esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Sensor Node Power Consumption
For the MNO UAN, we derived the power consumption characterization by performing measurements of the WISMO 228 GPRS radio platform [43], by using a N6750 DC Power Analyzer. For the DTN UAN, we used the same power analysis tool to model the radio-triggered wake-up receiver presented in [40]. For the WLAN and SO UANs, we characterized the power consumption of the RN-171 module in IEEE 802.11g mode and the TD1202 SIGFOX module, respectively, on the basis of information reported in their datasheets [24,44]. Typical transmit power settings for each technology have been considered, _i.e.,_ 0 dBm for LR-WPAN, DTN and WLAN, 10 dBm for SIGFOX and 33 dBm for GPRS. An equivalent message payload size of 100 bytes has been assumed (although SIGFOX limits the payload size to 12 bytes). This payload size is in the order of magnitude of the IEEE 802.15.4 frame maximum payload size, and therefore it represents a reference on the maximum expected size of messages used in sensor node applications (note that, if shorter messages are actually transmitted, sensor node power consumption will actually be lower than the one depicted in Figure 4 for any UAN class). For the GPRS study, a microcontroller consuming 1 \(\upmu\)A in sleep mode has been assumed. As shown in Figure 4, sensor node average power consumption is asymptotically dominated by the sensor node current consumption in sleep mode. LR-WPAN, with submicroampere sleep mode current consumption, achieves the best performance for a notification period beyond one minute. WLAN modules exploit their high bitrate, which compensates their high transmit power consumption, to provide the best performance for very frequent data communication (which however is not characteristic of Smart City applications). DTN sensor nodes permanently consume additional power compared with LR-WPAN platforms to feed the wake-up receiver, however they achieve an asymptotic behavior similar to that of WLAN modules. Sensor nodes that use a GPRS MNO interface are penalized due to the power consumed in actual communication, and only allow low-power operation for notification periods in the order of tens of hours. SIGFOX ultranarrowband 10 bit/s channels suffer a similar problem, whereas use of the SIGFOX 1 kbit/s rate reduces power consumption due to a lower transmit time, and achieves better asymptotic performance than WLAN or DTN solutions thanks to a lower sleep mode power consumption.
## 3 Dataset
### Noise-Network-Distorted Dataset
#### 3.1.1 Effect of VoIP Network QoS on Speech Quality  
The network condition in the VoIP system affects the quality of the transmitted speech. IP network conditions change with the network load. Commonly measured IP network conditions for network QoS include delay, jitter, bit rate, loss rate, forward error correction, and loss distribution. There is a substantial body of literature on the impact of network conditions on the perceived speech quality in VoIP communications [20; 21; 23; 31]. In this study, we examined the effect loss rate, burst packet loss, delay, and jitter, which are the wide studies' network condition parameters in VoIP applications.
## 3 Results and Discussion
### Verification of Pretreatment Effect of DLLME  
In this study, the DLLME/HS-SPME/GC-MS method was evaluated with 1 \(\mu\)g/mL maltol and ethyl maltol standards. As shown in Figure 2a, HS-SPME/GC-MS could effectively detect volatile maltol and ethyl maltol. The chromatographic peaks obtained were well separated (retention time was 13.3 min and 15.8 min, respectively) with symmetrical peak shapes. As compared with the HS-SPME method, the signal intensity of maltol (five times increase) and ethyl maltol (10 times increase) was improved significantly when DLLME was coupled with HS-SPME (Figure 3a).  
Figure 2: Chromatograms of maltol and ethyl maltol pretreated by dispersive liquid-liquid microextraction combined with headspace solid phase microextraction (A, without DLLME treatment and B, with DLLME treatment): (**a**) Chromatograms of maltol and ethyl maltol standard; (**b**) enlargement of the chromatogram of maltol standard; (**c**) enlargement of the chromatogram of ethyl maltol standard.  
Complex matrix samples such as seafood contain a high content of oil and protein [33], which significantly interfere with HS-SPME enrichment of maltol and ethyl maltol. Obviously, the signal intensity of maltol and ethyl maltol with sample matrix was individually reduced by five and nine times as compared with the samples without matrix (Figure 3c). However, the signal intensity of maltol and ethyl maltol with matrix was significantly improved after DLLME pretreatment prior to HS-SPME (Figure 3b).  
