esa-sceva/satcom-chunk-collection huggingface dataset:
# DeepProfile: Accurate Under-the-Clothes Body Profile Estimation
## 5 Experimental Results and Discussions
### Training and Comparison for Contour Keypoints  
To compare with state-of-the-art methods, we chose Hourglass [33], CPN [36], HRNet [38], SimpleBaseline [31], MSPN [37], DARK [39] and DEKR [40], seven existing mainstream methods, to train on front-view and side-view images separately. We used their publicly available codes and trained them on our proposed training set for a fair comparison. For front-view images, we generated a bounding box based on the contour keypoints to remove some background. Especially, for CPN, \(\sigma\) was set as 2,3,4 and 5 to generate the heatmaps, because these values obtained higher accuracy results in our dataset. For our method, we used the Adam optimizer. The learning rate started with \(2.5\times 10^{-4}\) and decreased to \(2.5\times 10^{-5}\) at the 180 th epoch, then decreased to \(2.5\times 10^{-6}\) at the 220 th epoch and, finally, stopped at the 240 th epoch. The batch size for training was 24. For side-view images, we trained these methods the same way as we trained them on front-view images. The result comparison of contour keypoint estimation between our method and other methods is shown in Table 1; our method outperformed the second one by 0.53 in respect to front-view images and 0.94 in respect to side-view images.  
To further prove the effectiveness of our SMSD block, we designed an ablation experiment to show the results with different numbers of SMSD blocks in the network. As shown in Table 2, the single-SMSD-block model performance was 79.24 with front-view images and 69.16 with side-view images. By contrast, the model with two SMSD blocks led to a 0.18 improvement with front-view images and a 0.94 improvement with side-view images. However, when we increased the number of SMSD blocks to three, it only led to a 0.02 improvement with front-view images and a 0.1 improvement with side-view images, while more parameters were required.  
Moreover, we also tried to remove the dense connections from \(m1\), \(m2\), \(m3\) and \(m4\) to \(p1\) and \(p2\) in the SMSD block to test the effectiveness of the dense connections. From Table 2, we can see thaet a single SMSD block with dense connections led to a 0.46 improvement with front-view images and a 0.6 improvement with side-view images. In addition, we compared our SMSD block with Res2 Net-50 [43]. Without these dense connections, Res2 Net-50 outperformed the one-SMSD-block model by 0.44 with front-view images and 0.46 with side-view images. However, with these dense connections, the one-SMSD-block model outperformed Res2 Net-50 by 0.04 with front-view images and 0.12 with side-view images.
## 3 Results and Discussion
### Initial Cell Concentration Impact  
The influence of the initial cell concentration of _Candida glabrata_ on the microwave discharge inhibition efficiency was observed using undiluted culture, 10- and 100-fold dilutions of _Candida glabrata_. In general, we can say that the size of the inhibition zones increased with the lower cell concentration, namely that the largest zones could be observed for the 100-fold dilution, even after the shorter plasma exposure time (see Figures 10 and 11).  
However, for the 100-fold dilution, a homogeneous distribution of the cells over the entire surface of the Petri dish cannot be guaranteed, and therefore the parallel measurements differed more than the in case of the original concentration or the 10-fold dilution. Additionally, it was impossible to calculate the inhibition zone at the 100-fold dilution due to the low yeast concentration after the 24 h incubation.  
Based on our previous experimental results obtained by optical emission spectroscopy [34], we suppose the action of reactive oxygen and nitrogen species (RONS) produced by our torch that can play a significant role in the decontamination process. Even though we used pure argon as the carrier gas, molecules contained in the surrounding air (oxygen, nitrogen, water vapor, etc.) were swept into the plasma flow where they were excited or ionized. Therefore, the presence of RONS such as nitrogen monoxide radical (NO), hydroxyl radical (OH), atomic oxygen (O), peroxynitrite molecule (ONOOH) or ozone (O\({}_{3}\)) was determined in our plasma torch [34]. The NO radical is very reactive and, in a reaction with water, forms nitrova acid, which decreases the substrate pH. This effect might contribute to the deactivation of some kinds of microorganisms. In addition, the OH radical is well  
Figure 11: Dependence of the inhibition zone size on the exposure time using power of 12 W.  
Figure 10: Dependence of the inhibition zone size on the exposure time using power of 9 W.  
known as one of the strongest nonselective oxidative species. Furthermore, UV radiation of the NO radical belongs to the UVC region, which is well known for its bactericide effects [14].
