esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Window** & **Stage** & **\textless{}54 (\%)** & **54–70** & **70–180** & **180–250** & **\textgreater{}180** & **\textgreater{}250** & **Mean Glucose** & **CV** \\ \hline \multirow{3}{*}{6-h post} & _Baseline_ & 5.4 & 12.3 & 69.0 & 12.3 & 18.6 & 6.3 & 136.2 & 0.48 \\  & _First_ & 3.7 & 13.3 & 69.4 & 14.1 & 17.4 & 3.2 & 128 & 0.43 \\  & _Last_ & 3.7 & 10.0 & 62.9 & 19.3 & 27.1 & 7.8 & 148.2 & 0.42 ** \\ \hline \multirow{3}{*}{12-h post} & _Baseline_ & 5.8 & 11.8 & 70.4 & 14.6 & 17.9 & 3.3 & 131.3 & 0.44 \\  & _First_ & 4.3 & 11.5 & 66.1 & 19.2 & 22.4 & 3.2 & 134.6 & 0.42 \\  & _Last_ & 9.3 & 17.2 & 57.1 & 19.5 & 25.7 & 6.1 & 139.5 & 0.47 \\ \hline \multirow{3}{*}{24-h post} & _Baseline_ & 5.8 * & 12.0 & 67.2 & 17.1 & 20.8 & 3.6 & 134.2 & 0.43 \\  & _First_ & 3.0 & 10.0 & 62.5 & 21.9 & 27.5 & 5.5 & 143.8 & 0.41 \\ \cline{1-1}  & _Last_ & 7.0 & 14.9 & 63.0 & 17.3 & 22.1 & 4.8 & 135.8 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average cohort isCGM values from FSL for different observation windows after the end of exercise interventions. Figure 3: Evolution of the mean glucose values and coefficient of variation (CV) evaluated with isCGM for every patient of the 24-h observation window. Each bubble is centered in the mean glucose value for each patient and stage of the study, while the area of each circle is proportional to the CV. numerically the tendency was similar, no significant differences were found in CBG changes in the _last stage_ (last two weeks of the study). Finally, HbA1c decreased by 0.4% (95% CI; \(-\)0.28-1.08%; \(p\) = 0.066) during the study. Four out of six subjects reduced their HbA1c values (range from 0.2 to 1.7%) without getting worse for any patient.
_Article_  
**Hydrochemical Processes and Isotopic Study of**  
**Geothermal Springs within Soutpansberg,**  
**Limpopo Province, South Africa**  
**[PERSON] 1,*\({}^{\text{\textregistered}}\)2, [PERSON] 2, [PERSON] 3 and**  
**[PERSON] 1**  
1 Department of Hydrology and Water Resources, University of Venda, Private Bag X5050,  
Thohoyandou 0950, South Africa; [EMAIL_ADDRESS]  
2 iThemba Labs, Environmental isotope laboratory, Private bag 11, Wits, Johannesburg 2050, South Africa; [EMAIL_ADDRESS]  
3 Directorate of Research and Innovation, University of Venda, Private Bag X5050,  
Thohoyandou 0950, South Africa; [PERSON] Correspondence: [EMAIL_ADDRESS]; Tel.: +27-780-642-299  
**Abstract:** Geothermal springs and boreholes within the Soutpansberg Group were sampled and analysed for their major ion chemistry and stable isotope compositions in order to ascertain the possible sources and geochemical processes of the waters. The temperature of the geothermal springs ranges from 41 \({}^{\circ}\)C to 49 \({}^{\circ}\)C (thermal/hot waters) and 53 \({}^{\circ}\)C to 69 \({}^{\circ}\)C (scaling/hyperthermal waters). The major water types are Na-Cl and Na-HCO\({}_{3}\), which are typical of marine and deep groundwaters influenced by ion-exchange processes. The hydrothermal parameters suggest that thermal gradient, carbonate weathering, mineral dissolution, ion exchange, and evaporation are the main geochemical processes controlling the geothermal springs. The \(\delta^{18}\)O and \(\delta^{2}\)H values vary from \(-5.82\%\) to \(-4.82\%\)o for \(\delta^{18}\)O and \(-33.5\%\)o to \(-24.6\%\)o for \(\delta^{2}\)H for all the geothermal spring water. The isotopic ranges of the groundwater are relatively smaller and more depleted than those of rainwater (\(\delta^{2}\)H = \(-9.8\%\)o and \(\delta^{18}\)O = \(-2.7\%\)o). The \(\delta^{2}\)H and \(\delta^{18}\)O signatures reveal a significant infiltration before evaporation. The \(\delta^{2}\)H and \(\delta^{18}\)O values further confirm that the waters are of meteoric origin, which implies that modern rainfall is the fundamental component of recharge derived from the infiltration of local precipitation with significant contribution of another type of water in the deeper part of the aquifer. These results provide further insight into this basement aquifer, which is a vital resource for the region.  
