esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
Figure 3 shows the total traffic volume at each measured hour in each intersection. Table 4 presents similar results for each vehicle type in each intersection. The results for weekdays revealed peaks in the total traffic volume representing rush hours around 8:00 in the morning and around 17:00 or 18:00 in the evening. Meanwhile, there were no similar peaks in the total traffic volume on the weekend. The total traffic volume gradually \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Green Light Duration on Weekdays (S)/on Weekends (S)**} & \multicolumn{2}{c}{**Traffic Signal Cycle Duration (S)**} \\  & **Phase I** & **Phase II** & **Phase III** & **Phase IV** & **Weekday** & **Weekend** \\ \hline Int. 1 & 35–45/36–45 & 41–46/29–37 & 72/41–47 & 42–47/30–38 & 200 & 150 \\ Int. 2 & 70–75/74–84 & 11–15/15 & 43–48/40 & 62–69/61–71 & 200 & 200 \\ Int. 3 & 15/15 & 65–85/45–55 & 70–85/50–60 & 30–35/25–30 & 200 & 150 \\ \hline \end{tabular}
\end{table} 
Table 2: Durations of each green light phase and traffic signal cycle. \begin{table}
\begin{tabular}{c c c} \hline  & **Min.–Max. (Number of Vehicles per 10 min)** \\  & **Weekday** & **Weekend** \\ \hline Intersection 1 & 1020–1648 & 531–1135 \\ Intersection 2 & 986–1839 & 621–1480 \\ Intersection 3 & 747–1541 & 263–944 \\ \hline \end{tabular}
\end{table} 
Table 3: Range of total traffic volumes during the measurement time period analyzed in each measurement day. increased from morning to evening. These characteristics of total traffic volume were found at all intersections. Table 4 indicates that the traffic volume of motorcycles was very high during rush hours (i.e., around 8:00 in the morning and 17:00 or 18:00 in the evening) on weekdays at all intersections, while that on the weekend gradually increased from morning to evening. The large number of motorcycles on weekdays appears to affect the total traffic volume. On weekend days, the traffic volume of motorcycles gradually increased from morning to evening. Similar results were found in the traffic volume of standard-sized vehicles on weekend days. On weekdays, a peak in the traffic volume of standard-sized vehicles due to the morning rush was found only at Intersection 1. At other intersections, the traffic volume monotonously increased during the measurement period. The traffic volume of large-sized vehicles was the lowest of all vehicle types, and there does not appear to be much difference in traffic volume for this vehicle type between morning and afternoon.
## 2 Background and Related Work
Considering how the concepts of microclusters and macroclusters are adopted, our proposal, called Macro SOStream, can be considered similar to the HCMstream, BEstream, and DyClee algorithms. These algorithms have only online learning (except for BEstream), the merge function, and arbitrary macrocluster shapes. The difference is in spherical microcluster shapes, which makes it simpler. Furthermore, Macro SOStream proposes to use the Self-Organizing Map (SOM) concept to create and manage clusters. The SOM, or Kohonen, network transforms high-dimensional data to low-dimensional data and preserves the characteristics of the data [35]. The SOM learning is based on the competition of neurons and the winning neuron influences neighborhood neurons. Thus, on Macro SOStream, the winning microcluster influences the neighborhood of the microcluster.
## 6 Simulation Results
### Simulated Time Delay Estimation Performance  
In Figure 4, we plot the time delay estimates \(\hat{\tau}\) for the duration of the simulated flight path. In Figure 4a, we plot the true time delay \(\tau\) in red and the radial velocity \(\hat{\tau}\) in blue. In Figure 4b,c, we plot the estimates generated by the optimal first- and second-order one-shot estimators described in [9], labeled STP1 and STP2, respectively. Both of these estimators successfully estimated the time delay to a precision of about \(\sigma=1\) cm and the radial velocity to a precision of about \(\sigma=15\) cm/s. In Figure 4d,e, we plot the estimates produced by the proposed first- and second-order EKF tracking solutions, labeled EKF1 and EKF2, respectively. The first-order method, depicted in Figure 4d, achieved slightly better performance and converged to a solution after only a single interaction cycle. The second-order method depicted in Figure 4e, however, took several cycles to converge, creating a direct tradeoff between the computational complexity and convergence speed.  
