esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Need for CS in Bio Electric Signals  
A continuous signal, sampled as per the Nyquist theorem, yields a large quantity of samples; hence, the traditional sampling method could be incapable for high-frequency signals [26]. Also, power consumption will be more due to the use of sensors in a large quantity. Hence, the compression techniques arise as an inevitable process in conventional signal processing. When monitoring bioelectric signals using WBSNs (wireless body sensor networks), some of the important parameters have to be considered such as power consumption, device cost and data compression [27; 28; 29]. In order to achieve the above limitations, data compression has to be done before transmission. However, conventional data compression methods are computationally intensive and fail to achieve these parameters. Hence, CS can be adopted as the efficient data compression methodology for bioelectric signals and other applications [30; 31; 32; 33]. It should also be noted that in spite of all the benefits of CS, it has a demerit in that the reconstruction is an iterative process and requires more computation [24; 25]. However, in an IoT framework, the computation is done in cloud support by cloud platforms.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 5 Future Directions
### New Protocols  
Currently, in order to meet the requirements of large-scale deployment of intra-DC services, such as AI/LLM training and HPC applications, various major technology companies have designed and developed their own transport protocols (e.g., FALCON, SRD, TTPoE, etc.). Based on their self-developed transport protocols, they have implemented and optimized RDMA, effectively enhancing the scalability, congestion control effect, and other related performances of RDMA in the intra-DC scenario. However, these solutions have not yet been adapted for long-range DCI scenarios. In the future, inter-DC-oriented features--such as tolerance for high latency and packet loss--could be integrated into these protocols. Alternatively, entirely new protocols could be developed to meet the specific demands of long-haul transmission.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 4 Receiver Architecture
### Phase Noise Impairments
The phase errors are evaluated, for three symbol rates, using the above expressions and the results are quoted in Table 5. From this table is apparent that a 3.8\({}^{\circ}\) rms phase error is obtained for a 500 ksym/s signal, whereas the same error reduces to 2.2\({}^{\circ}\) at 50 Msym/s. Figure 5: ADF5356 phase-noise: data-sheet vs model. The output frequency is 13.6 GHz (output B), the reference frequency to the phase-frequency detector (PFD) is 61.44 MHz and the loop filter has a 2 kHz bandwidth. The model parameters are: \(\beta=1.25\times 10^{-7}\,\)s, \(f_{L}=6\,\)kHz and \(f_{C}=100\,\)kHz. The final step is the estimation of the effective noise figure \(F_{RX}^{eff}\) and the comparison between \(C/N\) and SNR\({}_{eff}\). These computations are illustrated in Table 6 for a received power of \(-76.5\) dBm (i.e., the maximum level for a zenith link) and of \(-91\) dBm (i.e., 3 dB above the receiver noise floor for \(B_{IF}=50\) MHz). An antenna noise temperature \(T_{a}\approx T_{0}=290\) K is assumed. From the analysis of the table emerges that the ADF5356 frequency synthesizer is adequate for this kind of radio receiver and that its phase-noise is a limiting factor only for high quality signals, i.e., for low data rate (narrow IF bandwidths) and high received power levels.
## 5 State-of-the-Art and Open Challenges
### Dynamic vNF Placement
They formulated and solved the edge vNF placement problem using the fundamentals of the optimal stopping theory (OST). They presented a time-optimized scheduler for optimal placement of vNFs at the edge of the network, and considered an undirected network graph comprising hosts, links between hosts, and users on the network. An assumption was made by the authors that resources on the hosts are finite and that links have a physical limit when it comes to bandwidth along the path.
# Behavioral Game Theory Model in Pollution Control with Additional Supervision
## 4 Evolutionary Stability Analysis of Government-Enterprise Strategies under Third-Party Supervision  
### Solution of System Equilibrium Point and Jacobian Matrix  
The dynamic evolutionary system of government-enterprise strategies can be constructed immediately by combining Equations (7) and (8). Let \(F(x,y)=0\) and \(G(x,y)=0\); then, the five local equilibrium points of the system can be obtained, which are \(E_{1}(0,0)\), \(E_{2}(0,1)\), \(E_{3}(1,0)\), \(E_{4}(1,1)\), and \(E_{5}(x_{e},y_{e})\), where \(x_{e}=\frac{\mu(H+W_{2})-C_{3}}{\mu(H+W_{2})}\) and \(y_{E}=\frac{\mu W_{1}-C_{2}}{\mu W_{1}-P}\), which satisfies \(0<x_{E},y_{e}<1\).  
