esa-sceva/satcom-chunk-collection huggingface dataset:
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 4 MGBD Scheme and Adaptive MGBD Scheme
### Adaptive MGBD Precoder  
The adaptive MGBD precoder adjusts the number of data streams of each UE according to received SNR for each UE. The algorithm for the adaptive MGBD precoder is represented in Figure 3. BS can know the received SNR for each UE and \(\theta_{i}\) is the received SNR of the \(i\)-th UE which BS knows. Also, it is assumed that each UE has \(N_{R_{i}}\) receive antennas and BS has threshold table for \(\tilde{\theta}_{1},\tilde{\theta}_{2},\cdots,\tilde{\theta}_{N_{R_{i}}-1}\). The threshold table is an indicator for the number of data streams which provides maximum throughput. First, the adaptive MGBD precoder compares the received SNR \(\theta_{1}\) of the first UE with threshold SNR \(\tilde{\theta}_{1}\) which previously is obtained through training data. Then, the adaptive MGBD precoder continuously compares \(\theta_{1}\) to other thresholds and selects the suitable \(r_{1}\). Therefore, BS transmits \(r_{1}\) data streams which maximize throughput at \(\theta_{1}\). The adaptive MGBD precoder repeats these steps from the second UE to the \(K\)-th UE. Afterward, the adaptive MGBD precoder calculates Equation (28) by using \(r_{1},r_{2},\cdots,r_{K}\) in order to obtain precoding matrix. Through these steps, the proposed scheme achieves maximum throughput within the limited SNR of each UE.  
The initial threshold values can be obtained through mathematical analysis or the Monte Carlo method. However, the former method is very difficult to estimate threshold values for all channel environments and can lead to a large error due to the approximation processes for the number of data streams [22]. Thus, in this paper, the SNR points of switching the number of data streams are obtained by comparing the results which are gained through previous simulation. The previous simulation is performed by training data which is only used to obtain initial threshold values. Then, the BS stores these SNR points in the form of a threshold table.  
Figure 3: The algorithm of the adaptive modified generalized block diagonalization (MGBD) precoder.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
###### Abstract  
This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements' distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA's loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (\(C/N_{0}\))-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver's tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.  
Global navigation satellite system (GNSS); scalar tracking loop (STL); direct-state Kalman filter (DSKF); lookup table direct-state Kalman filter (LUT-DSKF); loop-bandwidth control algorithm (LBCA); adaptive tracking techniques +
Footnote †: This paper is an extended version of our paper published in Proceedings of the 2021 International Conference on Localization and GNSS (ICL-GNSS), Tampere, Finland, 1-3 June 2021; pp. 1-7.
## 2 Data and Methods  
### Studied Area  
The studied area includes the western part of the Russian Arctic within the geographical boundaries 60-75\({}^{\circ}\) N, 30-85\({}^{\circ}\) E in Figure 1. It includes the Republic of Karelia, Murmansk and Arkhangelsk Oblasts (Regions), Komi Republic, Yamalo-Nenets and Khanty-Mansi Autonomous Okrugs (Districts). Within this area there are railway sections between St. Petersburg and Murmansk and Arkhangelsk, railways to Salekhard and Yamburg, the 700 km section of the constructed NLR from the Obskaya station (Labytnangi) to Korotchaevo (Novy Urengoy), and the next section to Igarka and further to Dudinka in Figure 1.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation: Comparison with Laboratory Data
#### 5.1.4 Comparison with Standard Laboratory Methods  
Compared to laboratory methods, which use high-precision electrochemical pH meters, gravimetric TDS determination, and nephemometric turbidity analyzers, the Raspberry Pi-based system provides real-time, cost-effective monitoring while maintaining a minor trade-off in absolute precision. The differences observed in TDSs at high concentrations and turbidity at very low levels highlight areas where sensor calibration and post-processing algorithms could further enhance performance.