Figure 4: (**a**) True time of flight and radial velocity, (**b**,**c**) existing first- and second-order one-shot estimators [9], labeled STP1 and STP2, respectively, and (**d**,**e**) our proposed first- and second-order extended Kalman filter estimators labeled EKF1 and EKF2, respectively.
# Simulation of Partial Discharge Induced EM Waves Using FDTD Method--A Parametric Study +
## 3 Surface Discharge Simulation Using the FDTD Method  
### FDTD Principle  
The FDTD method is a powerful electromagnetic simulation tool that has been extensively used for studying radio wave propagation in multiple media. [PERSON]'s equations in the time domain are solved using finite-difference time approximations. Since the electric and magnetic fields are related in time and space, both space segmentation and time stepping are required. Space segmentation takes the form of box-shaped cells whose size are constrained by the wavelength of the EM excitation signals, and its maximum must be less than 1/10 of the smallest wavelength for greater accuracy, giving \(L_{max}=0.1(c/f)\), where \(c\) is the velocity of light and \(f\) the frequency. For propagation in insulation materials, \(c\) is reduced and sol the cell size will be reduced. The electric field (E) and magnetic field (H) components are staggered, with the E-field components centered on the edges of the box and the H-field components centered on the faces to form what is known as the Yee cell [19], as shown in Figure 3. The modelled volume space, including any material constituting the model is built by interconnection of these cells, forming the FDTD mesh.  
Figure 3: The Yee cell with the labelled field components.  
Figure 2: Examples of surface discharge signals measured using the ultra-high frequency (UHF) sensor: (a) time domain signals (b) noise level and (c) frequency spectra.  
Time stepping is achieved by quantizing time into small steps equal to the time required for the field to travel from one cell to the next. Like the E-field and H-field are offset in space, their values are also offset in time. These field values are updated using a centered-difference approximation leapfrog scheme where the electric fields and then the magnetic fields are computed at each step in time. In this work, the XFdtdR simulation package [20] was used to simulate the PD excitation source, the electrode assembly, the insulation sample with the location and extent of the discharge, and the UHF sensor. The excitation is introduced by applying a sampled waveform at one location. At each step in time, the value of the waveform is used to compute the field value. The surrounding fields propagate throughout the FDTD mesh depending on the characteristics of each cell and the material properties. The field computations continue until a state of convergence is reached. In this study, this is set by either specifying the threshold below which the computed fields decay (around \(-30\) dB in this application) or setting a maximum number of iterations. The extent of outer boundaries and the associated boundary conditions, the cell size and simulation time are set according to the modelled problem.
