esa-sceva/satcom-chunk-collection huggingface dataset:
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 7 Conclusions  
We propose CarrDet, the first deep learning-based framework that is specifically designed for carrier detection in wastewater microscopic images for the WWT process. By leveraging a carrier feature block attention module and a symmetry-based defective carrier detection module, CarrDet addresses the detection challenge of carriers with shallow edges and false positives caused by defective carriers, respectively. We construct a carrier detection dataset of wastewater microscopic images, which greatly facilitates the development of carrier detection. Experimental results show the superior performance of CarrDet, achieving a mAP of 94.32 and an IPS of 4.93, to accurately recognize carriers and improve water purification processes. Our industry practice proves the effectiveness of CarrDet in practical business WWT applications. In future work, we will try to optimize the performance of carrier detection for microscopic images that are derived from different stages of WWT, such as sewage mixtures and sludge.  
Formal analysis and data curation, S.L. and H.C.; software, R.L. and S.L.; methodology: S.L., H.C., R.L. and H.S.; validation, H.C., H.S. and C.H.; writing--original draft preparation, S.L. and H.C.; writing--review and editing, R.L., H.S. and R.S.; supervision, C.H. and R.S.; project administration, H.S. and R.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by Hunan 14 th Five-Year Plan Educational Science Research Project (No. XJK23 AJ)D022 and XJK23 AJ)D021), Changsha Science and Technology Key Project (No. kh2401027), Ministry of Education Industry-University Cooperation Collaborative Education Project (No. 220500643274437), National Natural Science Foundation of China (No. 62177046), Hunan Natural Science Foundation (No. 2023)J40772), Hunan Social Science Foundation (No. 22 YBA012), and the High Performance Computing Center of Central South University.  
Not applicable.  
Data Availability Statement: Due to confidentiality agreements with the company providing the data, they cannot be publicly disclosed. The dataset contains proprietary commercial information, and sharing it would compromise competitive advantages. Therefore, access is restricted to authorized personnel, and findings will be presented in aggregated or anonymized form.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Test Environments  
In this study, the TRM coupons were subjected to three different harsh conditions: chloride environment, high temperature and humidity environment, and sustained load.  
For the first test series, the TRM coupons were demolded one day after casting, and then cured for 27 more days in the laboratory condition (\(11\pm 2\)\({}^{\circ}\)C and 45% relative humidity), before being subjected to the chloride environment. Considering the ocean's seawater average salinity [40], a 3.5 wt.% sodium chloride (NaCl) distilled water solution was used (Figure 5). The TRM coupons were cured for 60 days in such chemical conditions. The TRM coupons were taken out of the sodium chloride solution three days prior to the direct tensile test.  
Figure 4: Illustrative details in the lap-spliced zone: (**a**) lap-spliced zone without surface treatment; (**b**) lap-spliced zone impregnated by epoxy; (**c**) lap-spliced zone coated with aluminum oxide after epoxy impregnation.  
For the second test series, the TRM coupons were prepared in a similar manner to the first test series. However, to create a high temperature and humidity environment, the TRM coupons were stored at a constant temperature of 50 \({}^{\circ}\)C and relative humidity of 95% in a chamber for 60 days (Figure 6). The TRM coupons were then taken out of the chamber two days prior to the fixing of the CFRP tabs onto the ends of the TRM coupons. Then, the direct tensile test was performed one day later.  
For the last test series, the TRM coupons were demolded one day after casting, and then adhered by CFRP tabs onto the ends. Next, the TRM coupons were cured for six more days in laboratory conditions, before being subjected to a sustained load (2.5 kN), as shown in Figure 7. The sustained load corresponded to 30% of the tensile strength at the seven-day aging. The last series was performed in the laboratory with the same conditions as those of the first two series. The TRM coupons were subjected to a sustained load for 60 days, and then the direct tensile test was performed.  
Figure 5: Test setup of TRM coupons subjected to the chloride environment: (**a**) scheme of the test setup of TRM coupons subjected to the chloride environment; (**b**) photo of test setup.  
