esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background of Microgrids
This paradigm shift allows end users to become \"prosumers\", acting as both consumers and producers of electricity for the grid. Different types of MGs, including DC and AC systems, as well as hybrid configurations, have been proposed for various applications. Islanded MGs find applications in sectors such as transportation, automotive, shipping, and rural regions, providing localized energy generation. Power electronic converters, specifically voltage source inverters (VSIs), often serve as interfaces between prime movers and MGs, allowing parallel connection to these converters. To ensure efficient power sharing without the need for extensive communication, the droop control method is frequently employed to eliminate circulating currents among the converters. This method involves simulating virtual inertias by adjusting the frequency and amplitude of each module based on proportional deductions from the output average active and reactive powers, utilizing control loops known as P-\(\omega\) and Q-V droops. While the droop control approach offers high reliability and flexibility, it has certain limitations that restrict its usability. For instance, traditional droop control is not suitable for interconnected systems sharing nonlinear loads, as it fails to account for harmonic currents and balance active and reactive power. Consequently, current-sharing methods that minimize circulating distortion power by addressing harmonic content have been proposed. These methods involve trade-offs by adjusting voltage to enhance current sharing accuracy. Recently, novel control loops incorporating output virtual reactors or resistors have been integrated into the droop approach, allowing for harmonic current content sharing through adjustments in output impedance [32; 33; 34]. Several research studies examine the control of individual MGs, where the different proposed control approaches such as predictive control, neural-network-based control, the sliding mode control, and Ho control are the most widely used robust control methods [35; 36; 37; 38; 39]. However, MG control has gained greater attention recently due to increased interest in the community MG concept. According to the literature survey, the community MG control approach can be classified as master-slave [40; 41; 42], peer-to-peer (P2P) [43; 44], and hierarchical control [45; 46; 47; 34]. This section discusses the overall definition and classification of the MG concept as it evolved over time. Following that, the benefits provided by MGs as well as the challenges in the MG evaluation are thoroughly discussed.
## 4 Conclusions  
MILs are superior to other MOFs for microextraction purposes due to their unique features like high surface-to-volume ratio, porosity, lower cost, and excellent chemical/mechanical stability. In this research, MIL-101(Cr) was enfolded into PPy layers during the electropolymerization of pyrrole on the surface of a stainless-steel wire. The coated fiber was applied as a novel and efficient sorbent for HS-SPME-GC sampling and quantification of MTBE in solid samples, without any sample pretreatment step. A comparison of figures of merit of PPy@MIL-101(Cr) nanocomposite with commercial SPME fibers demonstrated its superiority for precise and accurate determination of MTBE in contaminated soil samples. The proposed HS-SPME-GC method is solvent-free, fast, and simple in comparison with international standards. In this procedure, MTBE content of contaminated soil samples can be analyzed directly, without further sample preparation, and so there is no possibility of analyte loss. For filed analysis, complicated soil samples can be collected in SPME vials, transferred to laboratory (without considering and adjusting of the environmental parameters), and directly subjected to GC analysis. Unlike other methods, there are no limitations in terms of sample storage conditions and time. The developed procedure showed acceptable analytical performances compared to some of the most important similar methods that are reported in the literature. It was successfully applied for measurement of MTBE in contaminated soil samples.  
**Author Contributions:** Conceptualization, A.G. and J.D.; Methodology, J.D.; Formal Analysis, J.D.; Investigation, A.G. and J.D.; Writing-Original Draft Preparation, J.D.; Writing-Review & Editing, A.G.; Supervision, A.G.; Project Administration, A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 4 Performance Evaluation  
Regarding research question Q3, in this section, we compare the performance of the proposed method with other dynamic graph-based methods for detecting traffic incidents in a realistic road network.
# Chloride Resistance of Assembled Bridge Piers Reinforced with Epoxy-Coated Steel Bars
## 3 Results and Discussion
### Corrosion Current Density (\(i_{corr}\))  
As illustrated in Figure 10, the corrosion current density curves of all steel bars generally exhibit a distinct upward trend as the electromigration time increases, indicating that the corrosion rate of the steel bars is gradually accelerating. The corrosion current density curves for various steel bars, whether at Joint 1 or Joint 2, exhibit a descending order from highest to lowest: scratched > bent > knocked > untreated.  
Figure 9: Damage area ratio of epoxy coating.  
