esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Related Work
The pre-trained YOLOv5s model was identified as particularly effective for detecting plastic in riverine UAV imagery, striking an optimal balance between accuracy and computational complexity. Moreover, transfer learning techniques significantly enhanced the model performance, thereby improving plastic detection with minimal additional training required. The insights gleaned from this research contribute to the ongoing global efforts to decrease plastic waste and safeguard innumerable aquatic ecosystems. The key data used in this paper include aerial images captured by a DJI Phantom 4 drone from surveys of two rivers--the Houay Mak Hiao river in Laos and the Khlong Nueng canal in Thailand. The images covered 2 m \(\times\) 2 m patches of the rivers. Here, 500 aerial image tiles of 256 \(\times\) 256 pixels were extracted and manually annotated with bounding boxes around plastic objects for each river location. These formed the training and test datasets. Metadata like GPS coordinates and altitude were extracted from the raw drone images to georeference the tiles. The Houay dataset had 592 annotated plastic objects and the Thailand dataset had 796 objects. The tiles were split 70% for training and 30% for validation/testing for the deep learning models. The paper evaluated the performance of different deep learning models for plastic detection in rivers using UAV sensor data. YOLOv5s was found to be the most effective model, with a high mean average precision (mAP) of 0.81 with no transfer learning based on the Houay Mak Hiao dataset. Transfer learning was applied to improve the performance of the models. Pre-trained YOLOv4 with transfer learning achieved the highest accuracy, namely, a 3.0% increase in mAP to 0.83. YOLOv3 also showed itself to be significant for transfer learning, with an increase in mAP from 0.59 to 0.81. The pre-trained YOLOv5s model used the Houay Mak Hiao dataset provided the best tradeoff between accuracy and computational complexity.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Overview of the Proposed Model  
Figure 1 illustrates the comprehensive structure of the UPCCN model. UPGCN primarily consists of the UPEM and the multimodal two-step enhanced fusion method, which work together to achieve more precise representations of users and items. First, the enhanced signal is obtained by enhancing the item ID embedding using the transformed multimodal features of the item as the enhancing signal, thus obtaining \(\overline{e}_{v}\) and \(\overline{e}_{t}\). Then, the user representation is obtained by performing graph convolution on the user-item interaction graph using the multimodal features of the item as input, obtaining \(h^{v}_{u}\) and \(h^{t}_{u}\). Subsequently, the item representation is obtained by performing graph convolution on the same interaction graph using the user representation \(h^{v}_{u}\), \(h^{t}_{u}\) as input, obtaining \(h^{v}_{i}\) and \(h^{t}_{i}\) in different modalities. These item representations, along with the enhanced item ID embeddings \(\overline{e}_{v}\) and \(\overline{e}_{t}\), are fed into the enhancement module for further processing and fusion, producing \(\overline{e}_{i}\). Then, use the item representations \(h^{v}_{i}\) and \(h^{t}_{i}\) from different modalities as inputs, and perform graph convolution on the item-item graph in different modalities to obtain preliminary disentangled item representations \(\bar{h}^{v}_{i}\) and \(\bar{h}^{t}_{i}\) with reduced similarity. Finally, \(\overline{e}_{i}\), and the item representation \(\bar{h}^{v}_{i}\), \(\bar{h}^{t}_{i}\) obtained in different modalities are fused, and the final item representation \(\bar{e}_{i}\) generated by the multimodal two-step enhanced fusion method is output. Furthermore, to effectively extract user preference information across different modalities, the user representations \(h^{v}_{u}\) and \(h^{t}_{u}\) derived from these modalities serve as guiding signals for enhancing the user ID embedding. An attention mechanism is employed to quantify the user's focus on each modality's information, which is subsequently integrated into the user ID embedding for subsequent embedding learning. Building upon this framework, embedding learning is conducted on the user-item interaction graph to derive final representations of users and items that facilitate accurate predictions of user preferences.
