esa-sceva/satcom-chunk-collection huggingface dataset:
# The Local Integrity Approach for Urban Contexts:
## 3 Experimental Validation and Data Analysis
### Computation of the Local Protection Levels
In detail, the percentages of \"Normal Operation\" conditions (_i.e._, 100%, 99.1%, and 99.5% respectively) exactly agree with the percentages of positive cases in Figure 6b: this result provides a further validation of the performance, confirming the conservativeness of the LPL values. It is worth to remark that previous analyses were carried out on a limited amount of data logged by a single receiver in a small urban area (see Figure 4). The extension to a wider area is possible, but it would require comprehensive data collections and a remarkable computational burden for building the effective UERE data base, which were out of the scope of this work. However, a scenario with a large number of GNSS observations shared by collaborative vehicles could become realistic in the next future, due to the growing penetration of VANET technology and cloud computing facilities for providing new road services. Figure 5: Search for optimal multiplicative factors for the LPL computations. Figure 6: Results of the LPL analysis, assuming \(k_{AT}=k_{CT}=k_{V}=0.6\): (**a**) Absolute positioning errors _versus_ LPL values; (**b**) Conservativeness of LPL values _versus_ positioning errors.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 3 Sparse Codes
### Design of Constellation Mapping Function
: _The \(M\times K_{1}\) constellation mapping matrix satisfies following properties:_  
* _In the_ \(k_{1}=1,\cdots,V\)_-th column, a vector_ \(\mathbf{\tilde{e}}_{k_{1}}=\mathbf{\tilde{b}}\otimes\mathbf{1}_{2^{k_{1}-1}}\) _is repeated_ \(M_{0}=\frac{M}{2^{k_{1}}}\) _times._
* _For_ \(k_{1}=V+1,\cdots,K_{1}\)_, the_ \(k_{1}\)_-th column is equal to the_ \(k_{0}\)_-th column where_ \(k_{0}=\text{mod}(k_{1}-1,V)+1\)_._  
For example, if \(M=4\) and \(K_{1}=3\) in Figure 5, the first column of the matrix is generated by alternating \(1\), \(-1\) of period \(1\), and the second column of the matrix is generated by those of period \(2\). The third column is equal to the first column satisfying the second property in Remark 1. Hence, based on Remark 1, we propose an algorithm for optimizing the constellation mapping matrix as depicted in Algorithm 1. Using the low-complexity heuristic algorithm with arbitrary values of \(K_{1}\) and \(M\), we can easily optimize the constellation mapping function for both high-rate (e.g., \(M=16,32,64,\cdots\)) and long-range (e.g., \(K_{1}=4,5,6,\cdots\)) AmBC systems. Figure 5: Example of the constellation mapping function with respect to \(M\) and \(K_{1}\) for \(\tilde{M}=2\). (\(\tilde{\mathbf{b}}=[1,-1]^{T}\)).
# A Novel Bearing Lubricating Device Based on the Piezoelectric Micro-Jet
## 6 The Experiment Performance of the Micro-Jet
### Experiment Study of the Injection Performance
#### 6.2.2 The Influence of the Duty Ratio  
When the working frequency and amplitude were set to 2.1 kHz and 100 V respectively, the experimental injection states of the micro-jet under pulse voltages with different duty ratios were obtained, as shown in Figure 19. With the rising duty ratio of the pulse voltages, the lateral injection distance and the level of the dispersion of the droplets all increased gradually, which corresponded to the results of the simulation. In addition, through these experiments, we found that the micro-jet cannot work properly when the duty ratio of the pulse voltages is less than 20% or more than 70%. The main reason is that the mechanical responses of the vibrator requires a certain time; if a duty ratio of the pulse voltages is too high or too low, the vibrator will be unable to deform or return to its natural state.  
Figure 20 shows the relationship between a single droplet's volume and the duty ratio of pulse voltages. As we can see, the volume increases slowly with the increase of the duty ratio, but the change gradually slows down. The minimum volume of the droplets is 5.14 \(\times\) 10\({}^{-6}\) mL when the duty ratio of the pulse voltages is set to 30%.  