The corrosion rate of scratched steel bars is significantly higher than the others, suggesting that as the damage to the epoxy coating increases, the corrosion rate of the steel bar also tends to rise, with this phenomenon being more pronounced at Joint 1. This observation is consistent with the aforementioned rules [21] related to self-corrosion potential. It is considered that corrosion of steel bars begins when the corrosion current density exceeds 1 \(\mu\)A/cm\({}^{2}\)[22]. In Joint 1, steel bars treated with scratching began to corrode as early as 7 days of electromigration, and subsequently, their corrosion current density continued to rise. In contrast, the remaining three types of steel bars maintained a corrosion current density below 1 \(\mu\)A/cm\({}^{2}\) until 42 days of electromigration, displaying stable fluctuations. This suggests that the passive film on the surface of the steel bars remains intact, and its substantial resistance contributes to a lower corrosion current density. After 42 days of electromigration, the bent steel bars showed a noticeable increase in corrosion current density, indicating a compromise in the passive film that initiated corrosion. Steel bars subjected to knocking and those untreated began to corrode around the 63 rd to 70 th day, but their corrosion rate remained relatively low until the 84 th day. At the same electromigration times, the corrosion current densities of all steel bars in Joint 2 were higher than those in Joint 1. This means that the grouting interface in Joint 2 was less resistant to chloride ions than the post-cast strip interface in Joint 1. Chloride ions reached the steel bar surface more quickly and accumulated in higher concentrations within the same specimens, leading to earlier and more rapid corrosion of the steel bars. In Joint 2, steel bars treated with scratching and bending began to corrode after 7 days and 14 days of electromigration, respectively, and their corrosion current densities exceeded 10 \(\mu\)A/cm\({}^{2}\) at the 60 th and 75 th day, respectively, indicating a rapid corrosion state. Knocked and untreated steel bars also began to corrode within the 28 th to 35 th day interval.
## 2 Site Description and Test Shafts Details  
The field tests were conducted in Tai'an, China. All load tests were carried out at the same location. The geology of this area mainly consists of silty clay and coarse sand layers. Sufficient boreholes were sunk to obtain details of the soil layers distributions. Detailed soil profiles and corresponding physical characteristics along the axis of each test shaft can be obtained from Figure 2 and Table 1, respectively. As shown in Table 1, \(\omega\) is the moisture content, \(\gamma\) is the unit weight of soil, \(c\) and \(\varphi\) are cohesion and internal friction angle of soil, respectively. Sample preparation, test equipment, and test methods were all in accordance with Chinese standard GB/T 50123-1999 [24].  
Test shafts TS2, TS3 and TS4 were combined tip-and-side post grouted shafts, the ungrouted drilled shaft (TS1) was load-tested in the same site to quantify the improvement of shaft capacity after grouting. The bi-directional static test and top-down load test methods were adopted to determine the bearing capacity of shafts TS1, TS2 and TS3, TS4,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multirow{2}{*}{
\begin{tabular}{c} **Stratum** \\ **Description** \\ \end{tabular} } & \multicolumn{4}{c}{**Thickness of Each Soil Layer/m**} & \multirow{2}{*}{\(\omega\) (\%)} & \multirow{2}{*}{\(\gamma\) (kN/m\({}^{3}\))} & \multirow{2}{*}{\(c\) (kPa)} & \multirow{2}{*}{\(\varphi\) (\({}^{\circ}\))} \\ \cline{3-3} \cline{5-10}  & & **TS1** & & & & & & \\ \hline \multirow{3}{*}{1} & Silty clay & 4.4 & 4.4 & 10.8 & 10.8 & 25.7 & 19.0 & 41.1 & 11.7 \\  & Silty clay mixed & 5.6 & 5.6 & & 24.5 & 19.4 & 31.7 & 25.3 \\  & Coarse sand & 2.4 & 2.4 & & 30 & 18.4 & 18.5 & 6.1 \\  & Fine sand & & 3.5 & 3.5 & 26.3 & 19.1 & 21.3 & 9.4 \\  & Silty clay & 17.0 & 17.0 & 6.2 & 6.2 & 25.7 & 19.2 & 11.7 & 4.9 \\  & Coarse sand & 5.0 & 1.6 & & 26.2 & 18.4 & 3.7 & 18.9 \\  & Silty clay & 10.6 & & 12.7 & 12.7 & 23.7 & 19.2 & 44.3 & 15.8 \\  & Coarse sand & 1.0 & & 4.8 & 4.8 & 22.0 & 18.5 & 4.3 & 25.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of physical properties of soil.  
