esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Monitoring of a Drawbridge in Operation: Signal Analysis
## 3 Signal Processing
### Calculate Average Curve  
After every event is classified, their signals are used to obtain an average curve for each sensor, relative to both the opening and closing event types. This enables an individual comparison of an event with the average. Inconclusive events are not used in the calculation of any of the averages, but can later be compared with both.  
The first step in this process is to calculate a first and coarse average to be used as a basis to which the other curves are aligned, \(\overline{s}\). This alignment process is required since the signal noise can cause the triggering to occur in slightly different parts of the  
The \(s\) signal is then shifted by \(n\), defined as  
\[n=o_{r}-o \tag{8}\]  
Then, the location of a cross-correlation peak between the resulting signal, \(s\), and the coarse average, \(\overline{s}\), is obtained:  
\[d=\operatorname{argmax}_{i}(\{\overline{s}_{1}\dots\overline{s}_{i}\dots \overline{s}_{N_{s}}\}\star\{s_{1}\dots s_{i}\dots s_{N_{s}}\}) \tag{9}\]  
Then, the final adjustment for this event is given by  
\[n=o_{r}-o-N_{s}+d \tag{10}\]  
which applies to all signals for this event, from their original form.  
For the closing event type, the process follows a similar approach, where the offset is first removed from the reference sensor's signal, \(s_{i}\):  
\[s_{i}=s_{i}-s_{N},\ \ \ i\in\{1\dots N-1\} \tag{11}\]  
Then, a rough alignment is also performed considering two reference levels, \(ro\) and \(rc\):  
\[\begin{split} c&=\max_{i}(i\in\{1\dots N\}\quad| \quad s_{i}>r_{c})\\ c_{r}&=\max_{i}(i\in\{1\dots N\}\quad|\quad\overline {s}_{i}>r_{c})\end{split} \tag{12}\]  
The \(s\) signal is then shifted by  
\[n=c_{r}-c \tag{13}\]  
Finally, the cross-correlation peak between the resulting signal, \(s\), and the coarse average, \(\overline{s}\), is obtained:  
\[d=\operatorname{argmax}_{i-N_{s}+1}(\{\overline{s}_{N_{s}}\dots\overline{s}_ {i-N_{s}+1}\dots\overline{s}_{N}\}\star\{s_{N_{s}}\dots s_{i-N_{s}+1}\dots s_{ N}\}) \tag{14}\]  
The final adjustment for all signals of this event, from their original form, can be defined as  
\[n=c_{r}-c-N+2N_{s}+d-2 \tag{15}\]  
In both event types, a fine average can then be calculated for each sensor, following Equation (7), after shifting the specified amount and filling any gaps with NaNs.
## 3 Results
### Effects of N addition on C exchange Fluxes, ET, WUE and LUE in a Chronosequence
The effects of N fertilization on the carbon (C) component fluxes were estimated as the differences between the measured C fluxes and their corresponding modeled values. Figure 6: Artificial neural network (ANN) simulated and eddy-covariance tower measured monthly water-use efficiency (WUE) and light-use efficiency (LUE) in all three stands for the four post-fertilization years 2007–2010. (**a–c**) for WUE and (**d–f**) for LUE. The effects of N fertilization on WUE and LUE were estimated as the differences between the measured WUE and LUE values and their corresponding modeled values. Figure 7: Comparisons of measured and artificial neural network (ANN) simulated annual gross primary productivity (GPP), ecosystem respiration (\(R\)), net ecosystem productivity (NEP) and evapotranspiration (ET) at DF49 from 1998 to 2010. (**a**) for the ANN calibration years 1998–2004; (**b**) for the model validation years 2005–2006; and (**c**) for the four post-fertilization years 2007–2010. The error bars with \(\pm\) 1 SD are the standard errors for ANN-modeled annual values estimated using the optimized model parameters. \begin{table}
