esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
Footnote †: This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams of a 5G-based station (gNodeB). In the developed methodology, a multi-ellipsoidal propagation model (MPM) provides a mapping of the multipath propagation phenomenon and considers the directivity of antenna beams. To demonstrate the designation procedure of interference level we use simulation tests. For exemplary scenarios in downlink and uplink, we showed changes in a signal-to-interference ratio versus a separation angle between the serving (useful) and interfering beams and the distance between the gNodeB and user equipment. This evaluation is the basis for determining the minimum separation angle for which an acceptable interference level is ensured. The analysis was carried out for the lower millimeter-wave band, which is planned to use in 5G micro-cells base stations.
## 2 Materials and Methods
### Data Processing  
The data were preprocessed using Nexus 2.8.1 Software (Vicon Nexus; Vicon Motion Systems Ltd., Oxford, UK). The musculoskeletal model in Opensim was established by preprocessed data, adapting to the characteristics of each participant's posture. Joint angles and joint moments were calculated using inverse kinematics (ICs) and inverse dynamics (IDs) tools, combined with the three-dimensional motion data and GRF data, based on the established musculoskeletal model of the participants in Opensim. Normalize IKs and IDs data to 101 data points using cubic B-Spline, while standardizing IDs data by height (Ht) and body weight (BW).  
The CoM of the subjects was determined as the geometric center of the triangle enclosed by three markers: left antagonist superior iliac spine, right antagonist superior iliac spine, and sacrum. The double-leg CoP of the subjects was calculated from the single-leg CoP provided by each force plate and the corresponding vGRF.  
The kinematic variables of interest include the range of joint flexion angle, and the CoM-CoP angle (the angle between the line connecting the CoM and CoP and the vertical line passing through the CoP). The dynamic variables of interest include the joint flexion moment and the peak GRFs. We used the mean and standard deviation (SD) values of the biomechanical variables.  
This study used a stepwise screening method for multiple linear regression. Perform stepwise multiple linear regression using the kinematic and dynamic variables of interest in the two jumps as independent variables, and the total scores of the FMS and the times of sports injuries suffered by firefighters within a year as the dependent variables.  
The data processing procedures mentioned above were all obtained through custom MATLAB R2023a (The MathWorks Inc., Natick, MA, USA) programs.
# Quantum Natural Language Processing: Challenges and Opportunities
## 2 Theoretical Background
### Quantum Background
#### 2.1.2 Quantum Computation and Algorithms  
* **Noisy intermediate-scale quantum (NISQ) devices** are defined as quantum computers for which general-purpose quantum error correction is not feasible and hardware errors are expected [33]. These devices currently can handle 1000 two-qubit operations with tolerable error rates, having a memory size of 50-100 qubits. NISQ devices are currently the only hardware in place to run quantum algorithms; however, they present several limits concerning the number of qubits available to algorithms and the maximum size of quantum circuits.
* **Quantum Random Access Memory (QRAM)** is the quantum counterpart of the classical random access memory (RAM). QRAM is able to use \(n\) qubits to address any quantum superposition of \(N\) memory cells, where the classical RAM can only use \(n\) bits to randomly access \(N=2^{n}\) distinct memory cells. QRAM presents some advantages in terms of performance, exponentially reducing the need for a memory call from \(N\) to \(O(logN)\). The architecture has been presented by [34], but it still remains unrealized at the implementation level.
* **[PERSON]'s quantum search algorithm** is structured to get the correct answer using a state with an equal superposition of orthogonal states, which represent the answers as input. The output is a state with only basic states that have any probability of being measured correspond to correct answers [35].
* **Quantum volume** is the metric that allow a comparison between different quantum computers, each of which can present a different architecture. Performances are evaluated quantifying the largest random circuit of same width and depth successfully implementable by a quantum computer.
## 2 Related Work
### Stability of Petri Net  
In most industrial production processes, stable operation is the premise of normal production. Therefore, the stability of Petri net modeling cannot be ignored. In a Petri net, stability refers to whether there is a certain identification state in all potentially enabled transitions so that all enabled transitions are no longer enabled, so that the system is in a stable state and does not change. To judge the stability of a Petri net system, we can use a marking method or transition rule method. The marking method refers to finding an identification vector in the state space of the Petri net so that all enabled transitions are no longer enabled. The transition rule method describes the properties of the system by establishing transition protocols, such as T-protocol, S-protocol, R-protocol and so on.  