Therefore, for better uniformity of lubrication and longer effective distance, the higher duty ratio should be selected, but the duty ratio should be limited to less than 70%. Furthermore, in order to increase the effective working time of the lubricating system, the lower duty ratio should be selected, but the duty ratio should exceed 20%. Additionally, the appropriate amplitude should be chosen according to application situations and requirements.  
Figure 19: The injection states of the micro-jet under pulse voltages with different duty ratios: (**a**) the duty ratio is 30%; (**b**) the duty ratio is 40%; (**c**) the duty ratio is 50%; (**d**) the duty ratio is 60%.  
Figure 20: The relationship between single droplet’s volume and the duty ratio.
# Study on Active Tracking of Underwater Acoustic Target Based on Deep Convolution Neural Network
###### Abstract  
The active tracking technology of underwater acoustic targets is an important research direction in the field of underwater acoustic signal processing and sonar, and it has always been issued that draws researchers' attention. The commonly used Kalman filter active tracking (KFAT) method is an effective tracking method, however, it is difficult to detect weak SNR signals, and it is easy to lose the target after the azimuth of different targets overlaps. This paper proposes a KFAT based on deep convolutional neural network (DCNN) method, which can effectively solve the problem of target loss. First, we use Kalman filtering to predict the azimuth and distance of the target, and then use the trained model to identify the azimuth-weighted time-frequency image to obtain the azimuth and label of the target and obtain the target distance by the time the target appears in the time-frequency image. Finally, we associate the data according to the target category, and update the target azimuth and distance information for this cycle. In this paper, two methods, KFAT and DCNN-KFAT, are simulated and tested, and the results are obtained for two cases of tracking weak signal-to-noise signals and tracking different targets with overlapping azimuths. The simulation results show that the DCNN-KFAT method can solve the problem that the KFAT method is difficult to track the target under the weak SNR and the problem that the target is easily lost when two different targets overlap in azimuth. It reduces the deviation range of the active tracking to within 200 m, which is 500-700 m less than the KFAT method.  
DCNN; active sonar; tracking; Kalman filtering; underwater acoustic targets +
Footnote †: journal: Medical Engineering, Hangzhou Dianzi University, Hangzhou 310018, China; [EMAIL_ADDRESS] (M.W.); [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (H.X.); [EMAIL_ADDRESS] (C.Z.)
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.3 ZEB-HORIZON Data Processing  
After data acquisition was completed, the data was processed based on the laser SLAM system. The process included: first, to perform point cloud registration on each key frame of the front-end point clouds collected in the scene; second, to calculate the distance of the motion and attitude change of the LIDAR, i.e., the visual odometry, based on the poses recorded in each key frame of the point clouds; third, to reduce the poses through back-end optimization, thereby reducing the cumulative error caused by the visual odometry; and finally, to construct 3D point clouds of the scene and perform loopback detection to constrain back-end optimization to reduce the cumulative error in the 3D space. The data collected by the ZEB-HORIZON scanner was processed and computed in the GeoSLAM Hub software. The data set of the experimental area was copied from the data logger through the USB disk equipped with the instrument, and the data set was imported into the GeoSLAM Hub software and processed automatically. The processed overall plantation point cloud data format was exported as the Las general format, which would provide basic data for subsequent 3D point cloud post-processing and extraction of tree parameters. Its data processing flow is shown in Figure 7.  
Figure 4: ZEB-HORIZON equipment diagram.  
The 3D point clouds of the plantation forest constructed under the three path plans of straight line path, S-shaped line path, and loop path are shown in Figures 8 and 9. From the results of the straight line path (Figures 8a and 9a), it is evident that there is an error in the solution of the laser odometer trajectory when the constructed point clouds of the plantation are processed by the GeoSLAM Hub software. The point clouds constructed do not present a straight line path, resulting in confusion of feature matching and not being able to correctly construct the point clouds of the plantation. The results of the S-shaped path (Figures 8b and 9b) show that the laser odometer trajectories are crossed after being processed by the software, which leads to the misalignment of the point clouds in each key frame. This further results in the stratification of the plantation point clouds. The results of the loop path (Figures 8c and 9c) show that the laser odometer trajectories of the loop path are correctly solved with the complete feature information of the plantation. Therefore, our  
Figure 5: Data acquisition flow chart. ((**a**) shows the flow chart of data acquisition; (**b**) shows the data collection of the experimental area).  