\begin{tabular}{l l|c c|c c|c c|c c|c c|c c|c c|c c} \hline \hline \multirow{2}{*}{**Sound**} & \multirow{2}{*}{**Flux**} & \multicolumn{2}{c|}{**2007**} & \multicolumn{2}{c|}{**2008**} & \multicolumn{2}{c|}{**2009**} & \multicolumn{2}{c|}{**2010**} & \multicolumn{2}{c}{**Mean (2007-2010)**} \\ \cline{3-14}  & & \multicolumn{2}{c|}{**Measured**} & \multicolumn{2}{c|}{**Model**} & \multicolumn{1}{c|}{**N effect**} & \multicolumn{1}{c|}{**Measured**} & \multicolumn{1}{c|}{**Model**} & \multicolumn{1}{c|}{**N effect**} & \multicolumn{1}{c|}{**Measured**} & \multicolumn{1}{c|}{**Model**} & \multicolumn{1}{c|}{**N effect**} & \multicolumn{1}{c|}{**Measured**} & \multicolumn{1}{c|}{**Model**} & \multicolumn{1}{c|}{**N effect**} & \multicolumn{1}{c|}{**Measured**} & \multicolumn{1}{c|}{**Model**} & \multicolumn{1}{c|}{**N effect**} \\ \cline{3-14}  & & & & & & & & & & & & & & & & & & & & & & & \\ \cline{3-14}  & GPP & 2095 & 1943 & +152 & 2111 & 1936 & +175 & 2103 & 1982 & +
## 3 Queuing Model and Dynamic Spectrum Algorithm  
In a land communication system, the priorities of SUs are always decided by the data rate requirement of the service and the high real-time requirement of the services [30], while in maritime communication systems, the priorities lie in whether the service is about navigational safety. Services such as position reports and early warning information must have higher priorities than general services, which are not related to navigational safety. To differentiate whether the services involve the information of navigational safety, the CBS divides the communication requests of SUs into a high-priority request queue \(SU_{q1}\) and a low-priority request queue \(SU_{q2}\). Based on the two queues with different priorities, we introduce the idea of queuing theory and construct a regional spectrum allocation algorithm based on a queuing model with two queues and multiple servers.  
Figure 2: Schematic diagram of the data interaction between the secondary user (SU) and the central base station (CBS).
## 2 Material and Methods
### Estimation of the Total Imperviousness Dense Residential Suburbs  
Two methods were used to estimate total imperviousness in dense residential suburbs in this study. For this purpose, 67 suburbs with residential imperviousness more than 70% were selected. In method 1, the best relationship between population density and total imperviousness (see Section 2.4) was used. In method 2, residential imperviousness was predicted from population density using the best regression model explained in Section 2.5. Non-residential imperviousness was also predicted using the following steps:  
First, to prevent overestimation of non-residential imperviousness, a ratio (M) showing non-residential impervious area per person in the 67 dense residential suburbs, with more than 70% residential imperviousness, was calculated (Equation (4)).  
\[\text{M}=\frac{\sum_{\text{I}}^{\text{N}}\text{ISA}_{\text{c}}}{\sum_{\text{ I}}^{\text{N}}\text{P}} \tag{4}\]  
where, ISAc, P, and N are non-residential impervious areas, population, and number of dense residential suburbs, respectively.  
Second, non-residential imperviousness (I\({}_{\text{c}}\)) for each suburb was computed using Equation (5):  
\[\text{I}_{\text{c}}=\text{M}\frac{\text{P}}{\text{A}} \tag{5}\]  
where P is the population and A the area of each suburb.  
Total imperviousness was calculated by aggregating non-residential imperviousness (I\({}_{\text{c}}\)) and residential imperviousness (I\({}_{\text{r}}\)) (Equation (6)) for the suburbs:  
\[\text{I}=\text{I}_{\text{r}}+\text{I}_{\text{c}} \tag{6}\]  
Figure 2: Total imperviousness (%) at (**a**) Landsat pixel and (**b**) suburb scale. Numbers are in percentage (%). Figure has the same scale as Figure 1.  
Ultimately, estimated total imperviousness by the two methods were assessed by some commonly used statistical criteria, e.g., RMSE and MAE.
