esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Mode  
Consider the transmission system depicted in Figure 2. The bit sequence \(\mathbf{a}\) is encoded to generate the coded sequence \(\mathbf{c}\). After interleaving, every four bits from \(\mathbf{d}\) are finally mapped into a 16-bit chip sequence \(\mathbf{S}\). Here, the modulation scheme is MPSK, and the Rayleigh fading channel is considered. Specifically, within \(N\) symbol intervals, the complex baseband received chip sequence \(\mathbf{R}\) can be given as follows:  
\[\mathbf{R}=(\mathbf{r}_{1},\ \mathbf{r}_{2},\ \cdots,\ \mathbf{r}_{N}), \tag{1}\]  
where \(\mathbf{r}_{k}=(r_{k,1},\ r_{k,2},\ \cdots,\ r_{k,16})\) and \(1\leq k\leq N\). Here, \(r_{k,i}=\rho_{k,i}s_{y,i}e^{i\theta_{k,i}}+\eta_{k,i}\) and \(1\leq i\leq 16\). \(\{s_{y,i},1\leq i\leq 16\}\) represents the spreading sequence \(\mathbf{s}_{y}\), which are selected from \(\Lambda=\{\mathbf{s}_{y},1\leq y\leq 16\}\) as depicted in Table 1. \(\rho_{k,i}\) and \(\theta_{k,i}\) denote the fading amplitude and phase, respectively. \(\eta_{k,i}\) is a discrete, cyclic symmetric, complex Gaussian random process with zero mean and variance \(\sigma^{2}\).  
We assume that the fading amplitude \(\rho_{k,i}\) and phase \(\theta_{k,i}\) are unknown and random at the receiver, but are constant for some specified time period [19]. In other words, we make a piecewise constant approximation to these parameters, which is \(\rho_{k,i}=\rho\), and \(\theta_{k,i}=\theta\). In addition, \(\rho\) follows the Rayleigh distribution, and \(\theta\) is uniformly distributed in the interval \((-\pi,\pi)\).  
Figure 2: Traditional optimal noncoherent detection of coded MPSK in WSNs. \(\rho_{k,i}\) is the fading amplitude, \(\theta_{k,i}\) is the fading phase, and \(\eta_{k,i}\) denotes the complex AWGN.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## Proposed Method  
Human visual system (HVS) is sensitive to the intensity changes, therefore measuring the relationship between intensity values of different points in the receptive fields could get information indicating the properties of the images [26]. Moreover, the size of the receptive field determines the information extracting from the image: larger receptive field size obtains coarser information of the image, while smaller size may achieve finer details of the image [27]. To simulate the HVS and obtain more descriptive features from the distorted images, we introduce a multi-window method to generate multiple feature maps to represent receptive fields of different sizes. Specifically, we normalize the input image by a local nonlinear transformation following the method mentioned in [15], and multiple Gaussian filters of different window sizes are applied in the normalization procedure in our method.  
For a given image, the NFM is computed by  
\[T_{i}(m,n)=\frac{I(m,n)-\mu_{i}(m,n)}{\sigma_{i}(m,n)} \tag{1}\]  
where \(I(m,n)\) is the gray-scale image; \(m\in{1,2,\ldots,M}\) and \(n\in{1,2,\ldots,N}\) are the pixels indices; \(M\) and \(N\) are the image height and width, respectively; \(i\in{1,2,3}\) is the Gaussian function index; and \(\mu_{i}(m,n)\) and \(\sigma_{i}(m,n)\) are the local mean map and the local standard deviation map, which are defined as  
\[\mu_{i}(m,n)=\sum_{k=-K_{i}}^{K_{i}}\sum_{l=-L_{i}}^{L_{i}}\omega_{i}(k,I)I(m+ k,n+l) \tag{2}\]  
\[\sigma_{i}(m,n)=\sqrt{\sum_{k=-K_{i}}^{K_{i}}\sum_{l=-L_{i}}^{L_{i}}(\omega_{i }(k,I)I(m+k,n+l)-\mu_{i}(m,n))^{2}} \tag{3}\]  
where \(\omega_{i}(k,l)\) is the 2D circularly-symmetric Gaussian weighting function whose kernel size is \((2K^{i}\ +\ 1)\times(2L^{i}+1)\).  
