2010**} \\ \hline \multicolumn{2}{|c|}{**Sensys VDS [39]**} & \multicolumn{3}{c|}{**N1**} & \multicolumn{3}{c|}{**S1**} \\ \cline{2-10} \multirow{3}{*}{QN\({}^{1)}} & Avg. Speed & Abs. & \multirow{3}{*}{QN\({}^{1)}} & Avg. Speed & Abs. & \multirow{3}{*}{QN\({}^{1)}} & Avg. Speed & Abs. \\  & (mph) & Error & & & & & & & \\ \cline{2-10}  & GT\({}^{3)} & VDS & (\%) & & GT\({}^{3)} & PES &  
improved time synchronization protocol with piggy-backing the time synchronization information to the detection packet.