Figure 6: Photo of the test setup of TRM coupons subjected to the high temperature and humidity environment.
## 3 Results and Discussion
### Correlation between Accumulated LAI and Ground Measurements  
The R\({}^{2}\) between the accumulated S2 LAI and AGB for all the plots is 0.85, which is similar to GPP (R\({}^{2}\) = 0.82) and NPP (R\({}^{2}\) = 0.83) (Figure 7); individual R\({}^{2}\) ranges from 0.59 to 0.97 for the six crop types (Figure 8) with decreased R\({}^{2}\) for soybean, oat and canola. Notably, the nonlinearity between corn GPP or NPP and AGB is not observed in Figure 8 but the opposite is observed for oat.  
Although AGB may be estimated at a similar accuracy by using accumulated S2 LAI for a specific crop type, we believe that GPP- and/or NPP-based approaches produced a reliable overall result for the whole study area. Here, BEPS performs on par with accumulated LAI in biomass estimation because: (1) the accumulated LAI already contains adequate seasonal information for biomass estimation; (2) the meteorological variables do not change much since the study area is small; and (3) certain management information (irrigation, fertilizer use, crop-specific parameters) is unavailable to BEPS. If the study area is big enough to cover different climate zones, we could expect that the BEPS-based estimates will explain more variation in AGB than the accumulated LAI.  
Figure 7: Relationship between crop accumulated leaf area index (LAI) and ground-measured above-ground biomass for six crop types (oat, soybean, wheat, corn, canola and black bean) for SMAPVEX16-MB.
## 3 Results and Discussion
### Percolation Tests
#### 3.2.3 Portland Cement  
Figure 7 and Table S5 in Supplementary Materials present the results obtained from the percolation tests using Portland cement alone and enriched with MWCNTs or montmorillonite, both dispersed with ultrasounds and the surfactant SDBS. The results showed that the use of Portland cement without any other additive is effective in the retention of all heavy metals studied, as was already expected [10; 19; 48; 49; 51]. This high capacity to immobilize heavy metals is related to the chemical fixation of the HMs to binder hydration products, as calcium silicate hydrated (CSH). Portland cement is mainly composed of silicates and calcium elements (Table 3). When the cement is hydrated, the calcium and silicates react with water forming calcium silicate hydrated (CSH). CSH is a cementitious gel, which tends to react with the heavy metals, immobilizing these on the binder matrix while releasing calcium ions [52]: Equation (1), where M\({}^{2+}\) represents the heavy metal ion and C\({}_{3}\)S\({}_{2}\)H\({}_{k}\) represents the calcium silicate hydrated.  
\[\mathrm{C_{3}S_{2}H_{3}+M^{2+}\ \to\ MC_{2}S_{2}H_{3}+Ca^{2+}} \tag{1}\]  
The combination of Portland cement with MWCNTs had almost no impact on the amount of HMs immobilized. The combination of Portland cement with montmorillonite has a slight negative effect, resulting in a slightly lesser adsorption of Ni\({}^{2+}\) and Zn\({}^{2+}\). This suggests that the CSH are reacting with the montmorillonite particles, preventing some reactions between CSH and the HMs to occur due to a smaller number of adsorption sites.  
From the results obtained from the percolation tests, it is possible to conclude that Portland cement in a small dosage (25 kg of cement per cubic meter of soil compared to the usual amount of 100 to 250 kg/m\({}^{3}\) used for stabilization purposes [54]), is very effective without the need of any other additive. The addition of MWCNTs to the binder results in an additional cost without any significant advantage,  
Figure 7: Results from the percolation tests. Immobilization of heavy metals by the soil, the soil with Portland cement and the soil with Portland cement with MWCNTs/montmorillonite.  
except the eventual increase of the mechanical properties of the soil [35; 66], while the addition of montmorillonite to the binder has a negative effect.