The calculated matrix effect in seafood was \(-81.12\%\) for maltol and \(-88.72\%\) for ethyl maltol (Table 1). The enrichment factor of the DLLME method coupled with HS-SPME was 19 for maltol and 66 for ethyl maltol (Table 1). The results showed that DLLME improved the efficiency of the HS-SPME/GC-MS analysis of volatile flavor compounds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compounds** & **Maltol** & **Ethyl Maltol** \\ \hline Matrix effect (\%) & \(-81.12\%\) & \(-88.72\%\) \\ Enrichment factor & 19 & 66 \\ Calibration range (\(\mu\)g/g) & 0.25–25.00 & 0.05–40.00 \\ Regression equation 1  & \(y\) = 0.2806\(x\) – 0.0142 & \(y\) = 0.6744\(x\) + 0.2082 \\ R2  & 0.9967 \\ LOD (\(\mu\)g/kg) 3  & 5.0 & 2.5 \\ LOQ (\(\mu\)g/kg) 4  & 15.0 & 5.0 \\ Intraday precision & 5.7 & 2.8 \\ RSD (\(n\) = 3, \%) & 4.3 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calibration range, limit of detection (LOD), and limit of quantitation (LOQ) for flavor enhancers.  
Figure 3: Effects of different pretreatments (without DLLME treatment and after DLLME treatment) on extraction efficiency of maltol and ethyl maltol: (**a**) Effect of DLLME treatment on the extraction efficiency of maltol and ethyl maltol standards; (**b**) effect of DLLME treatment on the extraction efficiency of maltol and ethyl maltol in the matrix (dried squid samples); (**c**) effect of DLLME treatment on the extraction efficiency of maltol and ethyl maltol in the matrix with standards. C1/C0, the peak area of flavor enhancers (maltol and ethyl maltol)/the peak area of cyclohexanone standard.
## 2 Demand Response
### Reactive Power Market  
Reactive power control is an important technique for maintaining the power quality of voltage and power factor improvement. In recent years, due to the improvement of IoT technology, a system capable of giving and receiving reactive power from distributed power sources and household inverters' unused capacity has been proposed. Therefore, a reactive power market has been proposed, which buys and sells with respect to reactive power not normally consumed [39; 40]. However, the reactive power market is not opened for end-users of the power system. [PERSON] et. al. [41] have discussed reactive power sharing in microgrids with IoT technology. Against this backdrop resulting from IoT development, the mechanism that various customers can participate in for reactive power control is becoming a reality, but there are few papers that have discussed the incentives and benefits that they offer.
## 3 Results
### Analysis of Errors in IMERG Precipitation Data  
To further evaluate the errors in the IMERG estimates of hourly precipitation and investigate the correlation between IMERG data and gauge observations, the two datasets obtained for the four regions examined over the study period were ranked by the observation hours. Then, three metrics, i.e., CC, MAE, and RMSE, were calculated to analyze the variability of hourly precipitation for these two time series with same length.  
Table 2 shows that the correlation between IMERG data and gauge observations clearly varied across different regions. Notably, the lowest CC (0.308) was observed for European stations. However, in this region, IMERG data exhibited the smallest RMSE of 0.761 mm/h when compared to gauge observations. This discrepancy may be attributed to the prevalence of light rainfall and higher precipitation frequencies in Europe, resulting in a poorer correlation but smaller RMSE. In comparison, the CC for South Asia is 0.350, and higher RMSE and MAE values were obtained in this region, possibly due to the  
Figure 4: Average annual precipitation intensity from 2001 to 2020 obtained from gauge observations (**a1**–**d1**) and IMERG data (**a2**–**d2**), and the bias of IMERG data relative to gauge observations (**a3**–**d3**), for North America, Europe, North Asia, and South Asia, respectively.  
predominance of extremely intense convective rainfall. Similar CC values were obtained for North America (0.367) and North Asia (0.373). The RMSE and MAE values were also comparable between these regions. This was likely due to their geographical proximity and similar climatic conditions within the same latitude belt. Additionally, the higher number of meteorological stations in North Asia contributed to the better correlation between IMERG data and gauge observations.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 3 The Proposed Attention-Based Full-Scale Fusion Network
### Attention-Based Residual Ublock
The excitation operation adopts two fully connected layers \(F_{ex}\) to learn the weight for each channel. Note that the first fully connected layer is followed by a ReLU layer, and the second fully connected layer is followed by a sigmoid layer. The excitation operation can be expressed as follows:  
\[\omega=F_{ex}(g)=Sigmoid(FC(ReLU(FC(g)))), \tag{2}\]  
where \(FC\), \(ReLU\), and \(sigmoid\) refer to the fully connected layer, \(ReLU\) function, and \(sigmoid\), respectively. Finally, the output of the SE block is \(x_{C\times H\times W}^{\sim}=\Phi(g,\omega)\). The diagram of the SE block is shown in Figure 3.  