Figure 6: Path planning diagram. ((**a**) shows a straight path; (**b**) shows an S-shaped path; (**c**) shows a circular path).  
Figure 7: GeoSLAM Hub data processing flow chart.  
experiment indicates that the GeoSLAM Hub software cannot solve the 3D point clouds of the plantation constructed by the straight-line path and the S-shaped line path, and the loop path is the best to obtain the 3D point clouds of the plantation.
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 4 Experimental Results
### Mortality  
An outlier detection scenario is studied in [44], where the suggested approach _DetectDeviatingCells_ classifies cell-wise as well as row-wise anomalies in a data matrix. One of the tested experiments [44] refers to a dataset comprising male mortality in France from 19 th century forward, extracted from [45]. The aim is to discover outlying years, representative of the main iconic events in France history.  
Data is structured as a matrix. To adapt it to METEOR, age groups are regarded as variables, meaning that correlations among mortality rates of different ages can be modeled. Due to the excessive number of attributes, only a subset is selected. The normalized dataset can be seen in Figure 8, where each time series represent France's male mortality rates from 1841 to 1987 in a specific age group. The years in which measurements were obtained are regarded as time instants \(t\). With the assembling of longitudinal data, SAX pre-processing is applied to each series. Attributes \(X_{i}[t]\) are thus male mortality rates of specific age groups at particular years. It is worth noting that with all the transformations performed, the dataset is reduced to a single subject which portrays a MTS.  
Two experiments are presented in Figure 9. In the first experiment, 5 variables are selected, being ages 20, 30, 40, 60 and 80. Each variable is discretized with an alphabet size of 5 (\(r_{i}=5\) for all \(i\)) and all tests employ [PERSON]'s strategy in the score analysis phase. The objective is to determine unusual events such as wars and epidemics. The trained model involves a stationary third-order tDBN. Nodes are allowed to have at most one parent from previous slices. The reasoning behind the parameter choice is purely experimental. The problem exhibits a preference of attributes establishing connections with previous nodes which are not consecutive with themselves. It is worth recalling that having an order of three does not mean that every or even any relation has such lag, it just offers such possibility. Results confirm major events which shaked France history. These are displayed in Figure 9, representing both world wars, the influenza pandemic, the Franco-Prussian War and the European revolutionary wave of 1848. France was a belligerent in several conflicts as well as colonization wars in the 1850s.  
In the second experiment, a variable is added to the first set. The new age group represents the male mortality rate of children aged up to 10 years old. The aim is to capture the impact of youth mortality in the outputted years. Results are similar, being the differences observed in the 1860s and around the Spanish flu confirming that youth is more susceptible to epidemics.  
Figure 8: Normalized values of variables \(X_{i\in\mathbb{I},\dots,6}\) representing France’s mortality rates of males with ages 10, 20, 30, 40, 60 and 80, respectively, from 1841 to 1987. Each time stamp represents a year. Data is discretized with a SAX alphabet \(r_{i}=5\) for all \(i\).
## 3 Results
### Remarks on the Limitations of the Analysis  
The proposed methodology requires the definition of several parameters, criteria, and weights, whose choice resulted in being strongly dependent on the expectations of stakeholders and end-users. Thus, the obtained results should be seen as a first attempt towards the proposal of an MCDA methodology that does not require great mathematical expertise, is flexible, and can be easily adapted to many situations. Nevertheless, further efforts are necessary in order for the tool to be readily exploited by public authorities and decision makers. Furthermore, it is worth highlighting other limitations inherent in the present analysis.  
The first type of limitation is mainly related to the availability of data. Indeed, the choice of the criteria was based on the availability of the relevant information, which led, for some criteria, to a purely qualitative evaluation. Greater availability, accuracy, and ease of retrieval of the data would lead to the creation of a more complete and more precise analysis, and it could also contribute to the development of operational tools and software.  
Figure 14: Weights sensitivity analysis, multi-risk maps; (**a**) Scenario 2, population density weight increase; (**b**) Scenario 3, population density weight increase. The risk levels strictly indicate the relative priority ranking for decision-makers and do not indicate the effective safety level of the various municipalities.  