The concept of exponential stability of a Petri net system was proposed in [31]. When a Petri net system goes through a series of state transitions, if its state shows an exponential decay or growth trend with the passage of time, rather than divergence or shock, then we can say that the Petri net system has exponential stability. You usually need to use the Lyapunov function to make the judgment. Exponential stability is a subset of stability. If a Petri net is exponentially stable, then it must be stable because the exponential stability condition ensures that the incidence of transition in the system is bounded, thus avoiding infinite growth or attenuation of the system.  
According to the different modeling objects, the requirements for the stability of the model are different. For example, in disturbed control systems, communication systems that provide efficient services, and economic models in economics, systems with stability are needed. In non-real-time systems or analog mapping systems, the requirement of modeling stability is not high. So whether to consider the stability of the system depends on the specific application scenarios and system requirements.
## 5 Interpretation and Discussion  
The chemical composition of the studied samples is variable but generally comparable with the average shales documented by [21; 22; 23; 24; 25] (Supplementary Tables S3 and S4). The abundance of Al\({}_{2}\)O\({}_{3}\) was used as a normalization factor to make contrasts among the different lithologies due to their immobile nature throughout weathering, diagenesis, and metamorphism. In the shale and mudstone samples, major oxides like TiO\({}_{2}\), and K\({}_{2}\)O show a positive correlation with Al\({}_{2}\)O\({}_{3}\), whereas SiO\({}_{2}\), MgO, CaO, Na\({}_{2}\)O, and P\({}_{2}\)O\({}_{5}\) show no particular trend (Figure 6). The positive correlation of these major oxides with Al\({}_{2}\)O\({}_{3}\) indicates that they are associated with micaceous/clay minerals [14]. Also, a positive correlation of K, Ca, and Na oxides with Al\({}_{2}\)O\({}_{3}\) occurs in feldspars. However, the strongest evidence for the presence of mica/clay in the samples is the elevated levels of Al\({}_{2}\)O\({}_{3}\) as revealed by major element analysis. As a general rule, unweathered igneous rocks have Al\({}_{2}\)O\({}_{3}\) values in the range of 14-16 wt.%, and values of 17-20% are suggestive of clays and micas. The oxide wt% values in Supplementary Table S1 show shales and mudstones with alumina values in the 17-20% range, which is stronger evidence of clay/mica compositions than the bivariate plots in Figure 6. As Aluminium (Al) concentration is reasonably thought to be a good measure of detrital flux excellent positive correlations of TiO\({}_{2}\), and K\({}_{2}\)O.  
Relative to the Upper Continental Crust (UCC) and Post Archean Australian Shale (PAAS), the concentrations of major elements in the studied samples are generally similar, except for MnO and Na\({}_{2}\)O, which consistently yields much lower average relative concentration values than those of UCC and PAAS (Figures 7 and 8). The depletion of Na\({}_{2}\)O (!1%) in the Madzaringwe mudrocks and sandstones can be attributed to a relatively smaller amount of Na-rich plagioclase in them, which is consistent with the petrographic observation and XRD data. The ratio of K\({}_{2}\)O/Na\({}_{2}\)O also shows that K-feldspars dominate over plagioclase (albite) feldspar. The contents of K\({}_{2}\)O and Na\({}_{2}\)O and their ratios (K\({}_{2}\)O/Na\({}_{2}\)O!1) are also consistent with the petrographic interpretations. Compared with the North American Shale Composite (NASC), UCC, and PAAS, the shales are low in MgO, CaO, and Na\({}_{2}\)O. On the average, the shales have a similar concentration of SiO\({}_{2}\) with that of NASC.  
Figure 6: Bivariate plot of major oxides against Al\({}_{2}\)O\({}_{3}\) showing the distribution of shale and coal samples from the Madzaringwe Formation. Average data of UCC, NASC, and PAAS extracted from [5; 25; 26], respectively are plotted for comparison.  
Figure 8: Multivariation diagram of oxides for the studied samples normalized against UCC (after [5]).  
Figure 7: Multivariation diagram of oxides for the studied samples normalized against PAAS (after [26]).
