esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stave-Aware Optical Music Recognition on Monophonic Scores for Camera-Based Scenarios
## 3 Method Details
### Preprocessing  
For subsequent stave position detection, it is necessary to preprocess original sheet music photos. We use the same preprocessing method as [39]. Specifically, the steps for preprocessing are as follows:  
1. _Remove the background lighting._ All input images are converted into grayscale because color is an unnecessary element for OMR. Then, Gaussian blur [40] is applied to obtain blurred images. Finally, subtract the original grayscale image from the blurred image. The purpose of this step is to mitigate the brightness and contrast differences between different regions in the musical score image caused by uneven light distribution.
2. _Resize the image._ Firstly, we divide the image into a fixed number of columns, calculate the median gray scale value of each row within each column, and assign it as the value for that respective row. Next, convolve the modified image with a set of comb filters corresponding to different staff line spacing. We choose the spacing represented by the accumulated response of the most prominent comb filter as the distance between adjacent lines. Finally, we resize the image to ensure the lines' distance is fixed. The purpose of this step is to keep a constant distance between adjacent staff lines of the musical score.
3. _Morphological filtering._ We perform two rounds of morphological filtering on the image shown in Figure 2. The first round removes non-horizontal pixels, and the second round removes thin staff lines. Finally, subtracting the results of the two filtering steps provides the staff lines eliminated during the second round. The purpose of this step is to eliminate the musical notes from the image, preserving only the staff lines for stave detection.  
Through preprocessing, we improve the image by fixing uneven lighting and minimizing the impact of musical notes affecting stave detection.
## 2 Related Works
Channel state changes caused by high mobility result in the frequent update of channel information, causing signaling overhead and latency. Therefore, an appropriate method is required to maximize system energy efficiency while reducing signaling overhead and guaranteeing latency requirements. Typical optimization methods are highly complex and incur signaling overhead. The Q-learning method is not capable of processing high-dimensional data or learning directly the network data. Hence, there is need for research on a DRL based technique capable of reducing the signaling overhead of channel state updates generated from vehicle mobility. In this paper, we present a DRL based resource allocation algorithm with RRH grouping and vehicle clustering for cellular V2X communication while guaranteeing QoS and reliability requirements. Our goal is to maximize system energy efficiency. The proposed algorithm consists of three parts: One for RRH grouping, for vehicle clustering, and a DRL based resource allocation method. The RRH grouping method is a clustering technique that groups neighboring RRHs to reduce the communication overhead and maintenance cost of DRL. The vehicle clustering method clusters vehicles to reduce the signaling overhead of periodic messages. Each vehicle cluster constructs a jumbo frame from the periodic messages of cluster members and requests resource allocation. To allocate communication resources, the DRL based resource allocation is adjusted. Agents are deployed to each RRH, to learn to maximize the sum capacity of the system so that the RRH grouping and vehicle clustering method can minimize system energy consumption, and the DRL based resource allocation method can maximize the sum capacity of the system.
## 5 Proposed IoT Layered Models
### Generic IoT Layers and Data Fusion Model  
The generic architecture of the IoT model, from the authors' perspective, not sure if there are any similar thoughts in literature, as shown in Figure 1, consists of a device, cloud, and end-user layers. The device layer consists of a pool of wireless Internet-enabled sensor devices, data acquisition circuitry, and communication protocols to send data to local or remote storage for further processing. These devices allow the user to collect data in real-time with different acquisition frequencies. The cloud layer hosts the data collected from the sensors for further processing, noise removal, feature extraction, and data massaging. This data is later fed into a decision support system that runs complex data analysis and artificial intelligence to provide a decision regarding the person's health. The end-user layer, consisting of the receiving user, could be in different forms. Of concern is smart devices, where security and privacy challenges exist. Within the boundaries of these three layers, a list of sublayers or modules added to ensure the robustness of the decision support system. To ensure data is sent and processed promptly to provide a critical decision that cannot wait until the data is sent to the cloud, we introduce an edge computing capability that can make such smart decision, and at the same time save a copy of the data and send it to the cloud layer for processing and long-term storage. On certain occasions, we need to send commands or instructions to some wearables devices to update their acquisition rate or functionality, and this will require another protocol and security procedures.  