# Improving Traffic Load Distribution Fairness in Mobile Social Networks
## 5 Simulation and Discussion
### Simulation Results and Discussions
#### 5.2.2 SimBet vs. SimBet-TLDA
As described in Table 1, a lower friendship threshold means the influence of more central nodes is wider in their neighbourhood, and hence, many more less central neighbour nodes can increase their popularity and afterwards can become good relays. Moreover, the reduction in GINI index slightly impacts the delivery ratio, and SimBet-TLDA delivers messages to the destinations with a success rate as high as that of SimBet. However, as in Bubble-TLDA, the GINI index reduction in SimBet-TLDA increases the delivery time in both mobility cases. The explanation of this is similar to that given in the Bubble-TLDA before, as follows: when SimBet-TLDA successfully reduces the GINI index, some of traffic is diverted away from the shortest-paths (through hub nodes) on to the sub-optimal paths (via non-hub nodes); in turn, increasing the average delivery time. Finally, in terms of delivery cost performance, SimBet-TLDA performs as well as SimBet in both scenarios, i.e., SimBet-TLDA creates (redundant) message copies as many as SimBet in the network.
## 2 Wettability Measurement Methods
### Methods Based on Contact Angle Measurement  
#### 2.2.1 Visual Contact Angle Measurement Method  
Contact angle measurement techniques are widely used to determine the wettability of rock. A drop of liquid is applied to a cleaned and dried rock sample using a needle. The droplet lying on the surface of the sample is captured with a camera, and the software measures the angle between the droplet and the rock surface, as shown in Figure 2[14]. The contact angle value serves as a criterion for assessing the wettability, as shown in Table 3. A schematic illustration is provided in Figure 4[15].  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **USBM Wettability Index** \\ \hline water-wet & \textgreater{}0 \\ \hline mixed-wet & \textasci{}0 \\ \hline oil-wet & \textasci{}0 \\ \hline \end{tabular}
\end{table}
Table 2: Rock wettability and the USBM index.  
Figure 3: Separation test for materials: (**a**) oil-wet; (**b**) water-wet [11].  
The visual contact angle measurement method quickly and conveniently determines wettability at the surface but does not report information about wettability within the sample [16].
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 3 Control Scheme for PMSM  
The control objective is to design an asymptotically stable speed controller for PMSM to make the rotor speed track the reference trajectory correctly under different parameter perturbations and load torque disturbances. Therefore, the main signal error can be defined as,  
\[e_{\omega}=\omega^{*}-\omega_{r} \tag{7}\]  
where \(\omega^{*}\) is the desired rotor speed. If that the constant flow links have a linear relationship with the current in the stator; thereby its values can be estimate in an easy way. We can define the tracking error of the \(d\)-axis current as,  
\[e_{d}=i_{d}^{*}-i_{d} \tag{8}\]  
where \(i_{d}^{*}\) is the \(d\)-axis current reference value. In theory, the proposed controller forces the current \(i_{d}\) to have a value of zero in a finite time, so that the electric torque is proportional to current \(i_{q}\). Therefore, the current error in \(q\)-axis is defined as follows  
\[e_{q}=i_{q}^{*}-i_{q} \tag{9}\]  
where \(i_{q}^{*}\) is the \(q\)-axis current reference value. The proposed control system is based on a sinusoidal pulse width modulation (SPWM) scheme; \(m_{q}\) and \(m_{d}\) are the modulating signals in \(dq\) reference frame. The modulation signals are transformed to \(abc\) system using the Park transformation and rotor position \(\theta_{r}\), for comparison with high-frequency carrier signal. Figure 1 details the control scheme.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 3 High-Efficiency Mobile Positioning Method
### Pre-Deployment Stage
#### 3.1.2 Process  
Three steps comprise the mobile positioning method, as follows: (a) computation of the mean and standard deviation; (b) outlier filtering; and (c) location determination (see Figure 2).  
