esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Mechanism of Combined Remediation of Soil Heavy Metals by Material-Plant Collaboration  
According to the experimental results and related mechanism studies of the combined remediation towards the contaminated soil of the Zhongshan Huilong arsenic smelting plant in Guangxi, a mechanism was proposed, as illustrated in Figure 10.  
1. After combined remediation, the exchangeable and weakly bound forms of As in the soil were transformed into stable organically bound and residual forms, while  
Figure 10: Mechanism diagram for the stabilization of heavy metals in the soil.  
Figure 9: Genus-level species abundance composition stacked bar chart (S0: the soil before remediation; S1: samples taken after plant remediation; S2: material remediation; S3: combined remediation).  
the extractable and reducible forms of Cd in the soil were transformed into more stable reducible and residual forms [78].  
2. The introduction of combined remediation materials increased the content of Fe(III) and Mn(IV) in the soil, oxidizing highly active As(III) to less active As(V), thereby reducing the bioavailability of As in the soil [79].  
3. During the growth process of the remediation plants, organic acids released by plant roots formed complexes with Cd in the soil through chelation, increasing the stable organically bound form of Cd after remediation [80], and thereby reducing the bioavailability of Cd in the soil.  
4. After combined remediation, the abundance of microbial genera with high tolerance to heavy metals increased in the soil. These genera reduced the bioavailability of As and Cd in the soil through adsorption, chelation, or biological enrichment [81]. Moreover, certain genera could also promote plant growth, facilitating vegetation restoration in the contaminated area and enhancing the effectiveness of combined remediation.
## 3 Results  
### An Example of Optimal Differentiating FIR Filter Selection  
We considered four types of FIR differentiating filters, labelled as \(m=1,\,2,\,3,\,4\), for the calculation of the maximum amplitude derivative, according to Equation (5). These filters are characterized by \(k_{0}\) (the filter order) and \(i_{d}\) (the number of points used for approximation construction), which are assigned to the filter types as follows:  
* \(k_{0}=1\), \(i_{d}=2\), for \(m=1\);
* \(k_{0}=1\), \(i_{d}=3\), for \(m=2\);
* \(k_{0}=2\), \(i_{d}=4\), for \(m=3\);
* \(k_{0}=3\), \(i_{d}=5\), for \(m=4\).  
The ASP observatory and the daily data interval 22 June 2015 were chosen. For the sub-interval number \(n=n_{\text{SC}}=111\), corresponding to the SC appearance time 18:35 UTC and the filter types \(m=1,\,2,\,3,\,4\), we calculated the maximum amplitude derivatives of Equation (2) and determined the optimal filter type, which maximizes the maximum amplitude derivative  
\[m^{\circ}=\arg\left\{\max_{m=1:4}H_{N,d}^{\circ}(n_{\text{SC}},m)\right\} \tag{8}\]  
and leads to the result \(m^{\circ}=3\). The second-order differentiating filter with \(k_{0}=2\) and \(i_{d}=4\) min turned out to be the optimal one for the considered setting.  
Figure 2 shows the results of calculating the derivatives for the components and the total intensity of the geomagnetic field using the second-order FIR filter \(k_{0}=2\) and \(i_{d}=4\), \(r=0,1\ldots,3\), \(p=1\), \(H_{dx}=H_{N,d,11}(T(i))\), \(H_{dy}=H_{Nd,21}(T(i))\), \(H_{dz}=H_{N,d,31}(T(i))\), \(H_{d0}=H_{N,d,01}(T(i)),\ i=1,2,\ldots,N_{0}\), and \(N_{0}=1440\) (\(T_{N_{0}}=24\) h).  
The use of the optimized differentiating filter made it possible to reliably determine the beginning of the SC event. It can be seen that the SC event occurred in the interval with indices \(i=1115\div 1117\), corresponding to \(\approx\)18:35 UTC. In this interval, there was a sharp increase in the values of the derivatives for the components and the modulus, followed by noise components in the time interval \(\approx\)18:50-24:00 UTC.  