## 4 Simulation Results  
A set of simulations were developed using PSIM software in order to validate the proposed concepts. The boost converter was selected for examples with different complexity to validate the cases proposed in the theoretical development. To introduce the effect of the losses in the models, only the parasitic resistances of the inductor and the capacitor have been considered for simplicity. The parasitic resistances of power semiconductors are not considered but they can be included without modifying considerably the form of the model. Similarly, considering that switching losses could be included approximately as an equivalent series resistance [34], its inclusion in the model could be also developed for further analysis. Then, looking for simplicity, we consider a boost converter whose parameters are given in Table 1.
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
## 3 Transient SM Capacitor Voltage during Grid Faults
### Grid Fault SM Voltage Estimation
 \tag{22}\]  
where the input of the system can be regarded as a series of step signals in the \(s\)-domain  
\[ \begin{cases}i^{*}_{d+}(s)=\dfrac{I^{\prime}_{d+}-I_{d+}}{s}\\ i^{*}_{q+}(s)=\dfrac{I^{\prime}_{q+}}{s}\\ i^{*}_{d-}(s)=\dfrac{I^{\prime}_{d-}}{s}\\ i^{*}_{q-}(s)=\dfrac{I^{\prime}_{q-}}{s}\\ v_{d+}(s)=\dfrac{V^{\prime}_{d+}-V_{d+}}{s}\\ v_{q+}(s)=\dfrac{-V^{\prime}_{q+}}{s}\\ v_{d-}(s)=\dfrac{-V^{\prime}_{d-}}{s}\\ v_{q-}(s)=\dfrac{-V^{\prime}_{q-}}{s}\\ i^{*}_{cj}(s)=\dfrac{I^{\prime}_{cj}-I_{cj}}{s}\end{cases}  \tag{23}\]  
According to [32], the dynamics of the output current can be calculated as  
\[ \begin{split} i_{j\_tran}(t)&=i_{d+\_tran}\cos(\omega _{1}t-\varphi_{j})+i_{q+\_tran}\sin(\omega_{1}t-\varphi_{j})\\ &+i_{d-\_tran}\cos(\omega_{1}t+\varphi_{j})+i_{q-\_tran}\sin(\omega_{1} t+\varphi_{j})\end{split}  \tag{24}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Normal Operation Point** & **Grid Fault Operation Point** \\ \hline \(v_{d+\_tnormal}=V_{d+}\) & \(v_{d+\_fault}=V^{\prime}_{d+}\) \\ \(v_{q+\_tnormal}=0\) & \(v_{q+\_fault}=V^{\prime}_{q+}\) \\ \(v_{d-\_normal}=0\) & \(v_{d-\_fault}=V^{\prime}_{d-}\) \\ \(v_{q-\_normal}=0\) & \(v_{q-\_fault}=V^{\prime}_{q-}\) \\ \(i^{*}_{d+\_tnormal}=I_{d+}\) & \(i^{*}_{d+\_fault}=I^{\prime}_{d+}\) \\ \(i^{*}_{d+\_tnormal}=0\) & \(i^{*}_{d+\_fault}=I^{\prime}_{d+}\) \\ \(i^{*}_{d-\_normal}=0\) & \(i^{*}_{d-\_fault}=I^{\prime}_{d-}\) \\ \(i^{*}_{cj\_tnormal}=I_{cj}\) & \(i^{*}_{cj\_twait}=I^{\prime}_{cj}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quiescent operation point of the MMC. By applying the direct modulation [33], the insertion indexes of the upper and lower arms can be calculated as  
\[n_{uj\_tran}=\frac{v_{cj\_tran}-v_{j\_fault}}{V_{dc}} \tag{25}\]  
\[n_{lj\_tran}=\frac{v_{cj\_tran}+v_{j\_fault}}{V_{dc}} \tag{26}\]  
According to Equation (11), the dynamic response of the total SM voltage can be obtained as  
\[v_{uj\_tran}=v_{uj}(t_{0})+\frac{1}{C_{eq}}\int{(i_{cj\_tran}+\frac{1}{2}i_{j \_tran})n_{uj\_tran}} \tag{27}\]  
\[v_{lj\_tran}=v_{lj}(t_{0})+\frac{1}{C_{eq}}\int{(i_{cj\_tran}-\frac{1}{2}i_{j \_tran})n_{lj\_tran}} \tag{28}\]  
where \(t_{0}\) is the initial time of the grid fault. As the voltage across each SM in the same arm is assumed to be balanced, the voltage stress on each SM can be given as  
\[v_{smuj\_tran}(t)=\frac{v_{uj\_tran}(t)}{N} \tag{29}\]  
Thus, the maximum SM voltage stresses during the grid fault transient can be predicted as  
\[V_{tmax}=\max[v_{smuj\_tran}(t),v_{smlj\_tran}(t)] \tag{30}\]
# An Overview of Reinforcement Learning Methods for Variable Speed Limit Control
## 2 Application of RL in VSL Control  
The application of methodologies from the domain of artificial intelligence in traffic control provides a new opportunity for addressing the issues with current VSL control approaches. Optimization of VSL requires the determination of an optimal policy for posting speed limits as actions on Variable Message Signs (VMS) or sending them directly to vehicles in the case of the Vehicle to Infrastructure (V2I) communication environment, Autonomous Vehicles (AV) or to Connected Autonomous Vehicles (CAV). For this, RL can be applied without the need to explicitly specify how that task is to be achieved, as shown in [17; 18; 19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37].