## 2 System Model of Power Grid
### The Self-Triggered Controller  
Compared with the distributed control scheme, the advantage of the centralized control scheme in STC is that it reduces the conservativeness of control system, and further decreases the communication cost [40; 41]. In order to present the self-triggered control scheme, the power system is formulated as a linear dynamic system in a form of:  
\[\dot{x}=Ax+Bu+EW \tag{4}\]  
where \(x=(x_{i})_{n\times 1}\), \(1\leq i\leq n\) and \(i\in N^{+}\); \(A=(A_{ij})_{n\times n}\), \(A_{ii}=Ai\),\(1\leq i,j\leq n\) and \(i,j\in N^{+}\), if no connection exits between subsystem \(i\) and \(j\), \(A_{ij}=0\); \(B=diag(B_{i})_{n\times 1}\) and \(E=diag(E_{i})_{n\times 1}\), \(1\leq i\leq n\) and \(i,j\in N^{+}\); the control input \(u=(u_{i})_{n\times 1}=(-L_{ij})_{n\times n}\), \(x=-Kx\), \(L_{ii}=K_{i}\), \(1\leq i,j\leq n\) and \(i,j\in N^{+}\), and \(L_{ij}=0\), if there is no connection on subsystems \(i\) and \(j\); the disturbance is \(W=(w_{i})_{n\times 1}\), \(1\leq i,j\leq n\) and \(i,j\in N^{+}\). The control objective is to drive state \(x\) to origin zero by the linear controller.  
In a self-triggered control scheme [42], the local state \(x_{i}(t)\) can be acquired by observers (sensors). However, the information remains within the \(i\)th subsystems and is not shared within the system controller unless a pre-calculated triggering time is up, a self-triggered state is reached and a message is sent via data communication links. Thus, the control signal from controller remains constant and may change only after a self-triggered message is received. Self-triggered control promises the reduction of communication cost without sacrificing control performance. For a self-event triggered controller, the dynamic of power system in \(k\)th triggering is:  
\[\dot{x}(t)=Ax(t)+Bu(t_{k})+EW(t),t_{k}\leq t<t_{k+1} \tag{5}\]  
and the control output \(u(t_{k})\) is:  
\[u(t_{k})=(-L_{ij})_{n\times n}x(t_{k}),1<i,j<n\quad and\quad i,j\in N^{+} \tag{6}\]  
As shown in Figure 3, for a self-triggered control scheme, the controller obtains system state \(x_{1}\), \(x_{2}\), \(x_{3}\) from sensors or observers of each subsystem, when the time for triggering \(t_{k}\) is up. Then, the next triggering time \(t_{k+1}\) is calculated by using the previous system state \(x(t_{k})\). Meanwhile, the controller calculates the new control output \(u(t_{k})\) with the obtained new system state. The new control output \(u_{1}(t)=u_{1}(t_{k})\), \(u_{2}(t)=u_{2}(t_{k})\), \(u_{3}(t)=u_{3}(t_{k})\), where \(t_{k}\leq t<t_{k+1}\) is then applied to its corresponding subsystems. Above all, the procedure of self-triggered control is:  
1. First, obtain the system state of each subsystem, when the time for triggering is up;
2. Second, calculate the time for the next triggering;Finally, apply the new control output, which is calculated by using the system state obtained in step 1.
## 3 Interference Mitigation Schemes
### Power Control Approach
#### 3.1.6 Power Allocation Using GA  
In [22], a power allocation algorithm is designed to mitigate interference while guaranteeing the QoS requirements. In this algorithm, an optimization model is used to minimize the transmission power of the sensor in a WBASN by satisfying the required transmission power under the constraint of sensor QoS requirements. The GA is applied to WBASNs to solve the optimization problem. The model for GA is a biological evolutionary process in which the individual of the population is the sensor power and the fitness function is the optimal power allocation strategy with minimum total power under QoS constraints. The following steps are used to solve that problem: (1) Initialize the individual in the generation; (2) evaluate the fitness function; (3) gene selection; (4) crossover process; (5) mutation process; and (6) evaluate the fitness value. Further information can be found in [22].
# Characterization and Reactivity of Natural Pozzolans from Guatema
## 2 Materials, Equipment, and Methods
### Methods  
To determine the quantity of insoluble residue present in VP, the method specified in Standard UNE-EN 196-2 [24] was followed. According to this method, the sample was first attacked with HCl and finally with KOH. The insoluble residue represented the part of the material that was not reactive because it did not dissolve in the alkaline medium.  
