esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Model Parameter Identification  
#### 3.2.1 Identification of Vehicle Attributes and Road Properties Parameters  
1. Synchronous adhesion coefficient (\(\varphi_{0}\))  
When \(\varphi<\varphi_{0}\), the front axle locks up before the rear axle, resulting in a stable state. Therefore, in earlier vehicle designs, a larger synchronization adhesion coefficient (such as 0.8) was commonly used. However, an excessively large synchronization adhesion coefficient reduces the braking efficiency of the rear axle. This issue was only resolved after the invention of ABS. For vehicles equipped with ABS, \(\varphi_{0}\) is generally between 0.4 and 0.5 [22]. In this study, the coefficient \(\varphi_{0}\) is set at 0.4.
2. Maximum longitudinal friction supply (\(\mu_{xmax,supply}\))  
The maximum longitudinal friction supply is associated with pavement performance, tire types, and the complex interaction of them. The study showed the maximum braking deceleration rates (\(a_{xmax}\)) available for passenger cars was about 0.8 g, that for buses was about 0.7 g, and that for trucks was about 0.6 g under good pavement conditions (e.g., dry asphalt concrete pavement) [49]. The relationship between the maximum braking deceleration rates and the maximum longitudinal friction factor is given by  
\[\mu_{xmax,supply}=a_{xmax}/g \tag{53}\]  
Substituting \(a_{xmax}=0.6\) g into Equation (53), the maximum longitudinal friction supply is calculated to obtain \(\mu_{xmax,supply}=0.6\).  
The maximum longitudinal friction supply factors under different water film thickness conditions were proposed by [PERSON] et al. [40] as follows:  
1. Water film thickness: 0.5 mm, \(\mu_{xmax,supply}=0.5\) (\(\mu_{ymax,supply}=0.25\));
2. Water film thickness: 1 mm, \(\mu_{xmax,supply}=0.44\) (\(\mu_{ymax,supply}=0.22\));
3. Water film thickness: 2.5 mm, \(\mu_{xmax,supply}=0.34\) (\(\mu_{ymax,supply}=0.17\)). To sum up, the maximum longitudinal friction supply is given by \[\mu_{xmax,supply}=\left\{\begin{array}{cc}0.6,&WFT=0\\ 0.5,&WFT=0.5 mm\\ 0.44,&WFT=1 mm\\ 0.34,&WFT=2.5 mm\end{array}\right.\]
3. Maximum side friction supply (\(\mu_{ymax,supply}\)) The maximum side and longitudinal friction supply factors were proposed by [PERSON] et al. [50] as follows: \[\mu_{xmax,supply}=0.59-4.85\times 10^{-3}\times V_{DS}+1.51\times 10^{-5}V_{DS}^{2}\] (54) \[\mu_{ymax,supply}=0.27-2.19\times 10^{-3}\times V_{DS}+5.79\times 10^{-6}\ V_{DS}^{2}\] (55) where \(V_{DS}\) is the design speed, m/s\({}^{2}\).  
In this study, the design speeds (\(V_{DS}\)) are 120 km/h, 100 km/h, 80 km/h, and 60 km/h, respectively. One obtains \(\mu_{ymax,\ supply}=0.5\mu_{xmax,supply}\).
## 2 Preliminary Design of Prototypes  
Before developing and modeling the harvesters with ITDs, the validity of the basic concept, i.e., the addition of a cantilever DVA to a piezoelectric harvester, was assessed by means of prototypes built with a simple technology. The starting point was a Piezo Protection Advantage PPA 1001 harvester built by MIDE (Medford, MA, USA). It is a general-purpose unimorph harvester having a rectangular shape (length 41.1 mm, width 20.8 mm), with a PZT 5H piezoelectric layer and a stainless-steel structure.  
Figure 1 shows the cantilever DVAs used in the prototypes. The cantilever beams were made using harmonic steel wire (radius \(r\) = 0.15 mm, density \(\rho\) = 7850 kg/m\({}^{3}\)); this solution allowed to obtain a structure with low damping. The mass elements were small brass discs, whose masses were measured by means of a balance having a resolution of 0.1 mg.  
