esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Methods  
#### 2.2.1 Neural Network Setup  
All the setups consisted of a dense sequential network, where the nearby layers are fully interconnected (each neuron in a specific layer is connected to every neuron in the next layer). We used Keras and Tensorflow libraries [24] in Python to set up and train the NNs. While the NN design and the number of input variables were different for every setup (these are described in detail in the following sections), the output was always the same--a single forecasted value of either \(T_{\max}\) or \(T_{\min}\). The training was performed separately for each forecast lead time (the lead time was not used as a predictor). For example, the network for the 10-day forecast was trained independently of the network for the 100-day forecast. Thus each NN could focus on improving the performance at only one lead time, without heaving to worry about other lead times.  
To thoroughly analyze the behavior of each NN setup, we performed 50 realizations of the network training for every setup and thereby obtained an ensemble of NN models for each lead time. Due to random initialization of weights, each training produces a somewhat different NN, even though the network setup is the same and the NN is trained on the same data. Out of all available cases (3800 days), we used 90% for training and validation (of these 70% were used for training and 30% for validation), and the remaining 10% for testing. The 380 days in the testing set were selected randomly. The same set of testing cases was used to evaluate the performance of all setups and every training realization. Since the same test cases were used the comparison of performance of different setups and training realizations is more meaningful (as opposed to each setup using a different test set). Before the training, all the input data were scaled using the MinMaxScaler function from the Scikit-learn library [25], which normalized the input variables to the range from 0 to 1 (the normalization was performed separately for each input variable). For the training,  
Figure 2: Correlations of temperature \(T\), dew-point temperature \(T_{d}\) and relative humidity \(RH\) profiles with (**a**) \(T_{\max}\) and (**b**) \(T_{\min}\) measured on the same day. The correlations with measurements from the previous-day (\(T_{\max}(t-1)\) and \(T_{\min}(t-1)\)) and climatological values (\(T_{\dim}\)) are also shown by short vertical lines in the lower part of the graphs.  
we used Adam optimizer along with the mean absolute error metric (MAE, [26]) for the loss function. We experimented with the values for various hyperparameters such as batch size, number of epochs, activation functions and learning rate reduction (described in the following sections). Figure 3 shows an example of NN training.
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
## 7 Experimental Results  
In this section, we discuss some experimental results to complement our previously-established theoretical bounds. We simulated an RWA taking place in graphs evolving under the zero-step history model. We provided an experimental estimation of the value of the cover time for such a walk. To do so, for each specific graph and value of \(p\) considered, we repeated the experiment a large number of times, e.g., at least 1000 times. In the first experiment, we started from a graph instance with no alive edges. At each step, after the graph evolved, the walker picked uniformly at random an incident alive edge to traverse. The process continued till all nodes were visited at least once. Each next experiment commenced with the last graph instance of the previous experiment as its first instance.  
We constructed underlying graphs in the following fashion: given a natural number \(n\), we initially constructed a path on \(n\) nodes, namely \(v_{1},v_{2},\ldots,v_{n}\). Afterwards, for each two distinct nodes \(v_{i}\) and \(v_{j}\), we added an edge \(\{v_{i},v_{j}\}\) with probability equal to a \(randomThreshold\) parameter. For instance, \(randomThreshold=0\) means the graph remains a path. On the other hand, for \(randomThreshold=1\), the graph becomes a clique.  
