esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**2015**} & \multicolumn{3}{c}{**2017**} & \multicolumn{3}{c}{**2020**} \\ \hline \multirow{2}{*}{**Period of Time**} & **31** & **31** & **31** & **31** & **31** & **31** & **31** & **31** & **31** \\  & **January** & **March** & **July** & **January** & **March** & **July** & **January** & **March** & **July** \\ \hline Mean ROTI & 0.92 & 1.67 & 0.91 & 0.76 & 0.79 & 0.75 & 0.74 & 0.75 & 0.73 \\ \hline Max ROTI & 1.30 & 2.18 & 1.24 & 1.10 & 1.03 & 0.94 & 1.18 & 1.05 & 1.03 \\ \hline RMSE & 0.021 & 0.032 & 0.021 & 0.018 & 0.020 & 0.018 & 0.018 & 0.018 & 0.017 \\ \hline R-Squared & 0.82 & 0.80 & 0.82 & 0.83 & 0.82 & 0.83 & 0.83 & 0.83 & 0.84 \\ \hline Mean VTEC & 35.33 & 39.95 & 18.37 & 15.74 & 19.21 & 12.71 & 12.48 & 16.29 & 9.78 \\ \hline Max VTEC & 66.83 & 77.16 & 40.25 & 31.66 & 47.72 & 25.41 & 25.58 & 33.51 & 18.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The difference between predicted and observed ROTI and observed VTEC values during 24 h on 31 January, 31 March, and 31 July in 2015, 2017, and 2020. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**2015**} & \multicolumn{3}{c}{**2017**} & \multicolumn{3}{c}{**2020**} \\ \hline \multirow{2}{*}{**Period of Time**} & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** & **1–30** \\  & **January** & **March** & **July** & **January** & **March** & **July** & **January** & **March** & **July** \\ \hline Mean ROTI & 0.97 & 1.14 & 0.96 & 0.81 & 0.91 & 0.79 & 0.77 & 0.78 & 0.74 \\ \hline Max ROTI & 9.47 & 11.42 & 7.39 & 7.91 & 8.43 & 7.12 & 7.63 & 7.78 & 6.71 \\ \hline RMSE & 0.008 & 0.009 & 0.008 & 0.007 & 0.008 & 0.007 & 0.007 & 0.007 & 0.006 \\ \hline R-Squared & 0.91 & 0.90 & 0.91 & 0.91 & 0.90 & 0.92 & 0.92 & 0.92 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The difference between observed and GANN-modeled ROTI during the 30 days for January, March, and June in 2015, 2017, and 2020. Consequently, prediction accuracy parameters are improved. The accuracy of the predicted values for the days of 2020 is higher than that of 2015 and 2020. By referring to the RMSE and R-square, it can be seen that the maximum accuracy is measured on 31 July 2020, with an R-square and RMSE equal to 0.84 and 0.017, respectively. However, the lowest accuracy of prediction is measured on 31 March 2015, where the ROTI and VTEC have their maximum values. The R-square and RMSE are equal to 0.80 and 0.032 on 31 March 2015. According to accuracy parameters for a year, it can be said that 31 July of every year is more accurate than 31 January and 31 March of the same year. These values can be observed in Figure 4. The observed and predicted values of the ROTI are given for the three 24-h intervals. In Figure 4, the mean of VTEC GNSS is observed for GUAM station to obtain additional information on the ROTI and TEC fluctuation intensity.
## 1 Introduction
In most cases, organic pollutants could partially be mineralised but the formation of oxidation products is commonly observed, which could possess more toxicity than their parents' pollutants and thus, the toxicity assessment before and after water treatment is necessary [30; 31]. The Microtax protocol is a straightforward approach for the assessment of toxicity and provides the information on samples' potential to inhibit/promote bioluminescence, which is influenced by the tested compound's toxic profile [32]. Diiuron, terbutryn and terbuthylazine have been listed in the proposals of European Parliament Directives and the EU Council Amending Directives (2000/60/EC and 2008/105/EC) as hazard substances. Pharmaceuticals of gabapentin and sulfamethoxazole have also been listed as prominent emerging micropollutants (MPs) in water bodies. Thereby, these five MPs were chosen for the present study. So far, there have been a few reported studies using the Fenton oxidation to treat the above named micropollutants (MPs) and thus this study aims:  
* To compare the classical ferrous iron with Fe(0)-catalytic Fenton oxidation in removing and mineralising the selected MPs. * To use the Taguchi method to design experiments and to select and obtain the optimal operating conditions. * To estimate sludge production from the process and to assess the toxicity of the treated effluents.