The cantilever DVAs were connected to the free end of the harvester with a removable connection using wax for the accelerometers. In some tests the cantilever DVA was joined to the harvester using epoxy adhesive. No difference was found between the results obtained with the two joining techniques.  
A lumped-element approach was adopted for trimming the natural frequency (\(f_{t}\)) of the cantilever DVA (having stiffness \(k_{a}\) and equivalent mass \(m_{eq}\)) to first natural frequency of the PPA 1001 harvester:  
\[\omega_{t}=2\pi f_{t}=\sqrt{\frac{k_{a}}{m_{eq}}} \tag{1}\]  
Stiffness \(k_{a}\) depends on cantilever length \(L_{a}\), Young modulus \(E\) and moment of inertia \(I\) of the wire cross-section:  
\[k_{a}=\frac{3 EI}{{L_{a}}^{3}} \tag{2}\]  
Equivalent mass takes into account the lumped tip mass \(m_{a}\) and a fraction of the mass of the wire (having density \(\rho\)), making use of the Rayleigh method [23]:  
\[m_{eq}=m_{a}+\frac{33}{140}\rho\pi r^{2}L_{a} \tag{3}\]  
Equations (1)-(3) show that it is possible to trim a cantilever DVA with assigned tip mass \(m_{a}\) and wire radius \(r\) by varying the length (\(L_{a}\)) of the cantilever beam. The theory of DVA [23] shows that an increment in the tip mass (\(m_{a}\)) widens the frequency interval between the resonance peaks that substitute for the original resonance peak. Therefore, three different cantilever DVA trimmed to the same frequency (\(f_{t}\) = 125 Hz) but with different tip masses were developed.
## 3 Method: High-Level Overview  
A schematic view of the proposed framework is given in Figure 3. Given a set of vertices, \(\mathcal{V}\subseteq\mathbb{R}^{N\times 3}\), a set of edges \(\mathcal{E}\subseteq V^{2}\), and a set of faces \(\mathcal{F}=\{(k,l,m)\ |\ k,l,m\in\mathcal{V}\wedge(k,l),(l,m),(m,k)\in \mathcal{E}\}\), input mesh is defined as \(\mathcal{M}=(\mathcal{V},\mathcal{E},\mathcal{F})\). There are no assumptions about \(\mathcal{M}\) concerning manifoldness, closeness, resolution, or orientation. It is expected to be a geometric object representing a single human dentition (either a maxilla or mandible). The objective of our study is to model the function \(g\subseteq\mathcal{F}\times\mathcal{C}\), where \(\mathcal{C}\) is a set of classes such that \(\mathcal{C}=\{L1,\ L2,\ \dots,\ L8,\ R1,\ R2,\ \dots,\ R8,\ G\}\) and \(|\mathcal{C}|=17\), representing sixteen classes for individual teeth regions encoded as \(L\)(left) or \(R\)(right) quadrant followed by a number starting from the central incisor to the third molar (one to eight), and one class \(G\) that represents gingiva. Intuitively, function \(g\) assigns each face from \(\mathcal{M}\) a specific class representing 1 of 16 teeth or gingiva.  
Figure 3: Outline of our proposed projection-based tooth segmentation method. \({}^{\dagger}\)As the detector, we employ the multi-view landmark detection method presented in [16]. We utilize the detected teeth position information for transforming the input mesh to the canonical form using the ICP algorithm (occlusion alignment), localizing the segmentation over individual teeth (next-view sampler), and conditioning the training and inference via input tooth type signal (\(i_{tt}\) scalar). Generated 2D segmentation maps from \(n\) viewpoints are correlated by recurrent network, unprojected to mesh cells, and post-processed to suppress any region ambiguities (region voting) and isolated triangles (CCA).  