**Keywords:** geochemical process; geothermal springs; meteoric origin; Soutpansberg; stable isotopes; water type
## 4 Experiments
### Comparative Analysis
#### 4.2.2 Experimental Results
The comprehensive results of this experiment are presented in Table 6, revealing significant advantages of our method over existing industry models across the three types of test data. Our approach excels in terms of Precision, Recall, and F1-score, which are the three key evaluation metrics used for performance assessment.
# Reconstruction of Rainfall Field Using Earth-Space Links Network: A Compressed Sensing Approach
## 2 Principles of Reconstructing Rainfall Field
### Description of Rainfall Retrieved by ESL  
The total attenuation of an ESL caused by rainfall can be considered as the cumulative effect of the attenuation of all raindrops appearing in the path of the ESL. As a result, the rain attenuation coefficient \(\gamma_{R}\) (dB/km) can be expressed as [17; 18]  
\[\gamma_{R}=10\log e\int_{0}^{\infty}\frac{\pi D^{2}}{4}C_{ext}(D)N(D)d(D) \tag{1}\]  
where \(D\) denotes the equivalent diameter of the raindrop, \(C_{ext}(D)\) is the extinction cross section of the raindrop, and \(N(D)\) is the DSD. Furthermore, based on extensive experimental verification [26; 27], Equation (1) can be abbreviated as  
\[\gamma_{R}=\alpha R^{\beta} \tag{2}\]  
where \(R\) (mm/h) is the average rain rate along the path, and the coefficients \(\alpha\) and \(\beta\) can be obtained from ITU-R P.838-3 [27]. When the length of the ESL in the rainfall area is \(L_{R}\) (km), the rain attenuation \(A_{R}\) (dB) can be obtained by  
\[\begin{cases}A_{R}=\gamma_{R}\cdot L_{R}\\ L_{R}=(h_{0}+0.36)/\sin\theta\end{cases} \tag{3}\]  
where \(h_{0}\) is the height of the atmospheric 0 \({}^{\circ}\)C-Layer [28], and \(\theta\) is the elevation angle of the antenna.  