According to its definition, the Jacobian matrix \(J\) can be obtained by taking the first-order partial derivative of \(x\) and \(y\) in Equations (7) and (8) and arranging them in a certain way.  
\[J=\left[\begin{array}{cc}\frac{\partial F(x,y)}{\partial x}&\frac{\partial F (x,y)}{\partial y}\\ \frac{\partial G(x,y)}{\partial x}&\frac{\partial G(x,y)}{\partial y}\end{array} \right]. \tag{9}\]  
According to the nature of evolutionary game theory, with the help of the determinant \(Det(J)\) and the trace \(Tr(J)\) of the Jacobian matrix, the local stability of each equilibrium point can be analyzed. When \(Det(J)>0\) and \(Tr(J)<0\) corresponding to an equilibrium point, the equilibrium point is the evolutionary stable strategy of the system.  
Let \(a_{11}=\partial F(x,y)/\partial x,a_{12}=\partial F(x,y)/\partial y,a_{21}= \partial G(x,y)/\partial x,\)and \(a_{22}=\partial G(x,y)/\partial y\); then,  
\[Det(J)=a_{11}a_{22}-a_{12}a_{21}, \tag{10}\]  
\[Tr(J)=a_{11}+a_{22}. \tag{11}\]  
Substituting the five equilibrium points into Jacobian matrix \(J\), the corresponding expressions of \(Det(J)\) and \(Tr(J)\) can be obtained as shown in Table 2.  
According to the results in Table 2, \(Tr(J)=0\) corresponds to the equilibrium point \(E_{5}(x_{E},y_{e})\); thus, this point is certainly not the evolutionary stable strategy of the system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(Det(J)\) & Equilibrium Points & \(Tr(J)\) \\ \hline \((\mu W_{1}-C_{2})[\mu(H+W_{2})-C_{3}]\) & \(E_{1}(0,0)\) & \((\mu W_{1}-C_{2})+\mu(H+W_{2})-C_{3}\) \\ \(-(P-C_{2})[\mu(H+W_{2})-C_{3}]\) & \(E_{2}(0,1)\) & \((P-C_{2})-\mu(H+W_{2})+C_{3}\) \\ \((\mu W_{1}-C_{2})C_{3}\) & \(E_{3}(1,0)\) & \(-(\mu W_{1}-C_{2})-C_{3}\) \\ \(-(P-C_{2})C_{3}\) & \(E_{4}(1,1)\) & \(-(P-C_{2})+C_{3}\) \\ \(\frac{[\mu(H+W_{2})-C_{3}](\mu W_{1}-C_{2})(P-C_{2})C_{3}}{\mu(H+W_{2})(\mu W_{ 1}-P)}\) & \(E_{5}(x_{e},y_{e})\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Expressions of Jacobian determinant and trace of each equilibrium point.
## 3 Simulation Results
### Fractional Days  
Section 2.2 mentioned that fractional days require special treatment for the algorithm to work. We define as fractional days those days where the sunset occurs before the sunset within the 24 h UTC time window.  
For example, imagine that there is a day in which the sunrise occurs at 22 h and the sunset occurs at 4 h into the following day. Then, applying the algorithm midday is reported to occur at 13 h. This make no sense as midday occur at night. To avoid complex modifications of the algorithm the following strategy is employed: Midday of a fractional day within 24 h window is equal to the midday time calculated by the standard \(t_{midday}\) Equation (1) using the sunrise of the previous day adding 12 h and obtaining the module of this number by 24. Or, more precisely:  
\[t^{\prime}_{md}(d)=mod(t_{md,frac}(d)+12,24) \tag{11}\]  
where  
\[t_{md,frac}(d)=\frac{SR(d-1)+SS(d)}{2} \tag{12}\]  
Applying this strategy to the previous example, in which the standard \(t_{midday}\) equation gives a result of 13 h UTC, the new result yields a midday at 25 h UTC (13\(+\)12) or 1 h UTC of the same day (since 25 h UTC refers to \(d-1\)). The correct midday time is thus obtained. Figure 7 depicts the effect of this modification.  
This strategy allows the coordinates having the sunset of one day and the sunrise of the next day to be found on hardware platforms where the UTC days are treated independently. A small error is introduced, since the times of two different days are used. This error is practically insignificant since the difference of a sunrise (or sunset) times of two consecutive days is only a few minutes and by itself lower than the error of the light sensors used to detect sunrise or sunset times.  
Figure 7: Correcting the midday error by the initial midday equation.
