esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple-Attribute Decision Making ELECTRE II Method under Bipolar Fuzzy Model
## 2 The BF-ELECTRE II Method
### Construction of Outranking Relations  
In this subsection, an outranking relationship is established on the basis of above computed concordance and discordance indices for every pair of alternatives. Firstly, the concordance and discordance threshold values or levels are determined by decision maker for the better evaluation of alternatives. The strong outranking relation \(R^{s}\) and weak outranking relation \(R^{w}\) are designated by analyzing these threshold levels with respect to the concordance and discordance indices.  
Let \(f^{*}\), \(f^{\circ}\) and \(f^{-}\) be three strictly decreasing concordance threshold values, say, high, average and low concordance levels, respectively, denoted as \(1>f^{*}>f^{\circ}>f^{-}>0\). In addition, let \(t^{*}\) and \(t^{\circ}\) be strictly increasing discordance threshold values, say, low and average discordance levels, respectively, that is \(0<t^{*}<t^{\circ}<1\). With these specifications, the alternative \(\mathcal{P}_{\alpha}\) strongly outranks the alternative \(\mathcal{P}_{\beta}\), that is \(\mathcal{P}_{\alpha}R^{s}\mathcal{P}_{\beta}\) if and only if one or both of the following sets of conditions hold:  
\[\left\{\begin{array}{l}f_{\alpha\beta}\geq f^{*}\\ t_{\alpha\beta}\leq t^{\circ}\\ f_{\alpha\beta}>f_{\beta\alpha}\end{array}\right.\quad\text{or}\quad\quad \left\{\begin{array}{l}f_{\alpha\beta}\geq f^{\circ}\\ t_{\alpha\beta}\leq t^{\circ}\\ f_{\alpha\beta}>f_{\beta\alpha}\end{array}\right. \tag{11}\]  
The alternative \(\mathcal{P}_{\alpha}\) weakly outranks the alternative \(\mathcal{P}_{\beta}\), that is \(\mathcal{P}_{\alpha}R^{w}\mathcal{P}_{\beta}\), if and only if the following conditions hold:  
\[\left\{\begin{array}{l}f_{\alpha\beta}\geq f^{-}\\ t_{\alpha\beta}\leq t^{*}\\ f_{\alpha\beta}>f_{\beta\alpha}\end{array}\right. \tag{12}\]
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 3 Network Model
### Selection of Cooperative Nodes  
Within the search region of cooperative nodes, \(k\) nodes are selected as the cooperative nodes for the next hop. In our study, the ideal location for next-hop reference node \(m\) to select cooperative nodes for the next-hop is defined as follows:  
**Definition 2** (Strategic location for RCR-selection).: _For a certain reference node \(m\), the strategic location for selecting its next-hop cooperative nodes is represented by \(f_{m}\), which is defined as a location on the straight line from \(m\) to the sink \(t\), where the distance between \(m\) and \(f_{m}\) is equal to \(R\), i.e., \(|mf_{m}|=R\)._  
By pure use of the location as a benchmark, the nodes located in the selection region whose distance to the strategic location is among the first \(k\) shortest value (the premise is to satisfy \(|mf_{m}|=R\)) will be selected as cooperative nodes of the next hop.  
In order to achieve load balancing of the whole network [28; 29], the residual energy of sensor nodes should be considered. We assume that every node starts with the same initial energy corresponding to the full battery capacity. The remaining battery capacities represented as current residual energy of the sensor nodes are discretized into integer-valued quantized-energy-levels (\(L_{\text{QE}}\)). The criterion based on joint distance and residual energy is defined as follows:  
**Definition 3**.: _For a certain node \(u\) in the search region of cooperative nodes, \(L_{\text{QE}}(u)\) represents its quantized-energy-level (residual energy) and \(|uf_{m}|\) represents the distance between \(u\) and strategic location. The priority to be a cooperative node for the next hop of \(u\) is denoted by \(Q_{u}\), which is defined by \(Q_{u}=\sqrt{(1-\frac{|uf_{m}|}{R})^{2}+(\frac{L_{\text{QE}}(u)}{L_{\text{max} }})^{2}}\), where \(L_{\text{max}}\) is the full battery level (the initial energy)._  
The closer the distance from \(u\) to \(f_{m}\) is, the larger the value of \(Q_{i}\) will be, whereas the higher \(L_{\text{QE}}(u)\) (the residual energy) of \(u\) is, the larger the value of \(Q_{i}\) will be. Therefore, the nodes within the first \(k\) largest \(Q_{i}\) will be selected as the cooperative nodes under this criterion.
