esa-sceva/satcom-chunk-collection huggingface dataset:
# Korean Passori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding
## 3 Method
### Frame-Level Transcription
#### 3.1.3 Loss Function  
We compare focal loss [12] and binary cross-entropy loss in our experiment for the source task of vocal melody transcription. The goal of these loss functions is to classify each time step in an audio signal as either belonging to the vocal melody (positive class) or not (negative class). The loss function computes the error by comparing the model's output logits, typically representing the likelihood of the presence of a melody, with the binary ground truth labels. By optimizing this loss during training, the model learns to distinguish between vocal melody and non-melody segments in the audio, which is essential for accurate melody extraction. In vocal melody extraction tasks, there may be a significant imbalance between voicing and non-voicing frames in audio data. The presence of voicing frames is often the minority class. Focal loss is designed to address such imbalances by assigning higher weights to hard-to-classify examples, which in this context would be the voicing frames. Following [12], we set the value of parameter for focal loss as \(\alpha_{t}=0.25\), and \(\gamma=2\).
## 2 Antenna Design  
This section focuses on the design and description of the antenna that has been proposed. Figure 1 illustrates the schematic representation of the design procedure.  
The design process begins by initially developing a conventional single-element antenna in the form of a spherical patch. Subsequently, a fractal technique is applied to the patch, and the design is expanded to include a MIMO configuration consisting of two elements. The primary objective of this paper is the use of mm wave frequencies for 5G applications by establishing a MIMO antenna with a rectangular defection in the ground plane. The antenna is designed and simulated using HFSS 15.0 software. The antenna is 28.22 \(\times\) 44 \(\times\) 1.6 mm\({}^{3}\) and has a simple construction. An economical FR4 substrate material is used to manufacture the antenna. The subsequent parts will cover the stepwise design process, optimization, and the accompanying results.  
Figure 1: Design process. (**a**) Conventional spherical patch antenna; (**b**) fractal spherical patch antenna; (**c**) MIMO antenna; (**d**) MIMO antenna with DGS.
# Hardware Update through Digital TV Signals
## 4 DTV-Receiver Architectures and FPGA Reconfiguration Schemes  
DTV receivers (e.g., set-top boxes) are used to demodulate and decode the HDTV broadcast signal to recover the transmitted TSs carrying audio, video, and data packets (see Section 3). Typically, DTV standards specify reference receiver architectures to suggest design implementations to manufacturers [53]. Commercially available receivers usually present similar compositions and provide a set of device drivers with exemplary applications, which are taken as a reference to access and manipulate hardware devices.  
Figure 3 shows the necessary components present in receiver architectures. The air interface (e.g., frontend, also called a network interface module) device is responsible for demodulating any available DTV signal. It recovers the TS stream flows output by multiplexers, which were sent by DTV transmitters. The DEMUX component uses the frontend's (air interface) output (transport streamflow), filters packets related to a given PID identifier (see Section 3), and outputs them in separate continuous flows. Later, those flows are forwarded to their respective decoders (e.g., H.264 video decoder), which in turn decode the content (e.g., audio or video packets) in a continuous process. Finally, the information delivered by the video decoder is passed to its respective digital video encoder (DENC) module. The next step converts digital baseband video data into analog  
signals (e.g., Y/C and composite video broadcast signals) to provide a video interface with other equipment (e.g., TV sets and personal video recorders). The other flows present in transport streams are also filtered by DEMUX, according to DTV tables or a given PID provided by resident applications (e.g., filter software parser) for control.
## 4 Experimentation and Results
### Results and Discussion  
It is necessary to research the sensitivity and specificity of the diagnostic procedures currently used in the medical field. In the jargon of algorithm design, the ability of an algorithm to differentiate between accurate positive results and erroneous positive results is referred to as its \"sensitivity\". When determining how comprehensive a person's judgment is, one additional metric to consider is that person's ability to detect true negatives often. The classification techniques covered in this article provided results that were both true negatives and true positives, as may be seen here, and the confusion matrix is presented in Table 2.  
