esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Space and Time Complexity  
The proposed mechanism has modeled a grid neuron that acts like a bulb whose light intensity increases and decreases in 2D space as per the hexagonal grid sites exactly like grid neurons. At the same time, the other models like RatSLAM [17] have modeled grid neuron as a cell which is spatially placed in a hexagonal pattern in a two-dimensional space. In this view, these multiple hexagonal grids are used to learn places using a Mexican hat function. Since each cell covers a small space of the environment; therefore, their numbers depend on the size of the environment and makes them highly space inefficient. Whereas, in the proposed mechanism, a single grid neuron can generate a hexagonal pattern for any size environment, here, multiple grid neurons are used to increase the accuracy of localization, where the number is dependent on the accuracy level of localization, not on the environment size.  
Since the number of grid neurons is much less compared to the RatSLAM, therefore the proposed mechanism is highly space-efficient. The time complexity of the grid models directly dependent on the number of grid cells, which are much less in our case. That is why the proposed model would be superior in the time complexity as well. In RatSLAM, for a task of localization in an environment of size \(10\times 10\) m\({}^{2}\) the model has required 36 layers of hexagonal grids with \(0.25\times 0.25\) area for each grid cells. Thus the total number of grid cells in RatSLAM will be 57,600 [17]. Therefore correspond to each place in RatSLAM, activation values of 57,600 grid cells are required to be stored, whereas in the proposed model:  
'\(n\)' is the number of grid neurons  
Figure 26: Localization in an ambiguous environment.  
\(n\times(n-1)/2\) distance values,  
\(n\)--activation value  
\(n\)--quadrant angle of each grid neuron  
In this view, \(n=(n+5)/2\), for \(n=29\) (at which the highest accuracy has been achieved), the total number of parameters stored will be 493, corresponding to a place neuron.
## 3 Experiment Results and Discussion
### Experimental Results of the VFF Effect on Inertial Force  
For the CFF Case 1 (3 Hz) versus the VFF Case 4 (3 & 1 Hz), the total force, including the inertial and aerodynamic lift forces, are measured and shown in Figure 12. Since the total force measured by the load cell is a mixture of the inertial and aerodynamic forces, the inertial force was measured separately with the membrane skin removed from the wing in the same condition of \(\rm{f_{u}=f_{d}=3~{}Hz}\) as in case 1 and \(\rm{f_{u}=1~{}Hz},~{}\rm{f_{d}=3~{}Hz}\) as in case 4. The aerodynamic forces can be obtained from the difference between the total force and the inertial force of the FWR model. From Figure 12 for the baseline case 1 (CFF 3 Hz), the average inertial force, in a flapping cycle, is nearly zero, and results in an average total force (215 mN) almost the same as the average lift force (214 mN). The phase difference is about 90\({}^{\circ}\) between the inertial force and lift force, given the maximum vertical acceleration and minimum velocity occurs at the start of the up-stroke and down-stroke. During the up-stroke, the phase difference is slightly smaller than 90\({}^{\circ}\), while the phase difference is slightly larger than 90\({}^{\circ}\) during the down-stroke.  
Unlike case 1, the VFF in case 4 results in a positive average inertial force about 8.3 mN, which is about 10% of the average lift 80.5 mN. The results indicate that a reduced flapping frequency, during up-stroke, would be beneficial due to the power efficiency and the lift of the FWR model. It is worthy to note that this phenomenon is mainly due to the relatively rigid wing with limited twisting angle of the FWR model, which is different from a flying animal wing that is capable of folding or large twisting in up-stroke.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case No.** & **Flapping Mode** & \(f_{d}\) **(Hz)** & \(f_{u}\) **(Hz)** & \(\dot{\psi}\) **(Hz)** \\ \hline
2 & CFF & 2 & 2 & 1 \\
3 & CFF & 1 & 1 & 0.41 \\
4 & VFF & 3 & 1 & 1 \\
5 & VFF & 3 & 2 & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flapping frequency and corresponding rotational angular velocity.