## 3 Dataset of Historical Vietnamese Handwriting  
The dataset considered for experimental evaluation in this article is a collection of manuscripts written in Chu Nom used in Vietnam over 1000 years from the 10 th to the 20 th century, before it was replaced with a Latin-based alphabet. The script has Chinese origins but significantly extends the traditional Chinese writing system. Fewer and fewer people are still able to read Nom characters and therefore cultural heritage preservation is of a certain urgency. The Vietnamese Nom Preservation Foundation (VNPF) ([[http://www.nomfoundation.org/](http://www.nomfoundation.org/)]([http://www.nomfoundation.org/](http://www.nomfoundation.org/)) accessed on 25 May 2021) has collected a comprehensive collection of scanned manuscripts together with their transcriptions and also created the _Nom Na Tong Light_ font, which currently includes nearly 30,000 Nom characters in Unicode. For transcription alignment, we consider five different version of the _Tale of Kieu_ (1866, 1870, 1871, 1872, 1902) and the story of _Luc Van Tien_, which are published online by the Vietnamese Nom Preservation Foundation and were kindly made available to us for research purposes. Altogether, they include 899 scanned images and their transcriptions in Unicode, which contain column breaks but no information where on the scanned page the characters and columns are located. Please note that the text is read from top to bottom, right to left.  
For evaluating the character detection system, we consider an independent test set of 47 manuscript images used by [PERSON] et al. [3] with a manual ground truth for each character bounding box, which were kindly shared with us by the authors.  
Figure 1a illustrates both parts of the dataset. Please note that they differ significantly in terms of page layout, page background, page border, and handwriting style. Our proposed method will be applied, first, to align the 899 pages using only synthetic printed characters from the Nom font for training and, secondly, to self-train the detection system on the aligned pages in order to improve the character detection accuracy. To assess the improvement, we measure the detection accuracy on the 47 test pages before and after self-training.  
Figure 1: Dataset of historical Vietnamese handwriting.  
During the entire process, no human annotation is required for training the system, i.e., no bounding boxes have to be drawn around characters on the scanned pages. However, of course, we integrate the immense work of the scholars that was necessary to create the transcriptions and the Nom font.
# Anomaly Detection for Hydraulic Power Units--A Case Study
## 4 Discussion
### Contributions of the Article  
The contributions that this article brings into the domain of anomaly detection for hydraulic power units can be shown while discussing two aspects: commercial implementation and incorporation of IoTEdge and Microsoft Azure Cloud. The commercial solutions present on the market do not use, or do not allow, open insight into algorithms and solution architectures that are presented in the manuscript. The presented know-how in aspects such as integration with cloud, software, and hardware architecture and algorithms design and deployment are added value in the context of the paper's novelty and contribution. The combination of Microsoft IoTEdge and Microsoft Azure Cloud for anomaly detection purposes is a novelty among hydraulic power unit solutions. This article contains a description of the whole path from sensors through PLC, edge computer, cloud, and finally, hypotheses regarding anomaly detection. The on-premise part is described with the IoTEdge framework as the most important part. The cloud part of the solution involves a description of combining cloud services such as Azure Functions, Azure Storage, and Azure IoTHub into a single, working, commercial solution.  
The commercial context of the solution implied the need to organize work and develop algorithms in a strict time regime. The chosen approaches should have been easy to implement, easy to understand by experts in the hydraulic power units domain, and as effective as possible. Therefore, the scientific contribution could be defined as a description of the process of creating an anomaly detection solution for a hydraulic power unit that was limited in terms of time and availability of the object under investigation.  
Both aspects, commercial implementation and incorporation of IoTEdge and Microsoft Azure Cloud, could be named as scientific contributions to the domain. Both, in the specific context of hydraulic power units, have not been developed and described before.