## 4 Results
### Bibliometric Analysis
#### 4.1.3 Q3.  
What Are the Dominant and Emerging Research Themes Related to Digital TPM?  
Research on digital TPM shows a clear blend of traditional maintenance goals with new digital technologies. Our analysis of author keywords and their relationships, present in the Figure 5 below, highlights both established and emerging themes shaping the field today and in the future.  
Figure 4: Top 10 publication sources.  
At the center of the research landscape is the strong link between \"Total Productive Maintenance\" and \"Industry 4.0\". This connection shows that TPM is no longer seen just as a maintenance method, but also as a key part of smart manufacturing systems [6]. Keywords like \"predictive maintenance\", \"digital transformation\", \"operational performance improvement\", and \"management\" highlight a major focus on using digital tools to improve efficiency, reliability, and asset management. Terms like \"maintenance\", \"improvement\", \"quality\", and \"performance\" confirm that traditional TPM goals, such as reducing downtime and driving continuous improvement, remain essential but are now pursued in a digital context.  
Newer research areas are forming around advanced technologies. Keywords like \"IoT\", \"learning\", \"smart\", \"monitoring\", and \"system\" point to growing interest in connected, intelligent, and data-driven maintenance. This shift moves TPM from being reactive or proactive toward real-time and predictive models, enabled by artificial intelligence and smart sensors. Words like \"digital\", \"philosophy\", and \"4.0\" suggest that researchers are also exploring broader ideas, combining technical innovation with changes in management practices and organizational culture.  
As shown in the Figure 6 above, several main clusters of themes emerge from the keyword analysis. A strong core around \"Industry 4.0\", \"TPM\", \"predictive maintenance\", and \"digital transformation\" focuses on boosting performance and aligning maintenance with digital strategies. Another cluster deals with technological integration, where ideas like \"interoperability\", \"industrial maintenance\", and \"digital transformation\" show efforts to connect IT systems and physical operations. A third cluster, linked to \"lean maintenance\" and \"real-time monitoring\", reflects ongoing interest in applying lean methods to digital environments. Smaller but important topics such as \"association rules\", \"reliability\", \"maintainability\", and \"OEE\" suggest growing attention to data analysis and performance measurement.  
Looking ahead, the field is moving toward a full model of smart maintenance, with AI, cybersecurity, digital twins, and human-machine collaboration expected to play key roles. There is also more interest in adapting TPM 4.0 for specific applications, such as small businesses, airport logistics, and complex manufacturing, showing the wide reach of digital maintenance strategies.  
Figure 5: Word cloud of the author keywords.
## 2 Design of the Micromanipulation Device Using Piezoelectric Actuators
### Design of the Piezolectric Actuator-Based Robotic Manipulator  
The selected robotic arm is an electromechanical device consisting of three joints and their corresponding links made of aluminum, driven by direct current motors (a 90 RPM Precision Spur Gear Motor Servocity SKU 638238 for the first joint and 32 RPM Premium Planetary Gear Motor Servocity SKU 638290 for the other two joints). The proposed device includes motion sensors based on relative encoders (Relative, Quadrature, Magnetic Hall Effect, and 1,245.768 Countable Events Per Revolution) placed at the shaft extension of the motors. In addition, an individual encoder is fixed at each joint of the device. The proposed controller is implemented in a programmable microcontroller (Arduino Mega2556), which runs the dynamic evolution of gains and the control structure.  
The proposed robotic manipulator with a piezoelectric actuator is shown in Figure 1 with the three DOFs highlighted. The first DOF is found in the basement (\(\theta_{3}\)), exerting an axial movement. The other two degrees are at the vertical revolutes (\(\theta_{1}\) and \(\theta_{2}\)). This structure can support the gripper design, including the piezoelectric actuators.  