To extract discriminative features from the distorted image, we generate three NFMs by using three different Gaussian filters in normalization procedure with the Gaussian window size increasing from small to large. Specifically, the Gaussian filter size parameters \(K\) and \(L\) are set to 2, 4 and 6, and the standard deviations of the Gaussian filters follow the three standard deviations rule. As a result, three different NFMs ( named as \(T_{1}\), \(T_{2}\), and \(T_{3}\)) for a given image can be obtained.
## 5 Computational Experiments
### Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Average** & **Max** & **Average** & **Max** & **Average** & **Max** & **Solved** \\  & **Gap (\%)** & **Gap (\%)** & **Time (s)** & **Time (s)** & **Nodes** & **Nodes** & \\ \hline  
31.82 & 41.67 & 75.75* & 3600.51 & 31,208 & 92,674 & 3 \\ \hline \hline \end{tabular}  
* indicates that the corresponding value refers just to the instances in a dataset that have been solved within 1h computing time.  
\end{table} 
Table 2: Computational performances of the branch-and-price approach for Formulation 4 on supplementary instances of the MCSP. language such as Mosel. In order to obtain better insights about the computational limits of current implementation, we considered 36 supplementary datasets of the MCSP, each containing 10 random instances numbered from 0 to 9 and characterized by having a fixed number of rows and columns for the matrix \(A\). Specifically, we considered a number of rows \(m\) ranging in \(\{4,\ldots,9\}\) and a number of columns \(n\) ranging in \(\{5,\ldots,10\}\). Fixed the values of \(m\) and \(n\), a generic instance in a dataset is generated by creating a \(m\times n\) nonnegative integer matrix \(A\) whose generic entries are random integers in the discrete interval \(\{1,\ldots,H\}\), where \(H\) is randomly generated in the set \(\{3,\ldots,7\}\). A generic dataset is identified by the dimension of the intensity matrices encoded in its instances. For example, dataset \(4\times 5\) includes instances of the MCSP encoding intensity matrices having dimension \(4\times 5\). We used the Mersenne Twister libraries [26; 27] as pseudorandom integer generator and we obtained, at the end of the generation process, 360 instances of the MCSP downloadable from [24]. Table 2 shows the computational performances obtained on the datasets so generated. As a general trend, we have observed that the performances of the branch-and-price approach for Formulation 4 decrease in function of both the size of the input matrix and the value of \(H\), by reaching a maximum of 10 solved instances within 1h computing time for matrices having dimension \(4\times 5\) and a minimum of 3 solved instances for matrices having dimension \(9\times 10\). In current implementation, high values for \(H\) directly influences the size of each partition network as the number of edges in A partitions grow pseudo-polynomially in function of \(H\). Investigating possible approaches to overcome current limitations and improve the overall performances warrants additional research effort.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
### Practical Implementation, Attacks, and Decoy States
#### 1.3.3 E91  
E91 is an entanglement-based QKD protocol introduced by [PERSON] in 1991. The protocol is similar to BB84, except E91 utilizes a source that emits pairs of spin-\(\frac{1}{2}\) particles in a singlet state in order to generate a secret cryptographic key [31]. Because the particle pairs are entangled with one another, measuring one of the particles will instantaneously affect the other particle regardless of distance. Before measurement, the particles will be in a superposition of spin up and spin down states. If [PERSON] and [PERSON] measure in the same basis, then their results will be anti-correlated (e.g., if [PERSON] measures spin up, then [PERSON]'s particle must be spin down, and vice versa) [31]. They can use similar steps in the BB84 protocol to generate matching cryptographic keys by randomly selecting bases, communicating bases, and so on, as described in BB84. However, the main difference in E91 is that entangled particles are used. To check for eavesdroppers, [PERSON] and [PERSON] use the Bell Inequality, which can be used to indicate whether the source or transmission has been intercepted or compromised [11; 31]. A major advantage of E91 is not being limited to physical distance, as in BB84, making E91 more ideal in applications such as satellite communications. A number of entanglement-based QKD protocols exist and offer an alternative to the previously mentioned QKD methods.
