esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 5 Results
### Static Inversion  
#### 5.1.1 Two Model Families Which Explain the Data Equally Well  
Geometries of the best-fit model can be described by two families of models, which fit the data equally well (InSAR, continuous GNSS and campaign GNSS) for the whole eruption (Table 1). Because of the random search conducted, the best models belonging to one or the other family can result from inversions conducted with the same inputs as shown by Inv04a (F1) and Inv04b (F2). Family F1 corresponds to a curved fracture dipping to the east (blue model on Figure 6). Family F2 is a two-part intrusion consisting of a sill that turns into a dike before reaching the ground surface (red model on Figure 6). To better characterize these families, we also quantitatively compared geometries of the best-fit models by computing the average distance \(D\) between a node and the closest node belonging to a second mesh.  
Figure 6: Comparison between geometries of typical intrusions of family F1 (Inv04a in blue) and family F2 (Inv02a in red). The upper plots compare the geometry and fracture opening of intrusions of family F1 (**left**) and family F2 (**right**). Three views are shown for each subfigure, one from the east along the northern direction on the left-hand side, a map view on the right-hand side and a view from the south along the eastern direction at the bottom. The lower plot compares the eruptive fracture meshes for the two families, as seen from the east.  
Considering two meshes A and B with different node numbers, our average distance computation method (Figure 7) results in average distance from mesh \(A\) to \(B\), \(D_{AB}\), being slightly different from the average distance from mesh \(B\) to \(A\), \(D_{BA}\). Table 2 gives a summary of the average distance \(D\) between pairs of meshes. From this table, we conclude that the average distance between meshes of family F1 is \(94\pm 32\) m, the average distance between meshes of family F2 is \(103\pm 45\) m and the average distance between families F1 and F2 is \(160\pm 35\) m. Meshes from a given family are closer to meshes from this family than those of the other family, indicating that our classification into families is relevant. The best model geometries for inversions 02b, 03 and 04b are very close and all belong to family F2. Model geometry of inversion 04a is close to 01 and 05. The three of them belong to family F1. The greater average distance between models 01 and 05 could be due to a systematic shift even though the overall shapes are very close. This shift is compatible with model 04a lying between model 01 and model 05 but closer to model 05. Model 02a also belongs to family F2. The closest model to model 02a is 02b, which also belongs to family F2 and the furthest from model 02a is 05, which belongs to family F1. Apparently large distances for models 03 and 04b are artifacts due to the averaging of small parts of the meshes which did not superpose well, whereas the central part of the mesh is in very good agreement.
## 3 Machinability of AM Ti6 Al4V Alloy
### Selection of Cutting Conditions  
The selection of cutting conditions, environments, and inserts plays a crucial role in the effective machining of titanium alloys. Polycrystalline diamond (PCD) inserts have been reported to be particularly effective in high-speed machining processes, offering enhanced performance and tool life due to their superior hardness and wear resistance at elevated cutting speeds up to 150 m/min, Titanium diboride (TiB\({}_{2}\)) is used for intermediate rates up to 100 m/min, and cubic boron nitride (CBN) tools for lower cutting speeds ideally for finishing cuts [40]. Cutting conditions played a vital role in the analysis of energy consumption and surface quality in the machining of titanium alloy [58].  
The levels and cutting conditions, such as cutting speed, feed rate, and depth of cut, can be selected based on any previously reported work in the literature or tool manufacturers' guidelines, as listed in Table 6. Experiments can be repeated two or three times for data repeatability. However, a new insert needs to be used in each experimental run.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Density (kg/m\({}^{3}\)) & \(14.5\times 10^{3}\) \\ Ultimate tensile strength (MPa) & 3000 \\ Young’s modulus of elasticity (GPa) & 650 \\ Thermal conductivity (W/Mk) & 58.98 \\ Poisson ratio & 0.25 \\ Heat capacity (J/kg K) & 15.0018 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical properties of a tungsten carbide tool [56].  
Hardness refers to a material's resistance to plastic deformation, often resulting from rapid cooling. It is typically measured using scales such as Vickers, Rockwell, or Brinell, with the hardness value indicating the material's (or cutting tool's) ability to resist deformation and achieve the desired surface finish. Studies have shown that hardness is a key factor in determining cutting forces and burr formation during micromachining processes [7]. A comparison of hardness and toughness across different cutting tools reveals that polycrystalline diamond (PCD) exhibits high hardness but low toughness. In contrast, high-speed steel (HSS) tools demonstrate low hardness combined with high toughness. Ceramics and coated carbide tools offer a balance, displaying moderate levels of both hardness and toughness, making them suitable for a range of machining applications [51].