The technique followed for the electrical conductivity and pH measurements to monitor the pozzolanic reaction progress in the saturated lime/pozzolan mixtures was proposed by [PERSON] et al. [25]. In this method, the electrical conductivity and pH of the aqueous suspension of different Ca(OH)\({}_{2}\)/VP mixtures were measured for 7 days at 60 \({}^{\circ}\)C. In a 100 mL Erlenmeyer flask, 50 mL of deionized water was added at the following Ca(OH)\({}_{2}\)/VP proportions: 0.5:9.5, 1.0:9.0, 1.5:8.5, 2.0:8.0, 2.5:7.5, 3.0:7.0, and 0.0:10.0 (the last sample was employed to measure pozzolan's contribution to electrical conductivity). The total amount of solid mass was 1 g.  
The pozzolanicity test Is a standard method specified in EN196-5 [26]. Samples were prepared by mixing PC and VP with 15%, 25%, 35%, 45%, and 55% replacements. After preparation, the mixture was left in a closed polyethylene bottle at 40 \({}^{\circ}\)C for 8-15 days. After this time, the solution was filtered and analyzed by titration for CaO and OH\({}^{-}\).  
For the TGA analysis, the Ca(OH)\({}_{2}\)/VP pastes were prepared by mixing 6.7 g of hydrated lime with 13.3 g of pozzolan (1:2 mass ratio) and 14 g of water (water/binder ratio of 0.7). These pastes were cured at 20 \({}^{\circ}\)C and 40 \({}^{\circ}\)C. The PC/pozzolan pastes were prepared by mixing 7.5 g of cement and 2.5 g of pozzolan (25% replacement) at a water/binder ratio of 0.4. An additional control paste with only PC was prepared. These pastes were cured at 20 \({}^{\circ}\)C.  
The methodology and study of mortar properties were conducted in accordance with the European UNE-EN standard [23]. The mixtures were initially prepared using the CEM II/A-L 32.5N type with 15%, 25%, and 35% (by weight) replacements of cement with VP, as summarized in Table 1. The blended cement mortars 'compressive strength was measured after 28, 90, and 180 curing days. Cement mortars were prepared at a water/binder ratio of 0.47 (binder was the sum of CEM II and VP). Several mortars with 25% replacement (CEM II+25%R and CEM II+25%T series) were prepared at different w/b ratios: 0.41, 0.44, 0.47, 0.50, and 0.53. A set of mortars was also prepared using limestone filler instead of VP at the same replacement percentage (CEM II+25%LF series). The doses of the mortars are summarized in Table 2, where the letter X represents the quantity of limestone filler (LF) or pozzolan R or T added to the mixtures. The compressive strength was obtained at 28 and 90 curing days. The fresh mortars' workability was evaluated according to Standard UNE EN1015-3 [27].
# Would Forest Regrowth Compensate for Climate Change in the Amazon Basin?
## 3 Results and Discussion
### Sensitivity of Fluxes and Precipitation to Land Cover Change across the Basin
#### 3.2.1 Heat Flux  
Figures 4 and 5 show the effects of LCC on LH and sensible heat (SH) (only significant changes are shown here). According to Figure 4, the LH has increased by 30 Wm\({}^{-2}\) during May-November and by 15 Wm\({}^{-2}\) during December-April despite some extreme increases in the north side of the region. We found no pronounced negative changes in the domain-averaged mean SH across the region with reforestation. As the land surface has a complex relationship with the atmosphere, SH did not show significant sensitivity to changes in the land surface biophysical characteristics at a seasonal scale. There is only the northeast area of the basin which shows a significant decreasing trend for SH with reforestation. This decrease is the highest in December-April which is geographically consistent with the highest increase in LH during the same time period.  
Next, we looked at monthly changes. For regions with added tree cover, the LH has increased by 20, 50, and 30 Wm\({}^{-2}\) in July, August, and September, respectively. SH shows a decrease of 10 Wm\({}^{-2}\) in August and September at the same location. These months are in the dry season, therefore, an increase in the LH can provide more moisture to the environment if other criteria are met. By adding more vegetation cover through reforestation or forest rehabilitation, the transpiration rate and surface roughness increased leading to an increase in the LH and a decrease in SH. Since July has the highest LAI in the basin and it decreases toward the end of the year, we found the highest influence of LCC on exchanges of both SH and LH starting in July.  