Figure 2 shows the stretched version of the generic model. We can see the addition of the new layers; edge and fog. Both layers can overcome the latency issues from the reliance on cloud layer services and are able to make decisions faster. Edge computing occurs on the devices to which the sensors are attached to or physically close. They provide a real-time decision and control to the data sources, and at the same time, communicate with other layers to transfer the data for fusion, storing, and analytics. The fog computing layer moves the edge computing activities to more powerful computing resources that are connected to the local area network and physically more distant from the sensors and data sources [28]. These added benefits create more security and privacy challenges.
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 3 Background Knowledge
### Duplicate Bug Reports  
This study focuses on the challenge of identifying duplicate bug reports by determining whether a newly submitted report corresponds to an issue that has already been documented.  
For instance, Figure 2 illustrates a corrected bug report [29] titled \"Form based login does not work without cookie\" and its duplicate counterpart [30] titled \"Form based login without cookies not working\". These reports share significant lexical overlap, including terms such as \"form\", \"based\", \"login\", \"without\", \"cookie\", \"not\", and \"working\". By leveraging both lexical and semantic similarities, the model accurately identifies these reports as duplicates, ensuring consistent and reliable matching.  
To further exemplify, an Apache bug report identified as #2492, submitted on 21 July 2001, described a resolved issue. Subsequently, on 10 August 2001, another report labeled #3089 was submitted and later identified as a duplicate of #2492.  
This advanced functionality is achieved through the application of natural language processing (NLP) techniques, such as semantic similarity evaluation, which enable the model to identify both surface-level lexical overlaps and deeper contextual relationships between bug reports. Furthermore, by incorporating a feature selection algorithm, the system prioritizes the most relevant linguistic features, optimizing model performance and ensuring precise and efficient duplicate detection.
## 5 Conclusions
The F10.7 data were downloaded from the Canadian Space Weather Forecast Centre: [[https://www.spaceweather.gc.ca/solarflux/sv-5-en.php](https://www.spaceweather.gc.ca/solarflux/sv-5-en.php)]([https://www.spaceweather.gc.ca/solarflux/sv-5-en.php](https://www.spaceweather.gc.ca/solarflux/sv-5-en.php)) (last accessed on 27 August 2021), QBO data were downloaded from the Institute of Meteorology of Freie Universitat Berlin: [[http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/](http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/)]([http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/](http://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/)) (last accessed on 27 August 2021), and MEI data were downloaded from the NOAA Physical Sciences Laboratory [[http://www.esrl.noaa.gov/psd/enso/mei/](http://www.esrl.noaa.gov/psd/enso/mei/)]([http://www.esrl.noaa.gov/psd/enso/mei/](http://www.esrl.noaa.gov/psd/enso/mei/)) (last accessed on 27 August 2021). The ERA5 reanalysis dataset was downloaded from the ECMWF: [[https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)]([https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)) (last accessed on 27 August 2021). **Acknowledgments:** The authors would like to express their gratitude to University Corporation for Atmospheric Research (UCAR) for providing the GNSS RO data. We are also very grateful to the Canadian Space Weather Forecast Centre, the Institute of Meteorology of Freie Universitat Berlin, the NOAA Physical Sciences Laboratory, and the ECMWF for providing the data of F10.7, QBO, MEI, and the ERA5 reanalysis dataset, respectively. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
