esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Work
### Moft
#### 3.4.2 User Leave and Join Event  
When user 7 leaves, as in Figure 8, node 8 becomes leaf node 3. In the modified OFT node, 3 is issued with a new adjacent shared secret value \(K_{a3}\). The adjacent secret is blinded using a one-way function to obtain the blinded secret of node 3. Users 9 and 10 are supplied with a new blinded secret of node 3. SI\({}_{2}\) is calculated as \(\left(g(\mathrm{bls},\mathrm{bl}_{4}\right).\) Users 7, 8, 9, and 10 are supplied with the blinded secret of I\({}_{2}\). The keys that change during the user leave event are highlighted in Figure 8.  
When user 11 joins as in Figure 9, node 5's adjacent shared secret is changed to \(g(K_{a(5,6)},K_{5})\). The changed adjacent secret is issued to user 11 as \(K_{a(11,12)}\) encrypted using its individual secret key \(K_{11}\). The blinded secret of I\({}_{5}\) is calculated as \(\mathrm{f}(K_{a(11,12)})\). User 6's adjacent secret \(K_{a(5,6)}\) is blinded using a one-way function \(f(x)\) and becomes its blinded secret \(\mathrm{bl}_{6}=f(K_{a(5,6)})\). User 11 is issued with the blinded secret \(\mathrm{bl}_{6}\). Users 6, 11, and 12 calculate \(\mathrm{SI}_{2}=g(\mathrm{bls},\mathrm{bl}_{6})\). Users 7, 8, 9, and 10 are supplied with SI\({}_{2}\), which is encrypted  
Figure 8: User leave event, keys that change during leave event.  
Figure 7: Top-down growth during new user join.  
using the node secret of I\({}_{1}\). As in ROFT [39], another one-way function is performed on each one of the blinded node secrets along the trail of the joining user and supplied to them as a new blinded secret. This makes the MOFT collusion-resistant. The keys that change during the user join event are highlighted in Figure 9. Hence, the new users entering the group do not know any previously used keys, assuring backward secrecy. The modified OFT reduces the communication cost compared to the original OFT.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic Endoscopic Multitasking Platform
#### 2.1.1. Endomaster EASE System (EndoMaster Pte. Ltd., Singapore)  
The Endomaster EASE System (EndoMaster Pte. Ltd., Singapore), formerly known as the master and slave transendoluminal robot (MASTER), is a newly designed flexible robotic endoscopy platform for ESD. The system consisted of a console for remote control of the robotic arms and an independent endoscopic platform (Figure 1) [11]. The endoscopic platform has three working channels: two for the robotic arms and one for therapeutic accessories. The robotic arm was telemanipulated by a surgeon manipulating the controller of the console. Two robotic arms performed retraction and dissection with nine degrees of freedom; the left arm was designed for retraction and the right arm for dissection. Initial animal studies showed success in gastric full-thickness resection, hepatic wedge resection, stomach ESD, and colorectal ESD [12; 13; 14]. In a human feasibility study, successful gastric and colon ESD was performed [11; 15]. The Endomaster platform currently has no CE or FDA approval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Approval Status** & **Clinical Trial** & **Purpose of Use** & **Technical Features** \\ \hline Endomaster EASE System & & Animal & Stomach ESD &
\begin{tabular}{c} Console for remote control of the \\ robotic arms, Independent \\ endoscopic platform \\ \end{tabular} \\ \hline Endoluminal Assistant for & & & & \\ Surgical Endoscopy & & & & \\ (ICube Laboratory, & & & \\ Strasbourg, France) & & & & \\ \hline Flex Robotic System & & & & \\ (Medrobotics, Raynham, & & & \\ MA, USA) & & & & \\ \hline K-FLEX & & & & \\ (EasyEndo Surgical, & & & & \\ Daejeon, Korea) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary table of robotic endoscopic multitasking platforms.  
