esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 7 Conclusions and Discussion  
### Summary of Major Research Achievements  
This comprehensive review examines the current status and development trends of ETC data-based urban traffic state sensing and analysis technologies. The major achievements of this field are reflected in several key aspects:  
1. The unique value and technical advantages of ETC data in traffic sensing have been clearly established. ETC systems possess distinctive characteristics, including high vehicle identity recognition accuracy, extensive spatiotemporal coverage, and stable data quality, providing crucial data support foundations for refined urban traffic management. Compared with traditional traffic detection methods, ETC data demonstrates clear advantages in coverage scope, collection precision, and cost-effectiveness.
2. A relatively comprehensive technical system framework has been established. Significant progress has been achieved in key technical components, including data processing, feature extraction, state assessment, and multi-source fusion, forming a multi-level sensing and analysis system from microscopic vehicle behavior to macroscopic network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy.
3. The practical effectiveness of typical application scenarios has been validated. Practices in real-time monitoring and signal control optimization, traffic prediction, and artificial intelligence technology applications, and the transformation of highway technology to urban applications demonstrate that ETC-based traffic management can enhance management efficiency and service levels, providing robust support for smart city transportation system construction.
### Analytical Methods  
Service Life Prediction  
To analyze the service life (SL) of the structural elements in concrete, the methodology chosen focused on the estimation of the advancement of ion chloride into the concrete reinforcement and the observance of critical conditions in the armor. The choice was based on the location of the bridge: an environment with a high exposure to salt fog, and because chlorides are responsible for the most aggressive phenomenon related to the process of corrosion of the armors, producing a point corrosion, also known as pitting corrosion, as described by [PERSON] [35] and [PERSON] [45].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 9: Durability tests on concrete samples.  
Using the chloride diffusion coefficients (Dns) and adopting the [PERSON] second law of diffusion in the non-steady state, it was possible to generate a graph of concrete service life versus chloride penetration depth ([PERSON] [34] and [PERSON] [45]). Equation (1) was developed using the second law of [PERSON]:  
\[\begin{array}{c}P_{Cl}=2(z)\sqrt{Dt}\\ erf(z)=1-\frac{C_{cl}-C_{0}}{C_{s}-C_{0}}\end{array} \tag{1}\]  
where \(D\) is the diffusion chloride coefficient (cm\({}^{2}\)/year), \(t\) is the service life in years, erf is the Gauss error function (z), \(P_{Cl}\) is the chloride penetration depth in which the chloride concentration reached a critical level for depassivation of reinforcement (cm), \(C_{0}\) is the initial chloride concentration inside the specimens (mass, %), \(C_{s}\) is the chloride concentration on the surface of the specimens (%), and \(C_{Cl}\) is the concentration of chlorides in the depth \(P_{Cl}\) and time \(t\) (%).  
To generate the graph, some parameters were fixated. The upper limit considered for the depassivation of the reinforcement was 0.4%, related to the cement mass. This value is the only limit of the reinforced concrete according to the International Federation of Structural Concrete (CEB [46]), and it is the average value between the Brazilian norm (ABNT NBR 6118 [47]) of 0.5% and the American norm (ACI-318 [48]) of 0.3%. The chloride concentration adopted in the surface of the concrete (\(C_{s}\)) was 0.6%. [PERSON] et al. [49] evaluated concrete structures with an age over 15 years and different distances from the sea and obtained values around \(C_{s}=0.6\%\) for up to 630 m. Additionally, [PERSON] et al. [50] suggested 0.6% for a distance from the sea between 680 and 5000 m. Hence, the value adopted in the present study was consistent with the literature, admitting variations for the current application.
