esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Implementation
### Middleware Components  
Vehicle_OEM and the RADAR_OEM participate inside the blockchain as an actor in the smart contract and a node validator, respectively. They also participate through OffChain methods by providing the middleware required for collection, processing, transfer, reception and OAuth Access tokens both for raw RADAR data and road RADAR signatures. These business services for RADAR_OEM and Vehicle_OEM are implemented in Java JDK (Java Development Kit) eight language based on the Spring Boot Framework. Its architecture is a REST (Representational State Transfer)-based Model View Controller middleware. It deals with authentification, authorization, OAuth access token generation, verification, management, data storage, retrieval, and other API exposition necessary for the blockchain OffChain worker to interact. It also has the API service to encrypt and decrypt privacy bid payload for asset or asset service, which is Vehicle_OEM or RADAR_OEM. The Unified Modelling Language representation (UML) for the middleware is represented in Figure 8, which comprises the following aspects:  
* [noitemsep,topsep=0 pt]
* AuthController: It is the primary class interface in the middleware which accepts the request and redirects it to the necessary business service layer for storing assets, asset service data, OAuth token generation, as well as encryption and decryption process for the bidding procedure.
* UserController: This authentication service retrieves RADAR and RADAR service subscription data.
* Storage: This is the persistence layer for all the details regarding asset, asset service, authentication, and OAuth access tokens.
* Asset/Asset Service Feedback: The asset or asset service feedback in the form of review is also stored OffChain as additional storage.
* RADAR Data: This contains the data payload representation generated by the vehicle and transacted via the blockchain.
## 3 Research Methodology
### Feature Extraction  
This phase aims to extract the most important features from tweets. In our case, we use word embedding, which is one of the most popular representations of document vocabulary. It helps extract many useful features of a given word in a document (e.g., context, semantic, etc.). For this task, we rely on the GloVe model (Global and Vectors) which allows obtaining vector representations for words while integrating global statistics of words co-occurrence to obtain word vectors [42]. GloVe is developed as an open-source project at Stanford University and launched in 2014. Regarding our work, the pre-trained word vectors that are used are the GloVe Twitter word embedding (200 d), which are trained by using 2 billion tweets (containing 27 billion tokens and 1.2 million vocab). These data are made available under the Public Domain Dedication and License v1.0 [43].
# Robotic Button Mushroom Harvesting Systems: A Review of Design, Mechanism, and Future Directions
## 3 Mechanical System
### Robot Manipulators
The independent operation of these two arms significantly increased the picking speed, a key factor in large-scale mushroom industries. Although articulated arms have not been widely tested in commercial settings, they have been used for various mushroom harvesting analyses and laboratory tests. [PERSON] et al. utilized the Omron TM5-700 Cobot to evaluate the picking performance of a developed gripper [46]. Additionally, [PERSON] explored the potential of using similar articulated arms in fully automated harvesting [47]. [PERSON] et al. designed a SCARA manipulator as a harvesting robot using a probabilistic road map and an inverse kinematics approach [48]. [PERSON] developed a laboratory-based model with a six-DOF anthropomorphic flexible robot to test harvesting in a lab environment [49]. However, these manipulators have yet to be tested on multi-storied shelves, and the practical effectiveness of some--such as those developed by [PERSON] and [PERSON]--remains unproven [48; 49]. Various manipulators used in automated mushroom harvesting are illustrated in Figure 2. Cartesian manipulators generally offer a large workspace within Dutch shelves compared to articulated manipulators like SCARA. However, when dealing with multiple shelves, the SCARA arm provides greater flexibility due to its prismatic joint, which allows vertical movement between shelves. Additionally, articulated arms can easily extend beyond the shelves to interact with other components as needed, whereas Cartesian manipulators require an additional transport mechanism to perform tasks outside the shelves. That said, articulated arms themselves often rely on a Cartesian cart or mobile robot to move along the shelves.