## 1 Introduction
Supervised domain adaptation relies on labeled data from both source and target domains. Semi-supervised domain adaptation combines labeled data from the source domain with both labeled and unlabeled data from the target domain. Finally, unsupervised domain adaptation operates solely with unlabeled data in the target domain, focusing on learning features applicable across domains. The proposed method utilizes video clips of wheat fields and background video clips, along with only three manually annotated images. Acquiring video clips of fields and background scenes is straightforward and involves low data acquisition costs. Manual annotation of a few images is also achievable in a short period of time. Therefore, the proposed approach substantially decreases the time and effort needed to develop deep learning models for crop monitoring in agriculture. The closest work to our approach relies on extra manually annotated images and semi-automated image annotation for domain adaptation to improve model performance. In contrast, our approach does not use manually annotated data for domain adaptation. Our approach benefits from an architecture designed to bridge the domain gap. Following a multi-task learning paradigm, the proposed architecture utilizes both image reconstruction and mask prediction tasks and is trained end to end to facilitate the full utilization of both synthetic annotated data and real unannotated data, thereby enhancing the generalizability of the resulting model. In this paper, we develop a semi-self-supervised domain adaptation approach based on a probabilistic diffusion model that operates without the need for manual annotation of data from the target domain. This alleviates the need for further manual data annotation, accelerating the model development process. Self-supervised learning refers to methodologies where supervisory signals are generated computationally from the input data, enablinglearning data representations and extracting informative features without the need for manual annotation [16]. This approach allows for utilizing large-scale but unannotated datasets and has recently led to substantial progress in various image-processing tasks [16; 17]. Deep diffusion probabilistic models, such as self-supervised techniques, have shown remarkable results in image-processing tasks, especially in generative AI [18; 19; 20]. In the following, we provide a mathematical description of these models.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 3 Design of Robust Controllers
### Parameter Uncertainty Modeling  
The LC filter parameters are considered to be the uncertain parameters of IBDGs due to the \(\pm 10\%\) variations from their nominal values [34]. Therefore, the closed-loop control system is required to be robust against these variations. In the context of \(\mu\)-synthesisrobust control design, the parameter variations are modeled by multiplicative uncertainties as follows:  
\[\begin{array}{l}L_{i}=\bar{L}_{i}(1+\mathcal{E}_{L_{i}}\delta_{L_{i}})\\ C_{i}=\bar{C}_{i}(1+\mathcal{E}_{C_{i}}\delta_{C_{i}})\end{array} \tag{15}\]  
where \(L_{i}\) and \(C_{i}\) represent the uncertain values of the LC filter parameters and \(\bar{L}_{i}\) and \(\bar{C}_{i}\) represent the nominal values. Moreover, \"\(\mathcal{E}\)\" represents the maximum percentage deviation of a parameter from its nominal value and \"\(\delta\)\" corresponds to the normalized uncertainty variable such that \(|\delta|<1\).  
The multiplicative uncertainties defined in (15) are replaced into the transfer functions \(G_{inv_{i}}\) and \(G_{v_{C_{i}}}\) in Equation (3). Then, the uncertain transfer functions are obtained using the upper linear fractional transformation operator \(\mathcal{F}_{u}(.,.)\)[35] for inverter \(\#i\) (\(i=1,2,...,n\)), as follows:  
\[\begin{array}{l}G_{inv_{i}}(s)=F_{u}(\bar{G}_{inv_{i}}(s),\delta_{L_{i}})\\ \bar{G}_{inv_{i}}(s)\stackrel{{\Delta}}{{=}}\frac{1}{L_{s}} \left[\begin{array}{cc}-\bar{L}_{i}\mathcal{E}_{L_{i}}s&1\\ -\bar{L}_{i}\mathcal{E}_{L_{i}}s&1\end{array}\right]\end{array} \tag{16}\]  
\[\begin{array}{l}G_{v_{C_{i}}}(s)=F_{u}(\bar{G}_{v_{C_{i}}}(s),\delta_{C_{i}}) \\ \bar{G}_{v_{C_{i}}}(s)\stackrel{{\Delta}}{{=}}\frac{1}{\bar{C}_{s}} \left[\begin{array}{cc}-\bar{C}_{i}\mathcal{E}_{C_{i}}s&1\\ -\bar{C}_{i}\mathcal{E}_{C_{i}}s&1\end{array}\right]\end{array} \tag{17}\]  
where the transfer functions with a \"bar\" notation represent their nominal equivalence.  
To achieve a desired performance for the decentralized robust controllers, it is required that the following constraints be satisfied:  
\[H_{e_{i}}(s)W_{e_{i}}(s)<\frac{1}{\delta_{e_{i}}} \tag{18}\]  
\[H_{u_{i}}(s)W_{u_{i}}(s)<\frac{1}{\delta_{u_{i}}} \tag{19}\]  
where \(H_{e_{i}}(s)\) and \(H_{u_{i}}(s)\) represent the transfer functions from \(v_{ref_{i}}\) to \(e_{i}\) and \(i_{load_{i}}\) to \(u_{i}\), respectively. In addition, \(\delta_{e_{i}}\) and \(\delta_{u_{i}}\) (\(i=1,2,...,n\)) determine the tightness of the constraints. The two performance weight functions \(W_{e_{i}}\) and \(W_{u_{i}}\) respectively associated with the variables \(e_{i}\) and \(u_{i}\) play salient roles in the transient and steady state behavior of the time response of the closed-loop control system. Using trial and error, their parameters are selected to achieve the desired time response performance.