Figure 1: Endomaster EASE System (EndoMASTER Pte, Singapore) [11]. Colonic endoscopic submucosal dissection performed using the latest version of the EndoMaster EASE System. Robotic ESD was performed by grasping the mucosa with the robotic grasper and dissecting the submucosal plane using the needle-type dissecting knife with electrocautery.
# Warranty Cost Analysis for Multi-State Products Protected by Lemon Laws
## 4 Numerical Example
### Numerical Example for \(\{\tau,N\}\) Model  
According to lemon laws in Pennsylvania, personal and family vehicles sold in the state will be recognized as defective if the number of repair actions exceeds three or the repair time for one repair exceeds 30 days over one year ([4]). Based on the aforementioned lemon laws, we consider the following example.  
The multi-state product is renewed according to the [30,3] regulation. The whole warranty period \(W_{1}=360\) days and the distribution function of repair time \(G(t)=1-\exp(-\lambda t)\) with \(\lambda=80\). The product has four working states and four failure states, i.e., 1, 2, 3, 4 and 5, 6, 7, 8, respectively. At the initial time, it is new, that is, \(\mu_{0}=\begin{pmatrix}1&0&0&0\end{pmatrix}\). After repairs the product is restored to State 4, that is, \(\mu_{1}=\begin{pmatrix}0&0&0&1\end{pmatrix}\). The state transit rate matrices of the failure process are:  
\[\begin{array}{l}\mathbf{Q}_{UU}=\begin{bmatrix}-7.8&2.5&2&1.5\\ 3&-10.7&2.5&2\\ 2.5&3&-13&2.5\\ 2&2.5&3&-14.5\end{bmatrix},\;\mathbf{Q}_{UD}=\begin{bmatrix}1&0.5&0.2&0.1\\ 1.5&1&0.5&0.2\\ 2&1.5&1&0.5\\ 2.5&2&1.5&1\end{bmatrix}\end{array}\]Since the product cannot be in the same state as the new product after a repair action (no matter short repair or long repair), \(\mathbf{P}_{SDU}\) and \(\mathbf{P}_{LDU}\) are set as follows:  
\[\mathbf{P}_{SDU}=\begin{bmatrix}0&0&0.95&0.05\\ 0&0&0.9&0.1\\ 0&0&0.7&0.3\\ 0&0&0.6&0.4\end{bmatrix},\mathbf{P}_{LDU}=\begin{bmatrix}0&0&0.75&0.25\\ 0&0&0.65&0.35\\ 0&0&0.7&0.3\\ 0&0&0.55&0.45\end{bmatrix}.\]  
Suppose that the residual value of the old product \(c_{s}\) is $3000, the cost of updating the product \(c_{mw}\) is $10,000, and the cost for a short repair and long repair are $300 and $1500, respectively.  
According to the cost rate analysis in Section 2.3, the expected length of the warranty service is about 562 days and the expected warranty service cost over the whole warranty period is $11,954. The expected cost rate is $21.27 per day.