## 5 Simulation Results and Analysis  
### Simulation Settings  
This paper presents a comparative analysis of the DRFTM model and four existing models, namely the ARTMM [4], STMS [12], TEUC [17], and TUMRL [18]. The ARTMM is renowned as the pioneer trust model implemented in underwater environments. On the other hand, the STMS is a notable trust model specifically designed for layer-clustered networks. The TEUC stands out by utilizing a decision tree algorithm for trust evaluation, incorporating both event and time-based updating mechanisms. Lastly, the TUMRL is constructed on reinforcement learning principles for trust update. To evaluate the performance of these trust models, MATLAB 2021 simulation is employed. Specifically, the simulation incorporates sensor nodes that are uniformly distributed within a network space of dimensions 500 m \(\times\) 500 m \(\times\) 500 m. The default simulation parameters can be found in Table 1.  
The performance evaluation indicators are described as follows:  
* Detection accuracy (%): reflects the overall predictive accuracy of the model for all samples.
* Error rate (%): measures the model's ability to incorrectly predict negative samples as positive.
* Trust value: a visual reflection of the evaluation result of node trust.
# Fused Gromov-Wasserstein Distance for Structured Objects
## 5 Examples and Applications
### Unsupervised Learning: Graphs Clustering  
In the last experiment, we evaluate the ability of \(FGW\) to perform a clustering of multiple graphs and to retrieve meaningful barycenters of such clusters. To do so, we generate a dataset of four groups of community graphs. Each graph follows a simple Stochastic Block Model [60,61] and the groups are defined _w.r.t._ the number of communities inside each graph and the distribution of their labels. More precisely, labels are drawn from a 1D gaussian distribution specific to each community: each feature of a node is drawn from a law \(\mathcal{N}(n_{c_{1}}+n_{c_{2}},\sigma)\), where \(n_{c_{1}}\) is the index of the community and \(n_{c_{2}}\in\{0,1\}\) to create two modes per community. The dataset is composed of 40 graphs (10 graphs per group) and the number of nodes of each graph is drawn randomly from \(\{20,30,\ldots,50\}\), as illustrated in Figure 15. We perform a \(k\)-means clustering using the \(FGW\) barycenter defined in Equation (27) as the centroid of the groups and the \(FGW\) distance for the cluster assignment. We set the number of nodes of each centroid to 30. We perform a thresholding on the pairwise similarity matrix \(C\) of the centroid at the end in order to obtain an adjacency matrix for visualization purposes. The threshold value is chosen, so as to minimize the distance that is induced by the Frobenius norm between the original matrix \(C\) and the shortest path matrix obtained from the adjacency matrix. The evolution of the barycenters along the iterations is reported in Figure 15. We can see that these centroids recover community structures and feature distributions that are representative of their cluster content. On this example, note that the clustering perfectly recovers the known groups in the dataset. To the best of our  
Figure 14: Example of community clustering on graphs using \(FGW\). (**top**) Community clustering with four communities and uniform features per cluster. (**bottom**) Community clustering with four communities and bimodal features per cluster (and two nodes per cluster in the approximate graph).  
knowledge, there exists no other method that is able to perform a clustering of graphs of arbitrary size and to retrieve the average graph in each cluster without having to solve a pre-image problem.