## 3 System Model and Assumptions
### 5G C-RAN Architecture Model and Resource Allocation
It then determines whether to accept or reject the new call request \(i\) based on the values of \(R_{c}\) and \(\Delta R_{i}\). To calculate the new load increment (\(\Delta R_{i}\)), it is necessary to estimate the amount of bandwidth (\(B_{i}\)) required by a new call request i. The data rate requirement (\(DR_{i}\)) for each call request i can be defined in terms of its required RBs. Moreover, the bandwidth (\(B_{i}\)) required to provide the required data rates can be stated as:  
\[B_{i}=\frac{DR_{i}}{\log(1+SNR)} \tag{4}\]  
Assuming there are three different call request types, the new load increment (\(\Delta R_{i}\)) from a request \(i\) in terms of the required RBs can be computed as follows:  
\[\Delta R_{i}=\left\lceil\text{S}\times\frac{B_{i}}{BW_{RB}}\right\rceil \tag{5}\]  
where BW\({}_{RB}\) is the bandwidth of one resource block, S is the number of subframes (S = 10 in the 5G NR standard), and \(\left\lceil\text{x}\right\rceil\) maps x to the least integer greater than or equal to x [35]. Each request \(i\) can have \(C_{i}\) active connections, and each active user can occupy only one RB. The variable \(R_{c}\) denotes the total number of RBs that are currently utilized by all active users, which is the total current cell load. Therefore,\(R_{c}\) can be expressed as follows:  
\[R_{c}=\sum_{i=1}^{i=3}R_{c}^{i}\times\Delta R_{i} \tag{6}\]Therefore, the required RBs (\(\Delta R_{i}\)) for all active connections in our systems can be classified into three different loads (where \(i=1\) for URLLC, \(i=2\) for eMBB, and \(i=3\) for mMTC) and can be calculated as follows:  
1. for URLLC connection requests: \[\Delta R_{1}=\left\lceil\frac{B_{1}}{BW_{RB}}\right\rceil\] (7)
2. for eMBB connection requests: \[\Delta R_{2}=\left\lceil\frac{B_{2}}{BW_{RB}}\right\rceil\] (8)
3. for mMTC connection requests: \[\Delta R_{3}=\left\lceil\frac{B_{3}}{BW_{RB}}\right\rceil\] (9)
## 2 Mathematical Models of Pneumatic Flotation  
This section presents a mathematical model of pneumatic flotation for an R2R conveyance system based on a tension model and an empirical model of the tension variation due to air pressure. The dynamics of longitudinal behavior of a moving substrate is described as [17; 18; 19]  
\[L\frac{d}{dt}(\varepsilon_{2})=v_{2}(t)-v_{1}(t)+\varepsilon_{1}(t)v_{1}(t)- \varepsilon_{2}(t)v_{2}(t) \tag{1}\]  
where \(L\), \(v\), and \(\varepsilon\) are the span length (m), the tangential velocity of the roll (m/s), and the strain of the substrate, respectively.  
To include the effect of pneumatic pressure, the total strain can be assumed as a superposition of strains owing to longitudinal dynamics (\(\varepsilon^{\text{e}}\)) and air pressure (\(\varepsilon^{\text{air}}\)) [17]  
\[\varepsilon_{\text{eq}}(t)=\varepsilon^{\text{e}}(t)+\varepsilon^{\text{air}} (t) \tag{2}\]  
Substituting the derivatives of (2) into (1) and then applying linearization using the perturbation method and Hooke's law yields the total strain of the floating substrate  
\[\frac{d}{dt}\big{(}T_{\text{eq},2}\big{)}=\frac{v_{10}}{L}T_{1}^{\text{e}}(t) -\frac{v_{20}}{L}\Big{[}T_{2}^{\text{e}}(t)+T_{2}^{\text{air}}(t)\Big{]}+ \frac{AE}{L}[V_{2}(t)-V_{1}(t)] \tag{3}\]  
where \(A\), \(L\), \(T\), \(v\), and \(V\) are the cross-sectional area of the substrate, the length of the substrate, the variation in the tension, the tangential velocity of the roll in the initial state, and the variation in the tangential velocity of the roll, respectively. Equation (3) expresses the linearized dynamics for the tension in the substrate based on the pneumatic pressure. Then, the pneumatic pressure can be reflected as a tension variation due to the height of the floating substrate (4)  
\[T(t)=-k_{\text{air}}\bigg{(}\frac{\beta\Delta h(t)}{2}\bigg{)} \tag{4}\]  
where \(k_{\text{air}}\), \(h\), and \(\beta\) are the equivalent aerodynamic spring constant, flotation height and wrap angle, respectively. Combining (3) and (4) gives [17]  
\[\frac{d}{dt}\big{(}T_{\text{eq},2}\big{)}=\frac{v_{10}}{L}T_{1}^{\text{e}}(t) -\frac{v_{20}}{L}\bigg{[}T_{2}^{\text{e}}(t)+H(t)\bigg{(}\frac{-k_{\text{air}} \beta}{2}\bigg{)}\bigg{]}+\frac{AE}{L}[V_{2}(t)-V_{1}(t)] \tag{5}\]where \(H\) is the variation in the flotation height from the steady state.  