The maximum amplitude derivatives for the field component and total field intensity were \(\left|H_{Nd,l11}^{\circ}(n_{SC},m)\right|=391\) nT/min, \(\left|H_{Nd,21}^{\circ}(n_{SC},m)\right|=192\) nT/min, \(\left|H_{Nd,31}^{\circ}(n_{SC},m)\right|=903\) nT/min, and \(\left|H_{Nd,-1}^{\circ}(n_{SC},m)\right|=245\) nT/min for \(n_{SC}=111\) and \(m=4\). At that the derivative turned out to be equal to \(H_{Nd}^{\circ}(n)=903\) nT/min. Our analysis allows to conclude that the noise amplitude (about 48 to 60-70 nT/min) is 4-5 times less than the amplitudes of the maximum absolute derivatives.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
###### Abstract  
In wireless rechargeable sensor networks (WSRNs), a mobile charger (MC) moves around to compensate for sensor nodes' energy via a wireless medium. In such a context, designing a charging strategy that optimally prolongs the network lifetime is challenging. This work aims to solve the challenges by introducing a novel, on-demand charging algorithm for MC that attempts to maximize the network lifetime, where the term \"network lifetime\" is defined by the interval from when the network starts till the first target is not monitored by any sensor. The algorithm, named Fuzzy Q-charging, optimizes both the time and location in which the MC performs its charging tasks. Fuzzy Q-charging uses Fuzzy logic to determine the optimal charging-energy amounts for sensors. From that, we propose a method to find the optimal charging time at each charging location. Fuzzy Q-charging leverages Q-learning to determine the next charging location for maximizing the network lifetime. To this end, Q-charging prioritizes the sensor nodes following their roles and selects a suitable charging location where MC provides sufficient power for the prioritized sensors. We have extensively evaluated the effectiveness of Fuzzy Q-charging in comparison to the related works. The evaluation results show that Fuzzy Q-charging outperforms the others. First, Fuzzy Q-charging can guarantee an infinite lifetime in the WSRNs, which have a sufficient large sensor number or a commensurate target number. Second, in other cases, Fuzzy Q-charging can extend the time until the first target is not monitored by 6.8 times on average and 33.9 times in the best case, compared to existing algorithms.  
WSRN; Q-learning; on-demand charging algorithm; target coverage; connectivity +
Footnote †: This paper is an extended version of Q-learning-based, Optimized On-demand Charging Algorithm in WSRN. In Proceedings of the 2020 IEEE 19 th International Symposium on Network Computing and Applications (NCA), Cambridge, MA, USA, 24-27 November 2021.
## 3 Proposed Joint Resource Optimization for NOMA-Enabled Vehicular Networks
### Assumed Configuration  
This study assumes a power domain (PD) NOMA-enabled downlink vehicular network as shown in Figure 1 that encodes multiple signals into one by using different power levels at the transmitter [4; 11]. Regarding the signal propagation and channel model, the assumed system is primarily based on the configurations proposed in 3 GPP 5G standard [22] as follows. Although the proposed solutions do not depend upon the particular channel models and mobility, the urban macro scenario is assumed with line of sight and the logarithmic path loss model along with shadow fading. In terms of mobility, it affects channel condition and signal strength, which has been investigated in Section 4.  
An essential characteristic of PD NOMA is that it leverages the differences in channel gain among devices assigned to the same channel. Then, the receiver carries out SIC as part of a multi-user detection algorithm (MUD). In PD NOMA, it is common to allocate more power to the user experiencing low channel gain. For example, in Figure 1, RSU will allocate more power to VU1 which is further away from the RSU than VU2, where RSUs are treated as base stations for vehicular communication in this study. As pointed out in [11], the NOMA system becomes particularly effective in terms of the sum rate when the clustered users exhibit different channel gains [23]. In the assumed network, there are \(N_{VU}\) VUs and each VU is equipped with a single antenna [11]. Thus, a VU can be associated with only a single RSU for each time slot. On the other hand, an RSU \(b\) can associate with up to \(\kappa_{b}\) VUs, where \(\kappa_{b}\) is set to \(2\times N_{ch}\) in this study with \(N_{ch}\) being the number of orthogonal channels available to each RSU.  