Inspired by the excellent performance of the SE block [46], we integrate the SE block into the RSU block to form the attention-based RSU block (ARSU). As can be seen in Figure 2b, in the ARSU block, the SE block is applied to the output of the RSU module. In the proposed AFSF network (Figure 4), we stack several ARSU or ARSU-4F blocks to form the encoder and decoder. As illustrated in Figure 4, in the encoder stage, six ARSU or \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Definition** \\ \hline _Conv_ & A convolutional layer with a kernel size of \(3\times 3\) and a stride size of 1 \\ _BN_ & Batch-norm layer \\ _ReLU_ & \(ReLU\) activation function \\ _Downsample_ & Max pooling with a stride of \(2\times 2\) \\ _Upsample_ & A layer that uses the bilinear interpolation algorithm to expand the length and width of the input image twice, respectively \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The definitions of _Conv_, _BN_, _ReLU_, _Downsample_, and _Upsample_. Figure 2: Illustration of RSU and the proposed ARSU: (**a**) RSU, (**b**) ARSU. ARSU-4F blocks are connected in series through a downsampling operation; in the decoder stage, five ARSU or ARSU-4F blocks are connected in series through the upsampling operation. To generate the auxiliary prediction mask, we apply a mask generator, which consists of a \(3\times 3\) convolution layer, upsampling operation, and sigmoid function, to each output feature of the last encoder and all decoders. For clarity, we mark the modules connected to the mask generator in purple in Figure 4. For each purple module, its output feature map is copied into two copies: one for the input part of the next RSU or RSU-L module, and the other for the input to the mask generator. In addition, the output masks of the last encoder and all decoders are first concatenated in the channel dimension and then processed by another mask generator to produce the fusion mask. Note that, during the test (inference) phase, we adopt the fusion mask as the only prediction mask of the model.
## 3 Results and Discussions
### Findings from DOE 1, 2 and 3

Table 5: DOE 1, 2 and 3 thermal and structural results. Figure 3 shows the variation of cooling time and stress distribution with pitch distance (P) and the mold-to-channel centerline distance (L) for DOE-1. Figures 4 and 5 show similar trends for DOE-2 and DOE-3 (i.e., increase in cooling time with increase in P value). The same effect is observed for L. From these figures, it can be noted that with the increase in part thickness, the variation in cooling times decreases. Hence, the thicker the plastic part, the more critical the need for an improved conformal cooling channel design. When the plastic parts are thicker, the temperature is higher in the inner surface than the outer surface, and it is more important to keep the distance of the channels to the mold wall smaller in the core than the cavity. Figure 3: Trend of cooling time and maximum von Mises stress variation in DOE-1. Figure 2: Temperature distribution on plastic parts for comparable conformal and conventional cases in DOE 1, 2 and 3. Analyzing the thermal and structural results, it is noted that although the cooling times vary significantly among the cases in each DOE set, the value of the maximum von Mises stress is relatively constant. Those values are much smaller than the yield strength. Thus, the thermal performance is emphasized to determine optimum designs. In DOE 1, 2 and 3, considering the cooling times, the variances are 38.45 s, 35.72 s, and 8.27 s respectively, while the variances are 1576.56 MPa, 563.89 MPa, and 63.43 MPa, considering the von Mises stress. With the optimal designs obtained in the previous section, the study is further extended by considering the cylindrical and conical designs of plastic parts, which are the most commonly used in the injection molding industry. The top three optimized designs of cooling channels (from Table 7) for each thickness are chosen to set up new sets of DOEs. In terms of the channel cross-section, circular, square, and rectangular designs provide comparatively better thermal performance according to a previous study [38]. These three different channel cross-sections are incorporated here. Figure 4: Trend of cooling time and maximum von Mises stress variation in DOE-2. Figure 5: Trend of cooling time and maximum von Mises stress variation in DOE-3.
# A Graph-Cut-Based Approach to Community Detection in Networks
###### Abstract  
Networks can be used to model various aspects of our lives as well as relations among many real-world entities and objects. To detect a community structure in a network can enhance our understanding of the characteristics, properties, and inner workings of the network. Therefore, there has been significant research on detecting and evaluating community structures in networks. Many fields, including social sciences, biology, engineering, computer science, and applied mathematics, have developed various methods for analyzing and detecting community structures in networks. In this paper, a new community detection algorithm, which repeats the process of dividing a community into two smaller communities by finding a minimum cut, is proposed. The proposed algorithm is applied to some example network data and shows fairly good community detection results with comparable modularity \(Q\) values.  
community detection; graph cut; betweenness centrality; modularity +
Footnote †: journal: Medical Imaging  
1  
and applied mathematics, have identified their own needs for developing methods to analyze and detect community structures in networks. Graph partitioning problems in graph theory are also closely related to the community detection problem. Many algorithms for community detection and clustering have been proposed so far, and there are a few extensive review papers on them [1,2].  
The main contribution of this paper is to propose a new community detection algorithm, which repeats the process of dividing a community into two smaller communities by finding a minimum cut of the community. In order to compute the minimum cut of a community, the betweenness centrality values of vertices are used to choose the source and sink nodes. The modularity criterion is used in a greedy way to determine the local optimal partition among many candidate communities to be cut.  