In Tables 2-4, we display the average cover time, rounding it to the nearest natural number, computed in some indicative experiments for \(randomThreshold\) equal to 0.85, 0.5 and 0.15, respectively. Consequently, we provide estimates for a lower and an upper bound on the temporal cover time. In this respect, we experimentally compute a value for the cover time of a simple random walk in the underlying graph, i.e., the static cover time. Then, we plug in this value in place of \(C_{G}\) to apply the bounds given in Theorem 4. Overall, the temporal cover times computed appear to be within their corresponding lower and upper bounds.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Size** & \(\delta\) & \(\Delta\) & \(p\) & **Static Cover Time** & **Temporal Cover Time** & **Lower Bound** & **Upper Bound** \\ \hline
10 & 6 & 9 & 0.9 & 28 & 28 & 28 & 28 \\
10 & 7 & 9 & 0.5 & 28 & 28 & 28 & 28 \\
10 & 7 & 9 & 0.2 & 27 & 31 & 31 & 34 \\
10 & 7 & 9 & 0.1 & 29 & 50 & 47 & 61 \\
10 & 7 & 9 & 0.05 & 28 & 78 & 76 & 93 \\
10 & 7 & 8 & 0.01 & 28 & 356 & 83 & 413 \\ \hline
100 & 74 & 92 & 0.9 & 535 & 535 & 535 & 535 \\
100 & 74 & 91 & 0.05 & 530 & 543 & 535 & 543 \\
100 & 76 & 92 & 0.01 & 536 & 912 & 888 & 1003 \\
100 & 74 & 92 & 0.005 & 541 & 1476 & 1465 & 1746 \\ \hline
250 & 197 & 229 & 0.99 & 1551 & 1551 & 1551 & 1551 \\
250 & 194 & 228 & 0.75 & 1555 & 1555 & 1555 \\
250 & 192 & 225 & 0.01 & 1548 & 1744 & 1728 & 1810 \\
250 & 201 & 228 & 0.005 & 1538 & 2326 & 2259 & 2423 \\
250 & 198 & 225 & 0.001 & 1546 & 7948 & 7670 & 8603 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental results for randomly-produced graphs (\(randomThreshold=0.85\)).
## 3 Results and Discussion
### Assessment of the Methods
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**NO\({}_{2}\)**} & \multicolumn{2}{c}{**CO**} & \multicolumn{2}{c}{**O\({}_{3}\)**} & \multicolumn{2}{c}{**SO\({}_{2}\)**} \\ \hline  & **GBM** & **I-P** & **GBM** & **I-P** & **GBM** & **I-P** & **GBM** & **I-P** \\ \hline S1-Carapungo & 5.97 & 14.01 & 0.19 & 0.33 & 5.71 & 17.30 & 2.11 & 1.16 \\ S2-Cotocollao & ND & ND & 0.17 & 0.29 & 5.39 & 22.21 & 1.25 & 1.32 \\ S3-Belisario & 6.21 & 16.67 & 0.18 & 0.33 & 6.64 & 25.95 & 2.27 & 1.73 \\ S4-Centro & 6.50 & 16.86 & 0.22 & 0.26 & 5.55 & 17.93 & 2.43 & 2.39 \\ S5-Camal & 7.02 & 14.25 & 0.25 & 0.43 & 6.82 & 26.92 & 4.38 & 4.32 \\ S6-Guamani & 5.93 & 9.90 & 0.20 & 0.27 & 6.89 & 17.73 & 1.77 & 1.13 \\ S7-Chillos & 6.00 & 12.96 & 0.13 & 0.25 & 7.77 & 27.07 & 6.83 & 5.34 \\ Quito average & 6.27 & 14.11 & 0.20 & 0.32 & 6.17 & 21.34 & 2.37 & 2.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the performance between the two methods: gradient boosting machine (GBM) vs. inter-period analysis (I-P). The best accuracy (i.e., the lowest RMSE) is highlighted in yellow. lockdown impact on air pollution, the average values on the weeks [47] or the years [48] preceding the event are used as an approximation of the BAU. A limitation of the latter technique is the fact that it does not consider the meteorological conditions, which can significantly modify the assumed BAU pollution concentrations. In this study, we used the prior period averaging technique, but also provided a more robust estimation of the national protest on urban air pollution by developing a meteorology-normalized ML model [25,43] that takes into account the weather conditions during, and even after, the strike. The performance of the GBM method, compared to the averaging technique, is a more powerful method for predicting the true and lasting effect of an unusual event such as a protest on urban air quality. This unique investigation offers an example from South America, where the protests often get violent and involve a strategy of burning toxic barricade objects, covering the city in smoke. However, we show that BAU emissions from traffic in this high elevation city are larger than those from protest activities. Therefore, this study can serve as a valuable lesson to help better estimate the future changes in pollution due to changes in human activities, such as pandemics, protests, or strikes anywhere on the planet. In addition, it can serve as a call for urban planning strategies in developing countries to steer the city transportation problems towards greener and more sustainable solutions.