In this study, we assume multi-RSUs specifically to address the high density and mobility challenges in dynamic urban vehicular networks, where a single RSU may struggle to maintain connectivity, latency, and load balance. The multi-RSU configuration improves resource utilization and load distribution, helping to manage rapid changes in vehicle density. It also supports more effective NOMA-OMA switching, which enhances spectral efficiency. However, by introducing multiple RSUs, the global coordination may become more complex. The proposed algorithms are designed to minimize inter-RSU coordination, adding only modest overhead. VUs are generally resource-limited, and thus, grouping too many users into the same NOMA cluster can cause multiple iterations of SIC which will incur high processing delays [24]. Therefore, in this work, the size of the cluster is limited to two [11], meaning that at each time step, two VUs can occupy a channel for downlink transmission [23]. Different clusters are served with different channels in an OFDM manner [11], forming a multi-carrier NOMA system [4]. In addition, the time is slotted, and thus the time-frequency resources are allocated as a unit of resource block [25].
# Investigations into the Recognisability of Gear Damage
## 6 Conclusions  
In this paper we proposed an investigation into the recognisability of gear damage levels in vibration signals. The recognisability of damage levels was investigated in the vibration behaviour of two different gears. We showed that on the first gearbox stage, damage levels up to a single pitch deviation of 73.48 um could be recognised, and on the second gearbox stage damage levels up to 847.40 um could be recognised. This implies that detectable size is gear-dependent. The vibration signals were investigated in the time domain using cross-correlation and on the basis of the signal components occurring in the frequency domain. In addition, frequency bands were determined for the recognisable damage levels in order to estimate appropriate digital filter limits. For this, linear prediction coefficients were used to calculate smoothed frequency spectra. Afterwards, the digital filter limits were determined using difference spectra. By applying the filter limits in a multiband FIR filter, it was finally shown that the digital filters make it considerably easier to recognise the damage levels in the time domain of the vibration signal. For future work, we plan to transfer the method to gearboxes installed in vehicles. In this way, the approach will be validated for environments with more disturbing noise. In addition, the investigated method is to be validated for the analysis of wear phenomena. Since the presented methodology finds the different frequencies between intact and damaged teeth, we assume a good transferability to wear phenomena.  
Conceptualization, A.B.; methodology, A.B.; software, A.B.; validation, A.B.; formal analysis, A.B.; investigation, A.B.; resources, A.B. and K.-L.K.; data curation, A.B.; writing--original draft preparation, A.B.; writing--review and editing, A.B. and K.-L.K.; visualization, A.B.; supervision, K.-L.K.; project administration, K.-L.K.; funding acquisition, K.-L.K. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the German Federal Ministry of Education and Research project VIPER (16 ES0740).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 17: Results of multiband FIR filtering for the gearbox with damage on gear z\({}_{1}\) (**a**) unfiltered—(**c**) filtered and for the gearbox with damage on gear z\({}_{3}\) (**b**) unfiltered—(**d**) filtered. The rotational speed is 220 rpm and the torque is 70 Nm.
## Appendix A Primary Control  
The primary control includes outer control loops (for voltage regulation), inner control loops (for current regulation), and droop control loops to share the power between inverters. Grid-forming (comprised of the inner and outer loops) includes a proportional-integral PI controller to regulate the output voltage and frequency of the MG. Furthermore, the frequency and voltage amplitude can be addressed by droop control as shown in the Equations (A1) and (A2), [1]:  
\[\omega=\omega_{ref}-m_{p}(P-P_{ref})=\omega_{ref}-m_{p}\Delta P\] (A1)  
\[v_{dref}=v_{ref}-n_{q}(Q-Q_{ref})=v_{ref}-n_{q}\Delta Q\] (A2)  
where \(\omega_{ref}\) and \(v_{ref}\) are the nominal frequency and voltage, \(P\) and \(Q\) are the measured active and reactive power injection, and \(\Delta P\) and \(\Delta Q\) are the corresponding power input errors for the droop controller. Coefficients \(m_{p}\) and \(n_{q}\) regulate maximum deviations allowed in the MG [42; 63].  
Different lengths of transmission lines make the VSI output impedance different, causing unequal power sharing in droop control. Virtual impedance is an essential con cept [64] to fix the output impedance value and decouple the control of active and reactive powers. The virtual impedance can help to keep the voltage within certain limits and is used for applications such as harmonic voltage compensation and improved stability. Refs. [37; 65] show the implementation of virtual impedance. However, it is out of the scope of this research.  