## 3 Results
### Comparison of Evaporative Fraction (EF) with Soil Moisture Content and Matric Potential  
EF had strong correlations with soil moisture content (\(\emptyset\)) in individual groups (R\({}^{2}\) = 0.87, 0.78, and 0.82 for group C, BC1, and BC2 respectively, Figure 9). The ET-\(\theta\) fitting lines of two biochar groups BC1 and BC2 shifted toward higher \(\theta\) compared with the control group C. The slopes of the fitting lines were 0.29, 0.15, and 0.30 for group C, BC1, and BC2 respectively. The evaporative fraction had strong exponential relationships with soil matric potential (\(\psi\)) in individual groups (Figure 9b), with the EF-\(\psi\) fitting line shifting toward higher \(\psi\) after BC1 biochar application and toward lower \(\psi\) after BC2 biochar application.  
Figure 8: Overall relationships between soil matric potential (\(\psi\)) and (**a**) canopy chlorophyll content (CCC), (**b**) normalized difference vegetation index (NDVI), and (**c**) leaf area index (LAI) in the three groups BC1, BC2, and C.  
Figure 9: Relationships of evaporative fraction (EF) to (**a**) soil moisture content (\(\emptyset\)) and (**b**) soil matric potential (\(\psi\)) derived from UAV for the three groups BC1, BC2, and C. Dash lines in (**a**) indicate the extrapolation of the linear fitting lines (solid lines) to maximum EF (EF\({}_{\text{max}}\leq 1\)). Dash lines in (**b**) indicate the extrapolation of the exponential fitting lines (solid lines) to EF = EF\({}_{\text{max}}\), using sigmoidal curves.
# Resazurin-Based Assay for Quantifying Living Cells during Alkaline Phosphatase (ALP) Release
## 3 Results and Discussion
### Determining the Optimal Time of Incubation with Resazurin  
It is important to determine the resazurin incubation time, because resazurin reduction is affected by time and cell density. Cells were plated at the highest saturation density (\(\sim\) 3 \(\times\) 10\({}^{5}\) cell/mL), then serially diluted in a ratio of 1:2 to produce the range of cell density/well. After 24 h, the attached cells were incubated with a set volume of resazurin working solution for 2, 4, 12, or 24 h in order to cover all the recommended times (1-24 h). Figure 3A-D shows that the number of cells was strongly correlated with the values of resazurin reduction. This correlation was linear from two to four hours, but it started to lose linearity when the incubation time increased from 12 to 24 h. Linearity after two hours was positively proportional to cell densities, but it still demonstrated a low slope, which may underestimate cell numbers. A 4-h incubation time proved to be a preferable incubation time. The slope at the 4-h incubation time was almost two times higher than that of the 2-h incubation time. The occurrence of high density may form hydroresorufin faster. This high density was avoided in this research study. A 12-h incubation time showed an increase at low density, and then the rate of resazurin reduction started to decline by (\(\sim\) 1 \(\times\) 10\({}^{5}\) cell/mL). This incubation time was avoided to reduce errors for the rest of the investigation where proliferation needed to be detected. After 24 h, the incubation time levelled, since  
Figure 2: Optimization of the concentration of resazurin at various cell lines. (**A**) Balbc/3T3 cell lines; (**B**) A549 cell lines; (**C**) MCF-7 cell lines; (**D**) Ht-29 cell lines; (**E**) coefficient variance % of resazurin concentration of vs cells numbers. Data are expressed as the mean \(\pm\) SD of measurements of three independent experiments.  
the lowest density had the same rate as the highest, making the 24 h suitable when time parameters were not recommended. Coefficient variance (CV%) was highly reproducible in the non-linearity assays, although it was high at low densities of cells. Figure 3E shows that CV% exceeded a range of 20% at the lowest density of cells in the linear assays. It appears that four hours is the optimal time recommended. This is in agreement with [PERSON]'s contributions [32], which state that the cell density should be finely tuned in order to prevent the over-reduction of resazurin and ensure the applicability of the resazurin assay.
