esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 2 Materials and Methods
### Methodology
#### 2.4.1 Data Acquisition (DA)  
As is universally acknowledged within the field of training, it is imperative to employ the most accurate data possible. Consequently, the primary focus was directed towards the acquisition of 2D images of the welding seams, characterized by optimal quality and precision, in alignment with the experimental protocol that was to be executed.  
The images, which constitute our initial data, represent the most crucial element of an object recognition system based on deep learning techniques. The images were captured using a high-resolution camera, as illustrated in Figure 7. The camera was placed on the end effector of a robotic arm, with the aim of taking a total of seven poses of each weld bead, with different luminosities in order to have more images for the training/validation/test dataset. To do this, a simple program was created in the robot, where it had seven stopping points, taking an image of the weld beads at each of these points.  
The camera was situated on the end effector of a robotic arm, with the objective of capturing seven poses of each weld bead of varying luminosities to augment the images available for the training, validation, and testing datasets. To this end, a rudimentary program was developed within the robot, delineating seven designated stopping points. To accomplish this objective, a rudimentary program was developed within the robot that incorporated seven designated stopping points. At each of these points, the robot captured an image of the weld beads, as illustrated in Figure 5b.The distance at which the images were captured fell within the operational range recommended by the camera manufacturer, with a minimum distance of 270 mm and a maximum distance of 3000 mm [39]. Consequently, images were captured from a variety of positions and distances, ranging from 1200 mm to 1700 mm. The dimensions of the images were consistent across all captures, aligning with the size recommended by the camera for optimal quality.  
The three experiments that have been conducted differ in terms of the classes to be recognised and the manner in which the images are treated. In the initial experiment, weld seams were extracted in great detail, with two classes being attempted to be identified. In the second and third experiments, the complete image was utilized, with all welding seams, resulting in the detection of two classes in experiment two and four classes in experiment three.
# A Graph DB-Based Solution for Semantic Technologies in the Future Internet
## 3 GraphBRAIN Framework
### Architecture  
The GraphBRAIN framework consists of several interconnected components (see Figure 1) that work together to manage Knowledge Graphs (KGs) efficiently and provide advanced reasoning and analysis capabilities. Here is an outline of the key components and their functions:  
**Data Storage:** GraphBRAIN leverages Labeled Property Graph (LPG)-based graph databases, such as Neo4j, for efficient data storage. The data storage component manages the underlying KGs, where labels represent classes, nodes represent class instances, and arcs represent relationship instances.  
**Ontology Layer:** The ontological layer provides formal interpretations of data and supports semantic reasoning. On the one hand, it manages the schema for KGs, ensuring that the data adhere to user-defined constraints. The ontological formalism, expressed in XML, allows flexibility and easy mapping to various languages. On the other hand, the Semantic Web technologies used, particularly the OWL and RDF standards, enable semantic interoperability with the WWW.  
**Knowledge Integration:** This component integrates existing ontologies and allows knowledge engineers to create new domain-specific ontologies. It offers a modular approach, enabling the import and extension of ontologies, as needed.  
**Reasoning Engine:** GraphBRAIN goes beyond standard Semantic Web (SW) reasoners by incorporating automated multistrategy reasoning based on First-Order Logic. This component enables advanced logical inference and reasoning capabilities on the KGs.  
**Data Mining and Network Analysis:** The framework includes data mining and network analysis functions, which allow users to extract patterns, insights, and meaningful connections from the KGs. These analytical tools enhance the understanding of the data.  
**Public API:** GraphBRAIN functionalities are also accessible as a Java library. This makes it possible to build applications that meet specific requirements or simply customize the usability according to specific domains. Two example are  
* **Interactive Knowledge Browsing:** GraphBRAIN offers an interactive knowledge browsing interface that enables users to explore the KGs intuitively. This component  
Figure 1: Architecture.  
provides a user-friendly way to navigate and query the knowledge stored within the system.
* **Human-Understandable Descriptions:** To enhance the usability, GraphBRAIN generates human-understandable descriptions of processing results. This feature aids users in interpreting and making informed decisions based on the KGs.