# Automated Deep Learning Model for Sperm Head Segmentation, Pose Correction, and Classification
## 2 Materials and Methods  
### Dataset Collection and Preprocessing  
Our method uses three annotations during training: contour annotations for fine-tuning EdgeSAM, acrosome position to determine sperm polarity after angle correction, and morphology categories for training the classification network.  
The Human Sperm Head Morphology dataset (HuSHHem) consists of 216 RGB images, including 54 images of normal sperm heads, 57 images of pyriform sperm heads, 52 images of amorphous sperm heads, and 53 images of tapered sperm heads [5]. Most images are sized 131 \(\times\) 131, although some have slight deficiencies. Experienced male fertility specialists annotated the sperm head contours and the vertex (Figure 3). The dataset was split into training and testing sets at an 8:2 ratio. Table 1 shows the images used in the test set, while the remaining images are used to construct the training set.  
Chenwy Sperm-Dataset contains 320 RGB images with a resolution of 1280 \(\times\) 1024 [26]. The authors split the data into training and testing sets at an 8:2 ratio. Each image contains 5-6 complete sperm cells, with annotations for the contours of the sperm head, midpiece, and tail. The head is further divided into the acrosome, nucleus, and vacuole. However, this dataset does not provide sperm morphology categories, so it is only used to evaluate the model's segmentation performance. Based on the annotations, we extracted the sperm heads and used only the head contour and acrosome position annotations, resulting in a total of 1314 images resized to 201 \(\times\) 201 pixels.  
For preprocessing, all images in the HuSHem dataset were first resized to 131 \(\times\) 131 using reflection padding and then upsampled to 201 \(\times\) 201 to match the dimensions of the Chenwy dataset. Data augmentation techniques, including rotation, translation, brightness, and color jittering, were applied to expand the training data of both the HuSHem and Chenwy Sperm-Datasets, increasing the number of images from 8450 to 26,280.  
In addition, the training set was split at an 8:2 ratio for five-fold cross-validation. In each fold, the sub-training sets consisted of augmented images, while the validation sets contained only original images. To prevent data leakage, original and augmented images of the same sperm head were ensured not to appear in both the sub-training and validation sets within the same fold.
## 3 Case Study of the Port of Aviles
### Different Modeling Approaches
#### 3.2.3 HOMER Modeling  
While based on the same data used for HY4 RES modeling, HOMER(r) 3.16.2 had the advantage of collecting some data on renewable sources from NASA for the region of the Port of Aviles, namely an average daily radiation value of 3.37 kWh/m\({}^{2}\)/day annually. In July, the daily radiation value reached its maximum, measured at 5.330 kWh/m\({}^{2}\)/day. Based on 30 years of data from the NASA database, the annual average wind speed was 6.15 m/s, with the highest wind speed recorded in December, at 7.370 m/s (Figure 15).  
In the developed analysis and when involving hydrokinetic turbines, a water speed of 0.7 m/s was assumed. The power output of the hydrokinetic turbine was kept at a minimum level, considering the assumption that the water speed in the channel was lower.  
Regarding electric consumption, the same values of the HY4 RES model were used and HOMER(r) 3.16.2 Pro allowed for an interesting infographic representation (Figure 16).  
Figure 15: HOMER Pro data collection: (**a**) solar irradiation and (**b**) wind speed.  
The study developed with HOMER(r) 3.16.2 Pro used the following proposed hybrid scenarios:  
* **Scenario 6**--Solar, wind, and hydrokinetic energy and the electric grid;
* **Scenario 7**--Solar, wind, and hydrokinetic energy and BESS (battery energy storage system), keeping in mind that the primary renewable sources were solar + wind + hydrokinetic in all scenarios. It was not able to use a micro-PAT component in the hybrid energy solution module simulation due to the minimum limit possible for this element's implementation.