Figure 2: Randomly sampled cases from 3_DTetHSeg2_[18] dataset (**upper row**) and Poseidon3D, the dataset presented in this work (**lower row**). Focus your attention on the highlighted regions marked by arrows in the figure. These areas represent natural anatomical challenges (teeth crowding, missing, damaged teeth, retention, diastema, and others) relentlessly present in orthodontic cases. In 3 DTeethSeg22, such features also appear but not as commonly as in our dataset.  
Note that mapping \(g\) is not surjective since, in most cases, the scanned dental arch is incomplete, i.e., teeth are missing. To model \(g\), we employ a detect-and-segment approach.
# Coastal Wind Measurements Using a Single Scanning LiDAR
## 3 Results
### Data Availability and Accuracy Indicators
#### 3.3.2 Goodness of Fit for the Curve Fitting  
As well as the CNR value, the quality of the curve fitting is an indicator for investigating the accuracy of the retrieved velocities with the VVP method. For this examination, we analyzed the goodness of fit for the curve fitting performed for the VVP method. Figure 17 shows the instantaneous value of the radial wind speed and fitted curve for two illustrative cases. In one, the determination coefficient was relatively high (0.992), while in another it was relatively low (0.285). Cases in which the determination coefficient is close to 1.0 are compatible with the assumption used in the VVP method--that the wind field is homogeneous in time and space--whereas cases in which the coefficient value is low are not.  
Similar to the analysis of the relationship between the CNR values and the accuracy of the wind speed measurement (cf. Figure 14), the relationship between the RAE and the coefficient of determination (\(R^{2}\)) for Case 8 was investigated and the results are presented in Figure 18. The red circles with error bars in Figure 18 indicate the bin-averaged values and their standard deviation. As can be seen here, the RAE values increase rapidly when the value of \(R^{2}\) falls below 0.2. This result indicates that if the quality of the curve fitting decreases, the accuracy of the measurements also decreases. In other words, the determination coefficient derived from the curve-fitting process serves as an indicator for estimating the quality of the horizontal wind speed measurements using a single scanning LiDAR with the VVP method.  
Figure 17: Instantaneous radial wind speeds as a function of azimuth angles and the corresponding fits for the cases of (**a**) high and (**b**) low determination coefficients.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
###### Abstract  
In this work, we exploit a multi-antenna cooperative jammer to enable secure short packet communications in Internet of Things (IoT) networks. Specifically, we propose three jamming schemes to combat eavesdropping, i.e., the zero forcing beamforming (ZFB) scheme, null-space artificial noise (NAN) scheme, and transmit antenna selection (TAS) scheme. Assuming Rayleigh fading, we derive new closed-form approximations for the secrecy throughput with finite blocklength coding. To gain further insights, we also analyze the asymptotic performance of the secrecy throughput in the case of infinite blocklength. Furthermore, we investigate the optimization problem in terms of maximizing the secrecy throughput with the latency and reliability constraints to determine the optimal blocklength. Simulation results validate the accuracy of the approximations and evaluate the impact of key parameters such as the jamming power and the number of antennas at the jammer on the secrecy throughput.  
short-packet communications; physical layer security; multi-antenna jammer; secrecy throughput +
Footnote †: journal: Future Internet  
**Citation:**: [PERSON]; [PERSON]; [PERSON], [PERSON].;  
[PERSON]; Mull-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks.  
_Future Internet_**2023**, 15, 320.  