According to the results for the tested dataset, the algorithm was able to correctly recognize 852 occurrences, whereas it incorrectly labeled 166 instances. This information was gleaned from the dataset. Consequently, an algorithm was developed that had an accuracy of 84.8% and a sensitivity of 79.5%. In addition, the approach achieved a specificity of 90.6% and a precision of 90.5%. A graphical representation of the findings is provided in Figure 10 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N = 1018** & **Predicted Yes** & **Predicted No** \\ \hline \multirow{2}{*}{Actual Yes} & TP & FP \\  & 427 & 45 \\ \hline \multirow{2}{*}{Actual No} & FN & TN \\  & 110 & 436 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix for the CNN.  
Figure 9: The quadratic error of the network.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Methodology
#### 2.4.3 Support Vector Regression (SVR)  
SVR is another machine learning model which is mainly used for regression problems [74]. SVR is a variation of the famous classification algorithm--support vector machines (SVM). The main difference between SVR and SVM is that SVR is used for regression while SVM is used for classification. In classification, SVM constructs a line or hyperplane which can divide the data into different classes. SVR gives us flexibility in terms of error tolerance and maximizing margins [75]. In SVR, the input is initially mapped using a linear or non-linear kernel function depending on relationship between predictors and response variables, after which a linear model is constructed to minimize the errors of the model.
# Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios
## 2 Materials and Methods
### Metrics  
The trained models for weed detection were tested on the test subset and three additional small datasets mentioned in Section 2.2.2. The number of weeds per image was manually counted and reported as a ground truth value. Consequently, with this data, precision (P) and recall (R) were used as the evaluation metrics for weed detection. These model evaluation metrics are defined as follows:  
\[\mathrm{Precision(P)}=\frac{\mathrm{TP}}{\mathrm{TP+FP}} \tag{1}\]  
\[\mathrm{Recall(R)}=\frac{\mathrm{TP}}{\mathrm{TP+FN}} \tag{2}\]  
where TP consists of the true positives (when the algorithm correctly detects weeds with a bounding box); FP corresponds to the false positives (when the algorithm computes a bounding box in a location without weeds); and FN indicates false negatives (when a target weed is not detected). The IoU between the bounding box produced by the detection and the ground truth is calculated. For each image, If the IoU is over a predetermined threshold (0.5 in this study), a TP is produced; otherwise, the result is an FP. As mentioned in Section 2.1, the trained model provides a TP using bounding box coordinates and a confidence score (the model's confidence regarding each detection performed). The area under the precision-recall curve represents the average precision (AP).  
\[\mathrm{Average\ Precision(AP)}=\int_{0}^{1}\mathrm{P(R)dR} \tag{3}\]  
AP is a number between 0 and 1 used to summarize the different precision values obtained in the recall function. Furthermore, the mean average precision (mAP) is used to evaluate a model and is obtained by averaging the AP for each class.  
\[\mathrm{Mean\ Average\ Precision(mAP)}=\frac{1}{n}\sum_{i=1}^{i=n}\mathrm{Api} \tag{4}\]  
Generally, two maps are produced using two different thresholds: the mAP_0.5, the mean of the AP with a confidence score between 0 and 0.5, and the mAP_0.5:0.95, which is the mean of the AP with a confidence score between 0.5 and 0.95. Therefore, precision (P), recall (R), mean average precision 0.5 (mAP_0.5), and represent average precision 0.5-0.95 (mAP_0.5:0.95) are considered the most common metrics when evaluating object detectors [25].  
Models performance metrics (P, R, mAP_0.5 and mAP_0.5:0.95) obtained after the 5-fold cross validation have been subjected to a one-way ANOVA using statistical software R [55]. Data normality assumptions were assessed using the Shapiro-Wilk for normality and Levene's test for homoscedasticity using 'car' package [56]. Pairwise comparisons and mean separation were performed with a Tukey HSD post hoc test (FDR adjusted _p_-value) using'scmamp' package [57]. In Figure 2 is depicted and resumed the framework of the current study.
