esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Wearable Technology and a Deep Learning Model to Predict Occupational Physical Activities
## 2 Materials and Methods
### Model Training  
The ResNet-18 structure was pre-defined, only the weights were learned during training. Error was defined as the averaged cross-entropy loss over all samples [38] in one batch (Equation (2)). Training was not finished until the model converged (Figure 4a). A convolutional neural network (CNN), ResNet-18 [39] was used for categorical prediction. Its 18-layer implementation was robust to train the model efficiently and retain high accuracy. The time series data and kinematic data of activities based on the 15 OPA  
Figure 3: Wearable test for occupational physical activities: (**a**) wearable track device and location of IMUs and their orientations (orientations were displayed as two orthogonal vectors), (**b**) performing manual material handling tasks with prototype, (**c**) IMU used in this study.  
categories (Table 1) were converted to tensors (1 \(\times\) 60 \(\times\) 19) and divided into 60%, 20% and 20% for training, validation, and testing, respectively.  
\[\textbf{loss(x,i)}\textbf{=}\frac{\textbf{1}}{\textbf{N}}\left(-\textbf{x[i]+ log(\sum_{\textbf{j}}\textbf{e}^{\textbf{x[j]}})}\right) \tag{2}\]  
where, in each batch, i represented the correct OPA categories, j represented all of the OPA categories and N was the batch size. The lowest validation error across models did not necessarily identify the best model. Therefore, it was critical to save the current best model when the validation error decreased. After the training was complete, the best model was selected based on testing data accuracy (minimize empirical error). Model training and prediction were all performed using Python 2.7.  
The OPA categories were added to data of 15 activities as the activity's labels; the raw datasets were saved to 15 csv files. Each csv file represented one activity and was constructed in such a way that columns represented coordinates and time derivative information from one sensor and rows represented timestamps of each of the 8 sensors. Therefore, each row had 18 columns of sensor data, 1 column for the timestamp, and 1 column for the label.  
The final model input data was generated by combining multiple rows together to form an image-like window. The number of rows combined (window size) was a hyperparameter of the model. The window size with the highest accuracy achieved. Each window had a size of 60 rows \(\times\) 20 columns; in other words, 60 rows of sensor data, 20 columns of sensor data plus an activity label (Figure 3(b)). Finally, the overlapping window was cut. An overlapping window was defined as a window containing multiple activities and therefore it was redundant. For example, when a single window (1 s) contained both activity 1 and activity 2, this window was dropped (0.7%).
## 3 Results and Discussion
### 3D Processing Maps Establishment
As one can see, the optimal hot deformation parameter regions are in the field of red regions of power dissipation efficiency and the areas of flow stability (marked as S). For alloy 0.05 Sc these optimal parameters were a temperature range of 400-450 \({}^{\circ}\)C and a strain rate of 0.1-1 s\({}^{-1}\) at the beginning of deformation, and 380-420 \({}^{\circ}\)C and strain rates lower than 1 s\({}^{-1}\)--at high strain values. For the 0.1 Sc alloy, the optimal temperature range primarily decreased up to 360-400 \({}^{\circ}\)C with the same strain rates (0.1-1 s\({}^{-1}\)) at all stages of deformation. Furthermore, for the alloy with 0.15% Sc, the optimal temperature range was 360-450 \({}^{\circ}\)C during the first stages and near 360 \({}^{\circ}\)C at the end of deformation, with the lowest strain rates (near 0.1 s\({}^{-1}\)) for all stages. The specimens deformed with parameters at the lowest power dissipation efficiency (green areas) and flow instability regions (450 \({}^{\circ}\)C and 15 s\({}^{-1}\), for example) had cracks on the surface, as shown in Figure 4.  