The outcome of this preliminary photocatalytic study suggests that the DCF degradation reaction proceeds with a typical radical mechanism, as reported for other types of light absorbers [50], and is summarized in Figure 4.1 The process starts with the CZTS NPs' light absorption, so electron-hole pairs are generated: electrons are promoted from the valence band to the conduction band, leaving holes in the valence band. After the generation of electron-hole pairs, the corresponding charge separation occurs. After the migration of charge carriers, the redox reactions that lead to the degradation of DCF present in the surroundings are promoted. The photogenerated electrons typically reduce electron acceptors, such as oxygen molecules, forming reactive oxygen species (ROS)  
Figure 4.2 Proposed structure for the main degradation product. MW: 202 g/mol; formula: C8H10O6. Figure 4.3 DCF photocatalytic cycle mediated by CZTS NPs. Figure 4.4 (ESI+) MS/MS spectrum of the main degradation product after 120’ reaction time in visible-light-only photocatalysis mediated by Cu2 ZnSnS4 nanoparticles. like superoxide anions (O2*-). Parallelly, the holes oxidize electron donors, often water or hydroxide ions, to generate hydroxyl radicals (OH*). Both radical species are highly reactive and can attack and break down complex organic molecules by bond cleavage, converting them into less harmful or more easily manageable substances. The key advantage of CZTS NPs resides in their narrow bandgap of 1.5 eV. In this way, it is possible to drive the reaction by using only visible light and avoiding other possible degradation paths related to UV photolysis. These preliminary results are encouraging and could facilitate new ways for wastewater and primary water treatment against emerging micropollutants such as DCF, considering that, nowadays, the DCF degradation in industrial water treatment plants ranges between 20 and 40% [51; 52]. Moreover, though CZTS NPs' photocatalytic performance has been reported on and documented in the literature in case studies and reports of proof-of-concept experiments involving organic dyes, to the best of our knowledge, no direct research on a water micropollutant has been reported so far.
## 3 Results
### Influence of Large Particles on the Scattering Indicatrix of Milk  
We tested the behavior of the scattering indicatrix of milk with large-scale impurities. To this end, we compared the measurements made for pure milk with a fat content of 1.5%, and the same milk where somatic cells were added with a final concentration of \(10^{6}\) cm\({}^{-3}\) (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \((\theta,\beta)\) & \(0\) & \(1\) & \(2\) & \(3\) & \(4\) \\ \hline \(1\) (\(\theta=108^{\circ}\)) & \(57\) & \(-397.6\) & \(1049.2\) & \(-1264.5\) & \(602.8\) \\ \hline \(2\) (\(\theta=90^{\circ}\)) & \(237.15\) & \(-971.4\) & \(1488.2\) & \(-1016.3\) & \(263.5\) \\ \hline \(3\) (\(\theta=72^{\circ}\)) & \(137.6\) & \(-507.6\) & \(695.2\) & \(-421.6\) & \(96.8\) \\ \hline \end{tabular}
\end{table}
Table 1: Coefficients of \(A_{ij}\).  
Figure 6: Dependence of the scatterometric parameter \(X_{i}\) on the milk flow rate for milk with a fat percentage 3.2% at a temperature of 20 \({}^{\circ}\)C.  
Note that the addition of somatic cells has little effect on the intensity ratio of side and back scatter, that is, on the scatterometric parameter at the angles (72\({}^{\circ}\), 90\({}^{\circ}\), 108\({}^{\circ}\)). This is an essential result, which means that fat content measurements at these angles are not disturbed by the presence of somatic cells.  
