esa-sceva/satcom-chunk-collection huggingface dataset:
# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
###### Abstract  
The conventional torque sharing function (TSF) control strategy in a switched reluctance motor (SRM) has higher torque ripple due to the weak torque tracking ability with an increase in speed. A non-unity torque sharing function (NUTSF) is proposed in order to minimize the torque ripple. Firstly, the working principle of the conventional TSF is introduced, and the causes of higher torque ripple are analyzed. Secondly, the NUTSF control strategy at each sub-region, where the two-phase exchange region is further divided into region 1 and region 2 based on the inductance characteristics, is proposed, and an optimization algorithm at each sub-region is applied so that the TSF is more suitable for the inductance and torque characteristics of the motor. Finally, a three-phase 6/20 SRM is taken as an example for simulation analysis and a prototype experiment. The results show that the proposed control strategy can effectively reduce the torque ripple of an SRM at a wide speed range.  
switched reluctance motor (SRM); non-unity torque sharing function (NUTSF); sub-region; torque ripple; wide speed range +
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 3 Methodology
### Roughness Length Models  
Based on the aforementioned importance of \(d\), \(z_{\text{0m}}\), and \(z_{\text{0h}}\), the parameterization scheme should first optimize \(d\) and \(z_{\text{0m}}\) through the use of the roughness model and \(z_{\text{0h}}\) through use of the \(kB^{-1}\) model. In a previous study, the process was validated using EC measurements in which the model of [PERSON] [43] (SD00) indicated superiority for estimating \(z_{\text{0m}}\) but was compromised for \(d\)[47]. Since ET estimates from SEBS are more sensitive to \(z_{\text{0m}}\) and since \(z_{\text{0m}}\) directly impacts the \(z_{\text{0h}}\) value, the SD00 model was employed for parameterizing \(d\) and \(z_{\text{0m}}\). Three parameters, including forest height, LAI, and FAI are required for the SD00 model. The SD00 model is expressed as follows:  
\[f_{z}=0.3299L_{p}{}^{1.5}+2.1713\quad\quad for\quad L_{p}<0.8775 \tag{14}\]  
\[f_{z}=1.6771\exp(-0.1717L_{p})+1.0\quad\quad for\quad L_{p}\geq 0.8775 \tag{15}\]  
\[L_{p}=\frac{f_{p}}{f_{c}}LAI-\frac{f_{b}}{f_{c}}L_{b} \tag{16}\]\[f_{d}=1.0-0.3991\mathrm{exp}(-0.1779L_{p}) \tag{17}\]  
where \(L_{p}\) is the mean plant LAI, \(L_{b}\) is background LAI, \(f_{b}\) is the fraction of the canopy coverage, and \(f_{b}\) is the fraction of understory vegetation (\(f_{v}=\)\(f_{c}+f_{b}\)). Multiplying \(f_{z}\) on the right hand side of Equation (19) or Equation (20) yields roughness length as a function of both LAI and FAI. The \(d/h\) is quantified by multiplying Equation (17) by Equation (18).  
Here, it is worth mentioning that only understory mosses live on the forest floor and that the LAI derived from MODIS was used as a surrogate for \(L_{p}\) in Equations (14)-(17).  
The estimation for the normalized displacement height, \(d/h\), and the roughness length, \(z_{0\mathrm{m}}/h\), related to FAI (\(\lambda_{f}\)) is, as follows:  
\[\frac{d}{h}=1.0-\frac{1.0-\mathrm{exp}(-\sqrt{a_{1}\lambda_{f}})}{\sqrt{a_{1} \lambda_{f}}} \tag{18}\]  
\[\frac{z_{0\mathrm{m}}}{h}=a_{2}\mathrm{exp}(-b_{2}\lambda_{f}^{c_{2}})\lambda _{f}^{d_{2}}+\frac{z_{00}}{h}\hskip 14.226378 pt(\lambda\leq 0.152) \tag{19}\]  
\[\frac{z_{0\mathrm{m}}}{h}=\frac{a_{3}}{\lambda_{f}^{d_{3}}}\left[1.0-\mathrm{ exp}(-b_{3}\lambda_{f}^{c_{3}})\right]+f_{2}\hskip 14.226378 pt(\lambda>0.152) \tag{20}\]  
where \(a_{1}=15.0\), \(a_{2}=5.86\), \(b_{2}=10.9\), \(c_{2}=1.12\), \(d_{2}=1.33\), \(a_{3}=0.0537\), \(b_{3}=10.9\), \(c_{3}=0.874\), \(d_{3}=0.510\), \(f_{2}=0.00368\), and \(z_{00}/h=0.00086\). \(\lambda_{f}\) is the FAI, calculated from the frontal area, \(A_{f}\). By assuming that the frontal area of the stem is much smaller than the frontal area of the crown [(43)] for each individual needle tree, \(A_{f}\) is simplified, as follows:  
\[A_{f}=\frac{1}{2}h_{c}*w_{c} \tag{21}\]  
where \(h_{c}\) is the height of the crown (_i.e._, tree height-FBH) and \(w_{c}\) is the crown width. The \(\lambda_{f}\) is then calculated from the total \(A_{f}\) divided by the total area of the plot.