## 4 Experiments
### Dataset  
The approach presented in this paper was validated on the Blue Gene/L (BGL) dataset, prepared by the Usenix organization [36]. Blue Gene/L is an IBM supercomputer with 131,072 processors and 32,768 GB of RAM. Logs from this computer were collected over 215 days. The log file consists of 4,747,963 log lines, which amounts to 708 MB. The dataset is highly imbalanced. It contains 348,460 anomalous log lines, that is 7.3% of the total dataset. [PERSON] and [PERSON] [40] identified 41 anomaly classes in the BGL dataset through semi-automated log filtering combined with close collaboration with system administrators. The most common anomaly, accounting for 44% of all cases (152,734 log lines), is the _KERNDTLB_ alert, the typical descriptive message of which is: _data TLB error interrupt_. The next 48% of all anomalies (168,011 log lines) are: _KERNSTOR_, _APPSEV_, _KERNMNNTF_, and _KERNTM_. For the remaining 8% of anomalous log lines, there are 36 categories of alerts that occur much less frequently. Details of all alerts are described in the above-mentioned paper.  
Listing 3 presents sample raw log lines from the BGL dataset. The last entry is an anomaly and reports a hardware interrupt to the data storage medium--_KERNSTOR_ alert category.  
```
11178385700205.06.03 R02-M1-N0-C:J12-U11 2005-06-03-15.42.50.363779 R02-M1-N0-C:J12-U11 RAS KERNEL INFO instruction cache parity error corrected 11179738532005.06.05 R30-M0-NB-C:J14-U01 2005-06-05-05.17.33.516470 R30-M0-NB-C:J14-U01 RAS KERNEL INFO generating core.1280 11187694302005.06.14 R27-M1-NC-C:J06-U01 2005-06-14-10.17.10.932971 R27-M1-NC-C:J06-U01 RAS KERNEL FATAL instruction address: 0x00004 ed8 KERNSTOR 11187694302005.06.14 R24-M1-N0-C:J14-U012005-06-14-10.17.10.952598 R24-M1-N0-C:J14-U01 RAS KERNEL FATAL data storage interrupt
```  
Listing 3: Example logs from the BGL dataset.
## 4 Conclusions  
This paper evaluated the influence of freeze-thaw cycles on the performance degradation and fracture damage of SBS-modified asphalt mixtures with basalt fiber using the high-temperature compression test, low-temperature splitting test, and acoustic emission technique. Conclusions can be achieved as follows:  
* Acoustic emission parameters could well reflect the damage fracture characteristics of asphalt mixture specimen during the high-temperature compression and low-temperature splitting process. Meanwhile, the AE signal could be correlated with the fracture damage to study the influences of fracture damage.
* The fracture damage of the asphalt mixture specimens during compression or splitting can be classified into three stages by the variation of the AE signals, and the AE signal amplitude and count show clear correlations with the compression and splitting load level. Meanwhile, the AE signal clarifies the formation, development, and failure of internal damage for asphalt mixture specimens during the compression and splitting fracture.
* Damage gradually occurred inside asphalt mixture specimens under various F-T cycles. The intensity (value and density) of the AE signal parameters of asphalt mixture decreases with increasing F-T cycles. It is evident that the F-T cycle has a significant adverse effect on the mechanical strength of asphalt mixtures, which makes asphalt mixtures more likely to cause early failure.  