* _Scenario 6_--HOMER Modeling: Solar, Wind, and Hydrokinetic Energy and Electric Grid: According to the evaluation of Scenario 6 using HOMER(r) 3.16.2 Pro modeling, the amount of energy that needed to be purchased from the grid was calculated as approximately 546 kWh annually. This met the annual energy consumption of 1409 kWh, while 2385 kWh of energy was sold back to the grid. During certain periods of the year, due to increased energy demand, a grid connection was necessary to meet the energy needs in the scenarios without battery storage (Table 2 and Figure 17).  
Scenario 6 presented an excess electricity of 40.4 kWh/yr with no unmet nor capacity shortage.
* _Scenario 7--HOMER Modeling: Solar, Wind, and Hydrokinetic Energy and BESS_: According to the results, the energy produced was 3450 kWh/yr, which met the annual energy consumption of 1409 kWh (Table 3 and Figure 18).  
In Scenario 7, an excess of electricity production of 1834 kWh/yr was noticed, alongside an unmet electric load of 0.831 kWh/yr, representing a capacity shortage of 1.41 kWh/yr.
## 3 Sensor Network Architectures for Monitoring Underwater Pipelines
### Underwater Acoustic Wireless Sensor Networks
Communication among nodes located within the range of a few tens of kilometers may have a bandwidth of only a few kHz. The bandwidth can be increased to a hundred kHz, if the nodes involved are located closer (_i.e._, within several tens of meters). Second, propagation delay of acoustic signals underwater is very high and variable [29]. It is around five orders of magnitude higher than that in radio frequency channels. Third, due to the characteristics of the underwater channel, high bit error rates and connectivity losses can be encountered. Forth, battery power is consumed and it is difficult to renew or replace. In addition to the power needed to operate the nodes and their transceivers, in underwater communication advanced signal processing techniques are needed to reduce the impact of underwater communication characteristics; however, these signal processing techniques will consume more power and will reduce the life of the nodes' batteries. Figure 3: Automatic wireless/acoustic range. Acoustic communication is used for monitoring vibrations in the Langeled Pipeline installed at a depth of 800-1,100 meters on a hilly and rocky seabed. Several segments of the pipeline are not in contact with the seabed. With strong sea currents, high vibrations may be induced in these free spans. This introduces high and risky pressure on the pipeline segments. Consequently, it was a requirement to monitor vibrations on the pipeline. A wireless sensor network was installed on the pipeline to monitor vibrations [30]. The monitoring network consists of autonomous synchronized wireless acoustic nodes. These nodes use acoustic Clamp Sensor Packages (CSP) that are mounted to the pipeline at regular intervals and a Master Sensor Packages (MSP) that monitors the vibrations in longer pipeline free spans. The CPSs are equipped with batteries that last for six months. Remote operating vehicles (ROV) are used to replace dead nodes. This type of monitoring network requires periodic and costly maintenance due to the use of batteries for operating the nodes.
## 4 Results and Discussion
### Variation of Ventilation Volume with Solar Chimney Inlet Width  
Figure 12 shows the variation of the solar chimney ventilation versus the solar chimney inlet width at different outdoor wind speeds, where: the heat flow density \(W\) of the internal heat source is 900 W/m\({}^{2}\); the solar radiation intensity \(I\) is 600 W/m\({}^{2}\); and the geometry of the internal heat source is 4000 \(\times\) 2000 mm.  
As can be seen from Figure 12, the ventilation of solar chimneys does not always increase with increasing inlet width, and when the inlet width is greater than 0.5 m, the trend of increasing ventilation does not increase significantly or even decreases. When the inlet width is less than 0.5 m, the ventilation volume increases significantly with increasing inlet width. For example, when the external wind speed is 1 m/s and the inlet width is increased from 0.1 to 0.5 m, the increase in ventilation volume is 104.1%. However, for inlet widths above 0.5 m, the rate of rise in ventilation is very limited, at 3.25%. It can be confirmed that the optimum inlet width for a solar chimney differs from a design that does not take outdoor winds into account. Due to the counterflow at the chimney outlet, previous studies [25; 26; 27] favored 0.2-0.3 m as the optimum inlet width without taking outdoor winds into account. However, after taking outdoor winds into account, the optimum inlet width is located at a relatively large 0.5 m due to the wind pressure providing additional power to the solar chimney and the relatively small backflow at the chimney outlet.
