esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Discussion
### Performance Test
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test System** & **Algorithm** & \begin{tabular}{c} **DSO Operating** \\ **Cost (S/h)** \\ \end{tabular} & \
\begin{tabular}{c} **MGO Profit** \\ **(S/h)** \\ \end{tabular} &
\begin{tabular}{c} **Total Run Time** \\ **(min)** \\ \end{tabular} \\ \hline \multirow{6}{*}{IEEE test system} & PSO & 869,694 & 749,964 & 6.27 \\  & AMPSO & 866,047 & 749,165 & 1.95 \\  & SPEA & 865,147 & 750,132 & 2.03 \\  & GA & 870,486 & 750,641 & 5.83 \\  & NSGA-II & 863,208 & 746,927 & 8.54 \\  & MNSGA-II & 862,716 & 746,085 & 1.83 \\ \hline \multirow{6}{*}{Actual power system} & PSO & 2,339,648 & 994,648 & 22.74 \\  & AMPSO & 2,325,348 & 985,912 & 8.97 \\ \cline{1-1}  & SPEA & 2,328,321 & 984,315 & 10.84 \\ \cline{1-1}  & GA & 2,341,934 & 997,324 & 21.25 \\ \cline{1-1}  & NSGA-II & 2,322,496 & 981,073 & 24.31 \\ \cline{1-1}  & MNSGA-II & 2,320,075 & 980,610 & 5.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison result of different algorithms for Case 2. Figure 12: Optimization results for each system. PSO: particle swarm optimization; adaptive modified PSO; SPEA: strength Pareto evolutionary algorithm; GA: strength Pareto evolutionary algorithm; NSGA-II: non-dominated sorting genetic algorithm II; MNSGA-II: modified version of the non-dominated sorting genetic algorithm II: (**a**) IEEE test system, (**b**) Actual power system. The superior performance of the optimization process for multi-objective problem obtained Pareto-fronts between DSO and MGO using the MNSGA-II are shown in Figure 13. As shown in Figure 13a, when the weighting factors were \(w_{u}=0.3\) (upper-level) and \(w_{l}=0.7\) (lower-level), the convergence points of the Pareto-front for profit and operating cost in the IEEE test system were obtained after the 21 th iterative cycle. On the other hand, when the weighting factors were \(w_{u}=0.4\) and \(w_{l}=0.6\), the Pareto-front converged after the 28 th iterative cycle (see Figure 13b). On the basis of the results in Figure 13, it can be concluded that the performance of multi-objective optimization was superior, as it exhibited higher convergence speed and required fewer iterative cycles to converge. Therefore, the proposed approach results in optimal operational solutions and can assist in decision-making whenever there is a trade-off between the DSO and MGO in power systems.
## 3 Results
### The Analysis of the Conditions for Gamma-Decalactone Biosynthesis
#### 3.1.3 Effect of Surfactant Presence on Gamma-Decalactone Production  
Since the medium used in the biotechnological synthesis of GDL is an oil-in-water emulsion, the influence of surfactant addition on the GDL biosynthesis efficiency was also analyzed. Surfactants are chemical compounds with the ability to change the surface properties of the liquid in which they are dissolved. The decrease of the surface tension by these molecules at the water/oil interface contributes to the formation of stable emulsions [34]. In our research, the addition of two nonionic and one amphoteric surfactants on the biosynthesis of GDL was analyzed. The selected surfactants are included in the list of food additives and are commonly used in the food industry: nonionic: Tween 20-E432 and Tween 80-E433 and amphoteric: lecithin-E322.  
Figure 3 presents data on the concentration of GDL in media with added surfactants on particular days of a 7 day yeast cultivation. A control culture was carried out simultaneously without the addition of a surfactant. The collected data did not show a significant statistical effect of surfactant supplementation on the level of GDL biosynthesis. In case of the addition of Tween 20 (polyoxyethylenesorbitan monolaurate), the maximum lactone concentration on the 6 th day of culture 2.44 \(\pm\) 0.07 g/L was almost identical to that of the control medium 2.45 \(\pm\) 0.25 g/L (maximum on the 7 th day of the reaction). In the case of the addition of soy lecithin, the maximum level of the flavor compound was 2.05 \(\pm\) 0.11 g/L. The highest content of lactone was achieved in the medium with added Tween 80 (polyoxyethylenesorbitan monooleate) 2.99 \(\pm\) 0.24 g/L.  