## 5 Simulation Results
### Testing of the Proposed Control Strategy
#### 5.2.2 Case 2: Performance of Unknown Input Disturbances Rejection  
Case 2 is used to test the controller's ability to suppress unknown input disturbances. Since the MGT-CCP system operates in a high temperature environment, the wear caused by the frequent operation of the valve will affect its stability. Therefore, the impact of input disturbances on the MGT-CCP system cannot be ignored.  
Assume that the MGT-CCP system is running at working point #3. The input side of the system is subject to unknown input disturbances as shown in Figure 13, and the simulation results are shown in Figure 14. The simulation results show that the controllers can eliminate the influence of unknown input disturbances, but the proposed method suppresses the disturbance faster and the deviation from the operating point is smaller.  
Figure 12: Case 1: Load tracking performance of the MGT-CCP system (Solid in blue: proposed method; dot-dashed in red: PID; dot in green: reference). (**a**,**b**) Output variables; (**c**,**d**) Manipulated variables.  
Figure 14: _Cont._  
Figure 13: Unknown input disturbances.
## 2 Wireless Multimedia Sensor Networks
### Large Volume of Multimedia Data
#### 2.3.2 Higher Bandwidth Requirements  
Bandwidth has always been a scarce resource, and definitely more so in WMSNs. To illustrate, the nominal transmission rate of the IEEE 802.15.4 compliant camera sensor motes, such as MeshEye [60], iMouse [112], and Vision Mesh [113], is only 250 Kbit/s. But the required bandwidth for general multimedia applications could be at least one order of magnitude higher [4]. What is more, the need to relay multimedia data to the sink and the coexistence of scalar data traffic can easily raise the bar further on bandwidth requirements [54; 114]. In order to overcome the challenge, it is necessary to develop communication protocols that can utilize bandwidth more efficiently. To begin with, through the usage of multichannel MAC protocols, a group of nodes can communicate in parallel over different channels [74; 75; 76]. Instead of adopting a single routing path for data delivery, using multiple independent paths simultaneously to split the traffic will allow to achieve higher aggregated bandwidth [54]. In the meantime radio technologies with potentially higher bandwidth, such as the ultra-wideband (UWB), can also be used in WMSNs [77; 78].
## Glossary  
\begin{tabular}{l l} AC & Alternating current \\ BW & Bandwidth \\ DAB & Dual Active Bridge \\ DC & Direct current \\ \(\mathbb{D}\) & Duty cycle \\ \(\mathfrak{D}\) & Small deviation \\ GM & Gain margin \\ HF & High frequency \\ MF & Medium frequency \\ PWM & Pulse width modulation \\ PM & Phase margin \\ SST & Solid State Transformer \\ MGs & Microgrids \\ N & Turns ratio \\ Z & Load \\ U & Voltage \\ F & Frequency \\ L & Leakage inductance \\ \(\Phi\) & Phase shift \\ db & decibel \\ SGs & Smart-grids \\ I & Identity matrix \\ U\({}_{\rm s}\) & input voltage \\ U\({}_{\rm dc}\) & dc link voltage \\ U\({}_{\rm m}\) & peak ac input voltage \\ U\({}_{\rm inv}={\rm U}_{0}\) & inverter output voltage \\ U\({}_{\rm c}\) & voltage across the capacitor \\ \end{tabular}
## 3 Results
### Application of the Method: Spatial and Dosimetric Analysis  
The TRE values for each OAR and each registration are plotted in Figure 3a. The TRE was found to vary across the OARs of interest. The mean and standard deviation values (mean \(\pm\) SD) of the TRE over all fractions and patients were 4.6 \(\pm\) 4.6 mm, 2.6 \(\pm\) 1.4 mm, 2.7 \(\pm\) 1.5 mm, 2.1 \(\pm\) 1.0 mm, 2.0 \(\pm\) 1.2 mm, 1.8 \(\pm\) 1.2 mm, 5.6 \(\pm\) 3.3 mm and 2.5 \(\pm\) 2.0 mm for the body, left parotid gland, right parotid gland, larynx, oral cavity, mandible, brainstem and spinal cord, respectively. The large range of values of the TRE for the brainstem were due to the border of the ROI exceeding the CBCT boundary in the cranial direction in the MICE toolkit, when any mask was used. This could have induced unnatural and drastic shearing of the refDVF for the brainstem in the cranial section of the CBCT boundary, as reported in the refDVF data in Section 3.2. The testDVF generated with the Velocity AI showed a smoother DVF in that area, resulting in consequently larger TRE values. The differences in the TREs between the two fractions, compared on a voxel basis, were found to be insignificant using the Wilcoxon signed-rank test, with a \(p\)-value \(\approx\) 1.  