## 3 Receiver
### Channel Estimation
The information obtained from the BEM of the channel in the frequency and frequency Doppler domains is condensed in a doubly-indexed matrix, which can be expressed as follows:  
\[\mathbf{\Phi}_{q,r}^{k}=\mathbf{FB}_{q,r}^{k}\mathbf{F}^{H}, \tag{14}\]  
with  
\[\left[\mathbf{B}_{r,q}^{k}\right]_{n,n^{\prime}}=\phi_{q}{}^{I}[n+(k+1)(N+N_{ g})]\phi_{r}{}^{II}[(n-n^{\prime})_{N}] \tag{15}\]  
and the BEM coefficients of each Tx-Rx coupling given by:  
\[\rho_{q,r}=\sum_{m=0}^{3N-1}\sum_{l=0}^{L-1}h[r,t;m,l]\phi_{q}^{I}[m]\phi_{r}^ {II}[l]. \tag{16}\]  
Substituting the channel for its BEM in (9) and considering only Tx-Rx coupling yields the expression:  
\[\mathbf{u}^{k}=\sum_{q=0}^{M_{D}-1}\sum_{r=0}^{M_{\tau}-1}\rho_{q,r}\mathbf{ \Phi}_{q,r}^{k}\mathbf{s}^{k}+\mathbf{z}^{k}. \tag{17}\]  
By incorporating this representation of the channel into Equation (10) and considering only the positions where the transmitted and received pilots are present, we obtain the following expression:  
\[\mathbf{u}_{\mathcal{P}}=\mathbf{\Lambda}\boldsymbol{\rho}+\epsilon_{ \mathcal{P}}, \tag{18}\]where:  
\[\mathbf{\Lambda}^{T} =\Big{[}~{}~{}\mathbf{\Lambda}^{k-1T}~{}~{}~{}\mathbf{\Lambda}^{k}{}^{T}~{}~{ }~{}~{}\mathbf{\Lambda}^{k+1T}~{}\Big{]}^{T}, \tag{19}\] \[\mathbf{\Lambda}^{k} =\Big{[}~{}~{}\mathbf{\Phi}^{k}_{1,\mathcal{P}}\mathbf{s}^{k}_{\mathcal{P} }~{}~{}~{}\mathbf{\Phi}^{k}_{2,\mathcal{P}}\mathbf{s}^{k}_{\mathcal{P}}~{}~{}~{}\mathbf{ \Phi}^{k}_{l,\mathcal{P}}\mathbf{s}^{k}_{\mathcal{P}}~{}~{}\Big{]},\] (20) \[M_{\tau} =\lceil F_{s}\tau_{max}\rceil+1, \tag{12}\]0 (21) \[M_{\tau} =\lceil F_{s}\tau_{max}\rceil+1, \tag{12}\]1  
In the given context, the subscript \(\mathcal{P}\) pertains to the subset of vectors and matrices that correspond to the positions of the pilots. To simplify the notation, we introduce the indexed variable \(i=q+M_{D}(p-1)\), where \(0\leq p\leq M_{\tau}\) and \(0\leq q\leq M_{D}-1\). This indexing allows us to represent the pilots' positions more conveniently. Furthermore, the vector \(\epsilon_{\mathcal{P}}\) is introduced to encompass the contributions from noise, modelling errors, and intersymbol interference, with the purpose of simplifying the expressions. The assumption is made that the receiver possesses the matrix \(\mathbf{\Lambda}\) and the received vector \(\mathbf{u}_{\mathcal{P}}\). With these available, the estimation of the channel's coefficient vector for each transmitter-receiver coupling can be obtained using the least squares (LS) algorithm. \[M_{\tau} =\lceil F_{s}\tau_{max}\rceil+1, \tag{12}\]2  
Once these coefficients are obtained, various representations of the channel, such as the time-varying impulse response or the frequency response, can be calculated directly by computing the weighted sum of the base functions. This allows for the determination of the frequency Doppler and frequency response matrices for each transmitter-receiver coupling. The calculation can be performed using the following expression:  
\[M_{\tau} =\lceil F_{s}\tau_{max}\rceil+1, \tag{12}\]3
# Preliminary Results of an Astri/UWM EGNSS Receiver Antenna Calibration Facility
The combined phase center correction (PCC) is a function of PCO and PCV (1). \[\text{PCC}(\alpha,\text{z})=\text{r}+\text{PCO}\cdot e+\text{PCV}(\alpha,\text{ z}) \tag{1}\]  
In Equation (1), \(\alpha\) and z are the horizontal and zenithal angles of the SV position in the antenna body frame and \(e\) is the line of sight unit vector. The GNSS antenna PCV is still important issue in many various applications [4; 5; 6; 7]. Accurate PCCs are a key element, especially in field of geodesy, where GNSS measurements are used to establish reference frames [9; 10]. The results of many studies [11; 12; 13; 1] prove that the omission of antenna calibration correction or use of incorrect correction significantly affects results. In [11; 12], the authors demonstrated the differences in position results as caused by using mean calibrations instead of individual calibrations, where the differences can reach up to 1 cm in the up component and for the horizontal components are generally below 4 mm. In [13], the latest changes, improvements, and deficiencies of PCC models were presented. Appropriate PCCs are also important for various geophysical applications [14; 15; 16; 17]. One such application is precise time and frequency transfer and intercontinental and continental links for different metrology sites [14; 15]. Accurate antenna models also play an essential role in meteorology for the estimation of GNSS based troposphere zenith delays (ZIDs) [16; 17]. So far, absolute GNSS antenna models have been created using robot field calibration for two frequencies, i.e., global positioning systems (GPSs) and Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) systems. Besides the GPS and GLONASS systems, two other systems are approaching full operational capability. The European Union (EU), with the help of the European Space Agency (ESA), has introduced the Galileo positioning system. At approximately the same time, China has developed the Beidou system (BDS). Additionally, existing satellite navigation systems have evolved into new modernized forms. Modernized GPS and GLONASS systems transmit new signals using new carrier frequencies. The modernized GNSS satellites will broadcast at least three civil signals in a range of frequency bands. The development of new satellite systems and the introduction  