The typical microstructures of specimens after deformations at different conditions are shown in Figure 5. Primarily, the microstructures after all deformation conditions were non-recrystallised, and elongated deformed grains were present. However, upon deformation at rates of 5 and 15 s\({}^{-1}\) and temperatures of 450 \({}^{\circ}\)C, the microstructure was partially recrystallised, but there were also microcracks along the grain boundaries. Therefore, the main mechanism of energy dissipation during deformation was DRV. Under those deformation regimes, where DRV did not have time to occur, cracks formed and the specimens were destroyed. Thus, the optimal deformation parameters were a temperature range 350-400 \({}^{\circ}\)C and strain rates of 0.1-1 s\({}^{-1}\) for the 0.05 Sc and 0.1 Sc alloys, and 0.1 s\({}^{-1}\) for the 0.15 Sc. Figure 3:0 3D processing maps for Al4.5 Zn4.5 Mg1 CuO.12 Zr alloys with different Sc contents, and images of 0.1 Sc alloy specimens after deformations at 450 \({}^{\circ}\)C/15 s\({}^{-1}\) (with cracks) and 400 \({}^{\circ}\)C/0.1 s\({}^{-1}\) (without cracks). Figure 3:1 LM-images of the microstructures of the alloy 0.1 Sc after deformation at different conditions.
## 4 Application to Model Updating of an In-Service Bridge with Extra-Width
### FE Model  
Based on software of ANSYS, the three-dimensional FE model of Hunan Road Bridge was established shown in Figure 4c. The main girder, transverse beam, and tower are simulated with BEAM 44 element and due to the extra-width of box girder, it is divided into eight small girders with the connection of transverse beams and virtual weightless deck using SHELL 63 element. Both the main cable and hanger are simulated with LINK 10 element, only taking tension into consideration. The longitudinal and transverse pre-stressed steels are all simulated with LINK 8 element and the weights of cable clamps, main saddles, and secondary dead load are all transferred to MASS 21 element. The four connections of each tower and girder are simulated with COMBINE 14 element in which the connection conditions between the left tower and girder are restrained in the vertical and longitudinal directions while the conditions between the right tower and girder are restrained only in vertical direction. The six connections of the girder and foundation are also simulated with COMBINE 14 element in which the connection conditions are all restrained in vertical direction and, especially, in the transverse direction when referred to connections on the longitudinal axis of the girder. The bottom of tower is restrained in all directions of six freedoms. A total of 15,530 nodes and 20,875 elements are found in the whole model in which the gravity acceleration is 9.806 m/s\({}^{2}\), the damping ratio of material of girder, beam, and tower is 0.05 while it is 0.02 for the cable, hanger, and pre-stressed steel.
## 6 Conclusions  
A control technique based on the characteristics of PMSG has been proposed in this paper to deal with the power grid stability issues under high penetration of renewable energy sources. Initially, virtual inertia and mechanical power (VMP) have been embedded in an active and reactive power-based dynamic model of the interfaced converter as the main contribution of this paper, that could provide a stable operation for the power grid, with properly varying the virtual parameters. By considering the first order zero dynamic of the grid angular frequency, the active and reactive power variations, based on VMP, have been assessed. Based on this assessment, the accurate active and reactive power sharing have been improved by appropriately controlling VMP and the error of angular frequency. Moreover, the second order zero dynamic of the grid angular frequency has been used to investigate precisely the virtual inertia effects. This investigation has shown that the stable responses of the angular frequency have driven through the so-called low-high virtual inertia. From another side, the effects of the active power error on the reactive power, under the changing virtual inertia, have been comprehensively analysed. Within the future work, authors suggest that more detailed dynamic model of PMSG can be embedded into the active and reactive power-based dynamic model, presented in this paper to design more detailed controller. Simulation results confirmed that the proposed control technique can guarantee the stability of the power grid under high penetration of renewable energy sources. The main findings of this paper are that, for a wide range of virtual inertial with VMP, stable responses with very short transient time, have been achieved for the grid frequency and voltage magnitude, as well as the active and reactive power under high penetration condition. Figure 12: The grid voltage magnitude under (**a**) appropriate virtual inertia and VMPE, (**b**) very high and low virtual inertia, and (**c**) high and very high values of VMPE. **Author Contributions:** Conceptualization, [M.M., E.P. ]; Methodology, [M.M. ]; Software, [M.M. ]; Validation, [M.M., E.P. ]; Formal Analysis, [M.M., E.P. ]; Investigation, [M.M. ]; Resources, [E.P., H.S., F.B., M.R.A.C. and J.P.S.C. ]; Writing-Original Draft Preparation [M.M. ]; Writing-Review & Editing, [E.P., H.S., F.B., M.R.A.C. and J.P.S.C. ]; Visualization, [H.S. ]; Supervision, [E.P., M.R.A.C. and J.P.S.C. ]; Project Administration, [J.P.S.C.]. **Funding:** J.P.S. Catalao acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under SAICT-PAC/0004/2015 (POCI-01-0145-FEDER-016434), 02/SAICT/2017 (POCI-01-0145-FEDER-029803) and UID/EEA/50014/2019 (POCI-01-0145-FEDER-006961). **Conflicts of Interest:** The authors declare no conflict of interest.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## Abbreviations  