# A Stackelberg Game Approach to Model Reference Adaptive Control for Spacecraft Pursuit-Evasion
## 3 Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
### Stackelberg Equilibrium-Based Model Reference Adaptive Control Algorithm
 \tag{33}\]  
By selecting sufficiently large \(\mathbf{Q}\) and appropriate learning rates \(\mathbf{\Gamma}\), we ensure:  
\[\Delta\mathbf{V}_{t}\leq-c_{1}\|\mathbf{e}_{t}\|^{2}+c_{2}(\|\mathbf{\tilde{L}}_{p,t}\|^{2}+ \|\mathbf{\tilde{L}}_{e,t}\|^{2}) \tag{34}\]  
Defining the estimation error  
\[\mathbf{\epsilon}_{t}^{m}\overset{\Delta}{=}(\mathbf{\gamma}_{e}^{\mathrm{T}}\mathbf{\gamma} _{e})^{-1}\mathbf{\gamma}_{e}^{\mathrm{T}}(\mathbf{\tilde{f}}_{t+1}^{m}-\mathbf{\tilde{f}}_{t +1}) \tag{35}\]  
then (35) can be written as \[\epsilon_{t}^{m}=(\hat{\mathbf{L}}_{e,m}-\mathbf{L}_{e}^{*})\mathbf{\tilde{f}}_{t} \tag{36}\]  
According to (36), the design \(\hat{\mathbf{L}}_{e,m}\) of the adaptive update rule is  
\[\hat{\mathbf{L}}_{e,m+1}=\hat{\mathbf{L}}_{e,m}-\text{fal}(\epsilon_{t}^{m})\mathbf{\tilde{f} }_{t}^{T}\Gamma(M+\mathbf{\tilde{f}}_{t}^{T}\mathbf{\tilde{f}}_{t})^{-1} \tag{37}\]  
where \(M\) denotes a constant value, while \(\Gamma\) represents a matrix that is positively definite, denoting a nonlinear function \(fal(\cdot)\)  
\[fal(x)=|x|^{\tau}\cdot sign(x)\cdot\theta(|x|-\delta)+\frac{x}{\delta^{1-\tau} }\cdot(1-\theta(|x|-\delta)) \tag{38}\]  
where \(x\in\mathbb{R},\delta,\tau\in(0,1]\) is an adjustable parameter, \(\theta\) denotes the unit step function, and \(|x|-\delta\) is equal to \(1\) when \(|x|-\delta\) is greater than \(0\), and \(0\) otherwise. When \(\left\|\hat{\mathbf{L}}_{e,m+1}-\hat{\mathbf{L}}_{e,m}\right\|\leq\kappa\|\hat{\mathbf{L}} _{e,m}-\hat{\mathbf{L}}_{e,m-1}\|\) is satisfied, the controller sets \(\hat{\mathbf{L}}_{e}^{*}\leftarrow\hat{\mathbf{L}}_{e,m+1}\), where \(0<\kappa\leq 1\) denotes the adjustable parameter. Equation (38) shows that \(\Delta V_{t}\) is negative as long as \(|e_{t}|^{2}>(c_{2}/c_{1})(|\hat{\mathbf{L}}_{p,t}|^{2}+|\hat{\mathbf{L}}_{e,t}|^{2})\). This does not guarantee asymptotic stability, but it does prove that the tracking error \(\epsilon_{t}\) and parameter estimation errors \(\hat{\mathbf{L}}_{p,t}\), \(\mathbf{L}_{e,t}\) are uniformly ultimately bounded, as stated in Theorem 1. With the additional condition of Persistent Excitation (PE) on the regressor \(\hat{f}_{t}\), it can be further shown that the parameter errors converge, which in turn leads to the exponential convergence of the tracking error \(\mathbf{e}_{t}\) to a small neighborhood of the origin. When the persistent excitation condition (Equation (17)) is satisfied, the parameter error \(\left\|\tilde{\mathbf{L}}_{e,t}\right\|\) converges at an exponential rate, which in turn ensures that the tracking error \(\epsilon_{t}\) converges at an exponential rate. To consolidate the proposed method, Algorithm 1 provides a step-by-step procedure for implementation. The overall approach consists of an offline stage to compute the reference Stackelberg strategies and an online adaptive stage where the pursuer refines its strategy.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
###### Abstract  
In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user's motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worm location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off.  