## 4 Evaluation Metrics
### Routing Protocols Metrics
#### 4.2.2 Routing Overhead  
Routing overhead represents the number of control packets, such as RREQs, RREPs, RERs and HELLO messages. Basically, it is the amount of extra traffic that is distributed in the network in order to provide the possibility of sending data packets. However, in the case of flooding, there are no control packets, but there are multiple data packet transmissions. Therefore, in the case of flooding, we will quantify the overhead as the number of data packet transmissions.
# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 3: Multi-Scale Input  
The relationship between urban forests and ecosystem services has been shown to vary across space at different scales ([PERSON] et al., 2016; [PERSON] et al., 2016). Indeed, one of the key advantages of remote sensing is its scalability. Correspondingly, a more recent theme in remote sensing application in urban forests is the emergence of multi-scale studies, expanding from micro-scale (e.g., individual tree and park patch) to meso-scale (e.g., city) and macro-scale (e.g., regional analysis), although relatively limited attention has been given to this aspect (eight of 63 studies). Multi-scale studies are complex to carry out and require a thorough knowledge of different sensors and algorithms for fusing multiple images.
# Defect Detection in Striped Images Using a One-Dimensional Median Filter
## 4 Comparison to Other Study  
Here we used the deep learning method to detect defects and compare the results with those obtained by using the proposed method in the paper. The deep learning model was Faster R-CNN with ResNet-101, and the pre-trained model was on the MS COCO dataset. The number of images used for training and validation, the learning rates, and the total training steps are listed in Table 5. Due to the limited number of the images we have, we synthesized the images for training and validation with 128 different defects on 18 different defect-free images. We arbitrarily chose a defect and put it at a location on one of the 18 defect-free images to obtain a synthesized image. By using different combinations of the defect, the defect-free image, and defect location, we synthesized about 40,000 images in total. Among them, 30,159 images were used for training and 7500 images for validation. The validation accuracy was greater than 99%.  
After obtaining the trained model, we applied both methods to 83 images with defects and used 0.5 as the intersection over union (IOU) threshold. The average precision (AP) for the proposed method was 0.91, and that for the deep learning method was 0.74. One image with the defect detection results is shown in Figure 21. From the figure, we can see that the defect detection by using the proposed method can correctly find the bounding boxes of the two defects in the image, but the deep learning method failed to find one. For other images, the deep learning method even missed some defects. The training dataset may cause the deep learning results with lower AP. Although we used about  
\begin{table}
\begin{tabular}{c c} \hline \hline No. of synthesized images used & Training dataset: 30,159 \\ Validation dataset: 7500 \\ \hline \multirow{3}{*}{**Learning rates**} & Initial, 0.0003 \\  & Steps \textgreater{} 50,000, 0.00003 \\  & Steps \textgreater{} 100,000, 0.000003 \\ \hline
**Total training steps** & 184,914 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of deep learning setup.  
Figure 20: The enlarged scratches that the proposed algorithm failed to detected: (**a**) in the block, and (**b**) in the interval.  
30,000 images for training, the amount and the variety may not be enough to yield high AP. From the observation of the deep learning results, most of the poor predictions are related to missing defects and imprecise bounding boxes. The method proposed in this paper finds defects using the image difference, which is more robust to find the defects.
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
## 2 Materials and Methods
### Immunochemical Strip Kit Test  
The immunochemical strip kit test is widely used as an analytical method for the detection of expressed proteins in LM cotton. The test was immediately conducted using collected samples in the monitoring sites according to the manufacturer's protocol of the lateral flow QuickComb Kit (Envirologix, USA). The cotton leaf was ground using a pestle in a 1.5 mL tube and 0.7 mL of buffer solution was added to the it. Subsequently, four kinds of strip kits that can detect event specific proteins (2 M EPSPS, CP4 EPSPS, CRY2A/CRY1A, and PAT/BAR) were placed in the tube and allowed to react for 5-10 min. If the sample was negative (non-LMO), the strip kit only showed one line: A control band. Whereas, if the sample was positive (LMO), containing a specific target protein, two or more lines of test bands appeared on the strip kit.