Figure 2: Effect of heptane presence on the maximum level of GDL concentration in biotransformation medium. CO+P: medium with castor oil and peptone; CO+P+H: medium with castor oil, peptone and heptane. Different letters denote significant differences at \(p<\) 0.05.
# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 3 Proposed Method
### Prostate Volume Inference through Patch Voting  
Each quadruplet patch votes for each of the ellipsoid diameter endpoints at the voting space that has the same resolution as the input image. Each quadruplet patch goes through \(QDCNN_{T}\) or \(QDCNN_{S}\) classifier networks (depending on whether it is extracted from a transverse or sagittal image) to produce \(cd\) and \(co\) values. The actual voting happens along a circular arc where the arc center is the patch center. The arc radius is given by \(cd\) and the arc center angle by \(co\). The arc thickness and length are determined by the median and range of the distance and orientation class intervals. This way, a voting map for each diameter endpoint of a given sample is created whose peak gives the location estimation of the diameter endpoint.  
Figure 8 shows an example for the voting maps, arcs, and detected diameter endpoints. In Figure 8a, the arcs are drawn in red with the detected diameter endpoints in green. This sample image does not show the thickness to represent the locations of the arcs better. Figure 8b shows the detected points with red dots, while the manually annotated points are shown with green dots. Figure 8c shows the voting maps of each diameter endpoint. A Gaussian smoothing filter convolves these maps to suppress the noise in these images.  
In the test phase, for the transverse \(T^{j}\) and the sagittal \(S^{j}\) images of a given unseen patient \(j\), evenly spaced locations vote for the distance and the orientation class values in a sliding window manner. A quadruplet patch was extracted for each voting location where the quadruplet patch centers the location. The voting process proceeds by the classification of the quadruplet patches by the corresponding QDCNN. Figure 9 exemplifies the voting mechanism on an unseen transverse image \(T^{j}\). For each location \((x,y)\), a quadruplet patch \(PQ(T^{j}(x,y))\) was extracted and given as input to the trained \(QDCNN_{T}\) classifier to produce eight outputs, which are interpreted as \(cd\)-\(co\) pairs for each of the four endpoints. The final locations of the diameter endpoints were determined as the peaks of the corresponding voting maps. After obtaining the endpoints for the sagittal image \(S^{j}\) similarly, the standard ellipsoid formula was used to estimate the volume.  
Figure 7: The training process on m transverse images \(T^{1},\dots,T^{m}\). Around the diameter endpoints of each \(m\) training image, \(n\) quadruplet patches \((PQ(T^{1}(x_{1},y_{1}))),\dots,(PQ(T^{m}(x_{mxm},y_{mxm})))\) were extracted randomly. The transverse model \(QDCNN_{T}\) was trained to predict eight classes where eight is the \(number\_of\_tasks\times number\_of\_diameter\_end\_points\).
## 2 Methods
### CqSL Model Overview
#### 2.1.3 Segmentation  
The mask segmentor identifier \((S1)\) (Figure 3c) takes the output from the FiLM decoder \(f_{SK}^{F}\) as input and generates predicted segmentation mask \(SI(f_{SK})=\hat{y}^{l}\in\{0,1\}^{(H\times{\rm W}\times L)}\), where \(L\) is the number of categories (RV, LV, LV-Myo, and background) in the training dataset. We exploit a novel supervised loss, weighted soft background focal (WSBF) loss, \(L_{SI(seg)}^{\mathcal{L}}=\mathcal{L}_{WSFL}+\mathcal{L}_{BFD}\) for the base model, which is a combination of background focal dice loss (BFD) and weighted soft focal loss (WSFL):  
\[L_{SI(seg)}^{\mathcal{L}} =\Big{[}\alpha_{0}+y(\alpha_{1}-\alpha_{0})\Big{]}|y-\hat{y}|^{ \gamma}.w_{map}.CE(y,\hat{y})\ + \tag{3}\] \[\sum_{c}\Big{[}2-\frac{2\sum y\hat{y}+\epsilon}{\sum(y+\hat{y})+ \epsilon}-\frac{2\sum\overline{y}\overline{\hat{y}}+\epsilon}{\sum(\overline{ \hat{y}}+\overline{\hat{y}})+\epsilon}\Big{]}^{\frac{1}{\gamma}}\]  
where \(\alpha_{0}\) and \(\alpha_{1}\) are designed to account for class imbalance and are treated as hyper-parameters, the term \(|y-\hat{y}|^{\gamma}\) is used to down-weigh examples with backgrounds, where \(\gamma\) varies in the range \([1,3]\). The term \(CE(y,\hat{y})=-y\log\hat{y}-(1-y)\log(1-\hat{y})\) denotes the cross-entropy loss.  