Secondly, this analysis neglected cascade effects, an aspect that deserves further investigation in the future [11].  
Thirdly, the present contribution does not consider the impact of modeling assumptions on the seismic risk assessment. At the relevant scale of observations of the present analysis, specific structural aspects connected to the vulnerability levels of the buildings cannot be easily considered. In this regard, we recall that specific structural aspects and modeling assumptions play, among others, a key role for seismic risk evaluation at both the building and the urban scale [36]. A recent study focusing on South America has shown the uncertainties and biases that the use of simplified models or heterogenous data may produce in the determination of seismic vulnerability [36]. For completeness, seismic risk evaluation is extensively discussed, for instance, in the aforementioned contributions [36; 37; 38; 39] and the references cited therein.  
Finally, we recall that the seismic classification shown in Figure 2 has been merely used as a technical-administrative reference for establishing the priority of actions and measures aimed at preventing and mitigating seismic risk. It must not be used to determine the local seismic action or for the structural design of buildings, which, instead, rely upon more detailed maps highlighting, for instance, the presence of site effects due to the inherent geological structure of the ground or instability effects such as liquefaction. Therefore, the present analysis should be purposefully extended in order to consider the aforementioned local effects [40].
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 2 Related Works
### Selecting Appropriate Coordinator
The selection of the coordinators is based on the residual energy of the coordinator and the LQI. When the process is based on the coordinator, the node initiates the handover process whenever it verifies that the power of its coordinator is lower than a defined threshold. The remaining energy in the coordinator is periodically checked. When it is below the threshold, the coordinator sends a disassociation command to its nodes and the handover is performed. [PERSON] et al. [25] design a handover mechanism for IEEE 802.15.4 operating in beacon-disabled mode. The idea is that the APs can search for a specific MN instead of continuously monitoring the RSSI between the node and the corresponding AP. This mechanism is assessed considering an infirmary scenario, where patients have a set of sensor nodes attached to their bodies and can freely walk in this area. [PERSON] et al. [26] propose a fuzzy-based decision scheme to be used in the handover procedure of IEEE 802.15.4 standard operating in beacon-enabled mode. This scheme, named Group-Weighted Fuzzy Quantitative Decision Algorithm (group-weighted FQDA), assembles parameters (battery size, signal strength, etc) into groups (energy, link quality, etc) according to their proximity, defining profiles according to the type of the application. The group-weighted FQDA was assessed, and showed best performance when compared with other solutions. This algorithm can be extended to a large number of metrics and does not require computational or storage capabilities, thus allowing good adaptability to the problem of WSN mobility.
## 3 Results
### Scaffold's Geometrical Parameters and Main Findings
#### 3.5.3 Pore Size  
Of all the included studies, only one [34] did not report scaffold pore sizes. Some studies [26,32] show varied results with different pore sizes, while others [26] indicate that smaller pores may lead to better bone formation. [PERSON] et al. (2021) [26] observed that scaffolds with pores of 345 \(\pm\) 10 \(\upmu\)m and 365 \(\pm\) 30 \(\upmu\)m had different results under SEM (Scanning Electron Microscopy). The scaffolds with larger pores (365 \(\pm\) 30 \(\upmu\)m) (PLGA/TCP) showed irregular bone formation on the surface. In comparison, the scaffolds with smaller pores (345 \(\pm\) 10 \(\upmu\)m) (TCP) primarily presented fibrous tissue and infiltrated blood vessels. Although the PLGA/TCP scaffolds (365 \(\pm\) 30 \(\upmu\)m) also exhibited a structurally similar porous structure, the surface of the TCP scaffolds was more regular and smoother compared to the TCP scaffolds. In the Micro-CT analysis, the pore sizes were measured as 358 \(\pm\) 39 \(\upmu\)m (PLGA/TCP) and 335 \(\pm\) 11 \(\upmu\)m (TCP) [26].  