The mathematical model of the multibody robotic manipulator can be represented using the Euler-Lagrange equations and taking into account the effect of uncertainties and external perturbations, as follows:  
\[\frac{d^{2}}{dt^{2}}\theta_{t}{=}f\bigg{(}\frac{d}{dt}\theta_{t},\theta_{t} \bigg{)}+g(\theta_{t})\tau_{t}+\xi\bigg{(}\frac{d}{dt}\theta_{t},\theta_{t},t \bigg{)} \tag{1}\]  
The state vector \(\theta\in\mathbb{R}^{3}\), \(\theta=[\theta_{1},\theta_{2},\theta_{3}]^{\top}\), defines the set of joint coordinates for the robotic arm. Here, \(f:\mathbb{R}^{3}\times\mathbb{R}^{3}\rightarrow\mathbb{R}^{3}\) is the drift term of the robotic manipulator, while \(g:\mathbb{R}^{3}\rightarrow\mathbb{R}^{3\times 3}\) is the matrix that characterizes the input effect on the robotic dynamics.  
The term \(\xi:\mathbb{R}^{3}\times\mathbb{R}^{3}\times\mathbb{R}\rightarrow\mathbb{R}^{3}\) represents the effect of external perturbations and internal modeling uncertainties, which are assumed to belong to the set defined in Assumption 2.  
**Assumption 1**.: _The drift term of the robotic model defined by \(f(q_{b,t},q_{a,t})\) (with \(q_{b,t}=\dfrac{d}{dt}\theta_{t}\) and \(q_{a,t}=\theta_{t}\)) satisfies the inequality \(\left\|f(q_{b,t},q_{a,t})\right\|\leq L_{a}\|q_{a,t}\|^{2}+L_{b}\|q_{b,t}\|^{2}\) with \(L_{a}\) and \(L_{b}\) positive scalars._  
**Assumption 2**.: _The admissible class of mechanical perturbations \(\xi\) is defined by the following set (\(\xi\in\Psi_{\xi}\)):_  
\[\Psi_{g}=\left\{\xi:\mathbb{R}^{3}\times\mathbb{R}^{3}\times\mathbb{R} \rightarrow\mathbb{R}^{3}\mid\left\|\xi\right\|^{2}\leq\right.\]  
\[\left.\xi_{0}^{+}+\xi_{1}^{+}\|q\|^{2}+\xi_{2}^{+}\left\|\dfrac{d}{dt}q\right\| ^{2},\;\xi_{0}^{+},\xi_{1}^{+},\xi_{2}^{+}\in\mathbb{R}^{+}\right\} \tag{2}\]
## 5 Results and Discussion
### Distribution Characteristics of Plastic Zone  
The distribution of plastic zones is shown in Figure 16.  
Figure 16: Distribution map of plastic zones.  
Figure 15: Distribution of dissipated energy density.  
Regardless of the width of coal pillars, two primary forms of surrounding rock failure are observed along the roadway excavation. One is shear instability represented by both sides, and the other is a composite failure of tension and shear, represented by the shallow layer at the bottom and the immediate roof above the roadway.  
The mechanical properties of the surrounding rock are deteriorated due to water accumulation, leading to further expansion and development of disturbed plastic zones towards the inner side of the coal body, particularly in the vicinity of the goat side during excavation. When the coal pillar width is 6 m, there is no elastic core zone in the middle, and the plastic zone is completely connected. When the coal pillar width is 9 m, a stable elastic core zone can form inside, with the elastic core zone presenting as a rectangle with a width of 2 m. In this state, the coal pillar exhibits relatively high stability as a whole. When the width of the coal pillar exceeds 9 m, the influence of the goat on the plastic zone of the goat-side roadway diminishes, and the plastic zones become independent of each other, indicating a highly stable coal pillar at this stage.  
The development pattern of the plastic zone in the coal body with different coal pillar widths was obtained, as shown in Figure 17.  
When the width is 6 m, the plastic zone ratio is 79.1%. At this point, the elastic zone accounts for approximately one fifth of the overall area, indicating that the coal pillar has a certain bearing capacity. When the width is 9 m, the plastic zone ratio decreases to 53.1%. At this stage, the elastic zone and the plastic zone each account for about half of the area, indicating a relatively strong bearing capacity and stability of the coal pillar. When the width is 15 m, the plastic zone ratio is 31.6%. This marks the first inflection point of the plastic zone ratio. Prior to this point, the plastic zone undergoes significant changes as the coal pillar width increases, while afterward, it decreases.  
In summary, the failure modes of the goat-side roadway can be categorized into shear failure and tensile-shear composite failure. As the width increases, the influence of the water-bearing goat on the stability of the goat-side roadway gradually diminishes, and the plastic zone decreases accordingly. When the width is 9 m, a complete elastic zone forms within the coal pillar, occupying approximately one-fifth of the coal pillar's area. At this point, the coal pillar exhibits a strong bearing capacity and stability. However, when the width exceeds 15 m, the influence of the goat with water on the plastic zone becomes negligible. At this stage, the coal pillar is highly stable. Therefore, under these geological conditions, a coal pillar width of 9 m is deemed most appropriate.  