## 6 Future Work  
Though there has been significant progress in buffer-aided (BA) relay selection in the last few years, there are still challenges that need to be overcome before it can be widely adopted. Emerging Internet-of-Things (IoT) applications require ubiquitous access to the wireless medium and need flexibility in the transmission, scheduling, and distribution of data. Employing BA relays can result in significant gains in the robustness of such networks in supporting IoT applications. Device-to-device (D2D) communication can enormously benefit from BA relaying to afford improved frequency reuse and better off-loading, leading to increased revenues for the service providers. Proactive caching is another interesting domain wherein BA relay selection algorithms can be integrated to improve load balancing and reduce the bottlenecks in the core network.
# The Effect of Isomorphic Pitch Layouts on the Transfer of Musical Learning +
## 2 Materials and Methods
### Statistical Approach  
For each of the five tasks (scale training, scale retention, arpeggio training, arpeggio retention, melody transfer), a multilevel Bayesian beta regression was conducted on \(\textit{Accuracy}\in(0,1)\) as the dependent variable, with \(\textit{IsNonIsomorphic}\in\{0,1\}\), \(\textit{IsUntrainedKey}\in\{0,1\}\) (only in the melody transfer task), \(\textit{IsBosanquet}\in\{0,1\}\), \(\textit{LayoutNo}\in\{0,1\}\), \(\textit{PerfNo}\in\{0,1,2,3\}\), and \(\textit{IsMusician}\in\{0,1\}\) as the discrete regression inputs. All interactions between \(\textit{IsMusician}\), \(\textit{IsNonIsomorphic}\), \(\textit{IsBosanquet}\) (in all tasks), and \(\textit{IsUntrained}\) (only in the melody transfer task), and all interactions between \(\textit{IsMusician}\), \(\textit{LayoutNo}\), and \(\textit{PerfNo}\) were included. With one exception, group-level (random) effects were also added for all of the above predictors that were between subjects (as recommended by [PERSON] et al. [34]); the exception was the model for the arpeggio retention task, where it was necessary to remove the interaction \(\textit{LayoutNo}\times\textit{PerfNo}\) to achieve convergence.  
Because _Accuracy_ is a real number in the open unit interval, it was modelled with a _beta_ distribution, which is a standard choice for such dependent variables. A logit link was used to transform the \(\textit{Accuracy}\) score from the unit interval to the full real-line, thereby making it suitable for linear prediction. This means that the exponential of any effect gives the multiplicative factor by which the ratio \(\textit{Accuracy}/\textit{Inaccuracy}\) of a performance changes, where _Inaccuracy_ is defined as \(1-\textit{Accuracy}\), which also implies that \(\textit{Accuracy}=\frac{\textit{Accuracy}/\textit{Inaccuracy}}{1+(\textit{ Accuracy}/\textit{Inaccuracy})}\) (in Tables 1-6, we show the linear effect in the \"Estimate\" column and its exponential in the \"Eff.Fact\" column). For example, for an effect of 0.5, a unit increase in the corresponding predictor results in a multiplicative increase in \(\textit{Accuracy}/\textit{Inaccuracy}\) by \(e^{0.5}=1.65\), that is, an increase in \(\textit{Accuracy}/\textit{Inaccuracy}\) of 65%. More concretely, imagine an expected _Accuracy_ at a given set of conditions of 0.6; this implies that \(\textit{Accuracy}/\textit{Inaccuracy}=0.6/0.4=1.5\). If a predictor (whose effect is 0.5) is increased by one unit, keeping all else the same, the model tells us that the resulting \(\textit{Accuracy}/\textit{Inaccuracy}\) ratio would now have an expected value of \(1.65\times 1.5=2.48\); hence, the expected _Accuracy_ would become \(2.48/(1+2.48)=0.71\).