PDR; INS; GNSS; pedestrian navigation; sensor fusion Article  
A Real-Time GNSS/PDR Navigation System for Mobile Devices  
1567[[https://doi.org/10.390/s13081567](https://doi.org/10.390/s13081567)]([https://doi.org/10.390/s13081567](https://doi.org/10.390/s13081567))  
12 March 2021  
15 April 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**C copyright:** © 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Citation:**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-Time GNSS/PDR Navigation System for Mobile Devices. _Remote Sens._**2021**, _13_, 1567. [[https://doi.org/10.390/s13081567](https://doi.org/10.390/s13081567)]([https://doi.org/10.390/s13081567](https://doi.org/10.390/s13081567))
## 5 Performance Evaluation
### Packet Delivery Ratio  
Figure 5 represents the comparison of selected schemes for data delivery ratio and the effect of depth level, the number of sinks, and the mobility of nodes on the data delivery ratio. We can see in Figure 4(a),d, the increasing node mobility and the depth level decrease the data delivery ratio. While, with the increase in the number of sinks packet delivery ratio also increases as clearly shown in Figure 4(c). The reason is that, as the node mobility increases it increases the disconnections between the nodes causing the packet loss. On the other hand, the depth level has not serious effects on the packet delivery ratio. Figure 4(b) depicts the comparison of the packet delivery ratio among RTC, H2-DAB [22], and AUV-PN [24] schemes. The packet delivery ratio of the proposed scheme is greater than the both H2-DAB and AUV-PN. Because in RTC the less numbers of layers are involved in forwarding the data to the surface sink. Furthermore, if a FN with a data packet drifted into the neighboring sub-region due to water currents, it can easily convey its packet according to the TDMA schedule. However, a FN moved into the neighboring region will request the associated CH for forwarding the data. Then the new CH will assign a time slot and for the next round this FN will be consider as the SMNs of the new region. The H2-DAB has the lesser delivery ratio, as it  
Figure 4: Performance of RTC by considering the average end-to-end delay.  
delivers data towards the sink through many layers. As the more numbers of layers are involved, the probability of dropping the data packets is also increased. However, the delivery ratio of AUV-PN is lesser as compared to the RTC. Because AUV-PN has not defined any clear strategy to deal with the disconnection due to the water current movements.
# Feasibility of a 3D Surgical Guide Technique for
## 1 Introduction  
Supernumerary teeth (STs) are developmental anomalies that can affect the eruption of adjacent permanent teeth [1]. Most STs are observed in the anterior region of the maxilla and approximately 75% of STs are impacted [2]. STs can cause various clinical complications, such as ectopic and delayed eruption of permanent teeth, displacement or rotation of the central incisors, and cystic degeneration [1, 3]. Thus, early extraction of STs in the mixed dentition is usually recommended prior to occurrence of ST-associated complications.  
As STs in the early mixed dentition are often located close to the adjacent permanent teeth, removal of the STs can involve complications [4, 5]. Moreover, due to the complex anatomy of the maxillaryanterior region, there is a possibility of damaging adjacent permanent tooth germs or removing the wrong tooth [6]. Extraction of impacted ST is often challenging for dental surgeons.  
For impacted ST extraction, it is essential to confirm 3-dimensional (3D) localization of the tooth. Precise localization of ST is a critical step during surgery because it can affect the operating time, predictability of the surgery, and the incidence of damage to adjacent structures [7; 8]. For this purpose, cone-beam computed tomography (CBCT) is widely used to provide location data of impacted STs. Minimally invasive and efficient surgery can be achieved with the aid of CBCT [9].  
Surgical guides fabricated with computer-aided design (CAD) and computer-aided manufacturing (CAM) technology have been shown to be effective in maxillofacial surgeries [10; 11]. Efforts have been made to facilitate impacted ST extraction employing CAD/CAM surgical guide surgery [6; 7; 12]. However, previous reports are limited to case studies and a lack of reports on the feasibility of surgical guide surgery with respect to impacted STs is noted, owing to the limitations in simulating the same conditions of impacted STs in clinical circumstances. Thus, we designed a 3D printed simulation model for impacted ST extraction. A 3D printed simulation model and 3D analysis can facilitate various measurements regarding the amount of window opening or bone removal, and duration of surgery. Therefore, this study aimed to evaluate the feasibility of the surgical guide technique for impacted ST extraction using three-dimensional printed simulation models.