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 4 Multi-Agent System for Train Rescheduling Problems
### Collaborative Strategy: Condorcet Voting Method
The voting values \(\langle\tau_{m}|\tau_{n}\rangle\) and \(\langle\tau_{n}|\tau_{m}\rangle\) are calculated by the sum of the values for the objective function (total delay) of the best solution in a pair of voting sections in every conflict junction agent. Furthermore, a decision that a junction does not include in its solution table is viewed as extremely bad locally, so a very large integer is applied as the value of its objective function. The final decision on the train passing sequence depends on the numerical values of \(\langle\tau_{m}|\tau_{n}\rangle\) and \(\langle\tau_{n}|\tau_{m}\rangle\) as shown below. \[ \begin{split} i_{\tau_{m},\tau_{n}}^{I_{1}}=i_{\tau_{m},\tau_{n} }^{I_{2}}=\ldots=i_{\tau_{m},\tau_{n}}^{J_{N_{0}}}=i_{\tau_{m},\tau_{n}}^{I_{1 },J_{2},\ldots,J_{N_{0}}}\\ =\begin{cases}1&\text{\bf IF}\ \langle\tau_{m}|\tau_{n} \rangle<\langle\tau_{n}|\tau_{m}\rangle\\ 0&\text{,}\ otherwise\end{cases}\end{split}  \tag{24}\]  
Once a decision on the passing sequence of trains \(\tau_{m}\) and \(\tau_{n}\) is made, the collaborative agent saves this part of the solution as the final solution and discards all the individuals that include a different value for the decision variable in the solution table. It then moves to the next voting section until all voting sections are completed. The Condorcet voting mechanism was selected over simpler consensus-based approaches due to its unique ability to resolve cyclic conflicts inherent in decentralized railway rescheduling. When junction agents generate locally optimal but globally incompatible solutions (e.g., conflicting train sequences at coupled junctions), traditional averaging methods fail to guarantee operational feasibility. The Condorcet method addresses this by (1) decomposing cyclic preferences into pairwise comparisons of alternative sequences (Equations (22) and (23)), (2) evaluating each option's network-wide impact through summed delay penalties (Equation (24)), and (3) selecting the sequence with minimal total disruption. This approach maintains the MAS's distributed architecture while ensuring strict adherence to precedence constraints that averaging would violate through fractional solutions. Furthermore, the hierarchical voting process--where junction agents propose alternatives and the collaborative agent mediates conflicts--provides computational scalability that is unattainable with centralized resolution methods. The mechanism's effectiveness is particularly evident in scenarios with strong timetable coupling, where it prevents locally optimal but globally detrimental decisions by incorporating downstream delay propagation into the voting metric. This property aligns with real-world dispatching practices while overcoming the combinatorial limitations of alternative conflict-resolution approaches.