The voltage across the capacitor \(v_{abc}\) can be calculated using Equation (A3):  
\[v_{abc}(t)=\underbrace{v_{ref}sin(\omega_{ref}t)}_{v_{2}^{*}(t)}+\underbrace {iv_{ref}cos(\omega_{ref}t)}_{v_{\bar{p}}^{*}(t)}\] (A3)  
where \(v_{ref}\) and \(\omega_{ref}\) are the voltage amplitude and angular frequency \(\omega_{ref}=2\pi f_{ref}\) of the reference signal in time \(t\). The voltage derivative reference is obtained from Equation (A4):  
\[\frac{dv_{abc}(t)}{dt}=\underbrace{\omega_{ref}v_{ref}cos(\omega_{ref}t)}_{ \omega_{ref}v_{\bar{p}}^{*}(t)}+\underbrace{j\omega_{ref}v_{ref}sin(\omega_{ ref}t)}_{\omega_{ref}v_{\bar{p}}^{*}(t)}\] (A4)  
To track \(v_{ref}\) it is necessary to expand the last results to \(\alpha,\beta\) frame. The predicted currents \(i_{f}\) and measured currents \(i_{o}\) are calculated as [64], to predict the capacitor derivative voltage as follows:  
\[\frac{dv_{abc}(t)}{dt}=\underbrace{\frac{i_{f\alpha}(t)-i_{o\alpha}(t)}{C_{f} }}_{\frac{d\omega_{abc}(t)}{dt}}+\underbrace{j\frac{i_{f\beta}(t)-i_{o\beta}( t)}{C_{f}}}_{\frac{d\omega_{abc}(t)}{dt}}\] (A5)  
From Equation (A5), it can be seen that the derivative path of the voltage can be tracked if the error between the first term and the second (A4) and (A5) is minimized.
## 4 Results
### Mixed Paths  
Example two, shown in Figure 7, concerns a system with mixed paths. As in example, one, to improve the efficiency of the SOCP method, modifications have been made to the assignment of deadlock-free zones in [2]. The example also uses the zones shown schematically in Figure 1b. The results of the simulations carried out in the Delmia QUEST have been compared with the results published in [2] and are summarized in Table 4.  
Figure 6: A comparison of the results for unidirectional paths.  
Figure 7: The layout with mixed flow paths.  
In Figure 8, the empty circles show the results of the original SOCP method while the red circles show the results of this method using the deadlock-free zone shown schematically in Figure 1b. As can be seen, the use of a new type of zone enabled process reservations to be made in a staged manner which allowed for a significant reduction in transport task completion times of up to 45% when 10 AGVs were used.
## 1 Introduction
A test data transfer was made between the node and Sentry over the wireless optical modem from a 20 to 150 m slant range at data rates of 5 and 10 Mbps [16]. The WHOI optical modem was deployed on a seafloor Ocean Bottom Seismometer and on a REMUS 600 AUV. Test data were successfully transferred from the Ocean Bottom Seismometer to REMUS over the wireless optical modem at a \(\sim\)30 m slant range at data rates of 5-10 Mbps [17]. A study was performed on the docking of AUVs to a docking station using light and a light sensor system under the water. For this, a guiding system for an AUV loading sensor system composed of a lens, light sensor, signal processor, and control processor and a docking system with LED was proposed [18]. However, there is currently no research on AUV motion control that integrates real-time image transmission via UWOC between the underwater vehicle and an above-surface communication modem. In this study, a new control structure for the AUV motion based on real-time commands using the UWOC system is proposed. In this control structure, control inputs for the AUV attitude from outside of the water are transferred to the AUV for motion control, while its orientation data and motion images from the AUV camera are sent to the control station outside of the water via the UWOC system. For two-way communication between the ground and the AUV, a light receiver using an LED transmitter and a PIN photodiode was designed and installed in the AUV. In addition, four thrusters were installed on the AUV to control the water depth and attitude. The attitude control of the AUV based on the developed UWOC system was verified through underwater experiments.