###### Abstract  
An efficient routing using a virtual backbone (VB) network is one of the most significant improvements in the wireless sensor network (WSN). One promising method for selecting this subset of network nodes is by finding the minimum connected dominating set (MCDS), where the searching space for finding a route is restricted to nodes in this MCDS. Thus, finding MCDS in a WSN provides a flexible low-cost solution for the problem of event monitoring, particularly in places with limited or dangerous access to humans as is the case for most WSN deployments. In this paper, we proposed an adaptive scatter search (ASS-MCDS) algorithm that finds the near-optimal solution to this problem. The proposed method invokes a composite fitness function that aims to maximize the solution coverness and connectivity and minimize its cardinality. Moreover, the ASS-MCDS methods modified the scatter search framework through new local search and solution update procedures that maintain the search objectives. We tested the performance of our proposed algorithm using different benchmark-test-graph sets available in the literature. Experiments results show that our proposed algorithm gave good results in terms of solution quality.  
dominating sets; minimum connected dominating sets; scatter search; local search; wireless network design +
Footnote †: journal:  
_Article_  
Adaptive Scatter Search to Solve the Minimum Connected Dominating Set Problem for Efficient Management of Wireless Networks
## 4 Results and Discussion
### Results for Studies Providing Solutions for Both Challenges  
This subsection illustrates the systematic mapping results of works that focus on addressing both problems. Out of all the papers that were reviewed, only 26 met our selection criteria.  
Figure 16 illustrates the top ten techniques most used in the selected studies. Most of the works use a technique for each challenge; however, some techniques are used to solve both challenges, such as clustering, two-level aggregation, and client selections. The figure shows that knowledge distillation is the most widely used technique. Knowledge distillation can be used to solve both problems using the teacher-student approach to reduce communication overhead and overcome non-IID data problems by distilling knowledge. However, the selected studies used this technique along with another technique to solve the challenges. Knowledge distillation and personalized approach are used in 15% (four studies) of the studies, clustering is used in 12% (three studies) of the studies, and 8% (two studies) of the studies used one of the following techniques: adaptive approach, asynchronous method, client selection, lottery ticket, pruning method, quantization, or  
Figure 14: Percentage of the top commonly used datasets in communication-efficient studies.  
Figure 15: Heatmap between the most commonly used learning models and the respective datasets utilized in communication-efficient studies.  
two-level aggregation. The selected studies used these techniques to solve one or both challenges. The citation of these studies is presented in Appendix A Table A6.  
In Figure 17, we show the most used learning models. The figure illustrates the percentage of learning models used in more than one study. The results indicate that CNN is the most widely used model, while in Figure 18, we show the percentage of the datasets used in more than one study. The figure indicates that the Cifar-10 and MNIST datasets are the most widely used.  
Table A7 in Appendix A shows a mapping between the topmost commonly used learning models and the respective datasets utilized when employing these models. We show only three models, as the remaining model does not utilize a common dataset. They all use different datasets for each study, so we did not highlight them. As we can see, Cifar-10 is widely used between the different local models, as Cifar-10 was proven to be the most used dataset in these studies, as shown in Figure 19.  
Figure 16: Percentage of the top ten techniques commonly used in the selected studies that provide solutions to both challenges.  
Figure 17: Percentage of the top commonly used learning models in the studies that provide solutions to both challenges.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 2 Evolution of Single Antenna Element
### Performance Analysis
This shift in the slot's center improves the impedance match, resulting in a more stable response below \(-10\) dB, providing a simulated wide frequency band from 2.73 GHz to 6.3 GHz. The antenna design with the circular slot, shifted \(-4\) mm downward from the center of the patch, exhibits a wider frequency bandwidth and enhanced impedance matching characteristics.
## 4 Mission S-Net
### S Band Isl System  
The core components of the ISL system are the SLINK radio, the Spacecraft Network Controller (SNC), and the antenna system. SLINK accommodates the physical layer and data link layer (OSI layer 1 and 2). The SNC provides the higher communication layers and is responsible for the routing, control, and data logging, in particular. The antenna switch toggles all six ISL antennas to power the correct patch antenna during an ISL session.
## 2 Theoretical Foundation and Hypotheses Development
### Economic Factors  
#### 2.4.1 Income Disparity  
Over the past few decades, the adoption of modern technologies has repeatedly been attributed to widening income disparities. Technology has the potential to widen social and economic divides by disrupting certain business sectors and increasing the productivity gap between skilled and unskilled jobs [59; 60]. However, income inequality might occasionally be considered necessary for a free-market economy because it encourages workers to work hard and entrepreneurs to innovate. In a competitive developed market economy, income disparity may not be an issue of significant concern. Technology and income inequality have been extensively studied. After studying the causal relationship between the rapid expansion of trade and financial globalization, and the widening of economic disparities between nations over the past 20 years, [PERSON] et al. [61] found that advances in technology had a more significant effect on inequality. [PERSON] [62] explored how distributed ledger technology may affect income disparity and concluded that there is evidence of wealth concentration and inequality among cryptocurrency users. Considering this, we proposed the following hypothesis:  