In order to obtain the rain attenuation caused by rainfall to an ESL, the signal-to-noise ratio (SNR) when there is no rainfall is considered as the attenuation baseline when the ESL signal is recorded in real time using the receiver; the rain attenuation can then be obtained by the SNR when there is rainfall and the SNR when there is no rainfall, denoted as \(\text{SNR}_{no-rain}\) and \(\text{SNR}_{rain}\), respectively, and written as  
\[A_{R}=\text{SNR}_{no-rain}-\text{SNR}_{rain} \tag{4}\]  
Thus, combining Equations (2)-(4) allows the average rain rate retrieved by ESL to be written as  
\[R=\left[\frac{(\text{SNR}_{no-rain}-\text{SNR}_{rain})\sin\theta}{(h_{0}+0.36) \alpha}\right]^{\beta-1} \tag{5}\]  
To further investigate the correlation between the SNR of the ESL signal and the actual rain rate, we recorded the variation process of the ESL signal during rainfall using the ESL at the Ku band in Jiangning district, Nanjing. The variation process of the ESL signal with the rain rate during a rainfall event in June 2020 is shown in Figure 1. It is found that the SNR of the ESL signal decreases significantly when rainfall occurs. Moreover, the signal also produces small fluctuations during non-rainfall periods, which are mainly caused by noise. Further calculation has shown that the correlation coefficient (CC) between the SNR of the ESL signal and the rain rate is \(-0.714\). This indicates that there is a strong negative correlation between the two and that the relationship is the essential mechanism for retrieving rainfall data using the ESL. In our previous work [18], we carried out research on rainfall retrieval using a single ESL, which provides the basis for reconstructing rainfall fields using a network of multiple ESLs.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## 3 Understanding the Effect of Splitting a Dynamic Graph Based on Time
### The Surprise Index
However, the surprise index increases with the _ratio_ between the number of inductive and overlap nodes/edges, and this ratio does not necessarily have to be larger for edges than for nodes. For instance, suppose that four nodes, A, B, C, and D, interact with each other during both the training and test period, resulting in a total of six overlap edges. Now, suppose that a node E starts interacting with A during testing. In this case, the number of inductive nodes and edges are both 1. However, the node surprise index is \(\frac{1}{5}\), while the edge surprise index is \(\frac{1}{7}\), which is smaller. In contrast, if E had started interacting with all four nodes, then we would have an edge surprise of \(\frac{4}{10}\), which would  
Figure 3: Changing the test split ratio linearly from 0.1 to 0.5 changes the node and edge surprise indices differently depending on the dataset. The typical test split ratio of 0.15 is marked as “**” on the lines. be larger than the node surprise. Another example would be if two nodes E and F would start interacting but only with each other during testing. In this case, the node surprise index would be \(\frac{2}{6}\), while the edge surprise index would be \(\frac{2}{8}\), which is smaller. As such it is in itself an interesting pattern that the edge surprise index is generally higher than the node surprise index in all the considered datasets. Reporting both these indices in practice may give a good first overview of the difficulty of the DLP task at hand. **Domain Dependency**. The difference in growth rates between the node and edge surprise indices varies widely across different datasets. For some datasets, increasing the number of events in the test set will increase the proportion of nodes present in the test set that were not observed in the training set. For instance, in the CanParl dataset, increasing the test ratio from 0.1 to 0.3 increases the edge surprise index from 0.15 to around 0.78, while the node surprise index only undergoes a 0.1 increase. This means that while in both cases most of the nodes will already have been observed in the training set, the number of previously unobserved edges will increase significantly. This illustrates the fact that seemingly small changes in the evaluation setting can have a dramatic impact on the difficulty and type of task at hand. The exact values of the node and edge surprise indices, on the datasets from [PERSON] et al. [8], are provided in Table 1.
###### Abstract  
This study used a field-programmable gate array (FPGA) with a Xilinx Spartan-3 FPGA to implement Reflex charge control in a dual-axis solar tracking system with maximum power point tracking (MPPT). The chaos embedded particle swarm optimization method was used to search for the optimum gain constants of the PI controller and the Reflex charging frequency. This scheme not only increases the output power of solar panels but also has a significant effect on switching loss and oscillation of solar charging. The experiment results showed that the proposed method can also significantly improve temperature rise, and that charging efficiency is also better than it is in a traditional charge mode. The results also showed that charging power was enough for solar tracking and the requirements of the charging system. The most significant contribution of this paper is that the scheme can be applied to any active solar tracking and charging system.  