# Routing in Mobile Wireless Sensor Networks: A Leader-Based Approach+
## 3 A Leader-Based Routing Approach (LBR)
### Data Routing Protocol  
Once an initial spanning-tree has been created, an application-data routing protocol is executed. In contrast to the spanning-tree creation, where we use broadcast as the communication primitive, for application data, we use one-to-one communication.  
We have considered two different protocols for sending application data. The first one, which we have adopted in combination with LBR1, consists in the immediate forwarding of the received content to the \(sinkBy_{i}\) node (Algorithm 4).  
Alternatively, content can be buffered while it is received and be periodically sent piggybacked in leader messages. To do so, the \(broadcast(LEADER,i,actualRound,sinkDistance_{i}+1)\) primitive must be redefined as \(broadcast(LEADER,i,actualRound,sinkDistance_{i}+1,msgList_{i})\). This approach (Algorithm 5) is the one we have chosen in combination with LBR2.  
```
1 Task a:when\(m=(j,\textsc{content},\ldots,msg)\)isreceivedattime\(t\)suchthat\(j\in N_{i}(t)\){When receivingacontentmessage}
2 if\(msgTo(msg)=i\)then
3 if\(i=s\)then
4\(deliver(msg)\);
5 else
6\(broadcast(msg)\) to\(sinkBy_{i}\);
7
8 end if
9
10 end if
11
12 end if
```  
**Algorithm 4**Direct forwarding strategy executed by node \(p_{i}\).  
```
1 Task b:when\((j,\textsc{x},\ldots,msgList)\)isreceivedattime\(t\)suchthat\(j\in N_{i}(t)\){Wherexisanykindofmessage.}
2 foreachmessage\(m\)inmsgListdo
3 if\(msgTo(m)=i\)then
4 if\(i=s\)then
5\(deliver(m)\);
6 else
7\(push\_send\_buffer(m)\);
8
9 end if
10
11 end if
12
13 end if
14
15 end if
16
17 end if
```  
**Algorithm 5**Piggybacking strategy executed by node \(p_{i}\).
## 5 Conclusions  
In this paper, three traditional classification tree models of 11 July, 16 August and 26 September were developed based on three spectral features and corresponding group-truth samples for mapping aquatic vegetation types in Taihu Lake. Overall accuracies of three models were 82%, 79% and 84%, respectively.  
Based on traditional classification tree model, we presented two approaches for modifying the CT thresholds to generate maps of aquatic vegetation types for image dates when no ground-truth samples would be available. By test and verification, we found that we could modify CT thresholds successfully with linear models derived from the ranked values of spectral features from two image dates to produce maps. In comparison, an approach based on an assumed directed linear relationship between spectral feature values calculated for pixels from two image dates did not provide a good foundation for modifying CT thresholds.
## 2 Object Detection
### Classification-Based Object Detectors (Two-Stage Detectors)
#### 2.3.6 Mask R-CNN  
[PERSON] et al. [38] introduced Mask R-CNN, and it is the extension of Faster RCNN. Figure 9 shows that Mask R-CNN architecture is a two-stage pipeline. The main aim of Mask RCNN is to solve instance segmentation problems in CV applications; i.e., to separate different objects in an image or a video. Additionally, a mask branch on each region of interest (ROI) is included in Mask R-CNN for predicting an object works in parallel with the class label and bounding box (BB) regression branches. It produces three outputs: a class label, bounding box coordinates, and an object mask. Mask R-CNN efficiently detects objects in the input image or video and concurrently generates a high-quality segmentation mask for each instance detected object. It is conceptually simple to train, flexible, and is a general framework for instance segmentation of objects. To achieve excellent gains in both speed and accuracy, Mask R-CNN uses ResNet-FPN [37; 39] as a backbone model for extracting features. But the main drawback is it adds small computational overhead on the network and runs with a speed of nearly 5 Fps.