[[https://doi.org/10.3390/615100320](https://doi.org/10.3390/615100320)]([https://doi.org/10.3390/615100320](https://doi.org/10.3390/615100320))  
Academic Editor: [PERSON]  
Received: 28 August 2023  
Revised: 19 September 2023  
Accepted: 25 September 2023  
Published: 26 September 2023
# Unbiased Isotonic Regression Tree for Discovering Hidden Heterogeneity in Monotonicity Constraints
## 3 The Proposed Approach
### Unbiased Split Variable Selection
Similarly to GUIDE, the proposed approach divides the observations at the sample quartiles into four groups, resulting in a \(2\times 4\) contingency table. This contigency table allows for the computation of the \(\chi^{2}\) test statistic in Equation (1), which theoretically has \(\chi^{2}\) distribution with three degrees of freedom under the null hypothesis. * _Test of interaction effect:_ To improve variable selection performance, interactions between covariates can be taken into account [28]. Three types of interactions of split variables can be considered: the pair of two continuous covariates, continuous and categorical covariates, and two categorical covariates. For the pair of two continuous covariates \((Z_{a},\ Z_{b})\), the covariate \((Z_{a},\ Z_{b})\) is partitioned into four quadrants by halving each covariate's range at its sample median. This segmentation allows for a \(2\times 4\) contingency table to be constructed, with residual signs as rows and quadrants as columns. For each pair of categorical variables, URIT partitions the sample space using its respective value combinations. As an example, if \(Z_{a}\) has \(m_{a}\) values and \(Z_{b}\) has \(m_{b}\) values, the chi-square statistic and \(p\)-value are calculated using a table with two rows and \(m_{a}\times m_{b}\) columns, excluding any columns that have zero totals. Lastly, for each variable pair \((Z_{a},\ Z_{b})\) where \(Z_{a}\) is numerical and \(Z_{b}\) is categorical, UIRT splits \(Z_{a}\) at the sample median and partitions \(Z_{b}\) by its categories. If \(Z_{b}\) has \(m_{b}\) categories, this results in \(2\times m_{b}\) columns in a contingency table. In cases where an interaction has the smallest \(p\)-value in the chi-square test, the covariate in the interaction with the smaller \(p\)-value from a single covariate test is chosen. It is worth noting how UIRT can improve model performance by addressing selection bias through unbiased split variable selection. Selection bias occurs when variables are chosen for splits not because they are associated with the target response but due to properties having many potential cutpoints. Variables with numerous cutpoints are more likely to be selected even if they have no true relationship with a response variable. This leads to misleading splits and reduces the model's ability to generalize. UIRT eliminates this bias by focusing on the statistical dependency between residuals and split variables. In a well-fitting model, residuals should be random around zero and unrelated to any covariate. When residuals depend on a specific variable, they are no longer randomly distributed, and this indicates that the model can be improved by splitting that variable. UIRT uses a chi-square test to statistically identify significant dependencies, ensuring that splits are driven by meaningful associations. This approach removes selection bias and, thus, produces more statistically rigorous splits.
## 4 Architecture and Implementation
### AR User Interface
For instance, a semitransparent colored region can be superimposed over each connection. The color code can follow the same pattern as the general GUI, applied at the connection rather than the overall node level. For example, in Figure 12, there are connections in (1,1), (3,1), (2,2), (3,2) and (4,2), but only the connections in (1,1) and (3,2) exhibit anomalies. In addition to providing an adequate reflection of individual connection status, the grid division also allows the creation of buttons to request information on and interact with a given connection. When the maintenance worker clicks on one of the cells with a connection, the most detailed view will be displayed (Figure 13). This interface offers the following options:  
* Status of the target connection: only the cell of the selected connection will be enabled following the same color code as in the other modes of the GUI. * Power consumption of the connection: the bottom-left corner of the screen shows the power consumption of the connection in real time. * The bottom-right corner of the interface displays two buttons to open and close the target connection. If the connection is closed, the \"close\" button will be disabled (as in Figure 13), and if it is open, the \"open\" button will be disabled. Clicking the enabled button will switch the connection on or off. This is the only option in which the terminal can change the state of the node. Figure 12: Design for a more detailed user interface.