## 3 Solution Method
### Revised Sample Average Approximation
#### 3.1.1 Scenario Generation  
The wind power availability is closely related to the wind speed. Recently, a variety of theoretical distributions were proposed to fit the probability distribution of wind speed [24]. In the present study, we adopt two-parameter Weibull distribution to fit the probability distribution of actual wind speed. The Monte Carlo simulation is run to generate N scenarios of the wind power random variable \(\zeta_{uri}^{t,n}\), and each scenario has the same probability of 1/N. \(1_{(0,\infty)}(\cdot)\) is an indicator function, i.e.,  
\[1_{(0,\infty)}(t)=\left\{\begin{array}{c}1\ t>0\\ 0\ t\leq 0\end{array}\right. \tag{24}\]  
The chance constraints (10) can be replaced by  
\[\hat{P}_{N}(x)=\frac{1}{N}\underset{n=1}{\overset{N}{\sum}}1_{(0,\infty)}(p_{ wi}^{t}-\beta p_{wi}^{t}-\zeta_{uri}^{t,n})\leq\delta \tag{25}\]  
First, if \(\delta=\varepsilon\), as the sample size N goes to infinity, we can prove that the solution of the SAA problem converges to the solution of the true problem. Since N cannot be infinity, it is possible that the solution of the SAA problem is infeasible with respect to the true problem. To increase the confidence level for the solution of SAA problem to be feasible, we let \(\delta<\varepsilon\). Generally, and \(N=2/\varepsilon,\delta=\varepsilon/2\).
## 6 Conclusions  
In this paper the challenges and constraints when over the horizon (OTH) maritime surveillance service is built on an Internet of Things (IoT) as its backbone are examined. The service utilizes High Frequency Surface Wave Radars (HFSWRs) as primary sensors and relies on a satellite communication network as its communication infrastructure in a harsh environment. The whole IoT OTH maritime surveillance network is deployed in the Gulf of Guinea and it entered operational service. The performance analysis encloses multiple steps starting with the raw data gathering from the open sea. It continues with the data processing by HFSWRs at the remote sites and ends with the data transferring to the server on a C2 location, via the satellite network.  
Thorough statistical analysis has been conducted during the extreme periods (the driest and rainiest months) in order to mathematically describe the IoT service behavior as precisely as possible. The mathematical model of the IoT OTH maritime surveillance service behavior is verified with the Kolmogorov-Smirnoff test. It should be noted that during this analysis each empirical data set is tested with nearly 20 different PDFs. Analyses presented in the paper show that average service latency is about 90 s, but it can rise to about 120 s, which is used as a key information during the sensor data fusion algorithm design. Validity of the analyses is demonstrated through high quality of service with an outage probability  
Figure 21: Outage probability comparison between rainiest and driest months.  
of just 0.1% in the driest months up to the 0.7% in the rainiest months. According to the analysis presented in this paper it is shown that the IoT OTH maritime surveillance service based on HFSWRs which relies on the satellite communication infrastructure can be a viable and reliable solution in the areas defined by harsh meteorological conditions. Moreover, the IoT service behavior can be predicted very well by the statistical models presented in this paper. At the end, the work presented here may be used as a guideline for deployment of other maritime surveillance service solutions in other equatorial regions.  
For the future work we intend to expand the sensor network and gather new data in order to further improve the statistical description of the IoT OTH maritime surveillance service behavior.  
Conceptualization, R.P. and D.N.; methodology, D.S.; software, M.N.; validation, D.S. and Z.C.; formal analysis, R.P. and D.D.; investigation, R.P.; resources, R.P.; data curation, M.N.; writing--original draft preparation, R.P. and D.S.; writing--review and editing, Z.C., D.D. and D.N.; visualization, Z.C.; supervision, D.S.; project administration, R.P.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
The research is funded by the Vlatacom Institute of High Technologies under project #89.1. The APC is also covered by Vlatacom Institute of High Technologies.  
Authors of the paper would like to thank to [PERSON] for his help.  
The authors declare no conflict of interest.
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction
### HSV Color-Space-Based Automated Object Localization Algorithm
#### 4.2.3 HSV Color-Based ROI Merge Operation  
Algorithm 3 shows the algorithm of the ROI merge operation. The ROI merge operation retrieves the temporary ROI list for random sampling of ROI values with HSV similar to the reference ROI. ROI\({}_{\mathrm{Templist\_i}}\) represents the initial value of the index of the temporary array list that registers random sampling ROIs with high HSV color similarity, compared to the reference ROI. ROI\({}_{\mathrm{Templist\_max}}\) denotes the maximum index length of the temporary array. The ROI merge operation performs mask operation (OR bit operation) to merge the searched random sampling ROI and reference ROI data.  
The ROI merge operation is used to detect the shape (color, position) of the object of the product more accurately from the interference of light, illumination, etc., based on the HSV color base in the real environment. The final ROI merge operation is followed by the move to the automated object measurement.  