# Effective Frequency Range and Jump Behavior of Horizontal Quasi-Zero Stiffness Isolator
###### Abstract  
The quasi-zero stiffness (QZS) isolator shows excellent characteristics of low-frequency vibration isolation. However, the jump behavior caused by the strong nonlinearity is a primary reason for the failure of QZS isolators. In order to grasp the effective frequency range and failure mechanism of a horizontal QZS isolator comprehensively, the dynamics of the isolator were studied in the following two cases. In the first case, the isolator is subject to a base displacement excitation; in the second case, the isolator is installed on a linear structure that is subject to a harmonic force. The nonlinear algebraic equations describing the steady-state response of the two systems were derived via the complexification-averaging method, and the results obtained using the derived expressions were verified by comparing the results of the complexification-averaging method and the Runge-Kutta method. The effective frequency ranges of the isolator were then obtained, and the jump phenomena in the response amplitude induced by the strong nonlinearity of the isolator were analyzed. The results show that when the excitation amplitude is small, the vibration isolation system does not exhibit jumping behavior and the effective frequency range is relatively wide. With increases in the excitation amplitude, the system can exhibit jumping behavior when an additional impact load is considered, and this phenomenon leads to a narrowing of the effective frequency range. The characteristics of the jump phenomena produced in the two cases were analyzed, and the differences in the jump behaviors were elucidated. Furthermore, the effect of the isolator parameters on the effective frequency range was investigated.  
vibration isolation; strong nonlinearity; additional impact; lowest effective frequency +
Footnote †: journal: Applied Sciences
# Study of the Antimicrobial Effect of an Ethanolic Extract of Propolis in Periodantal Disease
## 3 Results  
The pH of propolis formulations (mean of three replications \(\pm\) SEM) was 5.9 \(\pm\) 0.48. Chemical analysis revealed that propolis used in the current study contained 388.2 \(\pm\) 8.2 mg GAE/100 g as total phenolics and 37.0 \(\pm\) 1.3 mg CE/100 g as total flavonoids.  
The results showed a significant clinical improvement (\(p<0.05\)) in the PPD and CAL comparing them at the beginning and one month after the periodontal treatment. Although the reduction was more apparent in the test group than in the control group, the differences were not statistically significant when comparing PPD and CAL between the two groups at the follow-up appointment (Table 1).  
One month later, the levels of bleeding on probe (BOP+) had been significantly reduced (\(p<0.05\)) by 90% in the test group vs. control 75% (McNemar test) (Figure 3).  
The presence of _A. actinomicetemcomitans_ was not detected in any patients included in the study, so the antimicrobial effect of propolisis extract for this bacterium could not be observed. Regarding the bacterial count of the other bacteria, after a month we observed a significant reduction (\(p<0.05\)) for _T. forysthemsis_, _P. gingivalis_, _P. intermedia_, _T. denticola_, in both groups. When comparing the percentage of decrease in bacteria between the groups, to evaluate the effectiveness of the irrigated substances, we found significant differences, decreasing the number of colonies for _T. forysthemsys_ and _P. gingivalis_ in the test group (\(p<0.05\)) (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Main Periodantal Parameters at Baseline (BL) and the Month (1 M)**} \\ \cline{2-7}  & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-7}  & **Mean** & **SEM** & **Median** & **Mean** & **SEM** & **Median** \\ \hline BL-PPD (mm) & 4.75 & 0.17 & 5.00 & 4.75 & 0.18 & 5.00 \\ BL-CAL (mm) & 5.75 & 0.29 & 5.50 & 5.80 & 0.33 & 5.50 \\
1M-PPD (mm) & 3.60 & 0.17 & 4.00 & 3.30 & 0.15 & 3.00 \\
1M-CAL (mm) & 4.55 & 0.28 & 5.00 & 4.45 & 0.26 & 4.00 \\ VAR. PPD (mm) & 1.20 * & 0.14 & 1.00 & 1.45 * & 0.15 & 1.00 \\ VAR. CAL (mm) & 1.20 * & 0.17 & 1.00 & 1.35 * & 0.18 & 1.00 \\ VAR. PPD (\%) & 36.42 * & 5.14 & 33.33 & 46.83 * & 6.02 & 33.33 \\ VAR. CAL (\%) & 30.63 * & 5.72 & 25.00 & 31.46 * & 4.24 & 25.00 \\ \hline \hline \end{tabular} SD. Standard deviation, SEM standard error of the mean. BL (Baseline) 1 M (one month later), PPD (probing pocket deep), CAL (clinical attachment level). Intra-treatment (Wilcoxon test) * \(p<0.05\) differences were significant when comparing PPD and CAL Intra-treatments. Between-groups (Mann-Whitney test) \(p\geq 0.05\), differences were not significant when comparing PPD and CAL between groups.  
\end{table}
Table 1: Effect of the intervention on periodontal status.  
Figure 3: Levels of bleeding on probing positive (BOP+) both groups after 1 month. No bleeding (BOP) was statistically significant in the test group. * \(p<0.05\).