## 3 Experiment and Analysis
### Evaluation Metrics  
To provide an objective assessment of the model's segmentation performance on the tilapia part segmentation dataset and facilitate comparisons with various methods, the following evaluation metrics were employed: mIoU, mPA, and mRecall.  
mIoU (mean Intersection over Union) is used to measure the degree of overlap between the predicted segmentation results and the true labels. In the tilapia fish segmentation task, a higher mIoU value indicates that the model can accurately capture the boundaries and shapes of various parts of the tilapia fish. The formula is as follows (8):  
mPA (mean Pixel Accuracy) measures the accuracy of the model in classifying each pixel. It calculates the proportion of pixels in the predicted results that match the true labels out of the total number of pixels. For the tilapia fish segmentation task, mPA reflects the pixel-level classification accuracy of the model for various parts. The formula is as follows (9):  
Figure 8: SENet module structure.  
mRecall (mean Recall) represents the proportion of correctly detected pixels by the model out of the total number of true pixels for a specific part. In the experiment, the mRecall value indicates the model's ability to detect various parts of the tilapia fish. The formula is as follows (10):  
\[mIoU=\sum_{i=0}^{k}\frac{TP}{TP+FP+FN} \tag{8}\]  
\[mPA=\sum_{i=0}^{k}\frac{TP+TN}{TP+FP+FN+TN} \tag{9}\]  
\[mRecall=\sum_{i=0}^{k}\frac{TP}{TP+FN} \tag{10}\]  
where \(k\) represents the number of segmentation classes, \(i\) represents the predicted class, \(TP\) represents the pixels predicted as fish body regions, which are actually fish body pixels, \(FP\) represents the pixels predicted as fish body regions but are actually not fish body pixels, \(FN\) represents the pixels predicted as non-fish body regions but are actually fish body pixels, and \(TN\) represents the pixels predicted as non-fish body regions and are actually not fish body pixels.
## 5 Conclusions  
In conclusion, the three sets of models constructed to estimate the DXA BMC using BIA estimates, as well as the subjects' physical and clinical characteristics, showed acceptable clinical correlation and low SEE values. Models 1 and 2 present adjustments for the BMC investigation using the BIA equipment that provided the estimated BMC values. Model 1 presents a simple adjustment for the investigation of the BMC only using the BMC value estimated by the BIA equipment, which is more suitable for the studies that only aim at monitoring the BMC in children and adolescents diagnosed with HIV infection. However, Model 2's adjustments resulted in better parameters. Thus, this model was the most suitable one for the studies that aim to investigate the healthy levels of BMC using BIA equipment which provide estimated BMC values. Models 3B and 4A present adjustments for investigating the BMC using BIA equipment that does not provide estimated BMC values, as well as tetrapolar equipment. Model 3B presents adjustments for the investigation of the BMC through half-body estimates (right-side), which is more suitable for the studies that aim to investigate the BMC in children and adolescents diagnosed with HIV infection using tetrapolar equipment. Model 4A showed the highest correlation result and the lowest SEE value compared to the other models, providing the best adjustment to estimate the BMC by BIA. Thus, among all of the models, this was the most appropriate model for investigating the BMC in children and adolescents diagnosed with HIV infection. Through these findings, the present study demonstrated a new approach and the suitability of an easy-to-use technique to estimate the BMC in children and adolescents diagnosed with HIV.  
Conceptualization, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; methodology, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; formal analysis, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; investigation, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; resources, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; data curation, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; writing--original draft preparation, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; writing--review and editing, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; visualization, J.A.C.d.C., L.R.A.d.L. and D.A.S.S.; supervision, D.A.S.S.; project administration, L.R.A.d.L. and D.A.S.S. All authors have read and agreed to the published version of the manuscript.  
W.D.A.S.S. was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)--Brazil--Finance Code 001, and D.A.S.S. is supported in part by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)--Brazil--ne 309589/2021-5.  