Conceptualization, C.L. and P.Z.; Methodology, C.L., P.Z., and G.M.; Validation, P.Z. and G.M.; Formal analysis, C.L. and J.M.; Investigation, J.M. and X.X.; Writing--original draft preparation, C.L. and J.M.; Writing--review and editing, P.Z. and X.X.; Project administration, C.L. and P.Z.; Funding acquisition, C.L. and P.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 9: The AE count and load level versus time during splitting at \(-10\)\({}^{\circ}\)C for SBS-modified asphalt mixture with basalt fiber under various F-T cycles. (**a**) 0 F-T cycle; (**b**) 3 F-T cycles; (**c**) 6 F-T cycles; (**d**) 9 F-T cycles; (**e**) 12 F-T cycles; (**f**) 15 F-T cycles; (**g**) 18 F-T cycles; and (**h**) 21 F-T cycles.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant numbers 51678271, 51978309, and 11702108), the Scientific and Technological project of Science and Technology Department of Jilin Province (grant number 20190303052 SF), the Jilin Province Development and Reform Commission Project (grant number 2019C041-5), and the Science and Technology Project of Education Department of Jilin Province (grant number JJKH20190150 KJ).  
**Acknowledgments:** The authors would like to appreciate anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hub-and-Spoke Network Design Considering Congestion and Flow-Based Cost Function
## 4 Solution Approach
### Master Problem  
The master problem would determine the domestic facility locations and choose the hub nodes among the domestic facility locations for an upgrade. The mathematical formulation is as follows:  
\[Minimize{\sum_{k}F_{k}h_{k}}+{\sum_{k}D_{k}s_{k}}+{\sum_{k}\sum_{l>k}l_{kl}y_{ kl}}+B\left({\sum_{m,l}P_{mn}-{\sum_{i,j}w_{ij}}}\right)+\vartheta \tag{29}\]  
Subject to  
\[y_{kl}\leq h_{k},y_{kl}\leq h_{l}\qquad k,l\
eq k \tag{30}\]  
\[h_{k}\leq s_{k}\qquad\qquad\forall k \tag{31}\]  
\[{\sum_{k}F_{k}h_{k}}+{\sum_{k}D_{k}s_{k}}+{\sum_{k}\sum_{l>k}l_{kl}y_{kl}} \leq Budget \tag{32}\]  
\[w_{ij}={\sum_{m,n}P_{mn}}{\frac{U_{mnij}\cdot s_{i}s_{j}}{\sum_{o,d}U_{mmod}s _{o}s_{d}}},\qquad\quad i,j\
eq i \tag{33}\]  
\[\sum_{k}h_{k}\geq 1 \tag{34}\]  
\[f_{kl}^{ij}\leq y_{kl}k\
eq l\qquad\forall i,j,k,l \tag{35}\]  
\[f_{kl}^{sm}\leq l_{m}k\
eq l\qquad\forall i,j,k,l \tag{36}\]  
\[f_{kl}^{ij}\leq h_{i}k\
eq l\qquad\forall i,j,k,l \tag{37}\]  
\[{\sum_{l}f_{l}^{ij}-{\sum_{l}f_{li}^{ij}}}=h_{i}\cdot h_{j}s\
eq m,s\
eq l \,\,\forall i,j \tag{38}\]  
\[{\sum_{k}f_{kj}^{ij}-{\sum_{k}f_{jk}^{ij}}}=h_{i}\cdot h_{j}i\
eq j,k\
eq j,l \qquad\forall i,j \tag{39}\]\[\sum_{k}f_{kl}^{ij}=\sum_{k}f_{lk}^{ij}i\
eq j,k\
eq l,l\
eq j,l\
eq i \forall i,j,l \tag{40}\]  
\[y_{kl},h_{k},s_{k}\in\{0,1\} \tag{41}\]  
\[f_{kl}^{ij}\in R^{+}\hskip 28.452756 pt\forall i,j,k,l \tag{42}\]  
In the above formulation, \(f_{kl}^{ij}\) represents the flow from node \"i\" to node \"j\" which enters the network at node \"k\" and leaves the network at node \"l\" (since \"k\" and \"l\" are the established domestic facilities and there is no facility in either \"i\" or \"j\"; thus, the passengers would travel to \"k\" as their start point and would leave at \"l\"). Initial investigation reveals that using only the original master problem, as the Benders master problem, may result in the non-connected graph. To overcome this issue, a set of constraints (34)-(42) are added to form the Benders master problem, as above [52].