# Design and Implementation of LoRa Based IoT Scheme for Indonesian Rural Area
## 4 Result and Discussion
### Analysis Using Default Parameter  
Firstly, the test was conducted to assess the default parameters from Dragino comprising the spreading factor of 7 and the bandwidth of 125 kHz. The parameter was carried out following the existing scenario explained in Section 3. Table 1 and Figure 8 show the detailed result of the experiment. As can be seen in Table 1, the average delay of the proposed scheme was around 145-260 ms. The experiment to get the real behavior of the available default parameters from Dragino was set to get the real data from a distance of 100 m to 400 m as a maximum distance. We also noticed that, the further the distance between the server and the client, the delay may increase, indicated by the data taken in the range between 100 to 300 m. However, an anomaly came in the data received from range 400 m. We further investigated that it may come from the shaking device caused by natural effects, such as wind or an animal passing through our device nodes in a real environment. This phenomenon, experiencing of the shaking device, may also affect the wiring connection, and then the LoRa module cannot smoothly process the data sending. A further experiment is necessary to support our findings.  
Figure 8: Delay graph from default parameters.  
Figure 7: The surroundings of the farming site.
## 4 Conclusions
Overall, both disorders show altered MNS-related activity, but ASD appears to be more influenced by attentional and contextual modulation, while MNS changes seem to be more variable in schizophrenia and may depend on symptom profile and disease phase. The overall findings support a model in which MNS abnormalities in ASD and schizophrenia are neither uniform nor wholly distinct, but shaped by broader cognitive and neurophysiological contexts. The controversies evident in the literature might stem from a variety of factors, such as the marked heterogeneity in terms of symptomatology and traits in ASD, as well as the small sample sizes and the different methodological designs across studies, or/and the targeting of limited cerebral areas. Similarly, the studies that have examined \(mu\) suppression in schizophrenia also present controversies in their outcomes. Reduced \(mu\) suppression in schizophrenia has been linked to negative symptoms, severity of symptoms and also thought disorder. In addition, divergent findings in the case of schizophrenia range from outcomes that depict intact \(mu\) suppression and MN activation, to severe MNS dysfunctions. Divergence in the results may be attributed to differences in study design, task selection, and size and representability of the samples. In total, the present review has demonstrated impairments in the MNS in both ASD and schizophrenia. Correlations between severity of symptoms or frequency of traits and level of \(mu\) rhythm suppression have been detected. In the case of schizophrenic patients, reduced levels of \(mu\) rhythm suppression were found to correlate with the severity of negative symptoms, whereas in ASD, reduced levels of \(mu\) rhythm suppression correlated with intense deficits in social cognition. Crucially, the overall findings of the selected studies demonstrate robust deficits in socio-cognitive understanding and Theory of Mind, i.e., the ability to mentalise, infer and understand emotions while observing actions in both clinical conditions. Further research in the field is of essential importance in order to reach consensus for the neural operations that determine the function of the MNS in both schizophrenia and ASD.
## 2 Study Sites and Database Description  
In this study, two real databases have been used; one collected in Tunisia and one collected in France. The Tunisian database, collected between 2009 and 2017, will not be described in the present study because it is well described in [PERSON] et al. (2016). It is composed of SAR data acquired by ASAR and Sentinel-1 sensors, NDVI values derived from optical images (between 0.08 and 0.86), and in situ measurements of soil moisture (between 4.0 and 39.7 vol %) and surface roughness (between 0.7 and 4.6 cm) for winter crop fields and grasslands. This Tunisian database was used in [PERSON] et al. (2016) to calibrate the Water Cloud Model (WCM) ([PERSON] et al., 2016) at the C-band. This parameterized water cloud model will be used in the present study for training the neural network, which will serve to estimate the soil moisture. Accordingly, this Tunisian database will not be used in this study to validate the inversion approach for soil moisture estimates because the WCM used was parameterized using this database.  
The French database collected in France at a study site near Montpellier (Southern France) is composed of SAR data acquired by Sentinel-1 sensors, NDVI maps derived from Sentinel-2 images, and in situ measurements of soil moisture. In the present study, only this French database will serve to validate the soil moisture estimation algorithm.
## 3 Example Analysis  
In this paper, the satellite observation data of the sc02 station in Friday Harbor Port, Washington, USA are selected for experimental verification. The sc02 station is subordinate to the plate edge observation PBO network of the \"earth scope\" of the United States. It is built near the sea and equipped with a Trimble netr9 geodetic receiver. It can accept the sea-surface reflection signal in the sea-surface direction at the direction angle of 45\({}^{\circ}\)-220\({}^{\circ}\)The height h of the antenna phase center is 5.389 m. A tide-gauge station is built near the station, which can provide tide level data once every 6 min. Figure 2 shows the surrounding environment of the sc02 station, and Figure 3 shows the geographical location of the sc02 station. It is said that under the influence of the gravitational pull of the moon and the sun, the sea-surface height changes periodically with the occurrence of the sea-surface height, and the sea-surface change period with time is about one month, so when the technology is used to achieve one-month tide level inversion, it can explain the effectiveness of the technology for a long time. Therefore, this paper uses one month's GPS satellite observation data from Friday Harbor sc02 station from 1 August to 31 August 2020 for the experiments.
