esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### BTEX Removal Efficiency  
Figure 11 shows the VOC concentration measured at the pre- and post-filter locations without a cabin filter installed. The excellent repeatability between the two repeats demonstrated the consistency in gas mixing created by the turbulence blades and the extraction through the multi-hole probes. However, observations also show a consistent difference in gaseous concentrations of 8-17% between the pre- and post-filter locations. The difference in gaseous concentration could be due to the sharp 90-degree bend in the filter housing unit (Figure 1) which is located right before the cabin filter location. This could create some eddies, but it also reflects the reality in an actual vehicle, which information will be taken into account in evaluating the efficiencies in various filters for removing BTEX.  
Figure 10: Particulate matter mass filtration efficiency over time for Group I (normal filters), Group II (electrostatic filters), and Group III (underperforming filters).  
Figure 9: Black carbon mass filtration efficiency over time for Group I (normal filters), Group II (electrostatic filters), and Group III (underperforming filters).  
Figure 12 summarizes the BTEX concentrations measured at the pre- and post-filter locations for a charcoal filter (ATP or Fram) and a non-charcoal filter (OEM). Since there is a consistent reduction in BTEX concentration between the pre- and post-filter locations (Figure 11), the averaged reduction amount was subtracted from the total reductions in BTEX concentrations for the all test filters in order to only reflect the change in BTEX concentrations due to the presence of a cabin filter. For the non-charcoal filter, the typical reductions in BTEX concentration were 3-5%. These changes are too small to be considered statistically significant, and they demonstrate that the non-charcoal filters have no impact on gaseous removal. In contrast, the two charcoal filters show significant removal of BTEX concentration, with one showing reductions of 11-17% with the other one showing reductions of 38-41%. These results suggest that even though there are variations in the BTEX removal efficiencies (RE) among different charcoal filters, charcoal filters are indeed useful in removing BTEX by typically more than 11%.  
Figure 11: Measured BTEX concentrations at pre- and post-filter locations without cabin filters. Uncertainties are 95% confidence interval error estimates. Dotted lines indicate the reductions in BTEX concentrations at the post-filter location for the different repeats.  
Figure 12: Measured BTEX concentrations at pre- and post-filter locations for various cabin filters.
## 4 Analysis of the Influence of High Peening Coverage on Almen Intensity and RCS
### Influence of High Peening Coverage on RCS  
In this section, as the RCS at different depth is difficult to obtain in the Almen test, the distribution of residual compressive stress (RCS) at different depth with the increase of peening coverage is analyzed in FE simulation. Negative value of the RCS represents compression.  
The peening coverage is 100%, 200%, 400% and 800%. The dent size of three cases is 0.125, 0.14, and 0.16 mm. With three dent sizes, the distribution of the RCS at different depth is shown in Figure 10 respectively. Figure 10a shown distribution of the RCS at 0.125 mm of dent size. Figure 10b,c shows the dent size at 0.14 and 0.16 mm, respectively.  
Figure 10: _Cont_.  
Figure 9: Variations of the Almen intensity with various peening coverage at different dent size. (**a**) 0.125 mm, (**b**) 0.14 mm, (**c**) 0.16 mm.  
From Figure 10, firstly, with dent size of 0.125, 0.14, and 0.16 mm, the absolute value of RCS firstly increases and then decreases with the depth increasing, there is a maximum absolute value of RCS around the depth of 0.02 mm. Secondly, with dent size of 0.125, 0.14, and 0.16 mm, at certain depth, the absolute value of RCS does not always increase with the peening coverage increasing.  
With dent size of 0.125, 0.14, and 0.16 mm, a more detailed analysis of peening coverage and RCS is shown in Figure 11. Surface RCS is the RCS of the Almen strip surface, while the maximum RCS is the minimum value of RCS at different depth as the value of RCS is negative. Figure 11 shows the relation of the surface RCS and the peening coverage, while Figure 11 shows the relation of the maximum RCS and the peening coverage.  
Figure 10: Residual compressive stress profiles for different impact velocity and peening coverage. (**a**) 0.125 mm, (**b**) 0.14 mm, (**c**) 0.16 mm.  