## 4 Results and Discussion
### Training and Validation of Machine Learning (ML) Models
#### 4.2.1 Training of Machine Learning (ML) Models
The MIKE11 model is accurate in simulating most stations; however, the accuracy of the simulation results (RMSE and NSE) for Shigang station is poor, mainly because this station is located in sub-catchment 7, and the generalization of the river network is not comprehensive because of the limitations of historical data. The largest difference in RMSE and NSE between Haiantong and Shigang stations is 0.17 and 0.62, respectively, indicating notable fluctuations in the simulation results of the MIKE11 model (RMSE and NSE). For example, [PERSON] et al. [33] calibrated and validated a conceptual hydrological model MIKE 11 NAM for rainfall-runoff simulations; the results exhibited that the integrated model can simulate the rainfall-runoff processes in the basin. [PERSON] et al. [25] present a real-time flood forecasting approach for the data-scarce Jhelum Basin, using the MIKE 11 Nedbor Astromings Model (NAM) and hydrodynamic (HD) and flood forecasting (FF) models. These indicators show good consistency between the observed and simulated hydrographs for the calibration and validation periods, resulting in a level of reliability sufficient for flood forecasting purposes. 3. For most stations, the simulation results of the MIKE11 and ML water level simulation models exhibited high accuracy, and the fluctuations in the simulation results (RMSE and NSE) of the ML water level simulation models were smaller than those of the MIKE11 model. For Shigang station, the RMSEs of the MIKE11, SVM, RF, and GBDT models were 0.276, 0.105, 0.085, and 0.081 and the NSE values were 0.353, 0.831, 0.842, and 0.845, respectively, indicating that the simulation accuracy (RMSE and NSE) of the ML water level simulation models was much higher than that of the MIKE11 model [8; 36]. For example, [PERSON] et al. [39] found the optimal configuration of a machine learning-based model and compared the quality of the results of different machine learning techniques and the MIKE-11 hydrologic forecasting model; the results have shown that the best algorithm in this study for seven days in advance is Bayesian linear which can reduce error from MIKE 11 22.02% and 44.40% for one day in advance. [PERSON] et al. [8] proposed a novel smoothing-based long short-term memory (Smooth-LSTM) framework for flood forecasting up to five days ahead and compared it with the benchmark LSTM (LSTM) model, an Artificial Neural Network (ANN) model, and the conceptual Nedbor Astromnings Model (MIKE11 NAM)-hydrodynamic (HD) (MIKE)-hydrological model. Overall, the Smooth-LSTM model is found to be robust in operational flood forecasting, with lower uncertainty and the least sensitivity to redundant input information.
## 2 Knowledge Discovery and Data Mining in Smart SCADA  
### Knowledge Discovery vs. Data Mining  
Knowledge Discovery (KD) and Data Mining (DM) have transformed the power engineering research. To carry out effective and meaningful research, a deep understanding of various aspects of data mining and knowledge discovery is necessary [23]. It includes the expertise of specialists who work in all components associated with the energy generation, transmission, and distribution of the chain representing the power system.  
There are some misunderstandings about the terms data mining and knowledge discovery defined in databases. Although many specialists and researchers use DM as a synonym for knowledge discovery, DM is not the entire knowledge discovery process. In addition to being defined as data mining, it comes with other names, such as information discovery or knowledge extraction. The KD is a process that aims to identify relationships and patterns in large datasets. It is defined typically as a non-trivial process that involves identifying novel, useful, and understandable patterns. In a narrow sense, KD refers to extracting information from a data source. While it can be performed through various methods, the term refers to obtaining knowledge from textual or database data. The combined process is referred to as the KD process.  
Figure 1 shows the steps of a KD process, integrating the Data Mining, and the details are introduced in the following [21, 23, 24].  
Data SelectionKD's initial step is data selection, which involves gathering information from the SCADA database. This process is carried out to create a raw dataset.  
Data Cleaning (Preprocessing)Ensuring that the data collected are of good quality is performed through preprocessing. This process involves handling noise, missing values, and inconsistencies.  
Data TransformationAfter cleaning the data, it is usually necessary to transform it into something suitable for mining. This can be carried out through various methods such as feature engineering and scaling. To make it easier for the machine learning tools, the label encoding was used to convert categorical data into a more readable format.  
Data MiningThe DM step involves uncovering patterns, anomalies, or relationships between the data. The DM process is composed of numerous steps, each of which is related to a specific discovery task. The extraction of knowledge involves the process of gathering and storing information. It also involves analyzing and visualizing the data, designing models for machine and human interaction, and learning how to use efficient methods. One of the most frequently used techniques is clustering, which enables us to group the data into distinct groups based on similarity.  
InterpretationAfter Data Mining, the next step is to interpret the results. This involves understanding the clusters (patterns) and their features.