The rest of this paper is organized as follows. Section 2 briefly reviews prior work on community detection methods. Section 3 presents the proposed algorithm, and Section 4 illustrates the results of the algorithm applied to a few example networks. Finally, Section 5 discusses the results and concludes the paper.
## Conclusions  
The present research concerned the evaluation of the mechanical performance of bioresorbable magnesium alloy stents with three different design patterns in planar stretching deformation and in crimping, expansion, and bending deformation. Mechanical tests were combined with FEA to reveal the deformability, scaffolding capacity, radial recoil, and bending flexibility of the stents. The following conclusions were drawn.  
1. In terms of scaffolding capacity and radial recoil, the performance of Pattern C was better than that of Pattern B.
2. A new stent design pattern proposed for magnesium alloys (Stent C) exhibited balanced radial strength and deformability. In comparison with the existing stent pattern designs (Stents A and B), Stent C had a compromised overall mechanical performance in expanding deformability, radial strength, and radial recoil. However, the expanding capacity of Stent C would need to be improved.
3. The crimping deformability of Pattern C was comparable with that of Pattern B, and better than that of Pattern A. Stent A exhibited non-uniform shrinkage and rotated axial links during crimping, and then cracking and fracture occurred. Thus, this design pattern would not be suitable for magnesium alloy stents.
4. Stent C had bending flexibility comparable with Stent B at low deflections and it had far better bending flexibility than Stent A. The advantage of Stent C over Stent B became apparent when the bending deflection exceeded 0.6 mm. Stent B had poor consistencies at large deflections due to the squashing of the stent circular rings, where Stent A and C performed much better.
5. Stent pattern redesign could open up new possibilities for magnesium alloy stents to meet the performance requirements of bioresorbable coronary artery stents, despite the low ductility of magnesium alloys at normal body temperature.  
[PERSON] and [PERSON] conceptualized the research; [PERSON], [PERSON] and [PERSON] conceived the experimental strategy and finite element analysis models; [PERSON] conducted finite element analysis and experimental validation with [PERSON]. [PERSON] wrote the original draft; [PERSON] and [PERSON] reviewed and edited the manuscript.  
This research was funded by National Natural Science Foundation of China (project No. 51375256) and the Beijing Natural Science Foundation (project No. 3142011).  
The authors declare no conflicts of interest.
# Crack Growth and Delamination Analysis in GFR Composite Materials
## 5 Crack Growth in Microscale Using Fracture Mechanics  
As mentioned, XFEM is a popular method for the solution of quasi-static problems containing cracks and interfaces that are mesh independent. The solution is attainable with the enrichment of elements that are intersected by the discontinuity, using special shape functions to handle the singularities and local discontinuities around the crack.  
In Figure 10, a cross-ply composite RVE model (with characteristics as mentioned in Section 3) is constructed and loaded in uniaxial tension that is applied through Dirichlet-type boundary conditions by using the strain results of the macroscale model. The cracking propagation simulated with XFEM is shown as the loading increases. In particular, as the crack propagates, the stress in the middle layer reduces while stress concentration in the crack tips is evident, in agreement with the macroscale model. In Figure 11, the stress concentration values at the crack tips are compared against the simulation predictions of microscale and macroscale models.  
However, the stress profiles cannot be directly compared due to the different representation scales. The macro model refers to the stress concentration upon a homogenized material and the micro model refers to the stress concentration upon the fiber. For their direct comparison, the stress in macro model needs to be localized using the stress concentration tensor of [PERSON]'s [35] theories, which invoke the following equations:  
\[\sigma_{f}=B_{f}:\sigma \tag{18}\]  
\[B_{f}=(M-M_{m})/\left[V_{f}(M_{f}-M_{m})\right] \tag{19}\]  
The quantities involved are the compliance tensors of the fiber (\(M_{f}\)), the matrix (\(M_{m}\)) and the homogenized composite material \(M\), \(V_{f}\) is the volume fraction of the fiber, and \(B_{f}\) is the [PERSON]'s fiber stress concentration tensor. The stress concentration tensor connects the  
Figure 9: Mean delamination length as a function of the number of finite elements.  
stress tensor of a constituent with that of the composite, making the direct comparison of the two models possible.  
Figure 10: Crack growth in a cross-ply RVE using XFEM.  
The maximum stress tensor of the homogenized material in the stress concentration area that occurred from the macro scale analysis in Voigt notation is \(\sigma=[1833.04\ -51.04\ -53.82\ 1.71\ 0.56\ -8]\). The von Mises stress of the fiber that occurred from the stress localization is 2958.3 Mpa, which is close to the prediction of the XFEM model around 2894 Mpa.