## 3 Results and Discussion
### Trapped Mass Estimation  
The second characterization step was to estimate the trapped mass at IVC. For this purpose, as previously stated, a model was developed in GT-Power. For each operating condition, the model was adjusted by considering measured parameters such as intake and exhaust temperatures, air mass flow, and maximum in-cylinder pressure. Compression ratio, valve timing, and the heat transfer coefficient were refined in an iterative process to obtain the optimum adjustment between the simulated and measured in-cylinder pressure as well as air mass flow. Special attention was paid to the pumping loop in the cycle, which is extremely important to guarantee good estimation of the trapped mass.  
Figure 10: Maximum deformation at 2.7 bar of intake pressure.  
Figure 9: Maximum deformation at 1.1 bar of intake pressure.  
For this purpose, it is very important to have an accurate valve timing and valve profile as well as intake and exhaust pressures and intake air temperature. Figure 11 shows a comparison between the simulated and measured in-cylinder pressure trace. It can be seen that the model was capable of capturing, with good agreement, the behavior in the low-pressure part of the cycle. In addition, the pressure in the closed cycle (compression and expansion) was also well predicted by the model.  
Figure 12 shows the comparison between the measured and simulated peak pressure and air mass flow used to verify the reliability of the model. The largest difference for the air mass flow was 2% and was less than 1% for the maximum cylinder pressure. These comparisons confirmed the prediction capability of the model. In this way, the main objective of this step could finally be achieved by obtaining the in-cylinder trapped mass for each engine condition, which are presented in Table 4. These values were used as input in the next step of the characterization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Intake Pressure [bar]** & **Trapped Mass [g]** \\ \hline
1.1 & 0.479 \\
1.5 & 0.644 \\
1.9 & 0.825 \\
2.1 & 1.009 \\
2.7 & 1.198 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The in-cylinder trapped mass estimated for each tested engine condition.  
Figure 11: GT-Power simulation versus the measured cylinder pressure.  
Figure 12: Maximum cylinder pressure (**left**) and air mass flow (**right**) for each tested condition.
# HFD: Hierarchical Feature Detector for Stem End of Pomelo with Transformers
## 2 Related Works
### Detection Methods  
There have mainly been two kinds of detectors since the advent of deep learning. They are the one-stage detection framework and the two-stage detection framework [24; 25]. The two-stage detection framework, which is represented by RCNN [26] and Fast RCNN [27], generates a series of sparse candidate boxes through CNN, and then classifies and regresses these candidate boxes. It has a more complicated training process because of the multistage complex pipeline. In practical applications, the time of inference is very long [24]. Theoretically, it is difficult for us to optimize. RCNN [26] uses CNN networks to extract image features from empirically driven artificial feature paradigms histogram of oriented gradients and scale invariant feature transform to data-driven representation learning paradigms to improve feature-to-sample representation. Fast RCNN [27] only performs feature extraction for the whole image full region once, introduces suggestion frame information, and extracts the corresponding suggestion frame features.  
By comparison, one-stage detection framework (Representative YOLO [28], SSD [29], etc.) can avoid the problems mentioned above. YOLO [28] uses the whole image as the input of the network and takes target detection as a regression problem to solve it. YOLO directly regresses the position and category of the preselection box on the output layer. SSD [29] extracts feature maps of different scales for detection. Large-scale feature maps (the feature map in the front) can be used to detect small objects, while small-scale feature maps (the feature map in the back) can be used to detect large objects. Moreover, SSD uses prior boxes (default boxes) with different scales and aspect ratios.  