## 3 Proposed Scheme: MC-TIRP
### Trust Evaluation Component
#### 3.2.1 Local Trust Evaluation
The proposed technique calculates the energy required for a resource-constrained device, as shown in Equation (4) below, where \(ERes_{i}\) is the residual energy of node \(i\), and \(EInit_{i}\) is the initial energy of node \(i\). \[Energy=\frac{ERes_{i}}{EInit_{i}} \tag{4}\]  
Using these derived factors, the node with insufficient residual energy calculates the energy-based QoS and trust value (\(EQTV\)), as shown in Equation (5). The \(EQTV\) equation calculates which metrics to prioritize: trustworthiness, QoS, and energy by weighting them based on the node's energy state. \[EQTV=(\omega_{1}\times Energy)+((\omega_{2}\times(PFR\times(1-ETT))) \tag{5}\]  
\[\omega_{1}=(1-Energy),\;\omega_{2}=(1-\omega_{1}) \tag{6}\]  
In this study, we propose a flexible weight module to distribute weights adaptively according to the status of a device. For this purpose, a flexible weight module that efficiently manages energy is essential. In a sensor network composed of trusted nodes, this technique applies a flexible weight based on the remaining energy. \(\omega_{1}\) is determined based on the residual energy, and \(\omega_{2}\) is calculated using Equation (6) based on \(\omega_{1}\). A node with sufficient energy assigns a higher weight to reliability and QoS, and a node with insufficient energy assigns a higher weight to energy. Therefore, it is possible to enhance survivability using an energy-based flexible weighting method. The derived trust value is calculated as the local trust level by applying the formula below. Local trust is the trust-based value of the k neighbor nodes of node \(j\) and is calculated as shown in Equation (7). Local trust is calculated as the average of \(EQTV\) values calculated by neighboring nodes, and it is possible to evaluate local trust more accurately. \(n\) denotesthe total number of \(k\) neighbor nodes and is derived by averaging the trust values evaluated by \(n\) nodes. \[LocalTrust=\frac{1}{n}\sum_{k=1}^{n}{{EQTV_{k,j}(t)}}. \tag{7}\]
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 2 Related Work
For example, in Figure 3, partition by large cells is more precise and the clusters are closed to the actual boundaries of the neighborhoods. However, partition by the smaller cell loses the geo-information of the neighborhoods. Semantically-based prediction methods claim that many behaviors of users are semantically-triggered. [PERSON] [19] discovered stops from trajectories and map these stops to semantic landmarks. Then a sequential pattern mining algorithm is used to achieve frequent semantic patterns. [PERSON] [20] utilized a hierarchical method to obtain geographic and semantic features from trajectories. [PERSON] [21] proposed a geographic-temporal-semantic pattern mining method for prediction, which also transforms trajectory into stay points. Then trajectory patterns are clustered to build the frequent pattern tree for detecting future movements. Semantically-based methods mainly focus on mining the stay points. The trajectories along the road are abandoned. Recently, recurrent neural networks (RNNs) have gained a breakthrough in sequence mining. [PERSON] [23] developed RNNs in word embedding for sentence modeling. Multiple hidden layers in RNN can adjust dynamically with the input of behavioral history, therefore, an RNN is suitable for modeling temporal sequence. [PERSON] [22] extended traditional RNN with geographical and temporal contexts to handle prediction problem of spatial-temporal data. An RNN performs well with short  
Figure 3: The accuracy of the prediction is influenced by the granularity of the cell partition. Track points from a courier in Hangzhou, China. (**a**) Partitioning into large cells and smaller cells; (**b**) The actual boundaries of the neighborhoods. location sequences, such as clusters and stay points. However, when considering the whole trajectory, the location sequences become longer and the precision of prediction of RNN may decline. In conclusion, in order to utilize machine learning models, the trajectory should be transformed to be model-friendly. Existing location prediction algorithms usually cluster trajectory into cells, regions, or stay points. Track points along the road are abandoned, but these points can be important to specific applications. To solve this problem, a spatial-temporal-semantic neural network algorithm for location prediction is proposed in this paper. First, in order to transform the whole trajectory into location sequences friendly to the prediction model, STS feature extraction is utilized to map trajectory to the reference points and maintain as much information as possible; Second, the LSTM model is built to handle the long sequences generated before, and to make further prediction.