## 3 Numerical Simulation
### Model Validation  
Figure 5a shows the PMIFJ under the conditions of stoichiometric ratio, \(Re=1000\), and \(H/d=2\). The simulated OH radical contour and the actual flame image are on the left and right, respectively. The height of the premixed cone (\(H_{\text{C}}\)) has been used as a validation index because it is a function of laminar flame speed and plays a key role in the PMIFJ's heat transfer. The contour of the OH mass fraction can be used to identify the reacting region and has been used to determine the height of the premixed cone [48]. Therefore, the \(H_{\text{C}}\) of PMIFJ at different Reynolds numbers are compared between the numerical simulations and the experimental results, as shown in Figure 5b. There is little difference between the simulation and experimental results. This study mainly focuses on the heat transfer behavior of the PMIFJ, so it also further verifies the total heat transfer rate of the impingement plate, as shown in Figure 6. The total heat transfer rate increases monotonically with \(Re\) raises. It can be found that the experimentally measured total heat transfer rate is low, which may be attributed to the local heat loss at the measurement location of the actual heat exchanger. Another reason is that condensed water droplets adhered to the surface of the heat exchanger during the experiment, and these water droplets absorb the heat generated by the PMIFJ. In summary, the flame behavior and heat transfer trend of the PMIFJ can be predicted using the CFD method and the GRI-Mech 3.0 mechanism.  
Figure 4: Local heat flux distribution on the impingement plate with different mesh numbers at the condition of \(\phi=\)1, \(Re=1000\), \(H/d=2\).  
Figure 5: _Cont._
## 3 Improved CKF Localization Algorithm Based on Volume Criterion  
In UWB indoor localization, the position of the phase centroid of the localization tag and the velocity information are used as state vectors for filtering, as follows [20]:  
\[\mathbf{x}_{k}=\mathbf{F}_{k,k-1}\mathbf{x}_{k-1}+\mathbf{\omega}_{k} \tag{9}\]  
where \(\mathbf{x}=\left[p_{x},p_{y},v_{x},v_{y}\right]\) represents the state vector. \(\left(P_{x},P_{y}\right)\) represents the planar position coordinates of the phase center point of the positioning tag. \(\mathbf{v}_{x},v_{y}\) represents the planar velocity of the phase center point of the positioning tag. \(\mathbf{\omega}_{k}\) represents the noise vector at time \(k\), and its corresponding covariance matrix is \(\mathbf{Q}_{k}\).  
\[\mathbf{F}_{k,k-1}\text{ represents the state transfer matrix at time $k$, as follows: }\mathbf{F}_{k,k-1}=\begin{bmatrix}1&0&\Delta T&0\\ 0&1&0&\Delta T\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix}.\]  
The measurement model of the system is as follows (10):  
\[\mathbf{Z}_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+\mathbf{v}_{k} \tag{10}\]  
where \(\mathbf{Z}_{k}\) represents the measured distance from the UWB positioning tag to the \(i\)th base station at time \(k\), where \(i=1,2,\ldots,\mathrm{N}\). \(\mathrm{N}\) represents the number of base stations. \(d_{i,k}\) represents the true distance from the positioning tag to the \(i\)th base station at time \(k\). \(\mathbf{v}_{k}\) represents the observation noise vector of the positioning tag at time \(k\), and its corresponding covariance matrix is \(\mathbf{R}_{k}\).  
\[\mathbf{H}_{k}\text{ represents the observation matrix at time $k$, where }\mathbf{H}_{k}=\begin{bmatrix} \frac{dd_{i,1}}{\partial p_{x,1}}&\frac{\partial d_{i,1}}{\partial p_{x,1}}& 0&0\\ \frac{dd_{i,2}}{\partial p_{x,2}}&\frac{dd_{i,2}}{\partial p_{x,2}}&0&0\\ \vdots&\vdots&\vdots&\vdots\\ \frac{dd_{i,k}}{\partial p_{x,k}}&\frac{dd_{i,k}}{\partial p_{x,k}}&0&0\end{bmatrix}.\]  
UWB ranging still has some outliers under non-line-of-sight error conditions, which seriously affects the accuracy and stability of the filtering results [21]. To solve this problem, this study combines the SRD model based on the TDOA algorithm with the CKF algorithm, utilizes the SVD decomposition method to solve the problem of the estimated covariance matrix being unable to meet the positive qualitative requirements in real time, and uses the Sigmoid adaptive function to update the measurement noise matrix \(\mathbf{R}\) in real time. They reduce the problem of inaccuracy of the model estimation affecting the filtered results under non-Gaussian noise, which allows for higher accuracy.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Rotation Angle of Double Gratings  
Since the reflector of the imaging spectrometer of the E-F structure is a collimating lens and a focusing lens at the same time, the focal length of the reflector, the distance from the slit to the mirror, and the distance from the mirror to the sensor are all \(s\). The grating is the aperture stop of the system. The distance from the slit to the center of the grating is \(h\). The two imaging bands are (\(\lambda_{1}\),\(\lambda_{2}\)) and (\(\lambda_{3}\),\(\lambda_{4}\)). In the actual design, in order to  
Figure 8: Transmission of FB390-10.  