Mean and Standard Deviation Computation  
For computation of the center, this study uses Equations (1) and (2) to calculate the means and standard deviations of longitudes and uses Equations (3) and (4) to calculate the means and standard deviations of latitudes, respectively:  
\[\mu_{x,i}=\frac{\sum\limits_{j=1}^{r_{i}}x_{i,j}}{r_{i}} \tag{1}\]  
\[\sigma_{x,i}=\frac{\sum\limits_{j=1}^{r_{i}}\left(x_{i,j}-\mu_{x,i}\right)^{2} }{r_{i}-1} \tag{2}\]  
Figure 1: The distributions of longitudes and latitudes.  
Figure 2: The steps of the mobile positioning method.  
\[\mu_{y,i}=\frac{\sum\limits_{j=1}^{r_{i}}{y_{i,j}}}{r_{i}} \tag{3}\]  
\[\sigma_{y,i}=\frac{\sum\limits_{j=1}^{r_{i}}{\left({y_{i,j}-\mu_{y,i}}\right)^{2 }}}{r_{i}-1} \tag{4}\]  
This step performs an outlier filtering mechanism to clean historical CVO data to determine location information precisely. The mechanism uses a threshold \(t\) that is defined as an outlier filtering criterion. The value of \(t\) can be set according to standard deviations \(\sigma_{x,i}\) and \(\sigma_{y,i}\). The longitude of record \(x_{i,j}\) will be filtered out when it is smaller than \(\mu_{x,i}-t\) or larger than \(\mu_{x,i}+t\). Latitude records \(y_{i,j}\) can be filtered with the same threshold \(t\).  
Location Determination  
After outlier filtering, the means of the longitude and latitude of each record are calculated with Equations (5) and (6), respectively. This method determines the longitude and latitude of the \(i\)-th cell-RSSI pair as \(\mu_{x,i}^{{}^{\prime}}\) and \(\mu_{y,i}^{{}^{\prime}}\):  
\[{\mu_{x,i}}^{\prime}=\frac{\sum\limits_{j=1}^{r_{i}^{\prime}}{x_{i,j}}^{ \prime}}{r_{i}^{\prime}},\text{where }\mu_{x,i}-t<{x_{i,j}}^{\prime}<\mu_{x,i}+t \tag{5}\]  
\[{\mu_{y,i}}^{\prime}=\frac{\sum\limits_{j=1}^{r_{i}^{\prime}}{y_{i,j}}^{ \prime}}{r_{i}^{\prime}},\text{where }\mu_{y,i}-t<{y_{i,j}}^{\prime}<\mu_{y,i}+t \tag{6}\]  
In these steps, the means and standard deviations can be calculated using cloud computing (e.g., the MapReduce programming model). The key is the cell-RSSI pair, and the values are longitudes and latitudes. The MapReduce programming model can be implemented for center computation.
## 4 Conclusions  
This paper assesses the content of anthropogenic radionuclides in tundra landscapes of the subarctic zone of Russia in the Nenets Autonomous Okrug. It examines the accumulation and migration patterns of these radionuclides. The study identifies possible sources of environmental pollution by calculating the isotopic ratios of Sr-90/Cs-137, Pu-238/Pu-239+240, Pu-239+240/Cs-137, and Am-241/Pu-239+240, and by determining pollution trajectories using the HYSPLIT model. The study's findings revealed that the Nes River basin has a radionuclide Cs-137 and Sr-90 pollution density that is two to three times higher than its available data for the Kaninskaya tundra, as well as for Russia and the world in general. This confirms the existence of a region with an increased content of radionuclides. The isotope ratio calculations conducted for layers at depths of 10-20 cm and 0-10 cm indicate that some parts of the tundra, specifically the tundra lowlands, exhibit signs of technogenic radionuclide accumulation and subsequent vertical migration. Conversely, the slopes, plakor, and hilly regions have a greater level of lateral migration, leading to reduced anthropogenic radionuclide activity in peat. Calculation of isotopic ratios of anthropogenic radionuclides and the air mass trajectory using the HYSPLIT model have indicated that the primary sources of pollution pertaining to anthropogenic radionuclides are the global atmospheric fallout and the Chernobyl accident.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app132312952/s1](https://www.mdpi.com/article/10.3390/app132312952/s1)]([https://www.mdpi.com/article/10.3390/app132312952/s1](https://www.mdpi.com/article/10.3390/app132312952/s1)), Table S1: Activity and pollution density of anthropogenic radionuclides in the Nes River basin (Nenets Autonomous Okrug).  