```
1:Start ROI Merge Operation
2:Set ROI\({}_{\mathrm{Templist\_i}}\), Retrieve ROI\({}_{\mathrm{Templist\_max}}\)
3:Set ROI\({}_{\mathrm{Templist\_i}}\) = 0
4:For i = 0: ROI\({}_{\mathrm{Templist\_max}}\)
5: Retrieve HSV Value, Upper and Lower threshold for ROI\({}_{\mathrm{Templist\_i}}\)
6: ROI\({}_{\mathrm{Templist\_i}}\) \(\rightarrow\) Image Masking According to Upper and Lower threshold
7:Bit Operation R\({}_{\mathrm{Templ\_rego}}\) and ROI\({}_{\mathrm{Templist\_i}}\)
8:Merge operation of ROI\({}_{\mathrm{Templ\_rego}}\) and ROI\({}_{\mathrm{Templ\_rego}}\)
9:IF(ROI\({}_{\mathrm{Templist\_i}}\) > ROI\({}_{\mathrm{Templ\_rego}}\)) then
10: Go to Automated Object Measurement through Equations (8) and (9)
11:Else
12:RO\({}_{\mathrm{Templ\_ist\_i}}\) = ROI\({}_{\mathrm{Templ\_ist\_i}}\) +1
13: Continue:
14:End
```  
**Algorithm 3** ROI Merge Operation
## 4 Model Calibration and Square Lattice Surrogate Models
### Relating the Diffusivities
#### 4.3.1 Equally Spaced Obstacles Model  
The effective diffusivity for the surrogate model \(\kappa_{\text{surr}}\) is first used to approximate the effective diffusivity for the continuous model \(\kappa_{\text{cont}}\). Specifically, slopes of the mean squared displacement response are matched by calibrating the commitment index \(M_{\text{C}}\) in the surrogate model (see Section 4.1).  
Substituting \(\langle r^{2}\rangle\) from (11) for the surrogate model into the second relation of (18), we obtain the relations,  
\[\kappa_{\text{surr}}=\frac{\langle r_{\text{surr}}^{2}\rangle}{4N\Delta t}\approx \frac{N(1-q)(\Delta x)^{2}}{4N\Delta t}=\frac{(1-q)(\Delta x)^{2}}{4\Delta t}=D. \tag{32}\]  
Since \(\Delta t\) is the same across all representations, it remains to calibrate \(\Delta x\), i.e., the effective spatial grid size for the square lattice model with theoretical diffusivity \(D\).  
Although \(L=10\) is a characteristic length scale for both the continuous and (surrogate) lattice models, it is not an appropriate choice for \(\Delta x\) in (32). A particle at an arbitrary location in a subdomain has many different net distances it can travel before crossing into and committing to a neighboring subdomain. We determine the spatial step size via the representation \(\Delta x=\frac{L}{\alpha}\), where \(\alpha>1\) will be calibrated based on our numerical simulations.  
Substituting \(\Delta x=\frac{L}{\alpha}\) into (32), we obtain  
\[\kappa_{\text{surr}}\approx D\big{|}_{\Delta x=\frac{L}{\alpha}}=\frac{(1-q) (\frac{L}{\alpha})^{2}}{4\Delta t}=\frac{1}{\alpha^{2}}D\big{|}_{\Delta x=L}. \tag{33}\]  
Hence, the ratio of the two diffusivities can be used to approximate \(\alpha\) via the formula  
\[\alpha\approx\sqrt{\frac{D\big{|}_{\Delta x=L}}{\kappa_{\text{surr}}}}. \tag{34}\]  
Using this coordinated approach, we ensure that all diffusivity estimates obtained via simulations using the continuous model and the surrogate model are consistent with theoretical estimates for 2D random walks on a lattice based on the choice \(\Delta x=\frac{L}{\alpha}\).