## 3 Wireless Sensor Network Environment and Modeling  
Required throughput and energy budget are important parameters to model the environment of a WSN. Although the throughput depends on the application, several recent works [21, 22, 23, 24] as well as off-the-shelf products for the IEEE 802.15.4 standard target a throughput \(T\) of 250 kb/s. According to [3] the amount of energy per bit saved due to the use of a correcting code (\(\Delta E\)) can be expressed as  
\[\Delta E=E_{TX,U}-E_{TX,C}-E_{enc}-E_{dec} \tag{7}\]  
where \(E_{TX,U}\) and \(E_{TX,C}\) are the amounts of energy per information bit spent to transmit one bit in an uncoded and coded system respectively. \(E_{enc}\) and \(E_{dec}\) are the amounts of energy per bit spent by the LDPC encoder and decoder. Assuming a Binary-Phase-Shift-Keying (BPSK) modulation, each \(E\) term in Equation (7) can be written as a function of the power consumption \(P\) and the throughput \(T\) of the corresponding task. For a fair comparison we assume that the throughput sustained by the transmitter is the same for both the uncoded and coded case. As a consequence, Equation (7) can be rewritten as  
\[\Delta E=\frac{P_{TX,U}-P_{TX,C}-P_{enc}-P_{dec}}{T} \tag{8}\]  
However, as shown in [25] and [26] the complexity and the power consumption of LDPC encoding is negligible with respect to decoding. As a consequence, in the following the \(P_{enc}\) term will be neglected. Moreover, as highlighted in [3], each \(P_{TX}\) term can be written as a function of the path loss \(A(d)\) at a given distance \(d\), the thermal noise \(N_{0}\cdot B\) (where \(B\) is the signal bandwidth and \(N_{0}\) is the noise power spectral density), the Signal-to-Noise-Ratio (SNR) at the receiver and the receiver noise figure \(F\):  
\[P_{TX}=A(d)\cdot N_{0}\cdot B\cdot 10^{(\text{SNR}+F)/10} \tag{9}\]  
According to [27],  
\[A(d)=\left(\frac{4\pi}{\lambda}\right)^{2}\cdot d^{n} \tag{10}\]where \(\lambda\) is the wavelength of the corresponding carrier frequency \(f\) and \(n\) is the path loss exponent, where \(n=2\) and \(n=4\) are good approximations for free space and dense environment propagations respectively. Assuming the same \(A(d)\) and \(F\) values for both uncoded and coded systems, Equation (8) can be rewritten as  
\[\Delta E=\frac{A(d)\cdot N_{0}\cdot B\cdot 10^{F/10}\cdot\left(10^{\text{ SNR}_{U}/10}-10^{\text{SNR}_{C}/10}\right)-P_{dec}}{T} \tag{11}\]  
where \(\text{SNR}_{U}\) and \(\text{SNR}_{C}\) are the SNR at the receiver in the uncoded and coded systems respectively. Thus, given the curves representing the BER of one system as a function of the SNR, we obtain for each BER value the amounts \(\text{SNR}_{U}\) and \(\text{SNR}_{C}\) with \(\text{SNR}_{G}=\text{SNR}_{U}-\text{SNR}_{C}\) representing the SNR gain achieved using error correction. So Equation (11) can be rewritten as  
\[\Delta E=\frac{A(d)\cdot N_{0}\cdot B\cdot 10^{(\text{SNR}_{U}+F)/10}\cdot \left(1-10^{-\text{SNR}_{G}/10}\right)-P_{dec}}{T} \tag{12}\]  
The expression obtained in Equation (12) will be used in Section 5 to show the effectiveness of the proposed LDPC architecture.
## 3 Results and Discussion
The ideal approximation of the analytical model is the main reason for the differences between measured signals values and the analytical model-predicted ones. For instance, the analytical model considers a constant magnetic permeability of the ferromagnetic material. In addition, this analytical model is suitable for defects with a completely rectangular shape. In the real defect, the shape is approximately rectangular with small inclinations along its edges. Also, the analytical model does consider a uniform distribution of both MMM signals around each defect. Figure 10: Normal MMM signal of the rectangular defect S3 of the ASTM A-36 steel pipe. Figure 9: Tangential MMM signal of the rectangular defect S3 of the ASTM A-36 steel pipe. The defect edges generate two peaks in the shape of the normal MMM signals, which indicate a polarity shift of the magnetic flux at the defect center. The defect with the largest depth achieves the highest value of the tangential MMM response, while the defect with smallest depth has the lowest value of tangential MMM signal. Thus, these parameters could be employed to predict the size (depth and width) and location of rectangular defects of ferromagnetic pipes using the MMM method. The measurement system could be used for real-time monitoring of rectangular defects on ferromagnetic pipes. This system does not require special treatment of the ferromagnetic sample, complex equipment and operators with extensive training. In comparison with other NDT techniques such as the Eddy current testing and magnetic flux leakage testing, the MMM method does not need to apply external magnetic field on the ferromagnetic pipe. It allows the decrease of additional equipment and special treatment on the pipe surface. On the other hand, the X-ray testing is an NDT that uses expensive equipment and operators with extensive technique experience. Another NDT testing is liquid penetrant inspection, which has low cost, but it can only detect surface flaws. In addition, it requires pipe surfaces free of contaminants. The MMM method could be integrated with other NDT testing such as electrical resistance measurement and acoustic emission technique. For instance, the comparison of analysis related to the electrical resistance monitoring and acoustic emission technique could be used in the assessment of damage and defects in structures [32; 33; 34; 35]. For structures formed with ferromagnetic materials, the results of both techniques could be used with those obtained through MMM method for monitoring the location, dimensions and shape of their defects. Thus, these NDT tests could be complementary for inspection of damages in structures with ferromagnetic materials.