Figure 17: Development pattern of plastic zone.
## 3 Environmental Applications  
Seventy-six studies have been selected (Table 1). Nine studies proposed the application of two different methods or approaches for sample preparation. In many cases, the method was applied to various aqueous matrices. The average number of PPCPs detected for each method was forty-one. The average number of PPCPs included in the studies reviewed increased over time, which may be justified by the growing interest in using methods that include as many substances as possible in the same analysis. In the studies published in 2018, the average number of PPCPs included was 34; in 2019 the average was 38 and up to May 2020 it was 60. In the Figure 5, the different studies are classified according to the number of compounds detected. Forty-six percent of studies covered between 10 and 25 PPCPs. The second range (between 26 and 50) included 30% of studies. Fifteen percent showed a \(50<\) PPCPs \(<100\) range. Lastly, a small portion (9%) included more than 100 PPCPs. A few methods reported a contaminants list characterized by a multiclass of compounds, not only PPCPS, but pesticides, drugs, flame retardants, etc., too. For these cases, only the total number of PPCPs was considered.  
Different types of water were collected. The principal parameters monitored were temperature, pH, EC (electrical conductivity, \(\mu\)S cm\({}^{-1}\)), TDS (total dissolved solids, \(\mu\)g L\({}^{-1}\)), DO (dissolved oxygen, mg L\({}^{-1}\)), TSS (total suspended solids, mg L\({}^{-1}\)) and BOD (biochemical oxygen demand, mg L\({}^{-1}\)). Influent and effluent wastewater samples from hospitals or wastewater treatment plants (WWTPs) were the most analyzed (46% of studies). This matrix was marked by complexity due to the presence of numerous interferents. Some studies investigated the presence of PPCPs in raw wastewater at the treatment plant, some just the effluents, and some investigated both [35]. Most of these studies also studied the efficiency of the elimination of the PPCPs in the WWTPs [35]. All these studies identified the WWTP effluents as one source of PPCPs to the environment.  
In the second block of studies, different matrices were regrouped into a single group: surface water (SW). The 40% of works analyzed and studied in this group investigated a large spectrum of water sources: streams, rivers, estuaries, lakes, seas, ground water, and urban and agricultural storm waters. Drinking and tap water (DW) constituted the third group, with an occurrence of 12% in the studies selected. DW was regulated by \"The Drinking Water Directive 98/83/EC\" that supervises the quality of water (for human consumption). It provided a general framework and a minimum value of 48 specific parameters that must be monitored regularly [24].  
Lastly, two works included other two aqueous matrices: thermal and swimming pool water (SP). Chemicals in these matrices can come from different sources, such as bathers, who continuously release organic matter mainly through sweat and urine [25].
## 3 Results
### AceF Affects the Expression of the T3 SS Genes through RsmY and RsmZ  
Given the role of AceF in carbon metabolism, we suspected that CrcZ might be involved in the regulation of the T3 SS in AaceF. The CrcZ level in the \(\Delta\)aceF mutant was 1,  
Figure 2: AceF influences the T3 SS. (**A**) Cytotoxicity of wild-type PA14, the \(\Delta\)_aceF_ mutant, and the mutant complemented with an _aceF_ gene or overexpressing the _exsA_ gene. A549 cells were infected with the bacteria for 3 h (MOI = 50). The cytotoxicity was determined by measuring crystal violet-stained cells. (**B**) mRNA levels of the T3 SS genes. Bacteria were grown in the presence and absence of 5 mM EGTA for 4 h. The relative mRNA levels of _pcV_, _exsA_, _exsC_ and _exoU_ were determined by real-time PCR. Data shown represent mean \(\pm\) standard error of mean from three samples. * \(p\) < 0.05; **_p_ < 0.01 by ANOVA.  