Figure 2: Soil distribution and details of test shafts.  
respectively. The details of the four test shafts can be obtained by Table 2. The elastic modulus of the concrete for the four test shafts \(E_{c}\) is 31 GPa. Furthermore, the elevations of the strain gauges for test shafts TS1 and TS2, the ring-shaped grouting pipes along shafts TS2, TS3 and TS4 are all presented in Figure 2. It should be noted that the grouting devices arranged for test shafts TS2, TS3 and TS4 are different. As shown in Figure 2, two ring-shaped grouting pipes were fixed at the side of test shaft TS2, while three for test shafts TS3 and TS4, the spacing between the ring-shaped grouting pipes of grouted shafts are all 10 m. Moreover, three straight grouting pipes were fixed to the bottom of test shaft TS2, while four straight grouting pipes were fixed to the bottom of test shafts TS3 and TS4, respectively, and the bearing layers of the four test shafts were all sand layers with similar properties.
## 3 Analysis of Simulation Results  
### Surface Deformation and Analysis of Roadway Surrounding Rock  
The observations show that (Figure 4) when the stress value of the roadway surrounding rock was less than 8.5 MPa, there was no obvious crack in the surrounding rock of the roadway. When the surrounding rock stress increased to 8.5 MPa, cracks began to occur in the roadway floor and in the zone where the upper zone of the roof was tangent to the parallel line of the rock stratum inclination, as show in Figure 4, 8.5 MPa red circle; with the increase of the surrounding rock stress, the cracks in these two zones gradually expanded. When the surrounding rock stress was 9.5 MPa, as show in Figure 4, 9.5 MPa red circle, the interlayer joint surface in the high and bottom corners of the roadway was separated; when the surrounding rock stress was 10 MPa, as show in Figure 4, 10 MPa red circle, the development of the upper of the roadway floor and the roof was very obvious.  
With the further increase of surrounding rock stress, the cracks in the roadway surrounding rock gradually expanded. When the surrounding rock stress was greater than 13.5 MPa, as show in Figure 4, 13.5 MPa red circle, the low upper zone of the roadway showed obvious layer separation along the joint surface of the rock formation. At the same time, there was also the phenomenon of interlayer slippage at the high-top bottom corner of the roadway, and the floor of the roadway had an obvious bottom bulge phenomenon in the center of the roadway.  
When the surrounding rock stress increased to 15 MPa (Figure 4), the roadway was structurally damaged, and in the upper zone where the roof and the low side intersected occurred a large deformation and damage, and the roadway floor heaving phenomenon was serious. This shows that these two areas were the weak links in the stability of the surrounding rock structure of the roadway, and the damage to the roadway surrounding rock first occurred in these two areas.  
Figure 4: Roadway surface deformation from 4.5 to 15 MPa.
## 4 Evaluation and Results' Discussion
### Performance Analysis
#### 4.2.2 Experiment 2  
We set the amount of data for the client and server to \(2^{8}\) and increased the number of participants from \(2^{4}\) to \(2^{9}\) for these experiments, which aimed at testing the runtime of individual clients and the server. The experimental results are shown in Table 3 and Figure 2. From the results, it is evident that the runtime of the clients remained constant in both our protocol and the protocol [28], regardless of the increase in the number (t) of participants. Moreover, in our protocol, the client's runtime was approximately \(1/(2^{log(t)})\) of the server's runtime when \(t\geq 2^{4}\). Our protocol ran more efficiently than the protocol [28]. This is mainly because the clients in [28] performed the \(ShDec0()\) operation, which involves exponential operations on a large integer \(n_{t}\) with large random values. The client \(P_{i}\) in our protocol needs to compute \(\Delta_{i}=\prod\limits_{f=1,f\
eq i}^{t}\frac{f}{f-i}\) only once and \(sh_{j,i}=(c_{j})^{\Delta_{i}\cdot sk_{i}}\)\(n_{t}\) times, and the computation related to the number of participants is only once for \(\Delta_{i}\), which is insignificant in the whole runtime of the client.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Data Size** & \(\mathbf{10^{2}}\) & \(\mathbf{10^{3}}\) & \(\mathbf{10^{4}}\) & \(\mathbf{10^{5}}\) \\ \hline \multirow{2}{*}{Paillier} & encryption & 0.502 & 4.158 & 39.910 & 328.851 \\  & decryption & 0.404 & 3.735 & 39.926 & 366.583 \\ \hline \multirow{2}{*}{ElGamal} & encryption & 0.184 & 1.957 & 16.695 & 160.772 \\  & decryption & 0.105 & 0.950 & 9.656 & 92.483 \\ \hline \hline \end{tabular} Select \(k=1024\) bit security parameter for public key encryption. The value of the encrypted data was fixed at \(10^{7}\). The encryption process used the same random number. The data volume increased from \(10^{2}\) to \(10^{5}\).  