## 3 Results and Discussion
### Comparison of Adsorption Quantity  
The adsorption capacity of the SWCNTs/Fe\({}_{\times}\)O\({}_{y}\) composites for the removal of BPA, as illustrated in this study, was comparatively assessed with data from other adsorbents reported in the literature, with the findings being summarized in Table 4. Figure 13 shows  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(C_{\mathbf{0}}\)} & \multicolumn{3}{c}{**Pseudo First-Order**} & \multicolumn{3}{c}{**Pseudo Second-Order**} \\ \cline{2-7}  & \(\mathbf{k_{1}}\) & \(\mathbf{q_{t}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{k_{2}}\) & \(q_{t}\) & \(\mathbf{R^{2}}\) \\  & \(\mathbf{min^{-1}}\) & \(\mathbf{mg\cdot g^{-1}}\) & & \(\mathbf{g\cdot mg^{-1}\cdot min^{-1}}\) & \(\mathbf{mg\cdot g^{-1}}\) & \(\mathbf{R^{2}}\) \\ \hline
0.025 & 1.40 & 25.69 & 0.9950 & 0.14 & 26.75 & 0.9998 \\
0.05 & 1.43 & 50.08 & 0.9830 & 0.06 & 53.16 & 0.9998 \\
0.075 & 1.08 & 60.69 & 0.9724 & 0.03 & 65.62 & 0.9994 \\
0.1 & 0.90 & 68.77 & 0.9422 & 0.01 & 78.86 & 0.9969 \\
0.15 & 1.50 & 80.71 & 0.9834 & 0.04 & 85.03 & 0.9997 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting parameters of kinetics.  
Figure 12: Weber–Morris curves (T = 25 \({}^{\circ}\)C, pH = 6, adsorbent dosage = 0.2 g/L).  
the adsorption mechanism of the SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) for BPA. Combined with Table 4 and Figure 13, it can be seen that the modified SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) had an excellent adsorption capacity for BPA. Previous studies have suggested that the two phenyl rings of the BPA molecule can be adsorbed onto the surface of CNTs in various configurations: namely, aligned parallel to the longitudinal axis of the tube, encircling the circumference, or positioned diagonally. This adsorption is purported to occur via a \(\pi\)-\(\pi\) electron donor-acceptor interaction, culminating in the establishment of a \(\pi\)-\(\pi\) conjugation with the \(\pi\) electron system of the CNTs. Furthermore, it has been suggested that the reliance of these \(\pi\)-\(\pi\) interactions on pH is minimal, indicating a robustness in the adsorption mechanism across varying pH conditions. In this research, the modification of the SWCNTs results in a larger surface area and the presence of numerous micropores and mesopores, which allows for more \(\pi\) electrons to be exposed for binding with BPA and to form \(\pi\)-\(\pi\) conjugations, thus accelerating the adsorption process. Furthermore, the hydrophobic nature of the carbon nanotube surfaces [39, 40] has been found to be effective in BPA adsorption, exhibiting a pH independence similar to that of \(\pi\)-\(\pi\) interactions. Additionally, the water solubility of BPA should be considered; in alkaline conditions, BPA exists in a salt form, which can also affect adsorption. Finally, the unique magnetic properties of the SWCNTs/Fe\({}_{\text{x}}\)O\({}_{\text{y}}\) are expected to facilitate the recovery and reuse of the adsorbent, minimizing the potential harm to humans or aquatic organisms.