Figure 1: PCC as a function of PCO and PCV. of new carrier frequencies to existing ones results in the need to include them in antenna calibration procedures. Recently, Geoscience Australia (GA) became one of the six GNSS antenna calibration facilities in the world and is the only one situated in the southern hemisphere [18]. The other five are the National Geodetic Survey (NGS) (Virginia, USA), University of Bonn (UB) (Bonn, Germany), SenB (Berlin, Germany), Geo++ (Hannover, Germany), and the Institute of Geodesy (IfE) (Hannover, Germany) facilities. The first results obtained by two new calibration facilities at Wuhan University [19] and Eidgenossische Technische Hochschule (ETH) Zurich [20; 21] have recently been published.
###### Abstract
In contrast, the PANAS incorporates ten positive and ten negative adjectives, containing moderate and well-balanced items, making it more suitable for capturing users' subjective psychological perceptions of architectural indoor elements. However, due to the possible uncertainty in subjective assessments, objective physiological indicators for detection can be utilized [4]. Various tools such as electroencephalogram (EEG), electrocoulogram (ECG), event-related potential (ERP), and functional magnetic resonance imaging (fMRI), have been widely employed for measuring physiological responses in recent years. Among them, heart rate variability (HRV) is widely used due to its advantages, such as easy operation, convenient signal collection, non-invasiveness, and portability, and it is often used in physiological feedback research [4]. In a study by [5], heart rate variability (HRV) and the profile of mood states (POMS) were utilized to assess the physiological and psychological responses of individuals exposed to two indoor environments aligned with or deviating from Feng Shui principles. Participants were instructed to wear head-mounted displays to experience the two virtual indoor settings. Results indicated that rooms designed with Feng Shui principles exhibited more favorable HRV and POMS outcomes. This suggests that indoor spaces incorporating Feng Shui elements elicit more positive emotional responses and are perceived as more conducive environments. In a book published in 2023, Doctor [PERSON] [6], a medical professional with expertise in both Chinese and Western medicine, introduced the novel concept of Spatial Medicine. By analyzing individuals' heart rate variability (HRV), brain waves, skin resistance, and other physiological data, [PERSON] conducted assessments in diverse indoor settings that adhered to or deviated from traditional Feng Shui principles. The findings revealed that individuals exhibit a more stable autonomic nervous system balance in environments that follow the Feng Shui principle. Conversely, in unfavorable settings, the data indicated a diminished autonomic nervous system balance, suggesting heightened physical and mental stress experienced by individuals in such conditions. Drawing upon the aforementioned analysis and deduction, this study employs the PANAS emotion scale as an objective assessment tool, heart rate variability (HRV) as a subjective monitoring technique, and formulates a computational approach for determining the design-weighting of indoor elements. This methodology has the potential to aid designers in establishing the order of design execution and the distribution of project funds to craft residential environments that adequately address the physical health, psychological welfare, and environmental satisfaction of occupants.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 3 Results
### Number of Publications (Study Area)  
The study area for the detection of phytoplankton blooms could be associated with the country for the first author or for the other/s. In Figure 3, the number of publications for each study area is summarised. Only study areas with at least two publications were represented; study areas with only one publication were not represented for map clarity. We used the ecoregions shapefile provided by [PERSON] et al. [32] to separate coastal and open ocean environments. The Marine Ecoregions of the World (MEOW) is a bioregionalization of coastal and shelf areas, and ecoregions are its smallest-scale units. [PERSON] et al. [32] suggest that the most appropriate outer boundary for coastal and shelf realms, provinces, and ecoregions is the 200-m isobath, which is a widely used proxy for the shelf. Coastal environments are 60% of the total represented studies, open ocean 25%, and inland environments 14%. So, the most studied along the entire study period are coastal environments.  