Equation (5) describes the dynamics of the tension induced by the pneumatic pressure as well as the velocity of the roll and the tension in the previous span. It is notable that empirical parameter \(k_{\text{air}}\) can only be obtained by measuring the flotation height and flotation-induced tension variation in the designated air-bar setup. The next section investigates design parameters such as the hole size, density, and distribution for the optimal design of pneumatic flotation.
## 2 Materials and Methods
### Model Calibration and Validation  
Spectral preprocessing significantly influences the prediction performance of Raman models. Appropriate preprocessing methods are crucial in enhancing the signal-to-noise ratio and refining model precision. The typical preprocessing procedure used in Raman-based models for monitoring the cell culture process encompasses first derivative (15-point window) followed by standard normal variate (SNV) [7; 8; 17; 18; 21; 23; 26; 29; 45; 46; 47]. In this study, before the selection of spectral variables, Raman spectra were preprocessed by Savitzky-Golay first-order derivative (the width of window was 15, and the order of the polynomial was 2) to reduce baseline shift and enhance resolution. Then, SNV was employed for scattering correction.  
Calibration models were established by PLS regression due to its widespread application in generating calibration models for cell culture monitoring [48]. Five-fold cross-validation was used to obtain root mean square error of cross-validation (RMSECV). The number of latent variables (LVs) was determined when RMSECV was the minimum. Studies have shown that the number of LVs selected by such a strategy is sometimes too large [49; 50], resulting in overfitting of the model, so a rule was established (LV \(\leq\) 15) to avert overfitting in our work. The performance of different variable selection strategies was evaluated using RMSECV from the initial 15 batches of data. The last batch (the sixteenth batch) was designated for validation to affirm the robustness of the optimized model. The performance of each model was assessed using root mean square error (RMSE)and correlation coefficient (R). Specifically, RMSEC represents the RMSE in calibration set, and RMSEP represents the RMSE in prediction set. R\({}_{\text{C}}\) represents the R from calibration set, and R\({}_{\text{P}}\) represents the R from prediction set. The performance improvement (PI) of Raman-based models is calculated according to Equation (1):  
\[\text{PI}\left(\%\right)=\frac{RMSEP^{before}-RMSEP^{after}}{RMSEP^{before}} \times 100 \tag{1}\]  
where \(RMSEP^{before}\) denotes the RMSEP of Raman-based models without spectral variable selection, and \(RMSEP^{after}\) represents the RMSEP of Raman-based models with spectral variable selection.  
The entire data processing was executed using MATLAB R2022b (MathWorks, Natick, MA, USA). Algorithms used in this study are from the libPLS 1.95 software package [51] and other in-house algorithms.
# Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period
###### Abstract  
To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the phase-plane was divided into statistical regions. Finally, anomaly detection and identification of the raw data were achieved by analyzing the statistical values of the phase-plane trajectory points in each partition region. This method was verified by a simulation test that used the measured data of the satellite momentum wheel rotation. The simulation results showed that the proposed method could achieve the pseudo-period extraction of the measured data and the detection and identification of the anomalous telemetry data.  