## 10 Conclusions  
A recently expanded database of production rates in mangrove forests strengthens the notion that mangrove forests per unit area are among the most productive ecosystems in the world. The revised mean rates of the mangrove, microalgal, and macroalgal GPP are 5.4, 1.5, and 17.4 g C m\({}^{-2}\) d\({}^{-1}\), respectively. The mangrove ANPP averages 3.9 g C m\({}^{-2}\) d\({}^{-1}\) as estimated by summing the biomass increments + litterfall, although both the GPP and ANPP rates vary greatly between methods and biogeographical regions. Only broad-leaved evergreen forests have greater rates of GPP. The mangrove GPP correlates positively with annual rainfall and temperature, and the ANPP correlates positively with rainfall and inversely with porewater salinity. Litterfall equates to only 55% of the NPP. The wood production averages 1.2 g C m\({}^{-2}\) d\({}^{-1}\), best fitting a non-linear peak regression with peak production rates between 5 and 12 y. The mean fine root production averages 1.0 g C m\({}^{-2}\) d\({}^{-1}\), being highest in deltas and estuaries and lowest in open-coast and carbonate settings. The total mangrove NPP ranges from 11.0 to 21.1 Mg ha\({}^{-1}\) y\({}^{-1}\) with the global mangrove NPP ranging from 143.0 to 383.4 Tg C y\({}^{-1}\). The mangrove GPP is forecast to increase to 2100 due to rising CO\({}_{2}\) levels which have a fertilizing effect.  
A revised mangrove carbon mass balance shows that the mangrove primary production and sediment DIC production are the largest and roughly equal ecosystem inputs. Canopy respiration and dissolved carbon exports are the largest ecosystem losses. Overall, the model is in balance but may become unbalanced with the increasing impacts of climate change.  
Globally, mangroves contribute 8% to 28% of the total blue carbon flow among low-latitude coastal ecosystems, which is disproportionate to their small area (6%). Macroalgae and tidal flats had the greatest ratio of P\({}_{\rm GPP}\)/R\({}_{\rm E}\), although all blue carbon ecosystems are currently net autotrophic.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/f16050747/s1](https://www.mdpi.com/article/10.3390/f16050747/s1)]([https://www.mdpi.com/article/10.3390/f16050747/s1](https://www.mdpi.com/article/10.3390/f16050747/s1)). Figure S1. Diagram of PRISMA protocol used in the meta-analysis.  
This research received no external funding.  
All data used in this study can be found in the data repositories of the referenced publications listed below. The raw data supporting the conclusions of this article will be made available by the author on request.  
The author declares no conflicts of interest.
# Analysis of Coating Loss from Coated Stainless Steel Orthodontic Wire
###### Abstract  
The leaching of metal ions from orthodontic appliances into the oral cavity is problematic for metal-allergic patients. Non-metal orthodontic appliances, such as plastic and ceramic brackets and elastomeric materials, have been clinically used and are effective for treatment of metal-allergic patients. However, only a few types of orthodontic wire are available for use in patients with metal allergies, thus restricting the establishment of orthodontic force systems in these patients. Wire surfaces can be coated with various substances to prevent leaching of metal ions. However, detachment of the coating may occur during orthodontic procedures, resulting in metal ion leaching from the substrate material. This study was performed to examine the resilience of various types of coated stainless steel wire to low pH, bending, and brushing. Wire surface characteristics were examined by stereomicroscopy and scanning electron microscopy. The degree of coating detachment due to bending of the wire was quantified by determining the change in the amount of metal ions eluted from the site of the bend immersed in hydrochloric acid. Exposure of the substrate metal by brushing was investigated by energy dispersive spectrometry. The results indicated that polyethylene naphthalene (PEN)-coated wire is resilient to bending, while gold (Au)-coated and titanium nitride (TIN)-coated wires are resilient to brushing.  
coated wire; coating loss; metal allergy Article
# Approximate Sensory Data Collection: A Survey
## 2 Model-Based Approximate Data Collection Algorithms  
One of the earliest pieces of research on approximate data collection algorithms is based on the mathematical models. Usually, the algorithms belonging to this category mainly consist of the following steps.  
Firstly, a mathematical model is selected to describe the correlations among sensory data, and the parameters of the chosen mathematical model are learned from the history sensory data.  
Secondly, the local prediction models are built according to all parameters obtained in the step above. Meanwhile, all the parameters of the local models are transmitted to the sink for constructing the global prediction model.  
Finally, when the local and global models have already been constructed, a newly arrived sensory value does not need to be transmitted if it can be predicted by the local model. Only the sensory values that cannot be predicted by the local model are required to be transmitted to the sink, and the sink node will use the global model to estimate the sensory values that are filtered by the local models.  