**H8.**_The Gini coefficient has a negative association with a country's central bank digital currency adoption_.
## 4 Discussion
### Acceleration Inputs  
Overall, the resultant \(a_{\text{in}}^{\text{peak}}\) measured on the LVP (0.8-1.2 g) are lower than previous research (\(\approx\)1.1-5.5 g) [16; 20; 21; 22; 23; 25; 26; 29]. Previous studies have shown that 30 Hz and low \(a_{\text{in}}^{\text{peak}}\) (\(\approx\)0.2-0.3) are enough to significantly increase bone mass density in long exposures [1; 3; 4] (long exposures: 8 months and 1 year). However, no significant effects were found in a short period of time (8 weeks) when applying intensities around 1-1.5 g of \(a_{\text{in}}^{\text{peak}}\)[28]. Regarding muscle activity, the resulting \(a_{\text{in}}^{\text{peak}}\) of between \(\approx\)0.5 and 1 g reported positive effects [5; 6; 20; 21; 38]. However, whereas differences were found when the vibration stimulus were applied [5; 6; 38], the increase in the \(a_{\text{in}}^{\text{peak}}\) did not result in a clear increase in muscle activation [5; 6; 20; 21; 38]. In terms of safety, an exposure below 1 g \(a_{\text{in}}^{\text{peak}}\) has been suggested by various investigations [11; 22; 30; 39]. Considering application and safety studies, platform inputs between 0.5 and \(\approx\)1 g of \(a_{\text{in}}^{\text{peak}}\) can be considered to have a good dose-response effectiveness whilst ensuring safety aspects in exercise protocols. As usual [16; 20; 21; 28], the LVP increase in \(f_{\text{set}}\) from 20 Hz to 25 Hz have shown a large increase (+30%) in the \(a_{\text{in}}^{\text{peak}}\), otherwise, similar outcomes were found at 30 Hz (Table 1). Conversely, previously investigated platforms can induce higher \(a_{\text{in}}^{\text{peak}}\) as the \(f_{\text{set}}\) increase [16; 20; 21; 28]. Between postures, the findings of this study showed differences on the LVP \(a_{\text{in}}^{\text{peak}}\) up to 0.2 g, whereas a previous study reported changes up to 0.4 g [19]. Overall, postures \(a_{\text{in}}^{\text{peak}}\) differences on LVP seems not to be a remarkable effect, taking in account the previous data reported by [PERSON] et al. (2013) [28] and [PERSON] et al. (2016) [5] who did not reported additional effects on bone turnover and muscle activity when applying +0.5 g of \(a_{\text{in}}^{\text{peak}}\). Given the \(f_{\text{in}}\) stability among \(f_{\text{set}}\), as can be expected, between the \(f_{\text{set}}\) and postures, the \(a_{\text{in}}^{\text{peak}}\) follows the \(D_{\text{in}}\) dynamics as explained above. These data support the feasibility of the vibration stimulus of the LVP in terms of \(a_{\text{in}}^{\text{peak}}\).
# Decentralized Public Transport Management System Based on Blockchain Technology +
###### Abstract  
The development of intelligent transportation systems (ITSs) is penetrating many economies around the globe. This paper presents three key innovations in the field of intelligent transportation systems, as follows: (1) a novel tokenization approach where each vehicle is represented as a macro-token subdivided into 500,000 micro-tokens for precise condition monitoring, (2) a comprehensive mathematical model for vehicle state assessment incorporating multiple operational factors, and (3) the GDEPZ method for optimizing data transmission via satellite communication. These innovations enable the autonomous control of technical conditions, transparent fleet management, and efficient data processing in hard-to-reach areas. Various researchers in both industry and academia are looking into more efficient management methods for both vehicles and related data processing aspects. A vast trend related to the latter is the distributed data processing of transmitted data. This article discusses approaches to the use of blockchain technology in ITSs. It explores the use of blockchains in modern transport industries. In particular, the paper proposes a novel approach to the maintenance of public transportation vehicles and buses. The specificity of the proposed approach is the autonomous control of technical conditions using information systems. When using blockchain technology, building a transparent vehicle fleet management system is possible. The specificity of the proposed approach lies in data processing. Within the organization, confidence in data increases, the possibility of manipulating transportation is eliminated, and the decision-making chain is reduced. As a result, the system can manage itself. This also helps to increase the service life of vehicles, makes it possible to predict their malfunctions, and improves the quality of data on their technical conditions.  