On the other hand, the data with no corresponding segmentation masks are trained by minimizing the unsupervised loss via a \(KL\) divergence based on least-squares GAN [46]. However, since the least-squares loss is not sufficiently robust, we introduce a new divergence loss function by incorporating it into a Geman-McClure model [47] fashion called _adversarial-Geman-McClure (adv-GM)_ loss between the ground truth of real mask \(y^{l}\) and prediction on unlabeled data \(y^{ul}\):  
\[L_{SI(adv-GM)}^{\mathcal{U}}\ =\frac{DI(SI(f_{SK}(x^{ul})))^{2}+(DI(\hat{y}^{ul})-1)^ {2}}{2\beta+DI(SI(f_{SK}(x^{ul})))^{2}+(DI(\hat{y}^{ul})-1)^{2}} \tag{4}\]where \(\beta\) is the scale factor which varies in the range of \([0,1]\) and we set \(\beta=0.5\) in our experiment.
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
## 2 Materials and Methods
### Assessment Scenarios  
The RF EMF exposure was assessed according to the following five representative scenarios for different patient assistant poses using the previously defined head and body coils. Scenario 1 is a posture in which the patient assistant is standing next to the patient. Scenario 2 is a posture in which the patient assistant is sitting next to the patient. Scenario 3 is a posture in which the patient assistant's hands are placed on the patient's hand. As previously mentioned regarding the head and body RF coils in this study, scenarios 1-3 assess both RF coils equally. However, scenario 4 is for the head RF coil only, with a posture in which the patient assistant's right hand holds the patient's left hand, and the patient assistant's left hand is on the patient's chest. Finally, scenario 5 is for the body RF coil only, under a posture in which the patient assistant's right hand holds the patient's hand, and the patient assistant's left hand is on the patient's head. Compared to scenario 4, scenario 5 creates a larger electrical current loop owing to the patient assistant's posture. The scenarios are summarized in Figure 5. The magnet and patient table were placed under each scenario to mimic realistic distances between the patient assistant and patient. Because the dielectric properties of the magnet and the patient table were considered to be those found in air, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Name** & **Age** & **Sex** & **Height (cm)** & **Mass (kg)** & **BMI (kg/m2)** & **Tissues (No.)** \\ \hline Patient assistant & [PERSON] & 34 y & M & 174 & 70 & 23.1 & 77 \\ Patient & [PERSON] & 11 y & F & 146 & 36 & 16.7 & 75 \\ Patient & [PERSON] & 6 y & M & 117 & 20 & 14.2 & 76 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Human body models.  
Figure 4: Circularly polarized (CP) excitation magnetic field (B\({}_{1}\) in Tesla) of the body RF coil model under the unloaded condition.  
presence of the magnet and patient table did not affect the SAR assessments. Additionally, the patient assistant is absent in scenario S\({}_{\text{ab}}\). For the SAR assessments of each scenario, 1 and 10 g averaged SARs were calculated using 1 W of scaled input power. The numerical simulations of each scenario were conducted on a 64-bit PC with 192 GB of RAM and an Intel Xeon 6226 CPU equipped with an NVIDIA Quadro RTX 8000 GPU. The grid size of the human body models was 2 \(\times\) 2 \(\times\) 2 mm\({}^{3}\). The computation time of each of the five scenarios took approximately 12-20 h, depending on the total number of grids used in each scenario.