The main challenges of such approximate data collection methods are how to construct and maintain the local and global models in order to make them be valid all the time. Meanwhile, the main advantage of these methods is that they only transmit a partial amount of sensory data to the sink node since a number of sensory values can be predicted by the models. The detailed information about the algorithms belonging to this category is as follows.
## 4 Frequency Response Function (FRF)
### FRF of the Beam Bridge Model with Continuously Distributed Mass (CDM)  
The equation of a bending line, describing the amplitude of the steady-state forced vibration of a single supported beam, has the shape:  
\[v(x)=C_{1}\cdot\cosh\Bigl{(}\lambda\frac{x}{l}\Bigr{)}+C_{2}\cdot\sinh\Bigl{(} \lambda\frac{x}{l}\Bigr{)}+C_{3}\cdot\cos\Bigl{(}\lambda\frac{x}{l}\Bigr{)}+C_ {4}\cdot\sin\Bigl{(}\lambda\frac{x}{l}\Bigr{)}. \tag{36}\]  
In the case of kinematic excitation at the right support point according to Equation (37):  
\[v_{bk}(t)=v_{bs}\cdot\sin(\omega\cdot t)=1\cdot\sin(\omega\cdot t). \tag{37}\]  
The integration constants have the following form:  
\[C_{1}=0,\;\;C_{2}=\frac{1}{2\cdot\sinh(\lambda)}\cdot v_{bs},\;\;C_{3}=0,\;\;C _{4}=\frac{1}{2\cdot\sin(\lambda)}\cdot v_{bs}. \tag{38}\]  
Then, it is possible to rewrite Equation (36) into the form:  
\[v(x,f)=\frac{1}{2\cdot\sinh(\lambda)}\cdot v_{bs}\cdot\sinh\Bigl{(}\lambda \frac{x}{l}\Bigr{)}+\frac{1}{2\cdot\sin(\lambda)}\cdot v_{bs}\cdot\sin\Bigl{(} \lambda\frac{x}{l}\Bigr{)}. \tag{39}\]  
Equation (39) depends not only on the \(x\)-coordinate, but also on the frequency \(f\). Let us introduce FRF as:  
\[\overline{v}\equiv\overline{v}(x,f)=\frac{v(x,f)}{v_{bs}}. \tag{40}\]  
Figure 8: Amplitude and phase characteristics of FRF at point 1, with 3 DOF.  
After dividing Equation (39) by the value \(v_{bs}\), we can write:  
\[\overline{v}=\frac{1}{2\cdot\sinh(\lambda)}\cdot\sinh\Bigl{(}\lambda\frac{x}{l} \Bigr{)}+\frac{1}{2\cdot\sin(\lambda)}\cdot\sin\Bigl{(}\lambda\frac{x}{l} \Bigr{)}. \tag{41}\]  
We will enumerate the values \(\overline{v}\) points \(x=l/4\), \(x=l/2\), and \(x=3l/4\), which are for the dimensionless coordinates \(x/l=0.25\), \(x/l=0.5\), and \(x/l=0.75\). The calculations will be performed in the frequency band \(f=0\div 40\) Hz with a step \(\Delta f=0.01\) Hz, \(f=\omega/(2\pi)\). The observed amplitude and phase characteristics for point 1 are plotted in Figure 9, where \((\mid\mathrm{FRF}_{1}\mid\equiv\mid\overline{v}\;(l/4)\mid\) if \(\mathrm{FRF}_{1}\equiv\varphi_{1})\). The functions \(\overline{v}\) have discontinuity points in places of natural frequencies \(f_{(1)}=4.0401\) Hz, \(f_{(2)}=16.1605\) Hz, and \(f_{(3)}=36.3612\). Theoretically, they reach infinitely large values here.