anomaly detection and identification; satellite telemetry data; data symbolization; pseudo-period; phase-plane trajectory +
Footnote †: journal: Applied Physics and Chemistry Letters
# Node Screening Method Based on Federated Learning with IoT in Opportunistic Social Networks
## 3 Methods  
### Model Description  
The basis of this paper is the opportunistic social network, and the data transmission model in the opportunistic social network is a very important aspect [28]. In the era of the IoT, the number of devices in opportunistic networks has increased dramatically, and data transmission between devices has become more complicated. Figure 1 is the overall model design diagram, which simulates the real scenes of various mobile devices and computing servers such as mobile phones and personal computers in the context of the IoT. The letters used in the model design of this paper and their definitions are listed in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Description** \\ \hline \(N\) & A collection of nodes representing terminal equipment in an opportunistic network \\ \(D\) & The set of edges between nodes \\ \(w_{u,p}\) & \(w\) represents the weight between \(u\) and \(v\) \\ \(M\) & Collection of servers \\ \(\Lambda\) & Collection of training tasks \\ \(U_{\underline{m},u}\) & The data set of the terminal covered by the server \(z\) \\ \(\Psi(t)\) & The degree of community modularity at time \(t\) \\ K & Indicates the total weight of the community node \\ \(\kappa_{a}\) & The total weight of all edges in the community a \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Symbol explanation. Figure 1: Overall model design. In the opportunistic social network, we define \(P=(N,D,w)\), where \(N\) represents the set of terminal device nodes in the opportunistic network, and \(D\) is the set of edges between nodes, which can also be expressed as \(D=\{(u,v)\}\,u\in N\), \(v\in N\}\), \(w\) represents the weight between \(u\) and \(v\). In the IoT network, multiple devices will be controlled by different servers for data transmission. The server will be stored in micro base stations and higher-level macro base stations. We set the set of these servers as \(M\), and each server \(m\in M\) has certain computing power. It covers several terminal devices through adjacent servers. \(U_{m,n}\)=\(\{x_{m,n},\)\(y_{m,n}\}\) to represent the data set of terminal n covered by server \(z\). To ensure the high speed and stability of data transmission, we need to use a better node selection method during the transmission process, which can also reduce the loss during the transmission process and improve the accuracy of the transmission [29]. In the IoT network, many devices including a large number of handheld devices are nodes in the network, and the data transmission between them has a certain degree of randomness and instability [30; 31; 32]. In this article, we mainly discuss the influence of node selection on data transmission during the forwarding process, and propose a FABD, FL node selection algorithm based on DPPO in IoT algorithm based on federated learning. It can ensure the reliability and high efficiency of data transmission and can make the selection of nodes fast and high-quality in the IoT network with a huge number of nodes.
## 3 Remote Sensing of Ecosystem Health  
Dynamic and integrated measurements of ecosystem attributes (vigor, organization, and resilience) allow an effective ecosystem health assessment and monitoring. For each ecosystem attribute, there are a number of indicators, although those indicators may be different for different ecosystems. This section will review the potential of remote sensing for estimating indicators of the three ecosystem attributes.  
Figure 1: Procedures to integrate the expertise of remote sensing experts and ecologists to develop a remote sensing-based Ecosystem Health Assessment and Monitoring System. The questions outlined in dotted lines shows the contribution of ecologists.
## 3 Results
### Results of S3 Set: Speed of Motion Patterns and Sampling Frequency  
The dispersion parameters and the rms distance values resulting from the analysis, for the different speed and sampling frequency values selected, are presented in Appendix A, Table 12. The number of frames (N\({}_{\ell}\)) considered by each algorithm is also presented.  
When the lowest (V1) and average (V2) speed are considered, a limited improvement in the estimation of AoR positioning is observed by rising the sampling frequency for configuration A, whereas no significant improvement is shown for configuration D. In particular, for the 1-DoF motion configuration, the linear dispersion and rms distance are halved at 1000 Hz (about 1 mm), while the angular dispersion falls below 0.3 deg (starting from 0.9-1.64 deg at 100 Hz).  
At highest speed (V3), the improvement in linear dispersion and rms distance obtained at a high sampling rate is evident for both motion configurations, although it is very clear especially for configuration A. The linear dispersion obtained at 100 Hz (10.6 mm) is reduced to 2.46 mm at 250 Hz, and it falls below 2 mm at higher frequencies. Similarly, the angular dispersion is generally reduced and achieves a very low magnitude (below 0.1 deg) at maximum frequency. The same behavior is observed for configuration D; however, the improvement in estimation accuracy is less significant.  
The AoR estimation based on the SARA method shows an almost constant level of accuracy for most of the trials analyzed, which is generally higher than the one observed for the IHA approach. Figure 7 shows the estimation of the angular velocity performed by the IHA algorithm and its comparison with the angular velocity set as input of the multi-body model, by choosing motion configuration A. As shown in Figure 7a, the evaluation accuracy of the angular velocity at low speed (V1) is very high, as the AoR estimation. Figure 7b shows the same signals when a higher speed (V3) is selected. In this condition, by assuming a constant sampling rate, the angular velocity is predicted with less accuracy as compared to the result shown in Figure 7a. This outcome affects the accuracy of AoRestimation performed by means of the helical axis approach, whereas it has no impact on SARA methodology.