From Figure 11, firstly, with dent size of 0.125, 0.14, and 0.16 mm, the absolute value of surface RCS and maximum RCS all increase firstly and then decrease as the peening coverage increases, there is a maximum absolute value of surface RCS and maximum RCS. Secondly, with certain peening coverage, the absolute value of surface RCS and maximum RCS increases as dent size increases.
## 3 Realize Point Cloud Fine Registration Using Improved ICP Algorithm
### Realize Accurate Registration of Point Cloud  
Conventional point cloud matching algorithms are applicable only between two-point clouds, and the two-point cloud sets have an inclusion relation. In the case of a big difference in the initial spatial position of the overlapping point clouds, the conventional algorithm is apt to fall into the local optimal solution [15; 30]. Therefore, under the premise that the standard ICP algorithm satisfies the minimum error function. The algorithms are detailed as follows:  
1. Downsample the point cloud to be registered \(P\) and the target point cloud \(Q\);
2. Search for the closest point of the target cloud using KD-tree, calculate the distance \(d_{i}\) in accordance with Equation (17); if \(d_{i}<k\), calculate the normal vector \(\theta_{i}\) of the two corresponding points in \(P\) and \(Q\), and the point is determined as the corresponding point in \(P\) if \(\theta_{i}<\theta\), otherwise, repeat the iteration;  
Figure 2: Flow of ICP.  
3. Calculate the average distance \(d\) of the corresponding point set. If it is less than the preset threshold, or if the times of iterations are greater than the preset maximum number of iterations \(\tau\), leave the loop and stop the iterations; otherwise, return to recalculate the transformation matrix until the convergence requirements are met. The registration flow of the algorithm proposed in this paper is shown in Figure 3.
## 6 Continuous Transmission of Packet Traffic
### AOA based  
Arrival Access can discern among signals delivered concurrently by numerous devices because codes scatter the user signal throughout the whole available band, making it resistant to the frequency selective fading caused by multi-path. Rake filters can be used at the receiver to take advantage of the time variation in underwater acoustic channels, thereby reducing the multi-path effect. This approach decreases the data packet retransmissions while boosting channel reuse, assisting the AOA in managing energy more efficiently.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 8 Evaluation
### Computation Overhead  
We implement a prototype of DPTracer in Python and ran experiments on an Apple Silicon 10-core M1 CPU with 32 GB RAM. Each collector node in our simulation receives a 128-byte report from external data sources in a single round (\(|R|=128\)). The aggregator nodes calculate an average from the inputs by the aggregating operation. In the logger, the hash operations for Process Tree use the SHA-256 algorithm with commitments sized at 34 Bytes (32 Bytes for SHA-256 plus 2 Bytes for the prepended version number), i.e., \(|C|=34\).  
Figure 7 illustrates the execution times required for computing commitments and generating interval proofs when the number of nodes varies from 10 to 990. Both tasks are the primary operations of the logger and occur during \(\mathsf{LogUpdate}()\). As depicted in Figure 7, the time to compute a commitment increases from 0.067 ms to around 0.6 ms when the number of nodes changes from 100 to 990, reflecting the expansion of the Process Tree. The time for proof generation grows logarithmically and peaks at 6.03 us when the node count increases to 650. The time required for the logger to generate a commitment is significantly longer: two orders of magnitude greater than the time required for proof generation.  
Figure 7: Computational overhead.  
Given this computational overhead, DPTracer is practical for real-world applications. For context, the average round times for price oracles such as Chainlink [9] and Band Protocol [10] are at least 5 and 6 s, respectively. Even in the high-frequency-update scenario of Pyth Network [14], where the round time is as quick as 400 ms [25], DPTracer remains well within the required performance; in this case, the computation time for commitment generation in DPTracer is under 0.067 ms.