In summary, one-stage detection frameworks detect objects in a single pass through the network. Two-stage detection frameworks use a two-stage process to detect objects. In the first stage, the network proposes regions of interest (ROIs) where objects may be located. In the second stage, the network classifies the proposed ROIs and refines their bounding boxes. One-stage detectors are faster and easier to use, but they sacrifice accuracy. Two-stage detectors are more accurate but are slower and more complex. In practical applications, provided that the real-time requirements are satisfied (FPS > 50), both one-stage and two-stage detection frameworks are suitable for distinguishing the stem end of pomelo from its black spots with higher accuracy.
## 3 Materials and Methods
### Data Acquisition  
The first stage is to acquire the data; for this, a total of six test subjects was used, both women and men, as well as two different vehicles and a different set of cell phones. This was chosen to avoid biases towards a specific item, such as the color of the clothes, the model of the mobile phone, the shape/color/clothes of the driver or the lighting conditions.  
To detect the use of the cellphone while driving, a camera with a wide angle lens mounted in the middle of the ceiling car is used. The camera, which is a GoPro Hero 5 sessions camera with an equivalent 14 mm field of view angle, is mounted as depicted in Figure 1(a) and pointed towards the driver's body, as shown in Figure 1(b). With the camera positioned, a series of videos with a resolution of 1980 \(\times\) 1080 and sample rate of 24 Hz were taken.  
With the camera mounted, in the second stage, test subjects were instructed to drive the car as they normally do, without any special position or restriction of movement. Then, they had to move the cellphone from anywhere they had it and send a text message or use an application, without restrictions on how they held the phone or the time between events (as in real life, when the cellphone is held for short or long periods of time). This process was repeated five times by each subject. For each subject  
Figure 1: Flowchart of the methodology proposed.  
Figure 2: Image acquisition setup.  
video, every frame was saved as an independent image; then, the said image was assigned a label (distracted, non distracted). Table 1 shows all the details of the data used, including, cellphone model, vehicle type, number of images by subject, etc. All this was performed under two different lighting conditions, daytime driving, and night driving, gathering a total of 85,401 images.
## 5 Results
### Performance Evaluation Metrics of Model  
The performance of the proposed model was evaluated using some metrics such as accuracy, recall, precision, F1 score, and the confusion matrix [24,25]. Accuracy measures the accuracy of recognizing traffic signs, while precision measures the proportion between the true positives to the total number of detected signs. Recall measures the ratio of true positives to the total number of ground-truthed signs, and the F1 score combines precision and recall into a single metric:  
\[Accuracy=\frac{(TN+TP)}{(TP+TN+FP+FN)} \tag{2}\]  
\[Precision=\frac{TP}{(TP+FP)} \tag{3}\]  
\[Sensitivity/Recall=\frac{TP}{(TP+FN)} \tag{4}\]  
Figure 3: Proposed CNN model architecture.  
A comparison of the experiment training and validation accuracies is presented in Figure 5. The fact that both values are nearly equal to each other suggests that the model fits the data well and exhibits neither overfitting nor underfitting.  
After that, the training and validation losses for the proposed LE-CNN model with different epochs were computed and are displayed in Figure 6. The fact that both values are convergent towards zero suggests that there is no overfitting or underfitting in the model. This also shows how well the model can be used as a predictive tool in general.  
Finally, a comparison was made between our proposed LE-CNN model and the ResNet, optimized ResNet, and VIT transformers [15]. As shown in Table 2, the LE-CNN model performed with an impressive accuracy of 96.5% while maintaining superior perfor  
Figure 5: Comparison of the proposed LE-CNN model’s training and validation accuracy.  