[PERSON] et al. (2017) [31] recognized that scaffolds with pore sizes in the range of 200-435 \(\upmu\) yielded similar results, with minimal new bone formation, and vascularization was not directly mentioned in this specific study. [PERSON] et al. (2022) [36] used scaffolds with pores of 500 \(\upmu\)m (PCL/\(\beta\)-TCP) and 191.9 \(\pm\) 74.6 \(\upmu\)m (\(\beta\)TCP). They concluded that they had significantly higher bone formation rates than the parallel groups of supports with larger pores (500 \(\upmu\)m).  
In a study by [PERSON] et al. (2019) [30], implants with intermediate pore sizes (300-400 \(\upmu\)m) exhibited greater bone formation compared to those with smaller pores. Additionally, angiogenesis-related genes were positively regulated in implants with intermediate pores, suggesting a relationship between pore size and vascularization. It was also observed that pore sizes between 200 and 435 \(\upmu\)m [31] demonstrate efficient scaffold degradation and the formation of new bone. According to [PERSON] et al. (2007) [35], scaffolds with high porosity and large pore sizes (450 \(\pm\) 50 \(\upmu\)m) facilitate better nutrient exchange and enhanced cell penetration. However, this structure also results in a decrease in the mechanical properties of the scaffold, which can lead to fragility. In this study, the authors observed a \(\beta\)-TCP scaffold with large pores (450 \(\pm\) 50 \(\upmu\)m) and no seeded cells fractured during surgery due to fixation with a titanium plate. In contrast, all constructs seeded with BMSCs maintained their shape without fractures [35]. None of the studies included in this review mentioned the pore shape.
## 3 Mechanical Modeling
### Modeling for Torsional Stiffness  
As the hub rotates relative to the outer ring, the contact points between the arcs on both sides of the clamping groove and the leaf spring undergo continuous positional changes. In Figure 4, \(O_{2}\) signifies the rotation center of the damper hub; \(O_{3}\) denotes the position of \(O_{1}\) after the torsion angle \(\theta\).  
The coordinates of point \(O_{1}\) are (\(O_{1x}\), \(O_{1y}\)), and the coordinates of point \(O_{3}\) are (\(O_{3x}\), \(O_{3y}\)), where:  
Figure 3: State of Euler-Bernoulli beam pre- and post-deformation.  
\[\left\{\begin{array}{l}O_{1x}=R-R_{1}\cos\alpha\\ O_{1y}=-R_{1}\sin\alpha\\ O_{3x}=R-R_{1}\cos(\alpha-\theta)\\ O_{3y}=-R_{1}\cos(\alpha-\theta)\end{array}\right. \tag{21}\]  
From Equations (9) and (19), it is easy to know that the coordinate of contact point \(N\) is \((L\,,\epsilon_{12}(L))\), where \(\epsilon_{12}(L)=w_{12}(L)-k\mathrm{x}\). It can be seen from the structure of the damper that there is the following positional relation during the rotation process: (1) the length of \(O_{1}^{\rightarrow}N\) is always the arc radius \(r\); (2) the tangent of the contact point \(N\) is always perpendicular to \(O_{1}^{\rightarrow}N\). Therefore, the following equations can be obtained:  
\[\left\{\begin{array}{l}(L-R+R_{1}\cos(\alpha-\theta))^{2}+(\epsilon_{12}(L) +R_{1}\sin(\alpha-\theta))^{2}-r^{2}=0\\ \delta_{12}(L)\ (\epsilon_{12}(L)+R_{1}\sin(\alpha-\theta))+L-R+R_{1}\cos( \alpha-\theta)=0\end{array}\right. \tag{22}\]  
where \(\delta_{12}(L)=\varphi_{12}(L)-k\). Based on the deformation compatibility conditions of two leaf springs at the \(T_{2}\) point:  
\[w_{11}(T_{2})-w_{21}(T_{2})=0 \tag{23}\]  
Simultaneously, Equations (22) and (23) are introduced into the specific damper parameters, and the numerical solution of relevant parameters is obtained by solving the nonlinear equations with mathematical tools. Then, the total hub torque \(M_{Z}\) of the damper is:  
\[M_{Z}=nF(R-L) \tag{24}\]  
where \(n\) is the number of circumferential leaf spring groups. The torsion angle of the hub is \(\theta\), so the torsional stiffness of the damper is:  
\[K=\frac{M_{Z}}{\theta}=\frac{nF(R-L)}{\theta} \tag{25}\]