## 3 Results and Discussion
### Wastewater Physico-Chemical Parameters
Also, the pH values showed a decrease in the first 96 h of the experiment, followed by a slight increase until 120 h, regardless of the tested sample, with the lowest pH values being observed in the case of synthetic wastewater with Pb\({}^{2+}\) at 96 h from the beginning of the experiment for both _R. mucilaginous_CMGB-G1 (P1, pH = 6.12) and _R. mucilaginous_CMGB188 (P2, pH = 6.14) strains. The decrease in pH during yeast development in the presence of Pb\({}^{2+}\) and Cd\({}^{2+}\) can be influenced by various factors, including the release of ions and the metabolic activity of the yeast strains, with the optimal pH for yeast growth being in the range of 4.0-8.5 [62]. These results are consistent with those reported by our group in a previous study [31]. While not the primary focus of this paper, it is essential to acknowledge several potential factors that could impact the removal efficiency of cadmium and lead ions by yeast strains [63]. However, it is important to note that the removal mechanism is very complex. It involves intricate interactions between the yeast cells and the metal ions, including adsorption, ion exchange, surface attachment, and intracellular sequestration. Additionally, factors such as cell wall composition, metabolic activity, and genetic variability among yeast strains can further influence the removal process [64]. For example, variations in the pH of wastewater solutions could significantly affect the adsorption capacity of heavy metals via yeasts. At lower pH levels, the abundance of H\({}^{+}\) ions prevails, overpowering the adsorption sites restricting the access of cations to these sites [65]. This phenomenon is particularly notable for Cd\({}^{2+}\) ions, indicating a greater susceptibility to pH fluctuations compared to Pb\({}^{2+}\)[66; 67]. Moreover, the toxicity of cadmium and lead ions can significantly impact the efficacy of yeast strains in removing them from wastewater. Our results suggest that the toxicity of cadmium on yeast strains tends to be higher than that of lead, as evidenced by the observations of growth rate inhibition in yeast exposed to these metal ions (Figure 2). These results are consistent with those reported in other studies, which have also highlighted the higher toxicity of cadmium compared to lead on yeast strains [31; 68; 69].
## 3 3D Interpreted Model
### Differences between Relevant Product Definition Model  
In order to better explain the relationship between 3 DAM, 3 DIM and extended annotation model (EAM) proposed in literature [6], this paper compares these related models from the perspective of product model content, as shown in Figure 4.  
Two conclusions can be drawn from the figure.  
* 3 DAM is a part of MBD dataset about product model, and also a part of 3 DIM about product geometric and technical information. That is to say: \[3 DIM\cap MBD_{dataset}=3 DAM\] (4)
* Both 3 DIM and EAM are product definition models formed by the integration and fusion of 3 DAM and design knowledge. The main difference is that 3 DIM has more  
Figure 4: The differences of related models from the perspective of product model content.  
complete design knowledge than EAM, that is, design knowledge includes not only the design intent of geometric information, but also the design justification, design decision, design option and design intent oriented to technical information. The differences of 3 DAM, 3 DIM and EAM can be expressed as follows:  
\[3 DIM=3 DAM\cup\{DI,DJ,DD,DO\} \tag{5}\]  
\[EAM=3 DAM\cup\{DI_{g}\} \tag{6}\]  
\[3 DIM=EAM\cup\{DI_{t},DJ,DD,DO\} \tag{7}\]  
On the basis of 3 DAM, how to describe the relationship between design knowledge product information, and how to organize these information and knowledge to form the representation structure of 3 DIM will be described in Section 3.3.
## 4 Simulation Results Analysis
### Performance Analysis of the EA-CA Model
#### 4.1.1 The Number of Communication Sector  
The evolutions of state using NA-AS strategy with the different number of communication sectors are shown in Figure 2. The number of agents in the candidate set decreases when the number of sectors is too small, which leads to the system converge to multiple local centers. If the number of sectors is set too large, though the convergence speed can be improved, the computational cost increases.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