# Effect of Rotational Speed Variation on the Flow Characteristics in the Rotor-Stator System Cavity
###### Abstract  
In this paper, to study the effect of dynamic and static interference of clearance flow in fluid machinery caused by changes in rotational speed, the model was simplified to a rotor-stator system cavity flow. Investigating the flow characteristics in the cavity by changing the rotor speed of the rotor-stator system is of considerable significance. ANSYS-CFX was applied to numerically simulate the test model and the results were compared with the experimental results of the windage torque of the rotor-stator system. The inlet flow rate and geometric model remained unchanged. With an increase in the rotating Reynolds number, the shear stress on the rotor wall gradually increased, and the maximum gradient was within \(l^{\prime}<0.15\). In addition to the shear stress, the tangential Reynolds stress \(R_{\rm{e}}\) contributed partly to the torque on the rotor wall. The swirling vortex formed by entrainment in the cavity of the rotor-stator system tended to separate at \(R_{\rm{e}}\)= \(3.53\times 10^{6}\). As the rotating Reynolds number continued to increase, the secondary vortex finally separated completely. The strength of the vortex in the rotor turbulent boundary layer decreased with an increase in the rotating speed, but the number of vortex cores increased with the increase of speed. Depending on the application of the fluid machine, controlling the rotating speed within a reasonable range can effectively improve the characteristics of the clearance flow.  
Keywords:rotor-stator system; clearance flow; Reynolds stress; vortex structure; torque coefficient
# Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
## 4 Experiments and Results
### Results
#### 4.5.3 Mixed-Type Defect Result  
Figure 12 shows the accuracy graph and confusion matrix for mixed-type defects. The test accuracy was more than 0.97 by epoch 15, and we could obtain results that maintained high accuracy without reduction.  
Table 3 details the detection performance for mixed-type faults. The multi-label accuracy for mixed-type defects reached 0.979. Sometimes, a mixed-type defect was judged to be a single defect when it had a combination of defects with similar morphologies or a combination of defects that mostly overlapped in positions. For this reason, the performance of the model was lower than for a single defect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Defect Type** & **Accuracy** & **F1-Score** & **IoU** \\ \hline Two-types Mixed & 0.979 & 0.982 & 0.645 \\ Three-types Mixed & 0.962 & 0.953 & 0.582 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mixed-type testing result.  
Figure 11: Segmentation Results in Single Defects: (**a**) Donut defect; (**b**) Center defect; (**c**) Edge-Loc defect; (**d**) Edge-Ring defect; (**e**) Scratch defect; (**f**) Loc defect.  
Figure 13 shows some examples of segmentation results of mixed defects. Mixed defects were also able to accurately predict most of the defects. However, it was difficult to detect all the defects when they overlapped by a certain ratio or more, for example, if Local and Edge-Loc defects overlap and are considered to be one Local defect, or if Scratches overlap with other defects and cannot be detected.  
Figure 12: Testing results of mixed-type defects: (**a**) testing accuracy graph. The accuracy of the model according to the epoch is shown; (**b**) normalized confusion matrix (C + EL, C + ER, C + L, C + S, D + EL, D + ER, D + L, D + S, EL + L, EL + S, EL + L, ER + S, L + S).  
Finally, we compared the performance with that of existing studies in Table 4. Our model was superior to the basic models, U-Net and FCN, in all aspects. We obtained improved results because the residual attention block focused on the defect and considered the surrounding defective dies to be noise. The better performance can be confirmed even in comparison with previous studies. A few studies did not provide IoU, so we could not compare them, but for accuracy or F1-score, our model showed an improvement of 1% to 18%.
## 2 Modelling
### Graphene Oxide Model  
To build the GO monolayer, we used an oxygen content that matched that of the experimental samples [23; 24]. Generally, the protonation or deprotonation of the hydroxyl groups and carboxyl groups (acid groups) is a function of the pH [42]. In our model, we assumed that the groups were in the neutral state (-OH and -COOH). The weight percentage of the monolayer was 59.5% C, 39.2% O, and 1.3% H, which was close to the elemental analysis of the actual GO samples used in the experiments (49-56% C, 41-50% O, 0-1% H) [42]. We introduced hydroxyl and epoxy groups on the basal plane of a graphene sheet and carboxyl groups on its periphery. The functional groups were distributed randomly on both sides of the sheet. Specifically, we used 87 epoxy groups, 101 hydroxyl groups, and 35 carboxyl groups. The resulting GO monolayer contained 916 atoms, of which 522 atoms were carbons (C), 258 oxygens (O), and 136 hydrogens (H).  