# Modes Control of Lamb Wave in Plates Using Meander-Line Electromagnetic Acoustic Transducers
## 2 Lamb Waves
The terms in Equation (10) have been split up into two systems of symmetric and antisymmetric modes, respectively:  
Symmetric modes:  
\[u_{x}=[ikA_{2}\cos(py)+qB_{1}\cos(qy)] \tag{11}\]  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)] \tag{12}\]  
\[\tau_{yx}=\mu[-2 ikpA_{2}\sin(py)+(k^{2}-q^{2})B_{1}\sin(qy)] \tag{13}\]  
\[\tau_{yy}=\mu[(k^{2}-q^{2})A_{2}\cos(py)-2 ikqB_{1}\cos(qy)] \tag{14}\]  
Antisymmetric modes:  
\[u_{x}=[ikA_{1}\sin(py)-qB_{2}\sin(qy)] \tag{15}\]  
\[u_{y}=[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{16}\]  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)]+[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{8}\]0  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)]+[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{8}\]1  
For free wave motion, we derive the homogenous solution by applying the stress-free boundary conditions at the upper and lower surfaces at \(y=\pm d\):  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)]+[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{8}\]2  
where \(d\) is the half-thickness of the plate. For the symmetric modes, the boundary conditions yield a system of two homogeneous equations for the constants \(A_{2}\) and \(B_{1}\). Similarly, for the antisymmetric modes, two homogeneous equations for the constants \(A_{1}\) and \(B_{2}\) are obtained. Since the systems are homogeneous, the determinant of the coefficients must vanish, which yields the frequency equation. Thus, we can obtain the well-known Rayleigh-Lamb frequency equations:  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)]+[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{8}\]3  
\[u_{y}=-[pA_{2}\sin(py)+ikB_{1}\sin(qy)]+[A_{1}p\cos(py)-ikB_{2}\cos(qy)] \tag{8}\]4  
Equation (20) corresponds to symmetric (S) modes and Equation (21) to the antisymmetric (A) modes. By reviewing the derivation process of the Lamb wave dispersion equations, we can see that if one wants to obtain a pure single-mode guided wave, the excitation force must satisfy the single-mode guided wave dispersion equation. When designing the transducer, it is necessary to consider the excitation force generated by the transducer to satisfy the desired dispersion equation of the Lamb wave mode.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
The fundamental contribution of this paper is to develop a low-loss active compensator to counteract the negative effects of grid impedance on current-controlled single-phase grid-connected VSI with an LCL filter. The active compensator is realized through a dual unit PCC voltage feedforward control mechanisms (VFFC), in which one is inserted into the inverter current control and the other generates voltage regulation of an additional low voltage source converter (LV-VSC) in series with the filter capacitor. The LV-VSC has no extra passive filtering elements and provides very low volt-ampere (VA). Thus, the inverter efficiency is not sacrificed. The rest of this paper is organized as follows. In Section 2, the rationale of the grid impedance compensator is elaborated. Then, the control strategy is thoroughly elucidated, and the system modeling is studied in Section 3. Simulation results are given in Section 4, and a single-phase inverter prototype with the compensator is built and evaluated in Section 5. Its steady-state and transient behaviors under different grid conditions are examined. Conclusions follow in Section 6.
## 4 D-PMU Placement Order Determination Based on Spatial Electrical Distance  
To ensure that the network is completely observable, the number of buses installed with D-PMUs accounts for a higher proportion of the total number of buses, generally more than one-third. Therefore, it is difficult to implement the whole solution of OPP at the same time.  
If a bus is installed with a D-PMU, the state estimation error variance of this bus is most significantly reduced due to the high accuracy of the D-PMU. Moreover, the state estimation accuracy of those buses which are close to the D-PMU bus is also significantly improved. The higher that the accuracy of the D-PMU's data are, the greater that the improvement in state estimation is. In contrast, the state estimation accuracy improvement of those buses which are not close to the D-PMU bus is not obvious. In order to ensure that the OPP method is easily applied in practical engineering, the D-PMU placement order determination method based on spatial electrical distance is proposed to maximize the state estimation accuracy of each stage. The reciprocal of the spatial electrical distance between the bus where the D-PMU is installed and the newly observable bus is selected as the weight. The greater that the weight of the location is, the higher that the priority of the installed D-PMU is.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 5 Results and Discussion
Along with the predicted blockage class of a given culvert (i.e., blocked, clear), the value of class prediction probability can help in estimating the model confidence of the prediction. Partial automation can be achieved by setting a threshold on prediction probability (i.e., 80%) to filter only those images for manual inspection for which prediction probability is less than a set threshold. Figure 12 shows the conceptual block diagram of the proposed framework for partial automation of the culvert visual blockage inspection process. Implemented CNN models were also compared for their processing times to investigate the relative response times. The purpose of these analyses was to investigate the hardware implementability of proposed models for real-world applications. Model inference time and image processing time were calculated as two measures to compare the models. Three different size images were used; image 1 of \(2048\times 1536\), image 2 of \(3264\times 2448\), and image 3 of \(4032\times 3024\). From Table 2 and Figure 8, it can be observed that MobileNet and DarkNet53 were fastest among others while the NASNet model was the slowest. In terms of accuracy, NASNet was the most accurate; however, MobileNet  
Figure 11: Selected ResNet50 CNN layers heatmaps. also exhibited comparatively good accuracy (i.e., 78% in comparison to 85% for NASNet) and was recommended as a suitable choice to implement for on-board processing. Figure 8 shows the graphical comparison of implemented models in terms of test accuracy and processing times. It is important to mention that reported processing times are for relative comparison between models and not the actual measure of cutting edge hardware performance. However, given the availability of efficient computing hardware, such as Nvidia Jetson TX2 [31] and Nvidia Jetson Nano [32], it is highly probable to implement any of the implemented models for real-world applications (e.g., pedestrian detection [33], wildlife tracking [34]).
# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 4 Taxonomy of High-Level Abstraction Category of Performed Process
### Taxonomy Discussion
For such input, the machine-learning-based solutions are expected to perform much better than other methods. Solutions that recognize objects from their point cloud representations and infer the type of the room based on its equipment are also very promising. However, such processing is an example of a very complex data structure that requires advanced and resource-intensive processing. **Machine-learning popularity. ** We can observe that the newer the solution, the more likely it is to use some form of artificial intelligence and machine-learning processing. This applies to both one-dimensional and multi-dimensional data inputs. Algorithmic solutions are much more vulnerable to even the smallest changes in the structure of processed data. Machine-learning-based solutions are better adjusted to nuances in the input and less likely to return completely false results. Their performance would worsen in a relatively progressive way and not with a single visible error threshold. **Hardware capabilities. ** We can observe an adjustment of the chosen processing method to the available hardware. This final conclusion is a continuation of the observation about the increasing complexity of input data with the upgrade of used hardware. We observe the same with the processing methods used in each distinguished category. For example, we can point to the precise classification bucket of solutions. Algorithmic processing requires fewer resources and is successful even in online solutions but requires more straightforward input data (e.g., graphic structures or radio readings). A powerful hardware platform was needed to carry out the same task category but for more complex structures (e.g., point clouds of whole buildings). However, in general, the more advanced the approach and the larger the amount of data to be processed, the more promising the results will be.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 3 Our Method
### Feature Extraction and Local Map Management  
De-skewed point cloud is segmented through attention PointConv network, and the points belonging to dynamic objects are removed. Then, we adopt lidar feature extraction [19] to select planar feature points, which are most likely on a plane.  
Since one single lidar frame is too sparse for feature matching, we build a local map that consists of feature points of several lidar frames \(\{o,\cdots,p,\cdots,i\}\), where \(o\), \(p\), and \(i\) are the first frame, the reference frame, and the last frame in the sliding window, respectively, as shown in Figure 4. For \(\gamma\in\{o,\cdots,i\}\), feature points \(\mathbf{F}_{L\gamma}\) are projected to reference frame \(p\) as \(\mathbf{F}_{L\gamma}^{Lp}\) and all projected features in the sliding window build a local map \(\mathbf{M}_{Lo,i}^{Lp}\). The frames before \(p\) are already optimized. For each point \(\mathbf{x}^{Lp}\in\mathbf{F}_{\mathbf{\mathrm{L}}}^{Lp}\) (\(\alpha\in\{p+1,\cdots,i,j\}\)), we adopt KNN algorithm to find its \(K\) nearest points \(\pi\big{(}\mathbf{x}^{Lp}\big{)}\) in \(\mathbf{M}_{Lo,i}^{Lp}\). Since these \(K\) points are most likely on the same plane, they have the plane constraint:  
\[\mathbf{n}^{T}\mathbf{x}+d=0,\;\mathbf{x}\in\pi\Big{(}\mathbf{x}^{Lp}\Big{)} \tag{7}\]  
where we denote \(m=[\mathbf{x},\mathbf{n},d]\in\mathbf{m}_{Lo}\). In this way, we formulate a relative constraint between the reference frame (\(n\) and \(d\) are defined in frame \(p\)) and the following frames (\(\mathbf{x}\) is defined in reference \(\alpha\)).  