\begin{tabular}{l l} DVL & Doppler Velocity Log \\ PLSR & Partial Least Squares Regression \\ SVR & Support Vector Regression \\ SINS & Strapdown Inertial Navigation System \\ ROV & Remotely Operated Vehicle \\ AUV & Autonomous Underwater Vehicle \\ IMU & Inertial Measurement Unit \\ KF & Kalman Filter \\ MCP & Magnetic Compass \\ PS & Pressure Sensor \\ GPS & Global Positioning System \\ APS & Acoustic Positioning System \\ GNS & Geophysical Navigation System \\ RBF & Radial Basis Function \\ \end{tabular}
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### Models
#### 3.2.1 Proposed Deep Neural Network  
Deep Neural Networks (DNNs) have recently become among the favorite approaches in various fields in the domain of Artificial Intelligence [58]. These networks, which are famously called models, are characterized by several layers that contain a huge number of computational units. These units, which are interconnected, meaning that the output of one unit is the input of the other, are conceived as the imitation of the physiological brain's structure. In mathematical terms, they are a set of parametrized linear and non-linear transformations capable of being adjusted in order to output abstractions of the input data [59]. This capability comes from the amalgamation of multiple layers full of perceptrons. Although a single perceptron cannot handle data that are not linearly separable, they are the basis of Multi-Layer Perceptrons (MLPs), whose ability to transform highly non-linear data makes them a powerful and efficient tool in machine learning [60].  
Figure 8: Illustration of NearMiss. This method refers to a collection of undersampling algorithms that select instances based on the distance of majority-class samples to minority-class ones.  
Furthermore, the first proposed method in this paper is a DNN-based model. The architecture of this model is demonstrated in Figure 9.  
As is observed in Figure 9, our DNN model comprises different layers, including a fully connected one followed by an activation function and a batch normalization layer. Then, a fully connected layer, an activation function, and a batch normalization followed by a dropout and a single neuron fully connected layer come after.
# Evaluation of the Consistency of Three GRACE Gap-Filling Data
## 2 Materials and Methods
### Global Mean Sea Level Change
Altimetry, SSA + Argo, IGG + Argo, and QF + Argo denote the corresponding total sea level changes after 12-month sliding average. (**b**) Annual variations in mean seawater mass. (**c**) Time series of Nino3.4 indicators. The bars and curves represent the monthly values and the series after 12-month sliding average, respectively. However, the four series exhibited substantial discrepancies after 2016, with lower values for SSA + Argo, IGG + Argo, and QF + Argo than the altimetric results. There are several possible causes: (a) Deteriorated data quality during the late stages of GRACE satellites operations and the reduced number of accelerometers after 2015 [51; 52]; (b) inaccuracies of Argo data [53]; and (c) uncertainties in the estimated degree-1 series [51]. Although these large discrepancies prevent a quality evaluation based on the sea level budget approach, the IGG + Argo and QF + Argo series consistently show closer agreement with Altimetry than SSA + Argo during the gap period, indicating that SSA may underestimate the mean seawater mass variation in the gap months. However, it is important to note that the IGG series suspiciously shows a strong seasonal increase in 2017 (marked by the box in Figure 8b) when the ENSO index is weak (Figure 8c). According to previous records, strong seasonal changes always occur when the ENSO index is extremely negative (e.g., in 2008, 2011, and 2012). In conclusion, Figure 8 shows that the IGG, SSA, and QF estimates of global ocean water mass began to differ from the Altimetry-Argo result toward the end of the GRACE mission. IGG appears to overestimate the annual amplitude of seawater mass, but the inter-annual mean sea level change derived by it is largely consistent with QF. SSA seems to underestimate the trend during the gap period. The cause of the large bias in the sea level budget after 2016 is unclear, and these data require further examination when used to analyze the global mean sea level changes in recent years.