The GO monolayer was optimized using steepest gradient energy minimization. Next, we built a three-layer structure of GOs by placing 12 GO monolayers, as illustrated in Figure 2. We set the interlayer distance at 9 A. This was the initial configuration of a bidimensional 50% superposition of the GO nanosheets. We used full periodicity in the simulation box. The box dimensions were 115 \(\times\) 76 \(\times\) 32.21 A\({}^{3}\).  
Figure 2: (**a**) GO layer structure. (**b**) Top view of GO box containing 3 layers of GO, each comprising 4 sheets. (**c**) Long and (**d**) short side view of the GO layers. In panels (**b**–**d**), each sheet is colored differently to make it easy to understand their spatial organization. Atoms are pictured with respect to their van der Waals radii.  
The multilayer GO structure was solvated in water by setting the water density to 1 g/cm\({}^{3}\). The solvation process introduced 15,936 water molecules (47,808 atoms) into the box. We performed a steepest gradient minimization followed by an NPT simulation over 0.5 ns to ensure that the water molecules were not overlapping with the Connolly isosurface (i.e., the surface traced by the van der Waals radii of the atoms) of the GO nanosheets. Consequently, we performed a long NPT simulation over 4 ns, using a timestep of 1 fs for data production.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 4 Topology
### Mobile Agent Based  
The main characteristic of this subcategory is the Mobile Agent, which is a program that migrates from node to node and performs tasks autonomously, based on environmental conditions. In what follows in this section, two typical examples of mobile agent based energy efficient routing protocols are described. _Multi-agent based Itinerary Planning_ (MIP) [132] protocol is used when a single agent based itinerary planning protocol is not sufficient as in a network of large scale. MIP defines an impact factor for every node and distributes it to every sensor node in the network. Then, the source with the greatest impact factor is selected. The main advantage of MIP is that lower energy consumption is achieved with small number of sources. Its disadvantage is that high delay is caused. _Itinerary Energy Minimum for First source selection_ (IEMF) [133] initially picks an arbitrary source sensor node \(v\) as a tentative \(S\) (1) leaving the rest of sources in a set \(V-\{v\}\). The \(v\) is set as the starting point, while the rest of the sources in the set will have their itineraries evaluated by visiting in sequence, everyone in the set, until all routes, starting from the base station to sources, are obtained. Finally, the route with the minimum cost of energy is selected. The Itinerary Energy Minimum Algorithm
## 3 Results
### Data Balancing: Application of Enhanced Sampling Methods
#### 3.3.3 Integrated Balancing Processing Results  
The integrated method of SMOTE+ENN and ADASYN+ENN was employed to process the dataset, aiming to control the relative balance of sample quantities for each label (Figure 6). Initially, datasets processed by SMOTE or ADASYN over-sampling algorithms exhibited rich and fairly balanced sample quantities for each class. Subsequently, after ENN filtering, samples with ambiguous class attributions were removed. At this stage, the sample quantities for each class became comparable yet distinct. Both sets of datasets achieved a relatively balanced state. Upon comparing the principal component frequency densities, it is evident that both integrated methods maintained the original distribution shape of samples in the principal component attributes. Specifically, the results of the SMOTE+ENN approach resemble the principal component frequency distribution of the original dataset (Figure 7h,k) more closely. These findings demonstrate that integrated methods can uphold the overall consistency of key features in the data distribution, making them applicable in intelligent lithology discrimination models.
## 4 Strain Measurement System Development and Results
### Experimental Setup for Strain Measurement  
The experimental setup for strain measurement in a spur gear is shown in Figure 6. It consists of a test rig, NI USB 6255 and NI SCXI 1314 data acquisition devices (both were provided by National Instruments Corp. Austin, TX, USA) and TML 1-11 3L strain gauges (provided by Tokyo Measuring Instruments Laboratory Co., Ltd., Tokyo, Japan) and slip ring MZ060-S20 (provided by Moflon Technology, Shenzhen, China). The drive gears are positioned on one side of the blocks, while the tested gears are positioned on the other side, where the gauges were glued in different places to capture the signals when the gear rotates.  