# Preparation of Modified Colloidal Gas Aphrons and Analysis of the Oil Displacement Effect
## 3 Results and Discussion
### The Performance of the Modified CGA Foam Flooding
Compared with the asphaltene content in the mixed crude oil of 4.87%, the changes in asphaltene content in the oil recovered by water flooding, CGA flooding, and modified CGA flooding were \(-\)4.31%, \(-\)8.83%, and \(+\)12.53%, respectively. As the modifier is a crude oil fraction, which is miscible with crude oil, the modified CGA system formed by adding GXJ to CGAs as a modifier clearly reduced the interfacial tension and played a role in dissolving the heavy components in crude oil. The experimental results showed that the modified CGA system had a good oil displacement effect. On the one hand, it reduced the oil-water interfacial tension, while, on the other hand, it had an elution effect on the asphaltenes deposited in the pores. That is, in the process of CGA flooding, small foam particles firstly entered the large pore channels, and the subsequent displacement fluid was forced to enter the small pore channels due to the foam resistance to displace the oil that could not be reached in water flooding, which enlarged the influenced volume. Compared with conventional CGAs, the modified CGAs reduced the interfacial tension between oil and foam and exerted a certain elution effect on the asphaltenes in crude oil. An emulsified oil zone was more readily formed with a notable oil displacement effect.
# Mechanical Performance of Built-Up Columns
## 2 Experimental Analysis
### Test Setup and Procedure  
The specimens were loaded horizontally by a testing equipment as shown in Figure 2. The specimens were fixed together with the loading frame through the stay bolts and fixed together with the loading plate through bolts. The horizontality of specimens was calibrated by a levelling rod. This loading equipment ensured the axial loading to the maximum extent possible. This was mainly because, the non-axial displacements of cylindrical steel block used to transfer the load were limited by two steel plates with openings. In addition, the cylindrical steel block had a slot along the generatrix, and the bulge at the edge of the openings of two steel plates could just fit into the slot. Therefore, the rotational displacement of the cylindrical steel block was also limited.  
As shown in Figure 2, the load was applied by a hydraulic jack of 500 kN capacity and measured by a load cell of 500 kN capacity. The measure devices were shown in Figure 3. Two Linear Variable Displacement Transducer (LVDT) (D1 and D2) were used to measure the axial shortening of the specimens along y-axis. Eight (LVDT) transducers (Dv1-Dv4 and Dh1-Dh4) were placed at the flange and web of four tubes at half the height of the column to measure the lateral displacement. In addition, the lateral displacement along z-axis at quarter and three quarter of the length of the column was measured by the LVDT Dv1/4 and Dv3/4. Sixteen strain gauges (T1-1-T4-4) were pasted on the four sides of the four tubes at mid-height and four strain gauges (B1-B4) were glued at the web of the connectors. A data acquisition system was adopted to record the applied load and readings of the LVDTs and strain gauges. The load increment was 5 kN/5 min until the column buckled. In the post-buckling range, the compression was increased with displacement control in a rate of 0.03 mm/s. Prior to testing, a preload was applied to specimens to make full contact with the loading frame.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Specimen** & **Length (mm)** & **S \({}^{1}\) (mm)** & **W \({}^{2}\) (mm)** & **Section Form of** & **Transverse** \\  & & & & & **Batten Plate** & **Diaphragm** \\ \hline S1 & L3000-S700-W200 & 3000 & 700 & 200 & channel & no \\ S2 & L3000-S700-W110 & 3000 & 700 & 110 & channel & no \\ S3 & L3000-S700-W200-Strip & 3000 & 700 & 200 & strip & no \\ S4 & L3000-S700-W200-X & 3000 & 700 & 200 & channel & yes \\ \hline \hline \end{tabular}  
* **S**: Spacing between connectors. \({}^{2}\) W: Spacing between tubes.  
\end{table}
Table 1: Details of specimens.  
Figure 2: Loading devices.
## 4 Results
### Integrating the Complexity Index into Point Estimation  
According to the methods described in Stage 3, a complexity index was introduced and integrated into the high-performance contingency prediction model: the ANN. The model's performance was then further evaluated to determine whether significant improvements were obtained, meeting Objective 2.  
Integrating the complexity index into the ANN model markedly improved the performance of the contingency prediction model. The efficacy of the models with and without the complexity index was evaluated with \(R^{2}\) values. As detailed in Table 6 and depicted in Figure 2, the integration of the complexity index significantly improved the model's performance, shown by an increase in the \(R^{2}\) value from 0.808 to 0.809. In addition, the MAE decreased from 93 to 68, the MSE decreased from 21,236 to 11,080, and the RMSE decreased from 145 to 105. Figure 2 illustrates that the predicted values align more closely with the actual values after the complexity index was integrated. These results demonstrate that incorporating a complexity index improves predictions of cost contingency compared to models that rely solely on project characteristics.  