## 2 Proposed Method and Environment
### Design and Fabrication of Experimental Rotating Equipment
The signals measured for 30 s were divided into 0.48-s units considering the measurement environment of the actual equipment, and each of the 0.48-s units was assumed to be one dataset. Machine learning was performed by dividing one dataset into 14 samples. Sampling was performed because a vibration is a periodic signal in the time domain [43], and most fault signals have periodicity. Therefore, sampling is used to examine the consistency and continuity of each condition using the features calculated from the signals. The signal segmentation for sampling was based on the rotational frequency of the rotor. Generally, in rotating equipment, the rotational frequency is the most dominant component, and the majority of fault components appear in the harmonic form of the rotational frequency. Therefore, the length of the sample of experimental data was set to 0.06 s. This was two times 0.03 s, which is the period of vibrations at 2000 RPM, and the number of samples was increased by overlapping half the signal. The total number of training and test data was 1056, and the dataset was divided into training and testing datasets by allocating 80% to the training dataset and 20% to the testing dataset. At this time, the training dataset consisted of 229 Normal condition data (no faults in operation), 199 Rubbing data, 205 Unbalance data, and 211 Misalignment data, and the testing dataset included 43 Normal data, 61 Rubbing data, 55 Unbalance data, and 53 Misalignment data.
## 4 Results and Analysis
### Correlation Analysis  
#### 4.2.1 Correlation Analysis  
Without considering the spatial distribution, the correlation between the annual index data and LE (Table 4) and the time lag correlation (Table 5) were calculated. It can be seen from the table that most of the index data are positively correlated with LE. The correlation between annual LST and the corresponding LE is small or negative; LST and NDVI in 2020 are negatively correlated with LE in 2020. Through spatiotemporal lag analysis, it was found that LST and Wet in 2010 and 2015 are negatively correlated with LE in 2015 and 2020, respectively.
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Threats to Validity  
The proposed model may face various threats to validity that could impact the reliability and generalizability of its findings. Here are some potential threats to validity for this paper:  
* Data bias: The choice of the dataset used for evaluation could introduce bias into the results. If the dataset primarily consists of code snippets from certain programming languages or specific domains, the model's performance may not generalize well to other languages or domains.
* Limited dataset size: The dataset size could be relatively small, leading to potential overfitting issues. With limited training data, the model might not capture the full complexity and diversity of code retrieval scenarios.
* Model selection: This paper might not explore a wide range of model architectures and configurations. This could limit the understanding of how other models perform in comparison to CodeT5 and Bi-LSTM or fail to identify the most suitable model for the specific task.
* Evaluation metrics: The choice of evaluation metrics could influence the perceived performance of the proposed framework. If the metrics do not align well with the end users' needs or real-world use cases, the reported results may not accurately reflect the system's true effectiveness.
* Implementation details: This paper may lack detailed information about hyperparameter tuning, learning rates, and other implementation specifics. These details are crucial for reproducibility and may affect the replicability of the experiments.
* Overfitting: There is a risk that the reported performance metrics are inflated due to overfitting on the test set. Proper cross-validation techniques and evaluation on an external, unseen dataset could address this concern.
* Lack of comparison with state of the art: Without comparing the proposed framework against state-of-the-art models or existing baseline approaches, this paper may not provide a comprehensive understanding of its advantages or limitations.
* Domain-specific limitations: The framework may perform well in a specific domain but struggle with different types of code or queries. This paper should clearly state the limitations and applicability of the proposed method.  
Interpretability: Deep learning models can be challenging to interpret. This paper should address how the model's predictions are explainable or provide insights into the attention mechanisms' behavior.
* Hardware and computational constraints: The hardware and computational resources used in the experiments could influence the model's performance and training time. A lack of information about these resources might hinder the reproducibility of the results.  
To mitigate these threats to validity, the authors should provide comprehensive experimental details, use diverse datasets, compare their framework with relevant baselines, consider different evaluation metrics, and perform thorough cross validation to ensure the robustness and generalizability of their findings.
# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 5 Discussion  
Comparative analyses between different methodologies mentioned in this study are summarized in Table 3. It is worth mentioning that due to the varied characteristics of the sign detection algorithms described in the research, only the performance metrics utilized in most of the literature are mentioned in the table. Some works also obtained good results [14], but they are not shown in the table because alternative metrics are utilized, which are difficult to compare. Through comparison examination of existing methodologies, present approaches for identifying CT signs in the lung disease contain the following basic characteristics:  
1. **CISs classification. ** Current work is able to obtain above 90% of the classification performance in one or more of the three assessment metrics: sensitivity, specificity, and accuracy. In terms of datasets, earlier work on sign classification mostly depended on the publicly accessible LISS dataset, although there was also some successful research using private data for CISs classification [20; 24]. It is not possible to directly compare the classification results because of the differences in datasets and the CISs studied, but the performance of model classification shows an upward trend on the whole, with deep learning methods outperforming traditional machine learning methods for both the classification of single signs and the classification of multiple classes of signs. 2. **CISs segmentation. ** Segmentation of lesions is essential to improve the performance of sign classification and detection. The major approaches are the threshold and morphological methods. As lung lesions contain additional blood vessels, the threshold method of segmenting lesions tends to produce a large number of false positive areas and is mainly used for the segmentation of larger organs, such as the lung parenchyma, while the morphological method can effectively remove blood vessels and is mainly applied to the segmentation of small lesion areas. 3. **CISs detection. ** While having achieved great progress, most of the present approaches largely focus on CISs classification tasks. There is a lack of research focusing on object detection of CISs. However, people are more interested in lesion detection tasks in medical imaging as opposed to classification tasks, which may be for the following reasons: objection detection networks are much more complex than classification networks and require higher quantity and quality of data. However, there is a wide variety of signs of lung lesions, and as the differences between signs are small, it is very difficult to accurately detect and quantify them. It is worth mentioning that most of the recent research on the lungs have concentrated on lung nodules or on the benign and malignant classification of lung disease, and much of the material discussed in this article was published two years ago. There is a lack of material focusing on object detection of CISs in recent years.
## 7 Supervised Classification
### Supervised Classification: Training, Validation and Accuracies  
Training of the classifiers was performed using the averaged features (49 in total) from a neighborhood of 100 m radius around each ground-truth data point. More precisely, the underwater videos have been converted into georeferenced snapshots every 100 m intervals, which constituted the ground-truth points after expert interpretation of the visible bottom types.  
The validation dataset was composed of the averaged features per segment with each segment characterized after the most frequent bottom class included in its extents, as derived by the detailed manual habitat map presented in Figure 4. This object-averaged version of the manual habitat map actually is the best possible classification that could be achieved through the given mapping scale, governed by the size of the segments, and so it is used as the basis for evaluating the classification accuracy achieved through supervised classification of each _data model_.  
A simple Naive Bayes classifier was selected to avoid over-segmentation (overfitting) of the feature space and to extract simple decision rules. Although using Random Forest (RF), Support Vector Machines (SVM), or Neural Network Classifiers (NNC) or other sophisticated classifiers could potentially produce higher classification precisions, Naive Bayes produces much more generalized decision rules, which constitute a fair baseline for comparison between different _data models_, avoiding any feature space over-segmentation. Simple decision rules need well separated classes in the Euclidean space, which is a desired hypothesis sought to be elaborated in this work. Classification was performed on the raw derivative points without performing any object-wise averaging. Objects were considered on a later stage, during which the majority class of the derivative points falling inside each segment was considered to construct the final classification maps per _data model_.  
Classification accuracy assessment per _data model_ was achieved through the Cohen's Kappa coefficient, estimated by the confusion matrix of the predicted and the manual classes for all segments. Classification accuracy assessment per Habitat type was performed using the \"recall\" measure. This isto unveil which system or which system's derived components worked better for each habitat type. This is important to exploit the inherent capacity of each system or feature extraction method towards the discrimination of certain bottom types.