The effects of LCC on the temperature are spatially different in May-November and December-April. Reforestation decreased the surface temperature by about 1.2 \({}^{\circ}\)C in the northeast part of the basin and about 0.2 \({}^{\circ}\)C on the west side of the basin (Figure 6), far from the reforested areas. The increased ET drives a significant increase in the cloud cover that gets advected westward. The cooling effect of reforestation is clearer on a monthly scale, especially in Aug and Sept by about 2 \({}^{\circ}\)C. This finding is consistent with Ref. [38] who found 2 \({}^{\circ}\)C warmer air temperatures as a result of deforestation, as well as Ref. [18] who found 0.3 \({}^{\circ}\)C warmer surface temperatures due to deforestation of the Xingu region along the arc of deforestation.
## 4 Numerical Results and Discussion  
### Mesh Independence Verification  
Table 4 shows the mesh independence verification results of the optimized model. When the total elements were beyond 9,461,970, the change in static pressure was small.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 4 Proposed Approach
### Models Development Phase  
After segmenting the training data into a set of clusters, the next step is to develop a classification model for each cluster. To do that, several classification algorithms are trained and evaluated on each cluster using the cross-validation methodology. The goal is to find the most suitable and expert model for each cluster. For example, suppose we have three classification algorithms called \(X\), \(Y\), and \(Z\). All algorithms will be trained and evaluated based on each cluster. For example, if algorithm \(Y\) produced the highest average accuracy over the cross-validation process based on a given cluster, then \(Y\) will be assigned to this cluster for future predictions because it showed higher prediction power than algorithms \(X\) and \(Z\). It is important to note that when there is a cluster of only one class the classifier works as one class classification algorithm. So, it trains based on one class in the training phase and detects the other class in the testing phase as outlier. After finishing this phase, each cluster will have its own expert model. Note that the best classifier can be different from one cluster to another. Figure 2 gives an example of this phase with three different classifiers trained on three clusters, and it shows how the classifiers in the final model are selected.  
In this work, two types of classifiers are implemented to produce two versions of the proposed approach. In the first version, basic classifiers are used. This version will be referred to as K-Means/Basic classifiers (KMB). In the second version, ensemble classifiers will be utilized. The latter version will be referred to as K-Means/Ensemble classifiers (KME).
## 4 Hardware Estimation and Comparison
### Performance Comparison  
In order to more intuitively to compare the hardware efficiency of each architecture, the comprehensive evaluation criteria area-delay-product (ADP) is proposed to [12]. However, ADP suffers high relevance to constraint rules and technologies. Thus, we adopt the transistor-delay-product (TDP) [14] to assess the hardware efficiency of architectures. The equation of TDP is shown in (7), where TC (Transistor Count) stands for the count of transistors and ACT (Active Cycle Time) is the computation time of an image in clock cycles, which can be calculated by ACT = N2/throughput.  
\[\text{TDP}=\text{TC}\times\text{CPD}\times\text{ACT}\;(\text{transistor}\cdot \text{s}) \tag{7}\]  
Hence, it can be considered that TDP takes into account the hardware consumption and total computing time for an image of the proposed architecture. Furthermore, the smaller the TDP is, the better hardware efficiency the architecture achieves.  
For the assessment of the transistor count, a method for calculating the number of transistors based on the ripple carry adders (RCA), RCA-based multipliers, D flip-flops register and single-port SRAM for all the memory words, which are assumed to be implemented for all our structures is proposed to [9]. Also, it is assumed that \(Tm=2 Ta\) and \(Ta=3.01\) ns.  
After paired with these factors, Table 2 shows the comprehensive comparison of the 3-level 2-D DWT using different architectures of \(512\times 512\) image size. As discussed previously, the proposed structure adopts an overall module design with a 2:1 parallelism ratio between the lower level and the above level, which reduces the temporal memory between two levels and eliminates the frame buffer compared with the folded structure. And the internal folded architecture reduces the consumption of computing resources. Meanwhile, the CPD of the proposed structure is also reduced to \(2 Ta\). Besides, all of these optimizations are reflected on TDP. For S = 8, the proposed architecture has the highest throughput rate. This architecture increases the hardware efficiency by more than 22.4% for S = 8 and 25.77% for S = 16 in TDP, compared with the existing parallel architectures.  