Globally, the areas with the most concentrated studies are the East China sea and the Yellow sea with 82 peer reviewed articles. It was followed by the next 10 areas: Baltic sea (54), Lake Taihu (52), South China sea (48), North Atlantic (47), Great Lakes (42), Arabian sea (41), Mediterranean sea (40), Gulf of Mexico (39), Bay of Bengal (32), and Southern ocean (32).  
Most studies focus on three areas, North America, Europe, and East Asia, while other areas have nearly no studies (Africa and South America).  
In Figure 5, we classified study areas into marine or inland waters; marine waters included coastal and offshore waters. We observed that marine studies were dominant along the entire study period, but since 2010, there is an increasing trend in inland water studies. Lake Taihu (China) and the Great Lakes (USA) were the most studied inland waters.  
Figure 3: Planisphere with number of publications for each study area. The 200-m isobath of ecoregions is used for separating coastal and open ocean environments according to [32]. The ecoregions legend is presented in a separate figure (Figure 4).
# Global Dynamics of a Stochastic Viral Infection Model with Laternally Infected Cells
## 5 Numerical Simulations  
In this section, we use Euler-Maruyama method for solving SDEs, as was discussed in detail in [32; 35], to have the discretization transformation of (2), as follows  
\[x_{j+1} =x_{j}+[\lambda-d_{1}x_{j}-\frac{\beta_{1}x_{j}v_{j}}{1+v_{j}}- \frac{\beta_{2}x_{j}y_{j}}{1+y_{j}}]\Delta t+\sigma_{1}x_{j}\sqrt{\Delta t} \zeta_{1,j}, \tag{20}\] \[l_{j+1} =l_{j}+[(1-\varphi)\Big{(}\frac{\beta_{1}x_{j}v_{j}}{1+v_{j}}+ \frac{\beta_{2}x_{j}y_{j}}{1+y_{j}}\Big{)}-(m+\gamma)l_{j}]\Delta t+\sigma_{2} l_{j}\sqrt{\Delta t}\zeta_{2,j},\] \[y_{j+1} =y_{j}+[\varphi\Big{(}\frac{\beta_{1}x_{j}v_{j}}{1+v_{j}}+\frac{ \beta_{2}x_{j}y_{j}}{1+y_{j}}\Big{)}+\gamma l_{j}-ay_{j}]\Delta t+\sigma_{3}y _{j}\sqrt{\Delta t}\zeta_{3,j},\] \[v_{j+1} =v_{j}+[ky_{j}-\mu v_{j}-\xi v_{j}w_{j}]\Delta t+\sigma_{4}v_{j} \sqrt{\Delta t}\zeta_{4,j},\] \[w_{j+1} =w_{j}+[gy_{j}w_{j}-hw_{j}(t)]\Delta t+\sigma_{5}w_{j}\sqrt{ \Delta t}\zeta_{5,j},\]  
where \(\Delta t>0\) is time increment, \(\zeta_{i,j},(i=1,2,3,4,5)\) are independent Gaussian random variables which follow the distribution \(N(0,1)\).  