ITS; distributed systems; blockchain; smart contracts; transportation management; public transport 2025
## 8 Simulation Setup and Analysis of Results
### Broadcast Traffic  
We evaluated the performance of BN-MAC and other hybrid MAC protocols under broadcast flood traffic. In this experiment, we measure the strength of the BN when floods are first sent to other regions.  
Figure 22 presents the packet delivery rate for BN-MAC and the other competing MAC protocols under broadcast flood traffic. The packet delivery ratio of the BN is calculated as the total number of flood messages received from all nodes and delivered to other regions, which is divided by the total number of distinctive messages generated by all nodes. Each message of the node consists of a sequential number to find the uniqueness of the message. The simulation demonstrated that BN-MAC outperforms all of the other hybrid MAC protocols.  
The BN-MAC curve is considerably higher than those for the other curves because the delivery ratio remains stable with the different network traffic floods. The high delivery rate is maintained because latency and idle listening are controlled. BN-MAC outperforms the other MAC protocols in high-traffic conditions.  
BN-MAC also avoids network congestion using a congestion window with 256 slots, whereas A-MAC, ADC-SMAC, and LPRT-MAC do not have the ability to support simultaneous transmission, thus causing collisions. As a result, the same message is re-transmitted multiple times, and the packet delivery rate is reduced dramatically.  
Figure 23 presents the latency of BN-MAC and the other hybrid protocols at different hops and traffic flows. BN-MAC provides uniform latency at various hops and different numbers of flows. The BN-MAC mechanism uses Anycast for scheduling and unicast for sending data at the one-hop neighbor node, which helps to improve the throughput and reduces the latency. There is also an extremely small probability of failure of a one-hop path. If a one-hop path fails, then a second alternative best one-hop path is chosen for intraregional data communication based on the stored information for the one-hop neighbor nodes. The mechanisms of the other MAC protocols support a multi-hop path technique. If one path fails, then it is difficult to immediately  
Figure 22: Delivery ratio under broadcast flood traffic.  
latency is increased, and the throughput decreases. Overall, BN-MAC has a low latency and outperforms the other hybrid MAC protocols.
## 4 Experimental Verification
### Parameter Identification Results
\[G(s)=\frac{1.5\times 10^{4}s^{2}+1.0\times 10^{9}s+1.3\times 10^{12}}{s^{3}+2.9 \times 10^{4}s^{2}+4.5\times 10^{8}s+1.3\times 10^{12}} \tag{12}\]  
Figure 10 displays the open-loop frequency-response results of the FSM system. The results indicate that the MHL model exhibits good agreement with the actual system dynamic response within the frequency range of 4000 Hz, thereby verifying the effectiveness of the identified model. Additionally, resonance phenomena are observed above 4000 Hz, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Model** & **Inverse Model** \\ \hline \(k_{1}\) & \(-0.001265\) & \(0.00036\) \\ \(b_{1}\) & \(-0.021\) & \(-0.0005\) \\ \(k_{2}\) & \(0.0007\) & \(0.000522\) \\ \(b_{2}\) & \(1\) & \(1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of \(H_{\text{D}}(v(t))\) and its inverse. Figure 9: Prediction results of rate-independent hysteresis model. accompanied by rapid phase attenuation, which is primarily attributed to unmodeled high-frequency dynamics. It is worth mentioning that during the modeling process in step 3, the control experimental platform shown in Figure 8 is not involved. Instead, continuous and undistorted input signals are first generated by using a signal generator and then applied to the FSM. Subsequently, the undistorted output signals of the FSM are directly collected by using a high-resolution oscilloscope. Therefore, the precise acquisition of high-frequency output signals can be achieved.
## 2 Spatiotemporal Grid Failures and Emergency Scenarios During Typhoons  
This section mainly presents the evolution of spatiotemporal grid failures during typhoons. First, considering typical grid-source equipment, including the wind power plants and transmission lines that are susceptible to typhoons, time-varying abnormal states of grid-source equipment and spatiotemporal grid failures are analyzed. Second, novel fault probability threshold-based emergency scenarios are developed considering the spatiotemporal grid failure characteristics.