The dose error values for each OAR are plotted in Figure 3b. Similar to the TRE analysis, the median dose error value varied according to the OAR of interest. The mean \(\pm\) SD dose difference values over all fractions and patients were 2.0 \(\pm\) 4.3 Gy, 2.2 \(\pm\) 2.4 Gy,  
Figure 3: Boxplots of **(a)** TRE difference and **(b)** dose difference distribution sorted by OAR. For each box, the central mark represents the median value, while the bottom and top edges of the box are the 25 th and 75 th percentiles, respectively. The ‘whiskers’ represent the range of values. Observations beyond the whisker length are marked as outliers (+). By definition, an outlier is a value that is more than 1.5 times the interquartile range away from the bottom or top of the box. An outlier appears as a red + sign.  
\(2.5\pm 2.7\) Gy, 1.8 \(\pm\) 1.8 Gy, 1.3 \(\pm\) 1.6 Gy, 1.2 \(\pm\) 1.8 Gy, 3.2 \(\pm\) 2.6 Gy, 0.7 \(\pm\) 0.7 Gy for the body, left parotid gland, right parotid gland, larynx, mandibula, brainstem and spinal cord, respectively.
## 4 Results and Analysis
### RMS Delay Spread
In 3 GPP TR 38.901, the mean of the RMS DS is also a fixed value, which is expressed as  
\[\mu_{DS}^{3 GPP}=\log_{10}(26\times\frac{V}{S}+14)-9.53, \tag{8}\]  
where \(V\) is the hall volume in \(m^{3}\) and \(S\) is the total surface area of the hall in \(m^{2}\) (walls + floor + ceiling). By substituting the volume and area values of the researched scenario into Equation (8), the mean was obtained as \(-7.74\)\(\log_{10}(\mathrm{s})\), which is the same as the average value of \(\mu\) in our research. This indicates that the 3 GPP model of the RMS DS for the factory scenario can be extended to apply to THz bands. For the standard deviation, the value for the visible light channel was much larger than that of the electronic wave channel, which was 0.23. For electronic wave channels, the standard deviation increased with the frequency, which can be modeled as  
\[\sigma_{DS}=0.034\times\log_{10}(1+f_{c})+0.10, \tag{9}\]  
where \(f_{c}\) is the frequency. The root mean square error (RMSE) was 0.009, indicating that the proposed model is in good agreement with the simulated data. Comparing the RMS DS of the industrial scenario with that of indoor hotspot scenarios, it was found that there was only a small difference in the electronic wave band. For example, at 28 GHz, the mean values of the RMS DS for the industrial scenario and the indoor office scenarios were \(-7.72\)\(\log_{10}(\mathrm{s})\) and \(-7.71\)\(\log_{10}(\mathrm{s})\), respectively. Furthermore, for the visible light bands, the RMS DS in the industrial scenario was greater than that in the indoor conference scenario, which was \(-7.93\)\(\log_{10}(\mathrm{s})\) and \(-8.13\)\(\log_{10}(\mathrm{s})\), respectively. This is because the obstruction of the metal instruments limited the propagation environment of most electronic wave signals to the space between the metal instruments on both sides, and the impact of the scenario on the signals was not significant. In VLC, the optical signal had a larger angle range due to Lambertian radiation, and the effect of obstruction on the signal was relatively small. The influence of the scenario size on the channel characteristics became increasingly prominent. Thus, the industrial scenario, which had a much larger size than that of the indoor conference scenario, had a greater RMS DS.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 6 The ns-3 Simulation Model
### Up-Link Outage Probability  
The full explanation of packet destruction detection is omitted in this publication. The LoRa physical layer's packet collision model is based on the work done in [33], which can be referred to for a complete overview. The model assumes that RF interference only comes from other LoRa transmissions, and we assume the partial orthogonality property of different SF to simulate packet collisions. The model also takes the capture effect into account.
# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 3 DAO-YOLO Model
### AMFE Module  
The AMFE module integrates multi-scale depthwise separable convolutions with a channel attention mechanism, facilitating efficient extraction of both fine-grained local details and comprehensive global context across multiple scales. This integration substantially improves the model's multi-scale feature representation capability.  