Intensity ratios analogous to (1) at forward-scattering angles o f \(\theta_{i}=\{0^{\circ},18^{\circ},\,36^{\circ}\}\) can serve as indicators of the presence of large particles in milk. It makes sense to introduce the index of large particles, \(LPI\), as a deviation of the values of these ratios from the normal case when there are no large inclusions in milk (pure milk):  
\[LPI_{i}=\frac{X_{i}^{(pure)}-X_{i}}{X_{i}^{(pure)}},\ \ \theta_{i}=\{0^{\circ},18^{ \circ},\,36^{\circ}\}. \tag{4}\]  
It follows from the graph in Figure 7 that the addition of somatic cells to milk at a level of 10\({}^{6}\) cm\({}^{-3}\) leads to an increase in \(LPI\) from zero to \(\sim\)0.1
## 3 Results and Discussion
### XRD Result Analysis  
Fe\({}_{3}\)O\({}_{4}\), magnetic polystyrene microspheres and sulfonated magnetic polystyrene microspheres were analyzed by an X-ray diffractometer (Bruker D2 Phase, Germany). It can be seen from Figure 5 that diffraction peaks with different peak intensities still appear at 20 = 30.121\({}^{\circ}\), 35.498\({}^{\circ}\), 43.205\({}^{\circ}\), 53.496\({}^{\circ}\), 57.022\({}^{\circ}\), and 62.603\({}^{\circ}\) for Fe\({}_{3}\)O\({}_{4}\) nanoparticles and prepared magnetic polystyrene microspheres, which is consistent with the position of characteristic diffraction peaks of Fe\({}_{3}\)O\({}_{4}\). Thus, Fe\({}_{3}\)O\({}_{4}\) nanoparticles could not destroy the crystal structure of ferrite during modification. It also shows that there is no change in the crystal structure of ferrite during suspension polymerization. In addition, the diffraction peak of Fe\({}_{3}\)O\({}_{4}\)@PS magnetic microspheres is weaker than that of Fe\({}_{3}\)O\({}_{4}\) nanoparticles, which is mainly caused by the weakening of the diffraction peak intensity of Fe\({}_{3}\)O\({}_{4}\) due to the presence of polystyrene. The large diffraction dispersion peak at 20 = 20\({}^{\circ}\) is a typical amorphous polystyrene diffraction peak. The diffraction peak of Fe\({}_{3}\)O\({}_{4}\)@PS magnetic microspheres is weaker than that of Fe\({}_{3}\)O\({}_{4}\)@PS, indicating that the magnetism of ferrites continues to be weakened by further functionalization of chlorosulfonic acid. However, the separation effect would not be affected.  
Figure 4: Hysteresis lines of Fe\({}_{3}\)O\({}_{4}\), Fe\({}_{3}\)O\({}_{4}\)@PS and Fe\({}_{3}\)O\({}_{4}\)@PS.
# Visual and Textual Sentiment Analysis of a Microblog Using Deep Convolutional Neural Networks
## 3 Methods
### Textual Features  
Before pre-training the word vectors, we extract all texts from the Sina Weibo Dataset including both labeled and unlabeled data and segment them using two methods: One is splitting all texts into Chinese phrases using the Python package named jieba, and the second is splitting them into single Chinese characters--but preserving the English words. We split all texts using a delimiter of one blank space.  
Based on these two different segmentation methods, we obtain two word vector vocabularies trained on all texts using the _word2 vec_ tool and all vectors have dimensionality of 300.  
Next, we train the textual DNN model (DNN_Text, see Figure 2) on top of the pre-trained word vectors through stochastic gradient descent (SGD) over shuffled mini-batches with the Adadelta update rule [34]. The model randomly initializes unknown words. Then, while training the model, it keeps all the word vectors static and learns only the other parameters. Finally, we extract textual features with a length of 300 from the second-to-last layer of the textual model.  
We use the hyperparameters similar to [7]: rectified linear units, filter windows of 3, 4, 5 with 100 feature maps each, dropout rate of 0.5, \(l_{2}\) constraint of 3, mini-batch size of 50, adadelta decay of 0.95 and epochs of 25.  
Figure 2: Architecture of our multiple deep convolutional neural networks approach.
## 2 Factors Influencing the Risk of Road Subsidence around Sewer Pipes
### Analysis of Pipeline Age (Installation Year) and Road Subsidence  
Pipeline age or the year of installation is the most significant factor in determining the priority of inspections and repairs using security cameras. In Japan, sewer pipes older than 30 years are inspected annually and prioritized for reinforcement or replacement [10]. It has also been reported that the installation year (pipeline age) is highly correlated with ground subsidence [11].  
Figure 1 shows the results of analyzing the pipeline age and the road subsidence rates for all sewer pipes managed in urban areas in Korea. Pipes older than 40 years were excluded from the analysis. The data showed that road subsidence occurred most frequently in pipes older than 40 years. However, 30% of the data errors were marked older than 100 years in cases where the exact installation year was uncertain. Therefore, the data on sewer pipes older than 40 years were excluded from the analysis. As shown in Figure 1, the rate of ground subsidence increases as the pipeline age increases.