field-programmable gate array (FPGA); solar tracking; chaos embedded particle swarm optimization; maximum power point tracking; Reflex charging frequency Article
The authors present, in [44], a high-overloaded autonomous grant-free MUSA transmission with 8 to 20 users, where the data were LDPC-encoded and BPSK-modulated and sent over a channel affected by deep flat fading. The performance of such a system was studied in the presence of real Fourier-related transform spreading OFDM (RFRT-s-OFDM) by comparison with the traditional single carrier technology based on discrete Fourier transform spreading OFDM (DFT-s-OFDM). At the reception, a blind MMSE-SIC detector was used. The scenario with one antenna at the transmitter and two antennas at the receiver was considered. Time offset and frequency offset are also discussed here. In [45], the authors compare the performance of three different NOMA schemes--MUSA, PDMA, and SCMA, a QPSK-modulated transmission over Rayleigh fading channels in underloaded, fully loaded, and overloaded scenarios, employing two different receivers that have perfect knowledge of the channels--ordered successive interference cancellation (OSIC)-based MUD and message-passing algorithm (MPA)-based MUD. In [46], the authors present some interesting simulation results when NOMA is involved in uplink visible light communication system, while [47] proposes a multiuser hybrid equalizer system for broadband uplink massive MIMO millimeter-wave (mmWave) system. Taking into account the information gathered from the papers mentioned above, our proposed system describes a communication process which involves one antenna at the transmitter and up to 30 antennas at the receiver. Besides the direct link (source-destination), a decode-and-forward (DF) relay is introduced as intermediary between source and destination in order to improve the coverage, ensuring a more reliable uplink connection in a network shared by up to 16 active users simultaneously. The multiple access technique used here is MUSA where the complex spreading codes were obtained following a code proposed by the authors taking into consideration all theoretical aspects included in the 3 rd Generation Partnership Project (3 GPP) documents. Starting from the several setsof codes that are presented in [33; 34; 35], whose performances are analyzed in [43], we introduced two new sets of complex spreading codes, MUSA and EMUSA, and an algorithm of allocation such that the intercorrelation should be as reduced as possible. Moreover, we analyzed the possibility of creating complex codes starting from PN (cPN), which is a novel idea proposed in this paper, whose results are very promising. The purpose is to evaluate the performance in terms of BER versus SNR, using Monte Carlo MATLAB simulations. The rest of the paper is organized as follows: Section 2 presents the system model; the simulation results are included in Section 3; and finally, the conclusions are provided in Section 4.
## 2 The Formulation and Resolution of Equations for Tunnel Load and Deformation
### The Solution of the Tunnel Settlement Equation  
In summary, considering the combined influence of the soil beneath the tunnel and the lateral soil, the settlement equation for the tunnel is as follows:  
\[\frac{\mathrm{d}^{4}u(x)}{\mathrm{d}x^{4}}-\gamma\frac{\mathrm{d}^{2}u(x)}{ \mathrm{d}x^{2}}+\lambda^{4}u(x)=\frac{q(x)D}{EI} \tag{10}\]  
where \(\gamma=2 tD/EI\) and \(\lambda=\sqrt[4]{(kD+2\sqrt{2 tk})/EI}\). The general solution of Equation (10) can be solved by setting \(q(x)=0\), and the result is given below:  
\[u(x)=e^{xx}[A_{1}\cos(\beta x)+A_{2}\sin(\beta x)]+e^{-ax}[A_{3}\cos(\beta x)+A _{4}\sin(\beta x)] \tag{11}\]  
where \(A_{1},A_{2},A_{3}\) and \(A_{4}\) are coefficients, \(\alpha=\sqrt{\lambda^{2}/2+\gamma/4}\) and \(\beta=\sqrt{\lambda^{2}/2-\gamma/4}\).  