Then the synthesis results and comparison with the existing architecture in the same TSMC 90 nm CMOS library with Synopsys Design Compiler are tabulated in Table 3. Meanwhile, the power is estimated at 20 MHz frequency. EPI (energy per image), the energy consumption of decomposing an image, is also calculated and listed in Table 3. It can be seen that, the proposed architecture achieves the least ADP, about 30.6% less than the others, and the least EPI.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multirow{2}{*}{**S**} & **Throughout** & \multirow{2}{*}{**Multiplier**} & **Adder** & **Register** & **MEM** & **CPD** & **TC** & **ACT** & **TDP** \\  & & **Rate** & & & & & & & & (\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text
## 3 Proposed Work
### Link Communication
#### 3.4.2 Cluster Head Communication  
Zone 2 and zone 3 are equipped with advanced nodes, which have higher energy than normal nodes. These nodes use clustering technique to deliver records to base station. The advanced nodes form a cluster, and the cluster head is elected among those nodes in the cluster, while the remaining nodes become an element of this cluster. These cluster associates sense the environment and then transmit data to cluster head. Cluster heads collect the records, processes and deliver them to the sink.  
Transmission of data to cluster heads occurs during the time slot allocation in each node. To save energy, only transmitting nodes remain active, while all other nodes in the cluster turn-off their radio. Cluster head will start the data processing, after the all nodes data transmission in the cluster is ended up. Cluster head receives the data and then aggregates it to remove any redundancy. In addition, compresses the data as much as  
Figure 2: Nodes connection to base station.  
possible for fair bandwidth utilization. Cluster head then forwards the data to the sink or BS. This stage is called Steady-State Stage.  
The entire above two processes are illustrated in flowchart as shown in Figure 3.
## 3 Waveguide Design: Cut-Off Analyses
### Hexagonal Ridged Waveguides
However, we selected Mode 1 and Mode 2 in Figure 8a,b because their E-fields are polarized along the two principal axes of our reference system (x and y) and can be easily compared to the ones of the quadridge structure. Similar considerations apply for the hexagon with six ridges, for which the field modes are shown in Figure 8d-f. Their distribution is actually closer to the quadridge waveguide due to their two-fold symmetry. Comparing the electric field distribution between the two hexagonal apertures, we should expect symmetrical scan loss and XPD (with respect to the broadside) and higher polarization purity (see Section 4.2) for the six-ridge configuration due to its two-fold symmetry. However, it should be mentioned that XPD performance is due to a combination of modes in the aperture (modes are coupled at the discontinuity between waveguide and free-space), such a performance is rigorously evaluated using the provided XPD plots. Figure 8: Electrical field distribution for the hexagonal waveguide modes. (**a**) Hexagon with three ridges (Mode 1); (**b**) hexagon with three ridges (Mode 2); (**c**) hexagon with three ridges (Mode 3); (**d**) hexagon with six ridges (Mode 1); (**e**) hexagon with six ridges (Mode 2); (**f**) hexagon with six ridges (Mode 3). Figure 9: Degeneration modes in a three-ridged waveguide mode.
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
## 3 Mathematical Model Description  
In this study, a comprehensive mathematical model of a UGS facility was used for the simultaneous simulation of real-time gas flow from the porous rock through the production string and surface equipment and electricity production incorporating a turboexpander. The model was based on an earlier mathematical UGS model described in detail in [45]. It consisted of several individual mathematical models (a reservoir model, a well model, and a surface pipeline model), which have now been upgraded and integrated with the GCAP model used for modelling the turboexpander parameters and calculating the power output. Using model-generated predictive case scenarios, it was possible to examine the immediate effect of uneven gas flow and pressure variation (resulting from gas storage natural working regime) on energy production via the turboexpander and to calculate the cumulative generated electricity via the turboexpander device during a typical withdrawal cycle. A mathematical model of the UGS facility was developed and validated using real field data from the Croatian UGS facility collected during both the production and injection storage work cycles, as documented in reference [43]. The model was validated on the production/injection history and was suitable as both a prediction and a simulation tool for the UGS process in Croatia. The data uncertainty was checked by a sensitivity analysis. Eclipse 100 software was used for the dynamic modelling of the reservoir, while the fluid flow in wells and process lines was modelled using the IPM 13.5 software package. The power output of the TE as a function of gas flow and inlet pressure was modelled using GCAP 9.3 software [41], and the IPM programme RESOLVE [42] was used for software coupling and integration.