We assign the following parameter values: \(\lambda=10,d=0.1,\beta_{1}=2,\beta_{2}=2,\varphi=0.5\), \(m=0.5,\gamma=5,a=10,k=2,\mu=10,\xi=3,g=2,h=4\).  
We insert the different values of the white noises to discuss dynamical behaviors for the model (2). Initially, we consider the white noise values \(\sigma_{1}=0.9,\sigma_{2}=0.9,\sigma_{3}=0.7,\sigma_{4}=0.9\), \(\sigma_{5}=0.8\), the threshold conditions of unique stationary distribution \(R_{0}^{\varsigma}=1.5886>1\) is satisfied. Figure 1 displays the time trajectories of the corresponding deterministic model of (2), which shows a stable behavior and also shows that the uninfected cells \(x(t)\), latently infected cells \(l(t)\), actively infected cells \(y(t)\), free virus \(v(t)\), and antibodies \(w(t)\) of (2) fluctuate randomly with respective stochastic mean respectively.  
Next, we increase the white noise values \(\sigma_{1}=3.9,\sigma_{2}=3.9,\sigma_{3}=3.7,\sigma_{4}=3.9,\sigma_{5}=3.8\), we obtain the condition of extinction results in Theorem 3 such that \(\bar{R}_{0}^{s}=0.5665<1\) is satisfied. Figure 2 shows that infected cells \(l(t)\), actively infected cells \(y(t)\), and free virus \(v(t)\) can all die out as the white noise value increases. As a result, this indicates that white noise intensity can significantly reduce the number of virally infected cells and inhibit the growth of infected cells, as well as eliminate all latently, actively, and free virally infected cells.  
**Remark 1**.: _Under certain conditions, the disease can be eradicated. Through numerical simulations, it has been shown that a small intensity of white noises can maintain a stationary distribution, whereas a large intensity of white noises can result in the extinction of the disease._
## 4 Experimental Process and Result Analysis
### Performance Analysis of the Improved MLP Model
Compared with the  
Figure 8: Experimental results of the proposed model on the three components of East, North, and Up. Figure 9: Comparison of the estimated and true values of the presented method (**left**) and the random forest algorithm (**right**). RF algorithm, the presented method has a more concentrated error distribution and the overall error estimate is smaller. The Stanford diagram can show how well the estimated positioning error matches the original positioning error. The closer to the original positioning error the estimated positioning error is, the closer the data points are to the diagonal. According to Figure 11, most of the data points of the presented method are in the lower left corner of the graph, and only a very small number of data points are distributed on either side of the diagonal. This shows that the presented method obtains more accurate estimates. However, part of the data in the positioning error estimate obtained by the RF algorithm is biased, and there are a large number of data points where the positioning error estimate is less than the true value of the positioning error. A residual is defined as the difference between the true value and an estimated value. The residual histogram clearly shows the residual distribution of the proposed method. The \(x\)-axis and \(y\)-axis in Figure 12 are the size and frequency of the residual value, respectively. Based on Figure 12, the residual histogram of the presented method has a higher kurtosis than the residual histogram of the RF algorithm, and the peak value is closer to 0. This  
Figure 11: Stanford diagram with the presented method and the RF algorithm. The red dotted line indicates the diagonal line. Figure 10: Comparison of the estimation error distributions between the presented method and the RF algorithm. shows that the residual distribution of the presented method is relatively concentrated, the overall value is small, and it has better estimation performance. Table 2 records the quantitative analysis results of the proposed method and the random forest algorithm. RMSE, mean absolute error (MAE), and standard deviation are used as evaluation indicators. It can be seen from Table 2 that the performance of the proposed method is better than that of the random forest algorithm in all three evaluation indicators.