A.P.: Conceptualization, Investigation, Methodology, Validation, Resources, Supervision, Project administration, Funding acquisition, Writing. E.Y.: Conceptualization, Investigation, Methodology, Validation, Formal analysis, Writing, Review & Editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Russian Science Foundation, number 22-27-20079.  
Not applicable.  
Data Availability StatementData is contained in the Supplementary Materials.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 5 Test Results
### Filtration Behavior of Silt
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Soil** & **Geotextile** & **Normal Stress (kPa)** & \(k_{\text{sg}}\) **(m/s)** & \(k_{\text{s}}\) **(m/s)** & \(k_{\text{R}}\) & \(\boldsymbol{GR}\) \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & & 0 & \(4.09\times 10^{-7}\) & \(4.18\times 10^{-7}\) & 1.02 & 1.02 \\  & & 5 & \(3.04\times 10^{-7}\) & \(3.49\times 10^{-7}\) & 1.15 & 1.15 \\  & GT1 & 10 & \(1.55\times 10^{-7}\) & \(2.14\times 10^{-7}\) & 1.38 & 1.38 \\  & & 15 & \(1.11\times 10^{-7}\) & \(1.61\times 10^{-7}\) & 1.45 & 1.45 \\  & & 25 & \(\boldsymbol{5.36\times 10^{-8}}\) & \(8.88\times 10^{-8}\) & 1.66 & 1.66 \\ \hline \multirow{8}{*}{\begin{tabular}{} \end{tabular} } & & 0 & \(3.54\times 10^{-7}\) & \(3.86\times 10^{-7}\) & 1.09 & 1.09 \\  & GT2 & 5 & \(2.49\times 10^{-7}\) & \(3.26\times 10^{-7}\) & 1.31 & 1.31 \\  & GT2 & 10 & \(1.78\times 10^{-7}\) & \(2.52\times 10^{-7}\) & 1.42 & 1.41 \\  & & 15 & \(1.10\times 10^{-7}\) & \(1.63\times 10^{-7}\) & 1.48 & 1.48 \\  & & 25 & \(\boldsymbol{6.22\times 10^{-8}}\) & \(1.10\times 10^{-7}\) & 1.77 & 1.77 \\ \hline \multirow{8}{*}{\
\begin{tabular}{} \end{tabular} } & & 0 & \(3.84\times 10^{-7}\) & \(4.27\times 10^{-7}\) & 1.11 & 1.11 \\  & & 5 & \(2.50\times 10^{-7}\) & \(2.73\times 10^{-7}\) & 1.09 & 1.39 \\  & GT3 & 10 & \(1.57\times 10^{-7}\) & \(2.30\times 10^{-7}\) & 1.46 & 1.46 \\  & & 15 & \(1.08\times 10^{-7}\) & \(1.72\times 10^{-7}\) & 1.58 & 1.58 \\  & & 25 & \(\boldsymbol{5.70\times 10^{-8}}\) & \(1.03\times 10^{-7}\) & 1.80 & 1.80 \\ \hline \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & & 0 & \(3.35\times 10^{-7}\) & \(4.10\times 10^{-7}\) & 1.22 & 1.22 \\  & & 5 & \(2.19\times 10^{-7}\) & \(3.16\times 10^{-7}\) & 1.44 & 1.44 \\ \cline{1-1}  & GT4 & 10 & \(1.47\times 10^{-7}\) & \(2.23\times 10^{-7}\) & 1.52 & 1.52 \\ \cline{1-1}  & & 15 & \(1.03\times 10^{-7}\) & \(1.74\times 10^{-7}\) & 1.69 & 1.69 \\ \cline{1-1}  & & 25 & \(\boldsymbol{5.09\times 10^{-8}}\) & \(9.85\times 10^{-8}\) & 1.93 & 1.93 \\ \hline \hline \end{tabular} Notes: Bold italic indicates that geotextile filters may be clogged, \(k_{\text{R}}=k_{\text{s}}/k_{\text{sg}}\), where the \(k_{\text{sg}}\) and \(k_{\text{s}}\) are the stabilized values at the end of the tests.  