## 3 Experimental Results and Discussion
### Reliability Performance and Complexity Analysis of Proposed Model
The generalization improvement firstly is due to the use of textural features, and secondly due to the replacement of the SoftMax classifier with a sophisticated SVM-based model. The overall reduced prediction time with no beatable accuracy per image for brain tumor classification indicates that it has the potential to act as a real-time tool during neurosurgery for accurate delineation of tumor margin using desktop computers [5; 25]. The post-operative condition depends on the exposure of the tumor position; therefore, surgical support at a low cost is highly demanding especially for avoiding a second surgical attempt [4; 29; 30; 31]. A comparison of numerous existing techniques for intraoperative brain surgery has been illustrated in Table 11. The iMRI scans along with UL-BTD Framework, can be used for tumor margin resections during real-time surgery. \begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Accuracy** \\ \hline [PERSON] et al. [32] & 97.30 \\ \hline [PERSON] and [PERSON] [25] & 96.56 \\ \hline Proposed UL-BTD & 98.46 (_ax._) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of best-known results with proposed work in the light architecture category (same dataset). Figure 9: Result of a consecutive run of the proposed system and for comparison with [25; 32] their number of repetitions have been assumed constant.
## 4 Transformer-Based Ultra-Wideband I/Q Signal Generation
In the transformer-based quadrature hybrid unit stage presented in [12; 18], the differential signal routes between the quadrature hybrid and unit stage are quite long, which increases the chip area and design complexity. Additionally, the meander lines for phase matching adopted in [18] increase chip area and introduce extra passive loss. In this article, ports, unit stages, signal routing lines and signal combination routes are carefully arranged. Therefore, the length of signal routing lines is minimized to reduce I/Q imbalance, and a meander transmission line is not necessary. Moreover, in order to reduce phase/magnitude mismatch generated by undesired magnetic coupling, ground walls from M1 to LD are placed around each quadrature hybrid. In this way, the quadrature hybrids can be arranged closer to each other to further reduce chip area and length of signal routing lines. Before the cascaded transformer-based quadrature hybrid, a wideband passive balun converts the single-ended signal into a differential signal, as shown in Figure 10. The primary and secondary coils of the balun are implemented using OB and OA layers, respectively. The coupling factor between the primary and secondary coils is 0.72, and the core balun size is 186 \(\upmu\)m \(\times\) 176 \(\upmu\)m. Series and shunt capacitors are added at the input  
Figure 8: Polyphase signal combination scheme. Figure 9: 3D EM model of the proposed cascaded transformer-based quadrature hybrid. and output terminals of balun to achieve wideband impedance matching. The simulated balun insertion loss is <2 dB, with an input return loss <\(-\)10 dB at 22.5-49 GHz, as shown in Figure 11.
## 3 Experiment and Result Analysis
### Construction of Dataset  
In order to better study the intelligent generation of Chinese landscape paintings, we train and test the proposed Paint-CUT on the constructed landscape painting dataset. This dataset includes landscape photo samples, as well as two of the top ten painting samples, i.e., the samples of Dwelling in the Fuchun Mountains and A Thousand Li of Rivers and Mountains. In order to generate high-quality results, a series of processing methods are applied to the samples. The construction process of landscape painting samples, using A Thousand Li of Rivers and Mountains as an example, is shown in Figure 4. Landscape photos cover various elements, such as mountains, water, trees, rocks, and houses, which are common and represent elements of Chinese landscape paintings. These photos are consistent with the Chinese landscape paintings in content, which can be used to generate landscape paintings. The size of the sample is 256 \(\times\) 256, including 5388 samples of Dwelling in the Fuchun Mountains, 5025 samples of A Thousand Li of Rivers and Mountains, and 3253 landscape photos. In addition, the entire dataset is randomly divided into a training dataset (80%) and a test dataset (20%). A detailed overview is shown in Table 1. This dataset can satisfy the requirements of experiments; examples of samples are shown in Figure 5.  
Meanwhile, in order to prove that the proposed Paint-CUT has good generalization ability, the ChipPhi dataset [16] is also used to train and test, which contains landscape photos and Chinese paintings, including 1630 horse photos, 912 horse paintings by [PERSON], 1976 landscape photos, and 1542 landscape paintings by [PERSON]. The landscape photos of the ChipPhi dataset contain various representative elements of Chinese landscape paintings, such as mountains and rivers, which can be used to generate landscape paintings. The ChipPhi dataset is divided into a training dataset (90%) and a test dataset (10%). A total of 1774 photos and 1388 paintings are used to train and 202 photos and 154 paintings are used to test.