## 3 Research Methodology
### Framework of the Fourth Scenario  
The fourth scenario is designed to resolve both problems (high dimensionality and imbalanced class) and to avoid biased results from the sampling techniques in the fourth scenario. Therefore, the ensemble FS techniques were applied using different sampled data purely for the purpose of providing effective FS techniques. Figure 2 illustrates the framework for the fourth scenario that include five primary steps.  
* **Step 1.** We load seven datasets after the data analysis step (**Step 2** in previous section).
* **Step 2.** We create a fourth set of differently sampled data purely for the purpose of performing FS techniques: 1. Datasets without sampling. 2. Datasets with SMOTE sampling. 3. Datasets with SMOTE and spread sub-sample with the parameter set to 1, where these parameters define the distribution spread ratio between the majority and minority classes as 50:50. 4. Datasets with SMOTE and spread sub-sample with the parameter set to 2.1, where these parameters define the distribution spread ratio between the majority and minority classes as 35:65.
* **Step 3.** We apply ensemble FS using two filter-based feature ranking techniques, namely relief and Pearson's correlation coefficient on all sampled data in **Step 2**.
* **Step 4.** We compute the average of the feature ranking techniques in **Step 3** across all sampled data in **Step 2**.  
Figure 2: Framework of the fourth scenario.  
* **Step 5.** We select the best ten features that have the highest ranking from the sampled datasets in the third scenario. Therefore, the main difference between the third and fourth scenario is performing ensemble FS techniques in the fourth scenario. However, both the third and fourth scenarios used the same ensemble sampling techniques, namely, SMOTE, spread sub-sample with 2.1 ratio, and randomize.
## 1 Introduction
### Voltage-Harmonics Mitigation  
#### 3.6.1 Proposed Definition and Its Justification  
Harmonic pollution is a potential problem in distribution grids due to the proliferation of non-linear loads [107]. For this reason, the EN 50160 standard specifies maximum voltage harmonic pollution in distribution grids: the total harmonic distortion at any node should be less than 8% for 95% of the time within a week. Additionally, requirements are specified for individual harmonics too, e.g., the 5 th harmonic should be less than 6%, even-order harmonics such as the 2 nd less than 2%, etc.  
It is noted that it is one of the DSO responsibilities to preserve the voltage quality within its distribution system. The harmonic pollution of the voltage is caused by the harmonic currents flowing in the feeders and drawn by the non-linear loads and DRES that are converter-interfaced. Various standards, e.g., the IEC 61000 series or the IEEE 519 specify limits on the harmonic pollution of load currents while various grid codes and recent standards, e.g., IEEE 1547-2018 [10] impose limits on the harmonic pollution of the currents injected by the DRES. Until now, most of the DSOs hope that they can limit the harmonic pollution of the voltages in their distribution grids by assuming that the prosumers will follow the limits imposed by standards and grid codes with respect to the injection of harmonic currents. However, there is no guarantee that this will keep on happening given the rapid expansion of the non-linear loads and the continuous reconfiguration and expansion of the distribution grids, i.e., the continuous change of the feeder impedances. Moreover, research has shown that the increased voltage harmonic pollution limits the DRES hosting capacity of the distribution grids [108; 109; 110].  
On the other hand, it has been shown that converter-interfaced DRES can act as active harmonic filters, thus assist in the mitigation of voltage harmonics [111]. These DRES actually control their currents so that- in addition to the fundamental-frequency current component-they inject harmonic currents with magnitude and spectrum determined by the harmonic content of the voltage at their terminals. The basic constraint is that the RMS value of the currents injected should not exceed the thermal limit of the converter switches. Therefore, the DRES can act as active filters only when their fundamental-frequency loading is below the rated one. On the other hand, given the fact that harmonic currents are of reactive nature only, the active harmonic filtering action of the DRES is available even when the primary energy source is not available. Moreover, the active harmonic filtering does not require any additional equipment, such as supercapacitors, thereby there is no additional investment cost in providing this service. However, additional operational costs, e.g., increase of power losses on the DRES converter, should be considered in a similar manner as for the reactive power provision.
# Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components
## 2 Problem Framework  
### Overview of Target Problem  
Figure 1 illustrates a typical microgrid model consisting of (1) CGs, (2) ESSs, (3) CLs, (4) electrical loads, and (5) VREGs. As already described, components 1-3 are controllable, and components 4 and 5 are uncontrollable. Detailed definitions of the variables are shown in Section 2.2. The operation scheduling problem in the microgrids is generally formulated to determine a set of start-up/shut-down timing and output shares of component 1, charging/discharging states of component 2, and charging states of component 3 in response to the forecasted values of net load on each time interval [26; 27; 30; 33]. The net load is calculated by the sum of the forecasted values of electricity consumption of component 4 and output of component 5.  
If the microgrid operators cannot manage the power supply and demand appropriately, the resulting electricity surplus/shortage must be eliminated by trading electricity with the main power grids. In this case, an extra payment is required for compensating the imbalance in the power supply and demand as an imbalance penalty. However, electricity trades in the operation scheduling stage have the possibility to reduce complexity in the power supply and demand balancing operations as compared to adjusting operations of the controllable components. In other words, an appropriate operation schedule considering the electricity trade often provides economic benefits for the microgrid operators by improving/keeping the reliability and quality of the power supply. For these reasons, the authors regard electricity trading as an additional controllable component, although components in the main grids cannot be controlled by the microgrid operators.
## 4 Analysis of the Field Measurement Data
### Analysis of the Monitoring Data
#### 4.3.2 Horizontal Displacement  
Similarly, the researchers utilized polynomials to present the curve fitting of the monitoring data, and the fitting results are shown in Figure 11. Figure 11 indicates the excavation of the pit at the shield initial well, which causes horizontal displacement toward the excavation direction of the existing tunnel. The displacement value of the middle part of the existing tunnel was the largest, measured at 1.85 mm. The displacement value of both ends gradually decreased and approached the initial value at the end, similar to the vertical displacement. This measured result is consistent with the research conclusions of [PERSON] et al. (2019) and [PERSON] et al. (2019). After the new right-line shield tunneling, the existing tunnel generated a severe horizontal displacement away from the foundation pit. The displacement value of the middle part of the existing tunnel was the largest, measured at \(-3.80\) mm, and the displacement value of the two ends gradually decreased.  
Figure 11: _Cont._  
The relief of horizontal displacement in the existing tunnel toward the foundation pit occurred following the over-crossing tunneling by the left-line shield. The overall tunnel's horizontal position value was minimized to varying extents. When the final data were connected to meet the requirements of the stop test, the existing tunnel was displaced a little further toward the foundation pit. Moreover, horizontal displacement away from the foundation pit occurred in the middle part of the tunnel, and horizontal displacement toward the foundation pit occurred slightly at both ends.  
During the entire construction stage, the horizontal displacement of the down-line of the existing tunnel was the same as that of the up-line; however, its displacement value was reduced compared to that of the up-line. The only difference is that after the left-line shield tunneling of the new tunnel, the horizontal displacement of the downward line of the existing tunnel in the direction of the foundation pit sprang back sharply and approached the initial position. However, the final state was still highly consistent with the up-line.