Next, we assume an infinitely long tunnel is subjected to a point load \(Q\) at \(x=0\). Since the length of the tunnel is assumed to be infinite, the settlement at \(x=\pm\infty\) is 0, the tunnel corner at \(x=0\) is 0, and the shear force there is \(Pb/2\). To determine the tunnel settlement under the concentrated load [26], the following boundary conditions are needed.  
\[u(\pm\infty)=0 \tag{12}\]\[\left.\frac{\mathrm{d}u(x)}{\mathrm{d}x}\right|_{x=0}=0 \tag{13}\] \[E_{p}I_{p}\left.\frac{\mathrm{d}^{3}u(x)}{\mathrm{d}x^{3}}\right|_{ x=0}=Pb/2 \tag{14}\]  
Taking Equations (12)-(14) back to Equation (11) gives the solution of the tunnel settlement induced by the point load as follows:  
\[u(x)=\frac{QD}{4E_{p}I_{p}\alpha\beta(\alpha^{2}+\beta^{2})}e^{-\alpha x}( \beta\cos\beta x+\alpha\sin\beta x) \tag{15}\]  
Assuming that the additional load caused by dewatering at a given point \(\eta\) on the tunnel axis is \(Q=q(\eta)\mathrm{d}\eta\), the tunnel settlement \(\mathrm{d}u(x)\) induced by this load can be determined according to Equation (15) as follows:  
\[\mathrm{d}u(x)=\frac{q(\eta)D}{4E_{p}I_{p}\alpha\beta(\alpha^{2}+\beta^{2})}e ^{-\alpha|x-\eta|}\cdot[\beta\cos(\beta|x-\eta|)\ +\alpha\sin(\beta|x-\eta|)]d\eta \tag{16}\]  
By integrating Equation (16), the tunnel settlement caused by dewatering can be obtained as:  
\[u(x)=\int_{-\infty}^{+\infty}\mathrm{d}u(x) \tag{17}\]  
which then helps to determine the momentum as follows:  
\[M(x)=-EI\frac{\mathrm{d}^{2}u(x)}{\mathrm{d}x^{2}} \tag{18}\]
## 2 Methods
### Analytical Model of the Normal Air-Gap Magnetic Field
#### 2.1.3 Model of the Armature Reaction Magnetic Field  
When analyzing the armature reaction magnetic field, there is current excitation in the stator slot winding, and the vector magnetic potential in the stator slot region satisfies the Poisson Equation:  
\[\frac{\partial^{2}A}{\partial r^{2}}+\frac{1}{r}\frac{\partial A}{\partial r }+\frac{1}{r^{2}}\frac{\partial^{2}A}{\partial a^{2}}=-\mu_{0}J \tag{14}\]  
The form of general solutions of armature reaction air-gap flux density is the same as that of air-gap flux density under no-load, but the value of undetermined coefficient is different. The interface conditions of each subdomain of armature reaction under load of the IPMSM are as follows: The vector magnetic potential and tangential flux density are continuous on the interface between stator slot domain and slot opening domain; the vector magnetic potential and tangential flux density are continuous on the interface between slot opening domain and air-gap domain; the tangential air-gap flux density is 0 on the interface between air-gap domain and rotor domain. According to the interface conditions, the constraint equations of the undetermined coefficients can be derived and described as  
\[\left[\begin{array}{cccccccc}0&\mathbf{K}_{12}&\mathbf{K}_{13}&0&\mathbf{ K}_{15}&\mathbf{K}_{16}&0\\ 0&\mathbf{K}_{22}&\mathbf{K}_{23}&\mathbf{K}_{24}&\mathbf{K}_{25}&\mathbf{K}_{ 26}&\mathbf{K}_{27}\\ 0&0&0&\mathbf{K}_{34}&\mathbf{K}_{35}&0&0\\ 0&0&0&0&\mathbf{K}_{46}&\mathbf{K}_{47}\\ \mathbf{K}_{51}&\mathbf{K}_{52}&\mathbf{K}_{53}&0&0&0&0\\ 0&\mathbf{K}_{62}&\mathbf{K}_{63}&\mathbf{K}_{64}&\mathbf{K}_{65}&0&0\\ \mathbf{K}_{71}&\mathbf{K}_{72}&\mathbf{K}_{73}&0&0&0&0\\ \end{array}\right]\left[\begin{array}{c}\mathbf{A}_{gat}\\ \mathbf{B}_{gat}\\ \mathbf{C}_{gat}\\ \mathbf{D}_{gat}\\ \mathbf{D}_{gat}\\ \mathbf{A}_{soui}\\ \mathbf{B}_{soui}\\ \mathbf{A}_{sai}\end{array}\right]=\left[\begin{array}{c}\mathbf{Y}_{1}\\ 0\\ 0\\ 0\\ 0\\ \mathbf{Y}_{2}\\ \mathbf{Y}_{3}\\ \mathbf{Y}_{4}\\ \end{array}\right] \tag{15}\]  