## 4 Discussion on the Corresponding Processes in Ionosphere
### The Comparison of Plasma Parameters at Different Altitudes
In our research, as shown in Figures 4 and 6, the geographic latitudes of equatorial ionization anomaly crests were located within \(\pm\)20\({}^{\circ}\), but the latitudinal differences between two crests were within 20\({}^{\circ}\), although they were pushed southward overall during the recovery time period. As for the local time effects, they pointed out that the PRE-related \(E\times B\) drift provide a conducive environment for irregularities generation during post-sunet hours at low latitude regions, especially in the Pacific Ocean west sector, but it is possibly not suitable to explain the variations, as shown in Figure 9b, with no significant enhancement during the main phase and continuous low values in the recovery phase. The variation mode in Figure 9b may not only include the local time effects, but the longitudinal and altitudinal influences cannot be ignored, which is similar to the variations of TEC in the South America sector [46] with no significant enhancement at main phase. Although the penetration electric fields still existed in local nighttime, as observed by CSES in Figure 3, the electron density at different positions (longitude and latitude) and local times and altitudes are still controlled by the local plasma environment of the ionosphere, including the distribution of O/N2, etc., [20; 46]. Here, the altitude is also taken into account because the local nighttime for Swarm-A and CSES are close to each other at around 02:00, but CSES showed clear enhancement of Ne at the main phase; although some depletion structure existed at equatorial area, the TEC enhancement was totally covered up from Swarm-A at medium and low latitudes.
# A Basic Experimental Study on Analysis of Leak Signal and Monitoring Method for Water Supply Pipe
## 4 Simulated Leak Piping System
### Smart Bolt Strength Tests and Results  
The tensile stress was calculated by multiplying the value of the strain caused by the torque of the smart bolt by the modulus of elasticity (205 GPa), as shown in Figure 6. The torque value and the allowable tensile stress of the bolt were compared.  
Table 3 shows the torque and strength measurement results of the tested smart bolts. There was no visual damage to the bolt when it was 60 N\(\cdot\)m, which is the torque value of M16 required by the standard specification for connection by the water supply pipe in  
Figure 5: Simulated leak piping system.  
Figure 6: Smart bolt installation and measurement of torque.  
Korea, was applied. The tensile stress calculated from the measured strain was found to be within the allowable tensile stress range of the bolt. With the torque of 70 N\(\cdot\)m, the stress of the smart bolts were also calculated to be within the allowable tensile stress range, so it was judged that the smart bolt was suitable for the water supply bolt application.  
In addition, the embedded strain gauge was a good sensor for linearity and could determine whether the bolt gets loosened by using the initial (reference) value after the initial bolt installation. As the torque changes, the strain rate of the smart bolt is proportionally changed. Therefore, it may be possible to monitor the bolt loosening by comparing the new measured value and the initial measured value at the corresponding torque.
## 5 Semantic-Aware Global Orientation Estimation  
### Local Dominant Direction Estimation  
In a real navigation environment, there is a large number of road markings on the ground, which is a good reference landmark for SLAM systems. However, there are also various disturbing noises on the ground, such as glare or water stains. Therefore, it is necessary to distinguish the marker lines from the noise lines. By observation, it can be seen that most of the marker lines are parallel or perpendicular to each other, while the noise lines are disordered. Inspired by this phenomenon, this section designs a segmentation method for marker lines based on the structural rules of the Manhattan world, which can be used to obtain from the image a collection of line segments that conform to the structural assumptions of Manhattan and the dominant orientation of these lines, as shown in Figure 4. The left (a) is the raw input image. The middle (b) is the extracted raw lines, and the right (c) is the line segmentation result. Green lines are the preserved marking lines, and red lines are the noisy lines. The bottom row shows three consecutive images. Green lines are marking lines; the orange arrow is the local dominant direction of the current frame, and the blue arrow is the global dominant direction.  