which is 9-fold lower than that in the wild-type PA14 (Figure S2). Since CrcZ antagonists the function of Crc that positively regulates the T3 SS, the downregulation of CrcZ in the \(\Delta\)aceF mutant is unlikely to cause the defective T3 SS. We next examined the expression levels of RsmY and RsmZ. qRT-PCR revealed upregulation of RsmY and RsmZ in the \(\Delta\)aceF mutant (Figure 3A). By using transcriptional fusions between a lacZ reporter gene and the promoters of rsmY and rsmZ (PrsmY-lacZ and PrsmZ-lacZ), we found that the promoter activities of rsmY and rsmZ were enhanced in the \(\Delta\)aceF mutant (Figure 3B). To examine whether the upregulation of RsmY/Z leads to the repression of the T3 SS, we deleted rsmY and rsmZ in the \(\Delta\)aceF mutant, which restored the expression of exsA and exoU and bacterial cytotoxicity (Figure 4A,B). However, compared to the \(\Delta\)aceFArsmY/Z triple mutant, the pcrV mRNA levels were higher in the \(\Delta\)rsmY/Z mutant in the absence and presence of EGTA, and the mRNA level of exsC was higher in the \(\Delta\)rsmY/Z mutant in the presence of EGTA (Figure 4B). These results indicate that AceF might regulates the transcription or stabilities of pcrV and exsC through an RsmY/Z-independent mechanism. Collectively, these results demonstrate that AceF affects the T3 SS and cytotoxicity mainly through RsmY/Z.  
Figure 3: Mutation of the _aceF_ gene upregulates _rsmY_ and _rsmZ_. (**A**) The bacteria were grown in LB with or without 5 mM EGTA to an OD\({}_{600}\) of 1. The RNA levels of _rsmY_ and _rsmZ_ were determined by real-time PCR. (**B**) PA14, the \(\Delta\)aceF mutant and the complemented strain containing the P\({}_{rsmY}\)-_lacZ_ (**left**) and P\({}_{rsmZ}\)-_lacZ_ (**right**) transcriptional fusion were cultured in LB with or without 5 mM EGTA to an OD\({}_{600}\) of 1, followed by determination of the \(\beta\)-galactosidase activity. Data shown represent mean \(\pm\) standard error of mean from three samples. \({}^{*}\)\(p<0.05\); \({}^{**}\)\(p<0.01\) by ANOVA.
## 5 Experimental Results
### Analysis of Multi-Step Prediction Results for Entry and Exit Flow  
The predictive performance of the model was also validated for multi-step metro flow prediction. Figure 5 illustrates the multi-step prediction results for the next eight steps (2 h). Compared with the baseline method, the proposed method is more accurate over a longer prediction period. Specifically, compared with the LSTM model, the mean values of MFP-EP + F1 + F2 + S1 + S2 for RMSE, MAE, and MAPE were reduced by 15%, 24.63%, and 34.02%, respectively, when the predicted step size is 2, and the proposed method was reduced by 6.5%, 7.26%, and 34.70%, respectively, compared with the mean of the baseline method when the predicted step size was 8.  
The prediction error increased with the prediction step size. There are two main reasons for this. Firstly, compared with single-step prediction, the error accumulates in the process of multi-step prediction. The larger the number of prediction steps, the greater the accumulated error. Secondly, with the increase in time steps, the traffic environment gradually becomes complex, and the nonlinearity and fluctuation of the flow data in and out of the station gradually increase.  
Compared with deep learning methods such as LSTM, the performance advantage of the proposed method in multi-step prediction is more obvious. This is not only due to the fact that the self-attention mechanism of the transformer model can capture more information in the time series prediction, but also because the model considers the temperature, the  
Figure 4: Attention matrices generated by the transformer framework. (**a**) Time interval: 8:45–9:00. (**b**) Time interval: 6:45–9:00.  
precipitation, the flow of the correlated stations, and the flow of different time granularities. The error accumulation rate of the method in multi-step prediction is much lower than that of methods such as LSTM.  
Figure 5: _Cont._  
Figure 5: Results of multi-step metro flow prediction using different methods. (**a**) MAPE of entry of Zhonglou station. (**b**) RMSE of entry of Zhonglou station. (**c**) MAE of entry of Zhonglou station. (**d**) RMSE of exit of Zhonglou station. (**e**) MAPE of exit of Zhonglou station. (**f**) MAE of exit of Zhonglou station. (**g**) RMSE of entry of Hangtiancheng station. (**h**) MAPE of entry of Hangtiancheng station. (**i**) MAE of entry of Hangtiancheng station. (**j**) RMSE of exit of Hangtiancheng station. (**k**) MAPE of exit of Hangtiancheng station. (**l**) MAE of exit of Hangtiancheng station.