# Provision of Energy- and Wavelength-Efficient Traffic
## 2 Scenario and Models of Traffic Grooming in the DSCNs
### Optimization Models
#### 2.2.1 Minimize the Energy Consumption
The flow conservation constraint is described as \(C_{6}\), where connection requests originating from the same nodes terminate at different destinations via several virtual lightpaths. Capacity constraint is provisioned in \(C_{7}\) to assure that massive traffic flows can be aggregated as long as the maximum capacity of sub-wavelength is not exceeded. \[ \begin{array}{lcl}C_{4}&:&\sum\limits_{i}\sum\limits_{j\
eq i}\ (\Theta_{mn}^{ij}+\Theta_{nm}^{ij})\leq W,\ \forall m,n\in V\\ C_{5}&:&\sum\limits_{p}\ \sum\limits_{w\in[1,W]}\theta_{p,w}Z_{ij}^{f,p,w}\leq 1,\ \forall f\in[1,F_{s,d}],i,j\in V\\ C_{6}&:&\sum\limits_{j\in V;j\
eq i}\ \Gamma_{ij}^{sd}-\sum\limits_{j\in V;j\
eq i} \Gamma_{ji}^{sd}=\left\{\begin{array}{l}F_{s,d},\ i=s\\ -F_{s,d},\ i=d\end{array},\ \forall s,d\in V\\ 0,\ otherwise\end{array} \right.\\ C_{7}&:&\sum\limits_{f^{\prime},f^{\prime}\
eq f}b_{s,d}^{f^{\prime}}Z_{ij}^ {f^{\prime},p,w}\leq\Omega_{s}-\Psi_{ij}^{f},\ \forall s,d,i,j,p,w\end{array} \tag{4}\]
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 6 Performance Evaluation and Analysis
However, our HCHA algorithm based on the DT, which can divide the area in to triangles immediate, the steps are less than COVEN. * The assistant number calculation. The COVEN algorithm calculation the assistant sensors number need consider the sum of triangles those belong to the same one cell, but our HCHA can calculate every triangle directly. * The assistant sensor position finding. The COVEN finding the assistant sensor position based on the cross-regional but our HCHA algorithm is based on each triangle unit. Secondly, the COVEN which mainly calculated coverage holes in every Voronoi polygon and every mobile sensor is located in the angle bisector corresponding to each Voronoi verets. However, HCHA estimates full coverage holes in a triangle, assisted sensor deployed in each triangle and considers multiple nodes deployment. The assisted nodes deployment strategy of HCHA and COVNE  
Figure 16: The standard deviation of simulation (200 times). Figure 17: Coverage ratio with obstacles (200 times). is shown in Figures 13 and 14. Besides we work out the concrete amount of assisted nodes in every triangle corresponding to different ratio \(R_{c}/R_{s}\). Thirdly, compared to DT-Score, our HCHA has a better evaluation in the without obstacles condition, but for the obstacles scenario, we need pay more attention to enhance the coverage.
## 3 Results and Discussion
### Mechanisms of Action of PAW-N and PAW-P on Different Hair Types

Table 5: Proposed mechanisms of action of PAW-N and PAW-P on different hair types, highlighting chemical interactions, structural effects, and hair integrity based on hair porosity and composition. (Stepwise oxidation leads to cleavage and conversion of disulfide bonds into sulfenic, sulfinic, and sulfonic acids, weakening the keratin matrix. )_  
(2) Oxidative degradation of Vitamin A (retinol) [31]:  
\[\mathrm{C_{20}H_{30}O+\bullet OH\to oxidized\ fragments+H_{2}O} \tag{2}\]  
_(Breaks down lipid-soluble vitamins in the hair's lipid layer, contributing to lipid barrier degradation. )_  
(3) Nitration of tyrosine residues by peroxynitrite [32]:  
\[\mathrm{ONOO^{-}+Tyr\to 3\text{-nitrotyrosine}+OH^{-}} \tag{3}\]  
_(Nitrative stress alters amino acid side chains, potentially disrupting protein structure and stability. )_  
These simplified mechanisms are consistent with the spectroscopic signatures observed (e.g., changes at 1040 cm\({}^{-1}\), 2925-2850 cm\({}^{-1}\), 1650 cm\({}^{-1}\)) and support the interpretation of oxidative and structural modifications induced by PAW. While not exhaustive, they provide a conceptual framework for understanding how different PAW formulations affect hair integrity at the molecular level. This comparative mechanistic understanding underscores the importance of tailoring plasma-activated formulations to specific hair types and desired outcomes, balancing treatment efficacy with structural preservation.
###### Abstract  
The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs' class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs' class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.  
artificial intelligence; convolutional neural networks; cephalometric radiographs; orthognathic surgery +
Footnote †: journal: Medical Imaging