Figure 6 illustrates the architecture of the AMFE module, and its implementation process can be expressed as follows:  
\[X_{G}=GAP(DSConv_{1}(X_{i})+DSConv_{2}(X_{i})+DSConv_{3}(X_{i})) \tag{2}\]  
\[W_{c}=Sigmoid(AConv1D_{z}(X_{G})) \tag{3}\]  
\[X_{o}=X_{i}\cdot W_{c} \tag{4}\]  
where \(X_{i}\in\mathbb{R}^{H\times W\times C}\) denotes the input feature map, \(H\), \(W\), and \(C\) denote the height, width, and number of channels of the input feature map, respectively, \(DSConv_{1}(X_{i})\), \(DSConv_{2}(X_{i})\), and \(DSConv_{3}(X_{i})\) denote depthwise separable convolutions with kernel sizes of \(1\times 1\), \(3\times 3\) and \(5\times 5\), respectively, \(GAP(\cdot)\) denotes the global average pooling, \(X_{G}\in\mathbb{R}^{C}\) denotes the global feature vector, \(AConv1D_{z}\) denotes an adaptive 1D convolution operation with a kernel size of \(1\times z\), \(W_{c}\) denotes the channel attention coefficient, and \(X_{o}\) denotes the output feature map. The extent of the convolutional kernel \(z\) is adaptively determined by the following equation:  
\[z=\left\lceil\frac{\log_{2}(C)}{s}+\frac{o}{s}\right\rceil_{old} \tag{5}\]  
where \(C\) denotes the number of input channels, \(s\) denotes the scaling factor that controls the growth rate of \(z\), \(o\) denotes the offset used as the initial value for the adjustment of \(z\), and \(\left\lceil\cdot\right\rceil_{old}\) denotes the operation of rounding up to the nearest odd number.  
The above implementation shows that the AMFE module has two distinct advantages over conventional channel attention modules. Firstly, the input feature map under  
Figure 6: Architecture of AMFE module.  
goes multi-scale processing via depthwise separable convolutions at three different scales, nnhancing the model's proficiency in capturing features across multiple scales. Secondly, the employed one-dimensional adaptive convolution determines its kernel size adaptively based on the input feature scale. Compared to traditional fully connected layers, this approach substantially reduces both parameter count and computational complexity, achieving superior balance between computational efficiency and feature extraction capability.
### Proposals  
In this work, a new AR system combined with IRT called MANTRA is proposed, which can be applied to industrial maintenance. In more detail, the main contributions of this work are:  
* To our knowledge, it is the first time that an AR system has been used in combination with IRT in the field of industrial maintenance.
* We develop a new AR system using a combination of multiple methods that can align virtual content on 3D objects automatically, precisely, robustly, and in real time. Specifically, the 3D object detection and pose estimation method is based on a modified LINEMOD method [15] with a built-in deep-learning method called YOLOV4 [16]. In addition, a 6 Degree of Freedom (6 DOF) pose tracking method, based on the work of [PERSON] et al. [17], was also integrated. Finally, it is important to note that the YOLOV4 detection method was trained only with photorealistic synthetic images generated automatically by BlenderProc [18].
* The MANTRA system allows for obtaining the temperature information of a 3D object precisely and in real time, as well as that of its components, using the information of the previously calculated object pose.
* A new calibration pattern is designed, to spatially calibrate RGB-D and IRT cameras.
* The 3D object detection and pose estimation method used by the MANTRA system is validated on different public data sets.
* Both quantitative and qualitative validation of the MANTRA system, as applied to industrial maintenance in a real use-case, demonstrate its effectiveness, compared to traditional maintenance methods and those using only AR.
## 1 Introduction  
Body composition analyses, in children and adolescents diagnosed with human immunodeficiency virus (HIV), have been used to investigate complications related to HIV infection or to the alterations on body tissues associated with HIV treatment [1; 2; 3]. Regarding bone loss, it has been reported that children and adolescents diagnosed with HIV have a low bone mineral content (BMC) and bone mineral density (BMD) caused by an altered bone metabolism [4; 5; 6]. Despite the knowledge of the occurrence of low BMC and BMD in this population, the consequences and pathogenesis of these body tissue alterations are still unclear [5].  