The study had the approval of the local Ethics Committee in Research with Human Beings of the Federal University of Santa Catarina (protocol No. 49691815.0.0000.0121).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
## 3 Analysis of Antenna Configuration
### Circular Arc 2 \(\times\) 2 Transceiver Antenna Array  
The channel phase of a MIMO system needs to satisfy Equation (18) to reach maximum channel capacity. Moreover, in a 2 \(\times\) 2 antenna array system, the phases can be estimated as:  
\[\boldsymbol{\varphi}=(0,0,0,-\pi)^{T}+k_{1}(1,0,1,0)^{T}+k_{2}(0,1,0,1)^{T} \tag{20}\]  
where \(k_{1},k_{2}\) are arbitrary constants. The channel matrix can be written as:  
\[\boldsymbol{H}=\left(\begin{array}{cc}e^{ik_{1}}&e^{jk_{2}}\\ e^{ik_{1}}&e^{j(k_{2}-\pi)}\end{array}\right) \tag{21}\]  
Then, the maximum channel capacity can be estimated as:  
\[C_{max}=2 log_{2}(1+\rho) \tag{22}\]  
where \(\rho\) is SNR.  
A 2 \(\times\) 2 transceiver antenna array can be designed as a circular arc antenna array according to Equation (21), as shown in Figure 3.  
Figure 2: Schematic representation of \(n\times n\) channel.  
According to Figure 3, \(d_{s}\) is the array element spacing, the receiving antenna is a circular arc array with a radius of \(d_{11}\), and the transmitting antenna is a linear array. \(d\) is the distance of the transceiver antennas. \(d_{11},d_{12},d_{21},\text{ and }d_{22}\) are sub-channel transmission paths.  
Let us assume that:  
\[\left\{\begin{array}{c}d_{s}=n\lambda\\ d=md_{s}=mn\lambda\end{array}\right.,m>0,n>0 \tag{23}\]  
Then the sub-channel transmission distance in the circular arc antenna array can be calculated as:  
\[\left\{\begin{array}{c}d_{11}=d_{21}=n\lambda\sqrt{1+m^{2}}\\ d_{12}=n\lambda\sqrt{2+m^{2}}\\ d_{22}=mn\lambda\end{array}\right. \tag{24}\]  
When \(m\gg 1\), \(d_{11}=d_{21}\approx d_{12}\approx d_{12}\). The phase difference changes dramatically due to the small distance difference \(\Delta d\), which cannot be ignored. Then the channel matrix can be rewritten as:  
\[\textbf{{H}}=\left(\begin{array}{cc}e^{j\varphi_{11}}&e^{j\varphi_{12}}\\ e^{j\varphi_{21}}&e^{j\varphi_{22}}\end{array}\right) \tag{25}\]  
According to Equations (21) and (25), if phases \(\varphi_{ij}\) of matrix \(H\) satisfied Equation (26), the channel capacity reaches  
\[d=F(\lambda,d_{s})=\frac{2d_{s}^{2}}{\lambda}-\frac{\lambda}{4} \tag{30}\]  
To sum up, Equation (30) is a general expression for 2 \(\times\) 2 LoS MIMO systems in the circular arc array.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 3 Background
* \(train:\mathcal{D}\times 2^{\mathcal{HP}}\rightarrow\mathcal{M}\), which formally gives an ANN model \(M\in\mathcal{M}\) from a dataset \(D_{t}\in\mathcal{D}\) and a set of hyper-parameters \(HP\in 2^{\mathcal{HP}}\). * \(eval:\mathcal{M}\times\mathcal{D}\rightarrow\mathcal{EV}\), which yields the quality of a model \(M\in\mathcal{M}\) in terms of a set of evaluation \(EV\), based on the evaluation dataset \(D_{e}\). The _integration phase_ is a critical step in our travel time prediction framework, as it leverages the trained model to generate accurate estimates of the travel times for various routes. These predictions serve as essential inputs in the planning process, enabling the development of optimal travel plans that enhance operational efficiency and improve user experience. The predicted travel times can be seamlessly integrated into a wide array of applications, each benefiting from real-time data to make informed decisions. Some examples include the following:  
* **Autonomous vehicle systems:** For autonomous vehicles, integrating our travel time prediction model enables these systems to make informed routing decisions. By understanding expected travel times, autonomous vehicles can optimize their paths, enhance safety, and improve overall traffic flow. This capability is particularly important in dynamic environments where real-time adjustments are necessary. * **Delivery platforms:** In the context of e-commerce and food delivery services, accurate travel time predictions allow companies to optimize delivery routes, minimize delays, and improve customer satisfaction. By utilizing our model, delivery platforms can provide customers with more accurate estimated arrival times, leading to enhanced service reliability. * **Logistics and freight distribution operations:** In logistics, timely and efficient transportation is crucial for maintaining supply chain effectiveness. Our travel time predictions can assist logistics managers in scheduling shipments, allocating resources more effectively, and anticipating potential delays, thereby reducing operational costs and improving service levels. * **Traffic light management:** Our predictions can also be utilized in smart city applications, such as traffic light management systems. By analyzing predicted travel times, traffic management systems can adjust signal timings to optimize traffic flow, reduce congestion, and improve overall urban mobility. This proactive approach to traffic management can significantly enhance the efficiency of transportation networks.