Figure 3: The whole process of attention PointConv network.
## 2 System Architecture  
### Architecture  
To design a secure location-sharing system for trajectory-based message delivery on VANETs, we consider the system architecture shown in Figure 2 which consists of trusted authority (TA), service manager (SM), roadside units, and vehicles.  
* TA is a fully trusted entity responsible for managing security parameters for the system and issues id-based key pairs to the registered RSUs and vehicles denoted as \(\mathcal{R}=\{RSU_{1},...,RSU_{m}\}\) and \(\mathcal{V}=\{V_{1},...,V_{n}\}\), respectively. TA also manages pseudonymous identities for the vehicles to guarantee anonymity of vehicles on VANET communications.
* SM is a manager which provides storage service on the cloud. To support secure trajectory data sharing, SM maintains encrypted trajectory data of vehicles and acts as a broker which handles to distribute re-encrypted trajectory data to RSUs authorized by the vehicle of the trajectory owner.
* RSUs are subordinated to the TA and sparsely deployed on the roads such as main intersections, and their geographical location information are available through the system. The roles of RSUs in \(\mathcal{R}\) are divided into socialspot RSUs and relaying RSUs. Socialspot RSUs (sRSUs), denoted as \(\mathcal{SR}=\{sRSU_{1},...,sRSU_{l}\}\subseteq\mathcal{R}\), are deployed on the specific locations of interest. A set of RSUs establish a local cloud with dedicated servers so that they collect and provide location-aware information to vehicles by means of trajectory-based message delivery. On the other hand, a relaying node RSU equips storage for temporarily holding messages to support message forwarding to the destination vehicles passing by its coverage.
* Vehicles are equipped with OBU and GPS-based navigation system with digital maps. A registering vehicle \(V_{d}\in\mathcal{V}\) selects sRSUs among \(\mathcal{SR}\) in which \(V_{d}\) is interested and generates a re-encryption key for the selected sRSUs to share its driving trajectory data through the cloud storage under the control of SM. Whenever \(V_{d}\) changes its preferred driving route, \(V_{d}\) uploads its encrypted driving route data to SM. Moreover, to clarify the proposed system, we also make the following assumptions.
* Public security parameters of TA are already known to all entities in the system.
* SM and socialspot RSUs are interconnected to each other through a secure and reliable channel.
* Locations and identities of RSUs are publicly available to the system so that vehicles can know which RSUs are deployed at which locations.
## 4 Results and Discussion
### The Training Time  
As it can be seen from Table 9, the static AWSCTD-CNN-S model demonstrates the fastest training time for the 1000 system calls sequence. The next model by speed is FCN, which is slower by around 21%. All other models were slower than AWSCTD-CNN-S, with results varying from 64-618% (Figure 9).  
The training times (Table 9) are given for one-fold. The differentiation in model speeds starts from the sequence of 200 system calls (Figure 9).  
The worst training time results were obtained for the LSTM-FCN and AWSCTD-CNN-LSTM models. This can be explained by the number of parameters of these models.
## 5 Experimental Results  
### Spectrum Measurement Using the Implemented Spectrometers  
The characteristics of the spectrum measured by the two spectrometers were compared and analyzed. The measured spectra are shown in Figure 7. The spectrum measured with the spectrometer developed using the ADL (S-ADL) is shown in Figure 7a, and the spectrum measured with the spectrometer using the TFL (S-TFL) is shown in Figure 7b. To compare the spectral shape of the spectrum, each spectrum was normalized. Because the two spectrometers use the same LSC, different spectral shapes might be due to the imaging lens in the spectrometer. The spectra measured by the two spectrometers were similar to each other, and both FHWM bandwidths were equal to 42 nm.
## 3 Results and Discussions
### Verification of Actual Measurements  
Figure 10 shows the days chosen for the model simulation are the same days used in Section 3.2. The measurements and simulation were done by the Department of Energy and Refrigerating Air-Conditioning Engineering of the National Taipei University of Technology. The simulation software used was \"Ansys 13 Fluent,\" and the results show that the temperature reduction using VGS was 7.046%. The energy saving calculation is seen in Table 5.