## 4 Experiment
### Evaluation
#### 4.3.2 Evaluation on Open Dataset  
The layout of documents will vary according to the editor's style. The layout of public datasets are mostly composed of text areas, tables, or pictures, among which the styles of ICDAR2013 are more diverse, while the data of this study mostly consist of large object tables and a few text areas, similar to TableBank and Github. Therefore, the detection results of ICDAR13 were slightly poorer than those of the other three datasets, as shown in Table 5. Although the validation results of the proposed model in public datasets show that there was no significant effect at the IoU threshold of 0.7, there was little difference between the models. When IoU was 0.9, the F1-score of each model decreased significantly, and as the IoU threshold increased, the detection performance decreased. Thus, it is very important for the table to predict as little position offset as possible and as accurately as possible. Our model was almost higher than the other five models when the IoU was 0.9, and the average IoU was quite close to the optimal result. As can be seen from Figures 9-12, U-SSD (brown line) has a lower decline than the other five models, and this result illustrates that the stability and model detection performance of U-SSD are very good, and the network architecture based on SSD shows good performance. of zooming in after the feature map size is reduced, which has great influence on table detection. Therefore, when dilated convolution was added and the convolution perception field was improved, feature loss was complete. The experimental results show that when VGG in SSD was replaced by U-Net and dilated convolution was added, the result F1-score under the average IoU reached 0.94. When the threshold was increased, its detection ability did not decrease too much, but remained at a certain level; therefore, the robustness and performance of the proposed model are good. Figure 11: The F1-score from IoU = 0.5 to IoU = 0.9 on TableBank. Figure 9: The F1-score from IoU = 0.5 to IoU = 0.9 on our dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**IoU**} & \multirow{2}{*}{**Avg.**} \\ \cline{2-7}  & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & \\ \hline SSD & 0.95 & 0.95 & 0.95 & 0.93 & 0.72 & 0.90 \\ \hline SSD + Dilation & 0.95 & 0.95 & 0.95 & 0.95 & 0.85 & 0.90 \\ \hline SSD + U-Net & 0.95 & 0.95 & 0.95 & 0.93 & 0.72 & 0.93 \\ \hline SSD + Dilation + U-Net & 0.95 & 0.95 & 0.95 & 0.94 & 0.89 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The F1-scores in ablation experiment of dilation and U-Net on our dataset. Figure 12: The F1-score from IoU = 0.5 to IoU = 0.9 on ICDAR13.
## 3 Motivation
### Proposed Scheme  
In this paper, we propose the FAPR scheme, which combines the proactive and reactive schemes to exploit their advantages, and allocates suitable backup paths for different types of flows to reduce the possibility of flow congestion or interruption in the subsequent forwarding process and improve the quality of network service. Take Figure 3 as an example, where host \(H1\) sends three flows, i.e., flow 1, flow 2, and flow 3, to \(H2\), and the primary path of these three flows is assumed to be S1-S3-S5. FAPR calculates multiple backup paths for each link of the topology. For instance, the proposed scheme calculates three backup paths for link \(S1\)-S3, which are marked with red, purple, and green solid lines, namely \(S1\)-\(S2\)-\(S3\), \(S1\)-\(S6\)-\(S7\), and \(S1\)-\(S4\)-\(S5\)-\(S3\), respectively, and these three backup paths are stored in the controller. In addition, we adopt the pro-VLAN [19] based on the protection mechanism to install the backup paths in the corresponding switches in advance simultaneously. When \(S1\)-\(S3\) fails, the switch will automatically switch to the pre-installed backup path (which is calculated and installed by pro-VLAN), and the controller will receive the failure notification information simultaneously. As the three flows walk through the link \(S1\)-\(S3\), they are all affected by the failed link. However, the three flows are different in traffic characteristics: one flow may require a wider bandwidth, while another flow is more sensitive to delay. FAPR analyzes the peculiarity of each flow and each backup path, and chooses the most suitable backup path for each flow. Moreover, the controller is responsible for removing overlapping paths to avoid forwarding loops. For instance, one backup path for link \(S1\)-\(S3\) is path \(S1\)-\(S4\)-\(S5\)-\(S3\), as shown in Figure 3. If the backup path is assigned for any flow mentioned above, then the forwarding path after the link failure is supposed to be S1-S4-S5-S3-S5, in which a forwarding loop exists. In this situation, the controller should remove the forwarding loop and update the forwarding path to be S1-S4-S5. After the update of flow rules, the packets shift paths again. In conclusion, FAPR contains two path shifts in total. The first shift to the pre-calculated backup path (by pro-VLAN) is at high speed to reduce the number of lost packets to the best. The second shift to the backup path is realized by the controller to reduce the possibility of flow congestion and interruption.  
Figure 2: An example of a reactive scheme.  
In FAPR, the pro-VLAN scheme not only recovers from link failures quickly, but also reduces the use of storage space of switches effectively. Simultaneously, the controller can adopt multiple algorithms to calculate multiple backup paths for each link in advance and store them in the controller. When a link fails, the controller allocates the most suitable backup path according to the characteristic of the affected flow. As a result, the FAPR scheme avoids the issue of excessive resource usage of the proactive scheme and the excessive delay of the reactive scheme simultaneously.