## 3 Results
### Sensitivity Analysis of the Albedo  
While the bifacial gain is known to increase linearly with increases in albedo [65; 72; 73], uncertainties in albedo measurements have been shown to have a non-negligible effect on bifacial performance [74]. Therefore, we performed a sensitivity analysis with the MoBiDiG VF tool using annual albedo values from 18% to 24% (\(\pm\)3% of measured annual average) in place of the monthly measured albedo data that were used in previous sections. The results from this analysis are shown in Figure 15, with winter months excluded for clarity.  
Figure 15 shows that differences in simulated bifacial gain are very small (\(<\)0.3%) when annual average albedo data are used in place of monthly averages. However, during the high solar resource months of May through August, using monthly albedo in the simulation tends to match the measured bifacial gain more closely. This demonstrates that monthly albedo data is preferred over annual albedo data, even at sites where the albedo does not vary greatly throughout the year, such as the site studied here.  
Figure 14: Temperature difference of the PV module and ambient versus frontside global POA irradiance on the HSAAT system. Back of module temperature measurements are shown in black and were only available for the monofacial HSAAT system.  
Figure 15 shows only few instances where the measured bifacial gain is outside the boundaries of the sensitivity. One such example occurs in April, where unusually high bifacial gain was measured. In the case of the HSAT, nearly 30% albedo would be needed to recreate the measured bifacial gain in that month. Since such uncertainty in the measured albedo is unlikely, this lends credence to our hypothesis that abnormally high pollen counts (i.e., soiling) in April caused the spikes in bifacial gain. Another instance where the measured bifacial gain is outside the sensitivity boundaries occurs in the fixed-tilt system in March and October. Since the fixed-tilt array experiences the most shading in these months, it is likely that power losses due to shading are not accurately accounted for in the simulation.
## 3 Results and Discussion
### Floquet Exponents  
Equation (109) allows simulating the real component of the Floquet exponents associated with the \(1T\)-periodic orbit. In Figure 7, \(k_{2}=-0.5\), \(\gamma=0.35\), and \(T=0.18\) are selected. This result shows that for \(K_{1}>0.3392\), the \(1T\)-periodic orbit is stable. A formula was found that gives sufficient conditions for the existence of \(nT\) periodic orbits (Equation (61)) and their respective stability (Equation (65)).  
Figure 6: Existence of stable orbits.
## 5 Conclusions and Perspectives  
This work presented a study concerning the control of a buck-type DC-DC converter by using the MPPT Perturb and Observe (P&O) algorithm without a fuzzy logic controller and with a fuzzy logic controller. The innovation in this work consists of introducing a fuzzy logic controller to the model issued in Simulink for the Perturb and Observe algorithm with the help of a function from the MATLAB program. Two programs in MATLAB-Simulink were created to control the buck converter through the P&O algorithm without and with a fuzzy logic controller. The analysis of the experimental results led to the conclusion that a higher quantity of energy is transferred from the source to the receptor circuit in the case of the buck DC-DC controller controlled through the P&O algorithm with a fuzzy logic controller. The Perturb and Observe (P&O) algorithm was implemented on an experimental stand built at the Laboratory of Electrical Machinery and Drives of the Engineering Faculty in Bacau, with the help of a program issued in the programming environment Arduino IDE. The analysis of the results showed that upon an increase in the power transmitted to the receptor circuit by the photovoltaic system, there was an increase in the filling factor of  
Figure 17: Experimental stand for the DC-DC converter.  
the PWM signal that controlled the power transistor that equips the DC-DC convertor. The P&O algorithm with a fuzzy logic controller can also be implemented on DC-DC converters in the structure of the driving systems of electric vehicles.  
Conceptualization, P.L.; Methodology, P.L.; Software, I.V.B.; Validation, P.L.; Formal analysis, I.V.B.; Investigation, P.L.; Resources, G.C.; Data curation, G.C.; Writing--original draft, P.L.; Writing--review & editing, P.L.; Visualization, S.G.V.; Supervision, P.L.; Project administration, G.C.; Funding acquisition, S.G.V. All authors have read and agreed to the published version of the manuscript.  
This paper is financed by the University \"Vasile Alecsandri\" of Bacau from the funds allocated for research.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.