###### Abstract  
In this study, we developed a numerical model of the coupled modulation transfer function (coupled-MTF) based on the discrete sampling system from the perspective of optical system imaging quality evaluation for coupled two-dimensional discrete sampling characteristics of the hexagonally aligned fiber-optic imaging bundles and CCD image elements. The results show that when the spatial frequency of the input target signal deviates from the Nyquist frequency by 1%, an increase in the number of fibers leads to a faster convergence of the oscillation of the coupled-MTF, and the coupled-MTF converges to a stable value when the number of fibers reaches \(1000\times 1000\). The deviation of the spatial frequency of the input target signal from the Nyquist frequency is within 1%, and the oscillatory convergence of the coupled-MTF accelerates with increasing deviation. The coupled-MTF oscillates with the deviation period of the wave peak of the input target signal from the initial position of the fiber center, and the theoretical oscillation spatial period is twice the fiber diameter. This study produces important guidelines for the selection of the number of fibers, input spatial frequency, and initial position deviation of the hexagonally arranged fiber imaging bundles.  
fiber optics; hexagonally aligned fiber-optic imaging bundles; coupled two-dimensional discrete sampling; coupled modulation transfer function; evaluation of imaging quality
## 3 Results and Discussions  
After carefully considering these design guidelines and optimising the design accordingly, the prototype is fabricated, and its performance is measured. A Windows CPU @ 3.18 GHz system with 16 GB RAM and three parallel cores system is used for the design of the antenna. The reflectarray's performance parameters are then measured and compared to simulations. The reflectarray has a dimension of 111.6 mm \(\times\) 111.6 mm, as shown in Figure 6.  
Figure 6: The proposed reflectarray prototype placed with VNA and in an anechoic chamber for measurement.
## 1 Introduction  
It is important for oral and maxillofacial surgeons to repair craniofacial defects on oral cancer patients or patients with congenital problems [1], but the treatment course for bone repair is time-consuming and expensive. Thus, surgeons want to solve this difficult situation by using bone grafting (transplantation) and implantation [2]. Artificial bone replacements are not like natural bones and cause obvious side effects. The biomaterials have the ability to promote the regeneration of bones and would be the potential materials for bone repair [3].  
Silicate biocercamics are considered to possess great prospects in clinical application for orthopedic tissue regeneration due to their excellent osteogenesis and angiogenesis properties [4]. Moreover, tissue engineering using single-phased silicate-containing calcium phosphate bioceramic scaffolds is a promising and highly effective approach for periodontal repair. This material provides appropriate mechanical properties, proper degradation, and good manufacturability [5].  
In the last years, sol-gel processes for biocercamics have been incorporated for chemical composition tuning and size control at the nanometric scale [6]. Various elements were used to add to the bioactivity of biocercamics, such as silver (Ag) with antibacterial features [7], magnesium (Mg) that induces the formation of an apatite layer, strontium (Sr) that stimulates the cellular response of HUVECs [8], zinc (Zn) for the inhibition microbial adhesions [9], titanium (Ti) for the killing or growth inhibition of bacteria, etc. In particular, the nanostructured TiO\({}_{2}\)-incorporated materials have gained much attention in the field of tissue reconstruction because of their osteoconductivity, excellent biocompatibility, and favorable mechanical strength [10].  
In this study, the proposed biocercamics composed of silica and titanium oxide with various concentrations of titanium oxide were fabricated using the sol-gel method for tuning the formula, examined for their feasibility for biomimetic mineral deposition through immersion in simulated body fluid (SBF), and evaluated for their bioactivity using the cell toxicity test for the human osteosarcoma cells (MG63) in vitro.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud Industrial Applications
#### 5.5.7 Education  
There is a high demand for Telco Cloud-based education, particularly in cases of epidemics such as COVID-19 [210,211]. Telco Cloud can assist in the provision of an advanced and interactive educational cloud environment in which students can attendclasses and laboratory experiments from home and access study materials without having to travel to education centers.