\end{table} 
Table 8: Summary of the gradient ratio test results at 48 h performed on the silt-geotextile combination. Figure 6: _Cont._  
In the following discussions, typical results of test cases using medium and thick geotextiles (GT2, GT4) will be selected to further investigate the influence of fabric structure and protected soil characteristics on the filtration performance of geotextile filters under different normal stresses.
# A Theoretical Study on the Resilience Evaluation Method of Operational Road Tunnel Systems
## 1 Introduction  
The 2022 China statistical bulletin on the development of the transport industry [1] shows that China has built 24,850 road tunnels. In particular, the emergence of numerous subaqueous tunnels has led to more attention on the operation security of road tunnels in China [2]. The factors affecting road tunnel operation safety can be divided into external disturbance events and system self-functional status. For external disturbance events, transport infrastructures are facing a sudden increase in the frequency and intensity of extreme climate due to the change in global climate in the 21 st century. Excessive vehicles beyond the design consideration will greatly affect the traffic capability of road tunnels and increase operational security risks. The technical condition of long-term operating road tunnels is generally at a low level due to the occurrence of numerous structural diseases and low operational management capability. These tunnels exhibit poor resistance and recovery when facing the effects of external disturbance events. So, it is necessary to grasp the functional state of road tunnels and improve their operational ability to deal with external disturbances.  
The emergence of the concept of system resilience provides a new perspective for systems engineering to cope with external disturbances. The aim of this study was to build a theoretical evaluation framework for road tunnel resilience. The basic concept of road operating tunnel resilience will be discussed in detail, and an evaluation model will be proposed to describe the functional change in road tunnels that are subjected to external disturbances. Finally, one method based on functional networks will be established to evaluate the resilience of road tunnels. It is expected to analyze the critical function nodes and paths that affect road tunnel function under external disturbance conditions.
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
## 4 State Estimation Scenario
In particular, the three-phase active and reactive power are modeled as the functions of time \(P(t)\) and \(Q(t)\):  
\[P(t) = P_{0}(t)\cdot\left(\frac{V(t)}{V_{0}}\right)^{K_{pp}}\cdot\left[1+ K_{pf}\cdot(f(t)-f_{0})\right]\] \[Q(t) = Q_{0}(t)\cdot\left(\frac{V(t)}{V_{0}}\right)^{K_{pp}}\cdot\left[1 +K_{qf}\cdot(f(t)-f_{0})\right]\]  
where the voltage and frequency sensitivity parameters \(K_{pp}=1.7\), \(K_{pf}=1.0\), \(K_{qp}=2.6\), and \(K_{qf}=-1.7\) are based on typical values as inferred from the EPRI LOADSYN program. The instantaneous frequency \(f(t)\) and voltage \(V(t)\), as well as the rated profiles of active \(P_{0}(t)\) and reactive power \(Q(t)\) are updated with the same reporting rate of the PMUs, i.e., every 20 ms. In this way, by varying the rated profiles, it is possible to reproduce different dynamic scenarios. A constant profile results in a quasi-stationary condition, whereas profiles taken from real-world acquisitions can be employed to reproduce more challenging and dynamic conditions. In order to avoid recursive and diverging effects, the frequency and voltage measurements do not come from the PMUs. As shown in Figure 3, the frequency is measured by a Phase Locked Loop (PLL) followed by a moving average filter to reduce spurious oscillations, whereas the voltage is given by a Root Mean Square (RMS) operator. Both the moving average filter and the RMS operator consider observation intervals of 240 ms, that are suitably partially overlapped in order to reproduce a reporting rate of 50 fps.