Figure 7: Image 1 and image 2 adopt \(-1\) order and \(+1\) order respectively, which can prevent the influence of high-order spectrum.  
separate the two spectrums, the range of the two bands could be slightly expanded. It has been discussed above that using the +1-order and -1-order spectra of the two gratings respectively helps to eliminate the influence of high-order diffraction. Therefore, it can be assumed that (\(\lambda_{1}\),\(\lambda_{2}\)) comes from the +1 order spectrum, and (\(\lambda_{3}\),\(\lambda_{4}\)) comes from the \(-1\) order spectrum. By rotating the two gratings to image \(\lambda_{1}\) and \(\lambda_{3}\) at one point, the sensor plane can be utilized to the maximum, and the angle of the gratings can be derived from the grating equation.  
\(\alpha\) is the incident angle of the grating, \(\beta\) is the exit angle of the grating, and \(\gamma\) is the angle of grating rotation (rotation to the slit is positive), as shown in Equation (1), where the first equation comes from the grating equation, and the second equation comes from the geometric derivation.  
\[\begin{cases}sin(\alpha)\pm sin(\beta)=\frac{m\lambda}{d}\\ \\ \alpha+\gamma=arctan(\frac{h}{s})\end{cases} \tag{1}\]  
In Equation (1), \(m\), \(d\), \(\lambda\), \(h\), \(s\) are known numbers. The exit angle \(\beta\) can be obtained by \(\gamma\), or the rotation angle \(\gamma\) can be inversely deduced by \(\beta\).  
Since \(\lambda_{1}\) and \(\lambda_{3}\) need to be imaged at the same point, in order to eliminate coma, this point can be positioned at the symmetry of the slit. According to the geometric relationship, we have Equation (2).  
\[\beta-\gamma=arctan(\frac{h}{s}) \tag{2}\]  
Through Equations (1) and (2), the grating rotation angle \(\gamma\) can be obtained by bringing in \(\lambda_{1}\) and \(\lambda_{3}\), respectively.  
\[\gamma_{1}=arctan(\frac{h}{s})-arcsin(\frac{\lambda_{1}}{2d_{1}}) \tag{3}\]  
\[\gamma_{2}=arctan(\frac{h}{s})-arcsin(-\frac{\lambda_{3}}{2d_{2}}) \tag{4}\]
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 2 Preliminaries
### Distance Metrics
Consider, for example, the data transmission scheme sketched in Figure 3. In this example, a phase shift keying (PSK) modulation scheme is used to transmit data over a complex additive white Gaussian noise (AWGN) channel. The transmission of the complex symbol \(x=x^{\text{re}}+jx^{\text{im}}\) yields the channel observation \(y=y^{\text{re}}+jy^{\text{im}}\) at the receiving end. Obviously, the system fed with the samples \(\mathbf{y}=[y^{\text{re}},y^{\text{im}}]\) in vector notation should preserve information on the transmitted modulation symbol \(x\) in this example. Considering outputs \(t\in\mathcal{T}\) to be from a discrete set of integers \(\mathcal{T}=\{0,1,\ldots,2^{q}-1\}\), the system conducts a \(q\) bit quantization of the continuous received samples \(y\) with a minimum loss of relevant information on the transmitted modulation symbol \(x\). In addition, each \(t\in\mathcal{T}\) implies a conditional probability distribution \(p(x|t)\). Therefore, the considered system can also be used straightforwardly for demodulation of the transmitted symbol \(x\). The considered system will be investigated further in Section 4.