Figure 5: The layout of the test rig with a closed loop.  
As shown in Figure 7, the gauges were affixed to the cleaned and polished surfaces of the tested gear at certain positions using a suitable adhesive, then connected to the slip ring. The gauges were wired to the rotating part of the slip ring, which rotates with the tested gears. The stationary part, on the other hand, remains fixed while they are in gold-gold contact. While the gears were loaded in the closed loop, the recorded signals were transmitted to the D/A converter and, finally, to the data acquisition and computer for signal analysis.  
To evaluate the suitability of the lattice structure, the strain of two pairs of gears was measured. Thus, two solid gears were considered as pairs to determine the strain, which is used as a reference in Figure 7a. The other pair consisted of a lattice-structure gear and a solid gear, as shown in Figure 7b. For both gears, the strains on the ring and hub were recorded in the radial and tangential directions. The strain was also recorded at the tooth  
Figure 6: Experimental setup for strain measurement.  
Figure 7: The tested gears with attached strain gauges: (**a**) solid body gear; (**b**) lattice-structure gear.  
root to detect localized stress concentrations. The torque applied during the test was 60 Nm, with the gear rotating at 310 rpm. The total duration of the measurement was 0.95 s.
## 3 Results and Discussion
### Sensor Data Validation  
In order to demonstrate the usefulness and viability of the deployed network of nodes, data registered over a significant period of time from an agronomic and sensor network reliability point of view are shown below.  
Figure 11 shows the evolution of the VWC at both 25 and 45 cm depth, measured by 10 HS sensors (node 04). Irrigation was scheduled every two or three days at 11 a.m. and it took 2 h. VWC values ranged between 0.413 and 0.336 m\({}^{3}\) m\({}^{-3}\) during the test period. The maximum values were reached during the irrigation events. After the irrigation events, VWC values fell due to infiltration and they stabilized at values close to 0.358 m\({}^{3}\) m\({}^{-3}\). The information registered by MPS-6 sensors (Figure 12) was complementary to that obtained by 10 HS sensors (Figure 11). In each irrigation event and depth, the value increased until it was close to \(-7\) kPa, being considered this as field capacity [48]. The falls of MPS-6 sensors were steeper than those registered by 10 HS sensors. Both types of sensors detected the irrigation events between 1 and 2 h after irrigation events started in both depths. During the test period, evaporative demand increased strongly, going from a reference crop evapotranspiration of 2.5 to 4.0 mm d\({}^{-1}\) at the end of the study period, which accelerated the falls of the VWC and soil matric potential values, as well as it showed a greater drop in values. The values showed both types of sensors at saturation level were similar to those reported by [49] on similar texture soil.  
Figure 11: Evolution of VWC sensors at 25 and 45 cm depth measured by node 04 with 10 HS sensors (year 2020). The arrows indicate the irrigation events.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & 6 & 5 & 3 \\ \hline
**Description** & Sensors preheating & Sensors reading & Access to microSD \\ \hline
**I\({}_{\text{average (mA)}}\)** & 46 & 170 & 150 \\ \hline
**Duration (s)** & 14 & 17 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Description of functional states of node B—mode (iii).  
Additionally, the nodes automatically and continuously monitored plant water status with LVDT sensors. Figure 13 shows trunk diameter fluctuations (TDF) where the daily cycle of shrinkage for RDI30 for three consecutive days can be observed. The maximum trunk diameter (MXTD) was reached at the beginning of the day and the minimum trunk diameter (MNTD) was recorded during the afternoon. From data obtained from TDF, two indices were calculated to establish plant water status. The daily trunk shrinkage values, calculated as the difference between MXDT and MNTD on the same day, were 271, 411 and 292 m for three consecutive days, respectively. The trunk growth rate values were calculated as the difference between MXTD of two consecutive days. In general, they were close to 0 m throughout the period considered. The trend and values obtained were in agreement with those reported by [50; 51].