Further analysis was conducted to ascertain the importance of individual features to the ANN model's predictive performance, utilizing permutation feature importance as defined in Equation (6). The analyses presented in Table 6 and Figure 3 indicate the significant role of the complexity index in improving the accuracy of the contingency prediction model. As Table 7 shows, the three most influential features affecting the cost prediction were approved PE, complexity index, and contract contingency. These findings align with the existing research, emphasizing the vital role of cost estimates [35] and contract contingencies [64] in project cost predictions. The results also emphasize the substantial impact of project complexity on contingency models, reinforcing the value of integrating a complexity index into future predictive models to improve their estimation accuracy. Furthermore, construction time-related variables, particularly the project start year, significantly influenced cost contingency predictions, while project type ranked fifth in importance (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Prediction Target** & **Metrics** & **Without Complexity Index** & **With Complexity Index** \\ \hline \multirow{4}{*}{Materialized cost contingency} & \(R^{2}\) & 0.808 & 0.889 \\  & MAE & 93 & 68 \\  & MSE & 21,236 & 11,080 \\  & RMSE & 145 & 105 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of ANN point estimation: before and after integrating complexity index.  
Figure 2: Comparison of testing results for cost and contingency prediction: before and after integrating complexity index. (**a**) Prediction of materialized cost contingency before integrating complexity index. (**b**) Prediction of materialized cost contingency after integrating complexity index.
## 4 Access Control Policy Conflict Detection and Resolution
### Policy Conflict Detection Based on TSABAC  
Access control policy conflicts often occur when a subject is granted two conflicting operation permissions at the same time. According to the entities causing conflicts, access control policy conflicts can be divided into several categories [19]. For any compatible policies _pol1_ and _pol2_, where _pol1_ = (_sa1_, _oa1_, _con1_, _op1_, _eff1_) and _pol2_ = (_sa2_, _oa2_, _con2_, _op2_, _eff2_), there is  
**A. Model conflict**. Model conflict occurs when an access control policy has a constant which is contradictory to the principle of access control model. For example, if _pol1_ is regarded as the MAC model but violates the principle of one-way information flow, a conflict occurs.
# Spiral Mobility Based on Optimized Clustering for Optimal Data Extraction in WSNs
###### Abstract  
Wireless Sensor Networks (WSNs) have led to tremendous growth in the development of sensor technology and offer numerous applications, such as wildlife monitoring, environment, healthcare, military surveillance and security systems. In terms of performance, the evolutions of WSN routing protocols play a vital role in extending the lifetime of networking operations. Due to the limited resources of power in sensor nodes, the design and implementation of an energy-efficient routing protocol comprise a key challenge for researchers. Before, in the case of a static sink, nodes at farther areas from the sink transmit their sensed data at a longer transmission range and die early, while nearby nodes face lesser transmission costs and stay alive for a longer period. This unbalanced energy distribution creates energy holes in ares far from the sink in WSNs. Mobility-based sink routing protocols are proposed to minimize the sub-optimal energy consumption in WSNs, as the sink mobility covers the sensor field, which alleviates the overall load-balancing among sensor nodes. In order to overcome the energy hole issue while prolonging the network lifetime, it is important to determine the optimal mobility pattern. In this regard, we propose the Spiral Mobility based on Optimized Clustering (SMOC) routing protocol and the Multiple sink-based SMOC (M-SMOC) routing protocol for large-scale WSNs. Performance evaluations of the proposed protocols are compared with various existing routing protocols based on static sink, random mobility and grid mobility. Different numbers of performance parameters are considered for evaluation, such as network lifetime, network stability, packet drop ratio, packet delivery ratio, end-to-end delay, average energy consumption, network connection time and the impact of different heterogeneity levels. Experimental results show the benefits of the spiral mobility pattern and how it improves the network lifetime and stability period over the existing state-of-the-art routing protocols.  
wireless sensor networks; sink mobility; optimal location; mobility patterns; energy efficient +
Footnote †: journal: _technologies_