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 4 Architecture and Ontology Models
### Machine Failure Ontology Model
Users instances include details about the failures and symptoms. To check for exceptions, we used Pellet reasoner [26] to verify the correctness of our ontology model. In addition to the failure ontology, we also created a machine domain ontology (Figure 4a) to describe the machine components to satisfy our requirement to link failures to specific machine components when and where they occur, as simply knowing about a failure is not useful on its own. We also need to identify the components that caused a failure or that show failure symptoms, so that they can be identified as candidates for repair or inspections. For simplification, we only described two types of machines in our machine domain ontology, a Tray Sealers Machine and a Packaging Machine. Each machine type is composed of many components (PLC, Drive, Actuator, Sensor, and others), connected through different communication buses. To integrate both ontologies, we created OWL Class _FailureAsset_, to associate failures to the corresponding components in the machine domain ontology. However, we faced another issue to identify the exact component of a machine that has failed or is impacted by the failure. For example, consider that a machine has two Servo Drives of the same type. These Servo Drives are described in the machine domain ontology as two instances (SDA and SDB) of the \"ServoDrive\" class and are associated with the instance of a machine. When a failure occurs  
Figure 3: Failure ontology. in SDA, we should be able to identify it through ontology. Such detailed identification is especially useful when the failure knowledge is shared with the other sites using the same machine type, as it will help them to recognize the exact component responsible or impacted by the failure. To address this issue, each component in the machine domain ontology has a unique number \"FunctionID\", to distinguish its role in the machine compared to other components of the same type. We used both desktop Protege [27] and Web-Protege [9] to create our ontologies. The latter allowed us to include machine domain experts in the ontology development process and to gather and organize their feedback. It is important to mention that our main focus in this work has been on the validation of the idea to the OEMs, that formalized knowledge about machines and their failures can be useful for quick resolution of failures and to improve Overall Equipment Effectiveness (OEE). The ontology, in its current form, will be extended to cover the needs of the OEMs. To guarantee the ontology generality, a potential extension basis could be the adoption of taxonomy of ISO standards for failure mode analysis (similarly to [11]). Relevant actors can be involved to extend these ontologies with new concepts and relationships by using appropriate tools like the ones mentioned in Section 7.1.
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 4 Experimental Results  
The experimentation phase of this article was carried out with the AERO-CAM platform, performing the various experiments described next. Real flights around and under two viaducts were performed to evaluate the localization solution. On the one hand, there is the railway viaduct _Arroyo del Espinazo_ in Alora, Malaga (Spain). This viaduct was inaugurated in 2006 and is currently in use. It has a length of 1.2 km with a maximum pillar height of 93 m and a width of 14 m. The pillars are equidistantly distributed and have a hollow square cross-section. On the other hand, there is the road viaduct _Puente de las Navas_ in Algodonales, Cadiz (Spain). This viaduct was built in the 1980s and is still active with the A-384 road passing over it; therefore, it withstands daily traffic. It is approximately 350 m long and consists of cylindrical pillars supporting in pairs three longitudinal beams on which the deck rests. Both viaducts are in a good state of conservation, presenting small aesthetic defects in the concrete during the inspections without danger. The utility of the experimental inspections is focused on predictive maintenance, being able to return in the future to carry out the same inspection and compare the evolution. The flights in Alora were pilot assisted in order to perform realistic routes while those in Algodonales were fully autonomous. A preliminary map of the viaducts was created using a Leica Nova MS50 total station.  
The trajectory followed in these experiments consists of a take-off close to the viaduct and a flight inspection of different areas that may include changes in altitude. Figure 10 shows some of these trajectories. In order to obtain the ground truth of the trajectory followed by the platform, a prism was installed on it and, using the Leica total station, the position with respect to the origin of the viaduct map was tracked. Note that the total station only provides position data, as it cannot estimate orientation. This position data is provided at 20 Hz.  
Figure 9: LIO-SAM adapted factor graph.  
Figure 10: Examples of trajectories followed from take-off to landing. The maneuvers were performed under a deck of the Algodonales and Alora viaducts. The graphs on the left illustrate X-Y, center X-Z and right X-Y-Z. (**a**) Alora sequence 5, (**b**) Alora sequence 7, (**c**) Algodonales sequence 3, (**d**) Algodonales sequence 4.