## 3 Results
### Decomposition Mechanism of PMVE/N\({}_{2}\)  
Figure 6 presents the primary decomposition pathways of PMVE and their corresponding relative energy changes. From a thermodynamic perspective, pathways B and C are more likely to occur than pathway A. In the ReaxFF-MD simulation, not only was the decomposition of PMVE observed, but the recombination processes between free radicals were also detected (as shown in Figure 7). Table 2 summarizes the decomposition and recombination processes along with their corresponding reaction enthalpy changes. Path B has two paths (B1 and B2). Path B2, generating C\({}_{2}\)F\({}_{4}\), is more common than path B1, which generated C\({}_{2}\)F\({}_{2}\). Pathway C, requiring the lowest activation energy of only 47.12 kcal/mol, leads to the decomposition of PMVE into C\({}_{2}\)F\({}_{3}\)O and CF\({}_{3}\), making it the most favorable pathway compared to paths A and B. Moreover, CF\({}_{3}\) generated in pathway C can recombine with free fluorine radicals to form CF\({}_{4}\), releasing 121.81 kcal/mol of energy. It is worth noting that CN radicals combine to form C\({}_{2}\)N\({}_{2}\) (cyanogen), but this product decomposes at high temperatures (1600-2600 K), so it primarily exists in the form of CN and C\({}_{2}\)N free radicals in this study [37; 38]. Additionally, the recombination of free carbon and oxygen radicals forms carbon monoxide (CO), a toxic gas that poses potential environmental hazards.  
Figure 6: Relative energy change in PMVE/N\({}_{2}\) decomposition process.  
From a kinetic perspective, there is a dynamic equilibrium between the free radicals generated during the decomposition of PMVE. This equilibrium helps maintain the concentration of perfluorocarbons such as C\({}_{2}\)F\({}_{2}\) and C\({}_{2}\)F\({}_{4}\) in the decomposition products. The insulating strength of these perfluorocarbons is comparable to that of SF\({}_{6}\)[4; 18; 39], thus contributing to the stability of the system's insulation properties to some extent. Thermodynamic analysis shows that the reaction enthalpies for pathways A2 and B2 (yielding C\({}_{2}\)F\({}_{2}\)) and for pathway A3 (producing CF\({}_{2}\)O) are positive. This means that energy must be absorbed for these products to form. In contrast, the reaction enthalpies for forming C\({}_{2}\)F\({}_{4}\), CF\({}_{4}\), C\({}_{3}\)F\({}_{6}\), C\({}_{2}\)F\({}_{6}\), CO, and C\({}_{2}\)N\({}_{2}\) are negative. These products tend to form spontaneously. The reaction enthalpy changes for molecules like CF\({}_{4}\), C\({}_{2}\)F\({}_{4}\), CO, and C\({}_{2}\)F\({}_{6}\) are consistent with previous studies [40; 41], further validating the reliability of the simulation.
# Constellation Encryption Design Based on Chaotic Sequence and the RSA Algorithm
## 3 The Proposed Design Method
### Specific Design Steps  
The following section shows the complete block diagram and provides a detailed analysis of the encryption at the sender and decryption at the receiver.  
Figure 2: Block diagram of the encrypted communication system.
## 2 Case Description and Methods
### HRV Monitoring during the Intervention Program  
We monitored the subject's HRV in real-time using a wearable heart rate sensor (H10, Polar, Finland) and a communication application (Elite HRV, Elite HRV Inc., USA) during the intervention program. The wearable sensor used in the investigation has been validated as a replacement for a Holter monitor in many previous reports [33; 34; 35; 36; 37]. After the intervention program, using the recorded inter-beat intervals, the statistical relation between the interventional events during the program and the change ratio in the HRV was analyzed to test our hypothesis.