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
###### Abstract  
Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.  
resource allocation; wireless power transfer; UAV; Internet of Things; dynamic game 13 February 2019; 1998; doi:10.3390/s19081908
# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 4 Performance Analysis in Simulated Scenarios
### Target Detection Performance  
To compare the fullarray and the AB-STAP schemes in terms of detection performance, we carried out a Monte Carlo analysis under different conditions of availability of the secondary data. We assumed the same parameters used in the previous example and, for simplicity, a uniform clutter scenario. The space-time AMF detectors in (1) and (4) were considered, with desired PFA level \(10^{-4}\). The Swerling 0 target model was assumed.  
In Figure 4a, the resulting probability of detection (PD) is shown as a function of the target input SNR. The target bistatic radial velocity \(v_{b}=7\) m/s was representative of a slow target condition, sufficiently close to the clutter notch. Two cases were considered, with different amounts of available training data. Specifically, \(K=24\) represents the case of enough training data for the full array solution (twice the number of the adaptive DOF), while \(K=16\) represents a condition of a limited sample support.  
When \(K=24\) training data are available (dashed curves), AB-STAP performs very closely to full array STAP. Although clutter cancellation is performed by means of only two spatial DOF on the A and B channels, their optimal coherent recombination allows us to avoid considerable detection losses. Notice that the slight remaining advantage of the full array case (<1 dB) can be further reduced if considering target velocities further from clutter notch or by adopting overlapped solutions for A and B channels (e.g., \(N_{0}=3\)).  
When the amount of training data decreased to \(K=16\) (solid curves), significant performance degradation was observed in the full array case, due to adaptivity losses. Conversely, the AB-STAP only showed a limited performance drop, thanks to the smaller number of adaptive DOF used in each step (half of the full array case). As a result, in this case, AB-STAP outperformed full array STAP by approximately 5 dB.  
The above considerations find confirmation also in Figure 4b, where the PD was evaluated as a function of \(K\), for a fixed target SNR of \(-50\) dB. As expected, the performance decreased significantly more in the full array case, as the number of training data reduced, while losses associated with the AB-STAP approach were remarkably smaller. Moreover, even for larger values of \(K\), AB-STAP showed minor differences in performance, compared to full array STAP, only for very slow targets, close to clutter notch. Moreover, this was obtained in favour of a lower complexity and computational cost.  
Figure 4: Detection performance comparison: (**a**) as a function of target input SNR, for target bistatic velocities \(7\) m/s; (**b**) as a function of the number of training data, for target input SNR \(-50\) dB. The desired PFA was set to \(10^{-4}\).
## 2 Materials and Methods
### Measurement of Intracellular ROS  
HCE-F cells were seeded in culture medium at a density of 2 \(\times\) 10\({}^{4}\) cells per well into 96-well plates. The day after, culture medium was replaced by SFM with 0, 50, 100, 250 \(\upmu\)M lutein, 0, 50, 100, 250 \(\upmu\)M astaxanthin or a mix of 100 \(\upmu\)M lutein and 100 \(\upmu\)M astaxanthin, and then cells were exposed for 30 min to blue-violet light at a final dose of 50 J/cm\({}^{2}\). The levels of intracellular ROS were analyzed using 2',7'-dichlorodifluorcescein diacetate (H2 DCFDA, Life Technologies, InvitrogenTM, Waltham, MA, USA 02451; cat. no. D-399) [30,31] according to the manufacturer's instructions. Briefly, adherent HCE-F cells were washed once with SFM and incubated with 10 \(\upmu\)M H2 DCFDA (loading buffer) at 37 \({}^{\circ}\)C for 30 min. The loading buffer was then removed and the cells returned to SFM. The fluorescence intensity (\(\lambda\)ex = 492 nm, \(\lambda\)em = 517 nm) was measured with the VarioskanTM (Thermo Fisher Scientific, Waltham, MA, USA 02451).