Dual energy X-ray absorptiometry (DXA), the most commonly used method to evaluate the BMC and BMD, is considered to be a precise technique to evaluate the body composition in children and adolescents, providing whole-body and regional estimates of the BMC, fat-free mass (FFM), and fat mass through the X-ray attenuation of the body tissues [7; 8; 9; 10]. In spite of its usability, the body composition evaluation using DXA equipment are costly and not widely available or accessible in underdeveloped countries such as Brazil, this limiting the use of this method in clinical practice [10; 11]. Therefore, reliable, easy-to-use, and non-invasive techniques, such as bioelectrical impedance analysis (BIA), have been developed and validated to investigate the body composition in children and adolescents due to their greater accessibility to clinical practice [7; 9; 12].  
The BIA is based on the values of resistance and reactance that allow us to make an estimate the total body water (TBW) and FFM [7; 12; 13]. Those estimations are based on the assumption that 73% of the FFM is composed by water [7; 12; 13]. The BIA has been commonly used to evaluate the TBW, intracellular and extracellular body water, FFM, and fat mass using tetrapolar single-frequency equipment [9; 13]. Recently, the BIA has also been used to estimate lean soft tissue mass (LSTM) and BMC by the division of the values of the FFM [9; 12]. Despite the knowledge regarding the BIA usability to estimate the FFM, there is not any substantial information on the use of the BIA to access the BMC [9]. In a non-systematic review developed within this study, only two studies that investigated the BIA's usability to estimate the BMC in children and adolescents were found [14; 15].  
Considering the need for BMC research in children and adolescents diagnosed with HIV and the lack of studies regarding the BIA's usability to estimate the BMC, the present study aimed to develop equations to estimate the BMC by BIA using DXA as a reference method.
## 4 Results
### Canopy N Concentrations Index Selection and Model Parameter Optimization  
Vegetation indices that have previously been used to estimate canopy N content were used to drive the model. The performance of each index was evaluated by comparing the accuracy of GPP estimated by the models driven by in situ meteorological data and these indices (Figure 4). Among all vegetation indices, the GPP simulation using CI\({}_{\text{green}}\) performed the best, followed by the chlorophyll/carotenoid index (CCI), the green normalized difference vegetation index (GNDVI) and the near-infrared reflectance (NIR).  
Moreover, we collected some measured canopy N concentrations data to demonstrate the correlation between CI\({}_{\text{green}}\) and canopy N concentrations. As shown in Figure 5, there was a strong correlation between CI\({}_{\text{green}}\) and canopy N concentrations (\(r\) = 0.82). Similarly, many studies also suggested that CI\({}_{\text{green}}\) can be used to quantify canopy N concentrations. For example, [PERSON] et al. [52] found that the R\({}^{2}\) between the canopy N concentrations of winter wheat and CI\({}_{\text{green}}\) at different viewing zenith angles reached 0.6\(\sim\)0.75; [PERSON] et al. [79] predicted the woodland leaf N concentrations using random forests and found that CI\({}_{\text{green}}\) had the highest importance among multiple vegetation indices; [PERSON] and [PERSON] [80] found that CI\({}_{\text{green}}\) and canopy N of grass and potato were linearly correlated (R\({}^{2}\) of 0.77 and 0.89, respectively). Therefore, we selected CI\({}_{\text{green}}\) as the nitrogen index (NI) in this study to characterize the canopy N concentrations and introduced it into the LUE model to calculate the \(\varepsilon_{max}\).  
As shown in Figure 6, we verified the accuracy of the GPP simulation using CI\({}_{\text{green}}\) and the ERA5 meteorological data based on the 10-fold cross-validation. The model overall has a high estimation accuracy (R\({}^{2}\) = 0.63 \(\pm\) 0.01, RMSE = 2.27 \(\pm\) 0.05 gC/m\({}^{2}\)/d, MAE = 1.48 \(\pm\) 0.03 gC/m\({}^{2}\)/d, IOA = 0.88 \(\pm\) 0.01). Parameters optimized according to different vegetation types were shown in Table 1. Among all vegetation types, the GPP estimates of DBF had the highest accuracy, followed by GRA, WSA, and WET. The GPP estimation accuracy of CRO, EBF, and MF was relatively poor, with R\({}^{2}\) below 0.5.  
Figure 4: (**a**) The coefficient of determination (R\({}^{2}\)), (**b**) the root-mean-square error (RMSE) (gC/m\({}^{2}\)/d), (**c**) the mean absolute error (MAE) (gC/m\({}^{2}\)/d), and (**d**) the index of agreement (IOA) of GPP simulations based on different vegetation indices.