where \(\mathbf{K}_{12}\)-\(\mathbf{K}_{77}\) are the equation coefficient matrixes under each boundary condition, depending on the motor structure parameters, and \(\mathbf{Y}_{1}\)-\(\mathbf{Y}_{4}\) are determined by the armature current density under load conditions. \(\mathbf{A}_{gat}\), \(\mathbf{B}_{gat}\), \(\mathbf{C}_{gat}\) and \(\mathbf{D}_{gat}\) are the undetermined Fourier series coefficients of the air-gap flux density general solution, respectively. \(\mathbf{A}_{soui}\) and \(\textbf{B}_{soui}\) are the undetermined coefficients of the flux density Fourier general solution in the stator slot opening domain, respectively, and \(i\) is the \(i\)th stator slot. \(\textbf{A}_{sai}\) is the undetermined coefficient of the flux density Fourier general solution in the stator slot region [19; 20]. By solving Equation (15), each undetermined coefficient can be obtained. Furthermore, the radial and tangential components of the air-gap flux density of the armature reactive of the IPMSM can be obtained.
# Electromagnetic Analysis of Vertical Resistive Memory with a Sub-nm Thick Electrode
## 4 Conclusions  
In summary, highly dense memory can be achieved by integrating a sub-nm thick plane electrode such as graphene in a 3D VRRAM structure. It was found that VRRAMs with such a sub-nm thick electrode exhibited a very different device response compared to that of a conventional metal electrode, resulting in low programming voltages and power consumption. The electromagnetic analysis showed that the electric field strength was increased in a 0.3 nm thick plane electrode by \(3.7\times 10^{8}\) V/m, accounting for approximately a 71% increase. In addition, the relatively thick Pt electrode has a peak electric field near the top and bottom edges, causing the formation of the branched CFs at two polar fields, which may lead to fluctuations during the resistance switching operations. On the other hand, the peak electric field strength is concentrated at the center of the edge for a sub-nm thick electrode. Based on the highlighted notions and experimental measurements, we derived a theoretical basis of the switching process of the VRRAM with conventional metal and graphene electrodes. As a result, the models that include the intrinsic responses of both devices were extracted. It is believed that the obtained results will be highly useful in the estimation of the potential of the graphene-based 3D memory at the circuit and architecture levels for designing in-memory processing and neuromorphic computing units.  
Figure 6: Experimentally verified Verilog-A compact model fitting of the VRRAM with (**a**) Pt and (**b**) graphene electrodes; (**c**,**d**) The resistance distribution from the experimental measurements and models with an injected stochastic feature for the Pt- and graphene-based VRRAM, respectively.  
**Author Contributions:** Conceptualization, B.A., S.L. and L.-w.K.; methodology, B.A., S.K.; software, B.A., S.K.; formal analysis, B.A., S.L.; writing and original draft preparation, B.A., S.K. and S.L.; writing--review and editing, L.-w.K., S.L.; supervision, L.K., S.L.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the technology innovation program (No. 10085646, Memristor Fault-Aware Neuromorphic System for 3D Memristor array) funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea) and by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(2020M5F3A2A01085755).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Organisation  
The remainder of this paper is organized as follows. First, we provide the related works on the proposed scheme in Section 2. The system model of the proposed scheme is presented with the network, energy, and success probability models in Section 3. Our SF-partition-based clustering and relaying scheme is described in Section 4. The performance evaluation and numerical results are presented and discussed in Section 5. Finally, the paper is concluded in Section 6.