In the initial frame, the line with the highest number of perpendicular and parallel to the other lines is considered to be the initial dominant direction \(x_{d}\), and the vertical or parallel between two lines can be evaluated by computing,  
\[\theta-\arccos(\frac{x_{i}\cdot x_{j}}{|x_{i}||x_{j}|})<\delta, \tag{3}\]where \(\delta\) denotes the tolerance and \(\theta\) can be set as 0 for the parallel lines' evaluation and \(\frac{\pi}{2}\) for the perpendicular lines' evaluation. Thus, we can build two sets for these two types of lines as \(L_{//},L_{\perp}\) for parallel lines and perpendicular lines, respectively. Then, we optimize the dominant direction:  
\[\min_{a,b}\sum_{l_{i}\in L_{//}}|x_{d}^{\prime}\cdot l_{i}|+\sum_{l_{j}\in L_{ \perp}}|x_{d}\cdot l_{j}|. \tag{4}\]  
The first term denotes that the parallel line intersects with the dominant direction at point \(x_{d}^{\prime}=[b,-a,0]\), and the second term means the perpendicular line intersects with the dominant direction at point \(x_{d}=[a,b,0]\).  
Due to the robot's dynamic constraints, the maximum directional change in adjacent frames is assumed to be \(\eta\). After successful initialization, in order to reduce the amount of calculation, subsequent frames will only look for a new dominant direction and the corresponding set of structure lines from candidate line features in the interval with the dominant direction of the previous frame less than \(\eta\).
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Network Architecture Overview  
It is clear that most networking on the solution VMs take advantage of the OpenFlow protocol in order to properly function. Additionally, it might be interesting to understand which protocols the implementation takes advantage of to communicate. Figure 9 provides an overview of the solution network architecture and the used protocols on each link.  
Hence, the Host is responsible for dealing with the Data Plane related tasks of the system architecture. If devices choose to use WiFi in order to access the gateway, traffic enters directly to the system Southbound Interface. On the other hand, in the case of BLE and LoRa devices, data firstly go through the SDWI module in order to be encapsulated. All those communication processes take advantage of the OpenFlow and MQTT protocols.  
The VM-Manager is responsible for running the administration features by means of Docker containers. It provides lodging for the SDN module, which includes the lightweight Ryu controller and FCMan API. Additionally, a web interface with the purpose of facilitating the network administrator actions is hosted on the machine. Furthermore, this important component can communicate with the VM-Application components through the system Northbound Interface. The VM-Application is responsible for dealing with the slice related networking, assigning each one to a newly created OvS switch. Additionally, it has the task of running all slice applications on separate Docker containers. All those actions are orchestrated and supervised by the VM-Manager, where the controller resides.  
Figure 8: LoRa network architecture and its relation with SOFTWAY4 IoT.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Orthogonal Modelling and Inversion Principles
#### 3.1.1 Basic Principles of Remote Sensing Inversion
The improved state vector \(x\) formula is as follows:  
\[x_{i+1}=x_{i}+(K_{i}^{T}S_{\epsilon}^{-1}K_{i}+(1+\gamma)S_{a}^{-1})^{-1}[K_{i }^{T}S_{\epsilon}^{-1}(y-F(x_{i}))+S_{a}^{-1}(x_{a}-x_{i})] \tag{3}\]  
where \(F(x_{i})\) is the forward model at \(x_{i}\), \(K_{i}=\frac{\partial F(x_{i})}{\partial x_{i}}\) corresponds to the Jacobi matrix, and \(\gamma\) is the Levenberg-Marquardt parameter. The process is iterated until the set \(\gamma\) convergence condition is satisfied, details of which are given by [PERSON] et al. [44]. In the inversion process, the profile concentration of CO\({}_{2}\) on the gas column is obtained. In order to calculate the XCO\({}_{2}\), one more step is required with the equation  
\[XCO_{2}=h^{T}\hat{u} \tag{4}\]  
where \(\hat{u}\) is the CO\({}_{2}\) concentration profile inverted over a fixed pressure layer and \(h\) is the pressure weighting parameter [45].