## 3 Experimental Results and Discussion
### Influence of Pre-Strain on the Ratcheting Effect  
Figure 6 shows the relationship of ratcheting strain with the number of cycles at both mean stress and stress amplitude of 400 MPa with different pre-strain (0, 0.4%, 0.8%, and 1.2%). It is evident from Figure 6 that the ratcheting strain decreased with the increase the amount of pre-strain. Both [PERSON] [42] and [PERSON] [43] discovered this phenomenon, which might be due to the fact that the complex dislocation structures formed during the pre-straining process are difficult to eliminate in the subsequent cycling process. Therefore, tensile pre-straining inhibits the accumulation of plastic strain [12].  
Figure 4: Uniaxial ratcheting tests with the constant amplitude of 300 MPa and different mean stresses (**a**) stress–strain hysteresis loop and (**b**) ratcheting strain evolution.  
Figure 5: Uniaxial ratcheting tests with the constant mean stress of 400 MPa and different stress amplitudes: (**a**) stress–strain hysteresis loop, (**b**) ratcheting strain evolution.
## 6 Conclusions  
This study employed wind tests to investigate the influence of attached vertical ribs with various moderate extensional depths on the wind pressure of a high-rise building. The wind pressure distribution on different building surfaces, layer force coefficient along the height, and overall base shear coefficient and bending moment coefficient were ana  
Figure 11: Base shear force and bending moment coefficients of a building under \(\theta\) = 0\({}^{\circ}\)–90\({}^{\circ}\).  
Figure 10: Resultant layer force coefficients along the building height (\(\theta\) = 0\({}^{\circ}\)).  
lyzed and compared between different rib extensional depths. Comparison of the layer force coefficient with that in different code provisions was also conducted. The main conclusions are:  
1. On the windward face, the negative pressure area increases with the rib extensional depth. This may result from the local recirculation caused by the outmost vertical ribs, which enhances flow separation around the building. The negative wind pressures on the leeward surface show a slight increase with the increase in the rib extensional depth.
2. The experimental results show that the resultant layer force coefficient along the height shows a shape of \"7\" and reaches the maximum value at a height of 0.8 \(H\). Compared to the experimental results, the resultant layer force coefficients provided by EN 1991-1-4:2015 and GB 50009-2012 are conservative for most cases, whereas those provided by the HK, China code lead to an underestimated evaluation. Increasing the rib extensional depth will significantly reduce the positive windward layer force by 28% but moderately increase the negative leeward layer force by 17%. The use of surface-attached ribs is beneficial in reducing the resultant layer wind pressure on the building by a maximum of 9%.
3. When _b/B_ is below 2%, the addition of vertical ribs has limited effects on the overall base shear and base bending moment. An increase in the rib extensional depth to 4% of the building width will reduce the maximum base shear and bending moment by 8.8% and 7.4%, respectively, indicating the effectiveness of vertical ribs in improving the wind-resistant performance of a high-rise building.  
Conceptualization, G.S., Y.J. and H.X.; methodology, G.S., Y.J. and S.Y.; investigation, Y.J., G.S., S.Y. and H.X.; writing--original draft preparation, Y.J., G.S. and H.X.; writing--review and editing, G.S. and H.X. All authors have read and agreed to the published version of the manuscript.  
The authors greatly appreciate the support by the National Natural Science Foundation of China (Grant No. 51578505 and 52178511) and Zhejiang Provincial Natural Science Foundation of China (Grant No. LGG21E080009). The opinions and statements do not necessarily represent those of the sponsors.  
Not applicable.  
Data availability Statement: Data is available upon reasonable request.  
The authors declare that they have no known competing financial interests or personal relationships that could influence the work reported herein.