Figure 6: Comparison of the proposed LE-CNN model’s training and validation loss.  
mance with a remarkably low inference time of 1.65 s. Also, LE-CNN was able to obtain a verification time less than the time required for the ResNet and the optimized ResNet v2 model, which was 719 s when trained without the k-fold verification technique, while LE-CNN was able to obtain a verification time less than the time required for the optimized ResNet v2 model, which was 3595 s when trained with the k-fold verification technique. On the other hand, the VIT transformer succeeded in achieving faster validation and testing times than the LE-CNN model, but this was at the expense of transformer accuracy.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 2 The Durability Prediction Method and Research Method
### Research Significance and Novelties  
The invention of the durability prediction method can directly guide the evaluation and conservation of the historical square rebar RC buildings. The main novelties are as follows:  
1. Fundamentally, the proposed durability prediction method accomplishes the issue that there has been no applicable and reliable method of durability evaluation for historical square rebar RC buildings.
2. The durability prediction calculation methods for a single concrete component focus on the historical concrete and square rebar components. It considers the poor properties of the historical concrete and the different models of corrosion-induced cover cracking between square rebar and round rebar.
3. The proposed important analysis method is improved from the existing theory on the realistic application, which is more accurate and efficient. The inclusion of the finite element model also enhances the efficiency of the evaluation works of the building.
4. GB/T 51,355 standard fosters the engineers or workers to choose a limited number of structural members to represent the status of the entire structure; however, the selection is performed randomly or inaccurately only depending on the experience. The proposed method ranks the structural members according to importance, which can reduce the potential human error and improve the reliability of the durability evaluation. Additionally, the component importance ranking is highly valuable and practical for the renovations and conservations of historic buildings that have aesthetic parts as colorful drawings and patterns. When feasible, the ranks can guide engineers to reinforce only the essential components rather than all components; hence, allowing the original paintings and patterns on the components to be preserved.
## 3 Results and Discussion
### Effects of VIAPW (M7) and IRWWW (M10) on Lipid Accumulation  
The effects of antioxidant peptides (M1-M10) on lipid accumulation in HepG2 cells pretreated with OA were studied. As shown in Figure 2, the intracellular lipid content of the model group pre-treatment with OA at 80 \(\upmu\)m after 24 h was dramatically higher than the lipid content of the normal control group (\(p<0.01\)), which indicated that the OA cell model was successfully established. In comparison with the OA cell model group, VIAPW (M7) could significantly decrease the lipid accumulation in HepG2 cells at 100 \(\upmu\)m (\(p<0.05\)), and IRWWW (M10) and the positive control of SV could very significantly reduce the lipid accumulation in HepG2 cells at same concentration (\(p<0.01\)). To directly observe the effects of VIAPW (M7) and IRWWW (M10) on the lipid accumulation of HepG2 cells, the tested HepG2 cells were dyed using Oil Red O, and their images were taken and shown in Figure 3. In comparison with the normal control group, higher cellular lipid content in the cell model group pre-treatment with OA was observed. In addition, more lipid globules inside cells were also found. However, the images (Figure 3) of VIAPW (M7)-, IRWWW (M10)-, and SV-treated cell groups (especially IRWWW (M10) and SV) were clearly different from that of the model cell group, which illustrated that they could dispose of most of the intracellular lipid.  
Figure 3: Effects of VIAPW (M7) an, IRWWW (M10) from miuuy croaker (_M. miuuy_) muscle at the concentration of 100 \(\upmu\)m on morphological characteristics of HepG2 cells for 24 h compared to normal and positive control (simvastatin (SV)) groups. The images were taken using an inverted microscope (\(\times 400\)).  
Figure 2: Effects of antioxidant peptides (M1–M10) from miuuy croaker (_M. miuuy_) muscle on lipid accumulation of HepG2 cells at 100 \(\upmu\)m. Means \(\pm\) SD (n = 3) are used to express the experiment data. \({}^{\#\!\!  
Excess lipid accumulated in the hepatic cells, which acts as an indicator of a lipid overload and a mediator of metabolic syndrome. Moreover, the steatosis may translate into nonalcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma with a range of cell histological alterations [29; 30]. Hence, VIAPW (M7) and IRWWW (M10) might be good lipid-lowering candidates, and their mechanisms were studied more profoundly and meticulously in the following experiments.