## 2 Results
### Performance of SPAD Prediction Regression Models
#### 2.3.2 Wheat SPAD Values Prediction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Testing Set**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) & **RMSE** & **MAE** \\ \hline MLP-FR & \(0.893\) & \(3.25\) & \(2.52\) & \(0.821\) & \(3.680\) & \(3.233\) \\ RF-FR & \(0.815\) & \(4.05\) & \(2.78\) & \(0.775\) & \(3.785\) & \(3.781\) \\ SVM-FR & \(0.791\) & \(5.10\) & \(3.90\) & \(0.747\) & \(7.903\) & \(4.252\) \\ ExGR-FR & \(0.670\) & \(7.88\) & \(5.12\) & \(0.621\) & \(8.202\) & \(6.102\) \\ ExG-FR & \(0.642\) & \(9.85\) & \(7.55\) & \(0.521\) & \(13.155\) & \(11.851\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation of the regression models. MLP-FR, RF-FR, SVM-FR, ExGR-FR, and ExG-FR represent the field-based regression models for the multilayer perceptron, random forest, support vector machine, excess green, and excess green–red segmentation methods, respectively.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 1 Introduction
Therefore, we propose a saturation-based dehazing method that preserves the information of objects by extracting only the hue of cast airlight. The proposed method addresses the existing color distortion issue by using the LAB color space in color correction and dehazing. First, the proposed color correction method uses a dominant color output method to cluster LAB color images and assign area scores to the classified clusters. These scores are used to identify the airlight areas, and the hue of the corresponding areas is detected to perform gray world-based white balancing. Furthermore, using the correlation between the luminance and saturation of the image and the depth of haze from the corrected image, this method generates a depth map equation. To address the loss of near-field information and the generation of far-field noise as visibility improves, this method uses the saturation of the restored image to separate and process far-field objects from airlight and applies a weighting value to the depth map based on the average luminance. The parameters of the defined depth map were estimated using multiple linear regression according to the linear characteristics and applied to the ASM for dehazing. The proposed method prevents excessive grayscale and color distortion while preserving object colors, thus improving upon the limitations of existing color restoration-based dehazing methods. The remaining structure of this paper is as follows. Section 2 discusses theories related to color restoration, and Section 3 describes the proposed dehazing method. Section 4 presents the experimental results and discussions, comparing our method with existing methods. Finally, Section 5 concludes the paper.
# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 3 Methodology
### Image-Based Logo Detection and Recognition
#### 3.2.3 Feature-Matching Logo Recognizer
_Bag-of-words Features._ Deep learning models are good at extracting high-level semantic features, whereas, for logos, low-level features (such as textures, corners, and edges) can be useful for recognition. To incorporate low-level features, we make use of the bag-of-words (BoW) features, which are the most commonly used image representations in image retrieval literature [40; 41]. In the training stage, local features were extracted using the scale-invariant feature transform (SIFT) descriptor [42]. This resulted in a large number of features for each logo image. To further find a compact and fixed-length representation of each image, feature quantization is required. This was achieved by visual codebook learning, after which, each local feature can be assigned to a visual word in the codebook. In this way, an image could be discriminatively represented by a histogram of these visual words. The codebook was learned by clustering gallery local features using the k-means algorithm and regarding the centers of clusters as visual words. In the inference stage, local features were obtained by the same method following the training stage. For each feature, the nearest visual word in a trained codebook was found and assigned to the corresponding bin of histogram, which resulted in a fixed-length feature vector for each testing image. _Fused Features._ Feature fusion has been proven to be effective when the classes of features to be fused are heterogeneous, which can result in better performance than the best single class of features. BoW features are essentially sparse histograms of low-level local features, and DML features are high-level semantic features with large receptive fields. Both of the extracted features are heterogeneous. Therefore, combining these two sets of features tends to give a better result, as they are complementary to each other. Following this intuition, we concatenated these two features and obtained 1,512-dimensional feature vectors. We then used principal component analysis (PCA) for dimensionality reduction [43]. This resulted in 500-dimensional reduced feature vectors as the final representation. The experiment results demonstrated the effectiveness of our fusion approach.