## 3 Test Results and Discussion  
In this study, the impact of various parameters--such as reinforcement bar type, bar diameter, embedment length, and concrete material--on bond behaviour are discussed. In the direct pull-out test, the bond stress of reinforcing bars and concrete is assumed to act evenly along the bond length. The following sections discuss the bond behaviour of the BFRP- and steel-reinforced SCGC with respect to the above-mentioned parameters. It is significant to note that the predominant type of failure in this study is the pull-out failure of specimens. This can be attributed to the sufficiently thick concrete cover provided to the pull-out specimens [3].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sl. No**} & \multirow{2}{*}{**Specimen No.**} & \multicolumn{2}{c}{**Bar Diameter**} & \multicolumn{2}{c}{**Embedment Length in**} & \multicolumn{1}{c}{**Embedment Length (mm)**} \\  & & \multicolumn{1}{c}{\(\mathrm{d_{b}}\) (mm)} & \multicolumn{1}{c}{**Terms of Bar Diameter**} & \multicolumn{1}{c}{**Length (mm)**} \\ \hline
1 & 6-1-1 & 6 & 5 \(\mathrm{d_{b}}\) & 30 \\ \hline
2 & 6-1-2 & 6 & 5 \(\mathrm{d_{b}}\) & 30 \\ \hline
3 & 6-1-3 & 6 & 5 \(\mathrm{d_{b}}\) & 30 \\ \hline
4 & 6-2-1 & 6 & 10 \(\mathrm{d_{b}}\) & 60 \\ \hline
5 & 6-2-2 & 6 & 10 \(\mathrm{d_{b}}\) &The pull-out tests were conducted on all specimens after curing under ambient conditions for 28 days. For all of the specimens, the failure type was by pull-out of the rebars. The maximum bond stress (\(\tau_{\text{max}}\)) is calculated as:  
\[\tau_{\text{max}}=\text{Pmax}/\pi\,\text{d}_{\text{b}}\,\text{L} \tag{1}\]  
where, Pmax is the maximum pull-out force on the rebar, \(\text{d}_{\text{b}}\) is the bar diameter, and L is the embedment length as per the CSA -S806-02 and ACI 440.3R-04 standards.  
The characteristic bond strength (\(\tau_{\text{max}}\)) is calculated as the mean of the maximum bond stress of three identical specimens of each configuration.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
###### Abstract  
In this work, we propose \(D^{3}\)-Tree, a dynamic distributed deterministic structure for data management in decentralized networks, by engineering and extending an existing decentralized structure. Conducting an extensive experimental study, we verify that the implemented structure outperforms other well-known hierarchical tree-based structures since it provides better complexities regarding load-balancing operations. More specifically, the structure achieves an \(O(\log N)\) amortized bound (\(N\) is the number of nodes present in the network), using an efficient deterministic load-balancing mechanism, which is general enough to be applied to other hierarchical tree-based structures. Moreover, our structure achieves \(O(\log N)\) worst-case search performance. Last but not least, we investigate the structure's fault tolerance, which hasn't been sufficiently tackled in previous work, both theoretically and through rigorous experimentation. We prove that \(D^{3}\)-Tree is highly fault-tolerant and achieves \(O(\log N)\) amortized search cost under massive node failures, accompanied by a significant success rate. Afterwards, by incorporating this novel balancing scheme into the ART (Autonomous Range Tree) structure, we go one step further to achieve sub-logarithmic complexity and propose the ART\({}^{+}\) structure. ART\({}^{+}\) achieves an \(O(\log_{2}^{2}\log N)\) communication cost for query and update operations (\(b\) is a double-exponentially power of 2 and \(N\) is the total number of nodes). Moreover, ART\({}^{+}\) is a fully dynamic and fault-tolerant structure, which supports the join/leave node operations in \(O(\log\log N)\) expected WHP (with high probability) number of hops and performs load-balancing in \(O(\log\log N)\) amortized cost.  
decentralized systems; distributed data structures; P2P; load balancing; fault tolerance +
Footnote †: journal: _Algorithms_