\end{table}
Table 2: Comparison of Paillier encryption algorithm and ElGamal encryption algorithm (in seconds).
## 7 Experiments and Results
On BEE4 (see Figure 10), the second of the two most challenging datasets, the topology of the validation curves is similar to the topology on BEE2_2S. All RFs kept improving their performance until the number of DTs in them reached 100, after which their accuracy stabilized around 82%. The PRF's accuracy did not decline as rapidly as on BEE1 and BEE2_1S with the addition of new DTs, but showed a slight decline after the number of DTs exceeded 125. On BEE3 (see Figures 11 and 12), the topology of the validation curves was similar. The accuracy spread was wider on BEE3 without SHADOW_BEE images after the number of DTs exceeded 50 and the accuracy varied between 85 and 87%. The accuracy spread was  
Figure 10: Validation accuracy (in %) vs. number of DTs of HQL RRFs and PRF on BEE4. Figure 9: Validation accuracy (in %) vs. number of DTs of HQL RRFs and PRF on BEE2_2S. narrower on BEE3 with SHADOW_BEE images after the number of DTs exceeded 50 and the accuracy stayed at \(\approx\)83%. One reason for the relative underperformance of DQL RFFs, especially DQL 1 RFFs, is the difficulty in finding optimal RRF with DQL, which we formulated and proved as Random Reinforced Forest Theorem 1 (RFFT 1) in Section 8 below. Another reason for the better performance of the SCNs may be their over-parameterization of the problem space [20], which allows them to generalize better with multiclass datasets that have relatively small numbers of training examples in each class. As the accuracies in the  
Figure 11: Validation accuracy (in %) vs. number of DTs of HQL RFFs and PRF on BEE3_NS. Figure 12: Validation accuracy (in %) vs. number of DTs of HQL RFFs and PRF on BEE3. SKL_RF and SKL_RF (Color) indicate, the addition of color to images did not improve the performance of the scikit-learn RFs.
## 1 Introduction
### Organization of the Paper  
The remainder of this paper is organized as follows. Section 2 describes the system model including propagation and energy consumption models, while Section 3 presents the problem formulation. Section 4 presents the proposed \(Q\)-learning-based UAV positioning mechanism, followed by discussing the simulation scenario and the results in Section 5. Section 6 concludes the the paper.
# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 2 State of the Art
### The Spatial and Temporal Structure of Road Traffic Noise in Large Urban Zones
#### 2.3.3 Groups of Roads Defined by Their Daily Total Traffic Flow \(T_{T}\)  
For building the Dynamap network upon a finite number of dynamical maps, and in particular within the pilot zone 9, it is convenient to define groups of road stretches, denoted as \(g_{i}\), according to their \(x\) values, Equation (2). We consider six groups (\(i=1,6\)) for Z9, which are defined in such a way to contain approximately the same number of road stretches in each group. They are reported in Table 1[83]. Thus, Dynamap[83; 86] generalizes the standard categorization of roads discussed in Section 2.3.  
Using the values of traffic noise calculated from the modeled traffic flows (see [86] for details), we display the equivalent noise levels at each road stretch \(s\) in zone 9 of the city of Milan, \(L_{\text{eq}}^{\text{(1h)}}(t_{\text{ref}},s)\), obtained at the 'rush hour' \(t_{\text{ref}}\in\) (8:00-9:00) a.m., for each one of the six groups \(g_{i}\) (\(i=1,6\)), denoted as the basic traffic noise maps (see Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & \(g_{1}\) & \(g_{2}\) & \(g_{3}\) & \(g_{4}\) & \(g_{5}\) & \(g_{6}\) \\ \hline \([x]\) & 0.0–3.0 & 3.0–3.5 & 3.5–3.9 & 3.9–4.2 & 4.2–4.5 & 4.5–5.2 \\ \(\beta_{1}\) & 0.99 & 0.81 & 0.63 & 0.50 & 0.41 & 0.16 \\ \(\beta_{2}\) & 0.01 & 0.19 & 0.37 & 0.50 & 0.59 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the six groups of roads, \(g_{i}\) (\(i=1,6\)), in terms of the non-acoustic parameter, \(x=\log_{10}(T_{T})\). Each group has \(x\) values within the range indicated in the second row (cf. Figure 4). The corresponding mean values: \(\beta_{1}(g_{i})=P_{1}(x[g_{i}])\) and \(\beta_{2}(g_{i})=P_{2}(x[g_{i}])=1-\beta_{1}(g_{i})\) are indicated in the 3 rd and 4 th rows, respectively.  
Figure 5: Calculated noise levels within Z9, \(L_{\text{eq}}^{\text{(1h)}}(t_{\text{ref}},s)\), for all road stretches \(s\) belonging to each group \(g_{i}\) (\(i=1,6\)), yielding the basic traffic noise maps (BTNM)[86]. Here, \(t_{\text{ref}}\in\) (8:00–9:00) a.m. The colors indicate the \(L_{\text{eq}}\) values on a scale from 40 db (blue) to 75 dB (red). The length scale of 2 km is indicated on the map.
## 2 Materials and Methods
### Image Quality Assessment  
All datasets were stored in DICOM format and protocol-related information was removed. A 3D post-processing platform (syngo.via, Siemens Healthcare) was used to display all datasets. Two board-certified radiologists (9 and 10 years of experience in head and neck radiology) analysed the images. The window setting could be changed by the raters at their own discretion. The default setting was W 2000 C 0. For the first cadaver head overall impression of the images was assessed according to the subjectively perceived image impression and rated (sufficient image quality vs. insufficient/noisy image quality).  
Image quality for the remaining three cadaver heads was measured by using an ordinal performance scale with 5 levels (5-point Likert scale). The demarcation of 10 anatomic regions (lamina papyracea, lamina chribosa, nasal septum, ethmoid air cells, sinus walls, orbital floor, lacrimal duct, carotid canal, tympanic cavity, mastoid cells) and incidental \"pathologic\" conditions (paranasal and mastoid fluid collections) was rated as follows: 5 excellent, 4 good, 3 moderate, 2 sufficient, 1 insufficient image quality.  
The overall rating for each protocol was defined by the worst rating of these 10 anatomic structures and two \"pathologic\" conditions. Image quality of 3 was considered \"sufficient\" for pre-operative evaluation for sinusitis surgery, and image quality of 2 to detect or rule out sinusitis [11]. Noise measurements were not performed since noise values do not adequately reflect the diagnostic value in high contrast objects [11]. For the evaluation of MAR a 3-point Likert scale was used, 3 = no beam hardening despite metal implants, 2 = moderate beam hardening artifacts, cortical structure of maxilla/mandible definable, but not reliably assessable everywhere, 1 = strong beam hardening artifacts, impairment of anatomical structures. [PERSON]'s K was calculated for inter-rater reliability by using SPSS Statistics 21.0 (IBM Corp).
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing
#### 3.1.5 SAR Intensity Difference Image and Texture Image  
To generate an intensity difference image and a texture image from the normalized image, sea surface values were extracted from the normalized image using a very large median filter [30]. In addition, to extract the sea surface values only, a median filter with a sufficiently large size of \(81\times 81\) was used based on the size of the ship on the image. The generated sea surface image was differentiated from the existing image where the ship appeared to create an intensity difference image, and a texture image was created by calculating the root mean square difference (RMSD). Through this, phenomena appearing at sea surface such as waves and ripples can be reduced, and at the same time, the contrast between the ship and the sea can be increased slightly.
## 3 Case Study  
### Data Source  
#### 3.1.1 Project Overview  
The data in this study is from Chengdu Metro Line 19 in China, with a total length of 43.186 km and a tunnel excavation diameter of 8.64 m. The location of the project is shown in Figure 4. All lines are underground, and there are 12 underground stations with an average station spacing of 3.58 km. Line 19 is still under construction, and we collected a large amount of data from five EPB shield machines in Xin-hong, Hong-tian, and Tian-hua sections. The total length of these sections is about 4 km. The surface layer is miscellaneous fill and plain fill, and the strata crossed are mainly medium weathered sandstone and medium weathered mudstone. The geological proportions along the line are shown in Figure 4. The overall geological condition is simple, suitable for excavation by EPB shield machine.  
All five EPB shield machines are made by the same manufacturer, China Railway Engineering Equipment Group Co., Ltd. (CREC), and the main technical parameters are summarized in Table 1. The EPB working parameters, such as advance rate, rota  
Figure 4: Chengdu metro line 19: (**a**) data area; (**b**) geological information.  
tional speed, cutterhead torque, and chamber pressure, are automatically collected at an acquisition frequency of 1 Hz, and the total data volume exceeds 180 GB.