esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Deep knowledge of how radio waves behave in a practical wireless channel is required for the effective planning and deployment of radio access networks in outdoor-to-indoor (O2I) environments. Using more than 400 non-line-of-sight (NLOS) radio measurements at 3.5 GHz, this study analyzes and validates a novel O2I measurement-based path loss prediction narrowband model that characterizes and estimates shadowing through Kriging techniques. The prediction results of the developed model are compared with those of the most traditional assumption of slow fading as a random variable: COST231, WINNER+, ITU-R, 3 GPP urban microcell O2I models and field measured data. The results showed and guaranteed that the predicted path loss accuracy, expressed in terms of the mean error, standard deviation and root mean square error (RMSE) was significantly better with the proposed model; it considerably decreased the average error for both scenarios under evaluation.  
Kriging; outdoor-to-indoor (O2I); path loss; radio propagation; shadowing +
Footnote †: journal: _Sensors_  
0021  
21G716[[https://doi.org/10.3390/s21206716](https://doi.org/10.3390/s21206716)]([https://doi.org/10.3390/s21206716](https://doi.org/10.3390/s21206716))
## 3 Results
### FGC Results  
Score plots (PC1 vs. PC2) obtained by PCA, developed considering 27 peak areas in the FGC first column chromatograms, are shown in Figure 8. Some discrimination according to the storage time was observed along the PCs, although it was less pronounced compared with prior results; in particular, fresh samples were mainly grouped in the fourth quadrant of the graph. In contrast, samples stored for one year were mainly placed on the opposite side in the second quadrant. The different distribution of the samples could be generated by a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **T0** & **T6** & **T12** \\ \cline{2-4}
**Volatile Markers** & \multicolumn{3}{c}{**Concentration (mg/kg)**} \\ \hline pentanal & 0.00 a & 0.10 b & 0.22 c \\ hexanal & 0.71 a & 1.18 b & 2.20 c \\ (\(E\))-2-heptenal & 0.00 a & 0.00 a & 0.056 b \\ nonanal & 0.00 a & 0.05 b & 0.06 b \\ (\(E\),\(E\))-2,4- & 0.00 a & 0.07 b & 0.24 c \\ heptadien & 0.00 a & 0.00 a & 1.09 b \\ propanoic acid & 0.00 a & 0.00 a & 0.07 b \\ \hline
4,8-dimethyl-1,7- & 0.74 a & 0.69 a & 0.00 b \\ nonadiene & 0.60 a & 0.28 b & 0.13 c \\ (\(Z\))-3-hexenyl & 0.04 a & 0.00 b & 0.00 b \\ acetate & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concentrations (mean values of samples analyzed at T0, T6, T12) of the seven oxidative markers and three “freshness” markers monitored during storage. The letters indicate significant differences (one-way ANOVA, Tukey’s HSD, \(p<0.05\)) among the samples.  
Figure 7: Bar graphs representing the sum of “freshness” and markers of oxidation during storage. Selected markers of lipid oxidation (red bars): pentanal, hexanal, (\(E\))-2-heptenal, nonanal, (\(E\),\(E\))-2-4-heptadiene, formic acid, and propanoic acid. Markers of “freshness” (green bars): 4,8-dimethyl-1,7-nonadiene, (\(Z\))-3-hexenal, and (\(Z\))-3-hexenyl acetate. Different letters above the bars indicate statistically significant differences as determined by one-way ANOVA (Tukey’s HSD, \(p\leq 0.05\)).  
model that discriminates less on the basis of the previously discussed peaks (related to the fruity and rancid attributes) compared with the SPME-GC-MS technique, which considers other variables. The loadings, illustrated in Figure 9, present the most important peaks that drive the discrimination, with red indicating components that are important for clustering stored samples, while green represents those relevant to fresh samples. No distinct groupings were observed related to the type of packaging used, the agricultural system adopted, or the olive maturity index [41]. This lack of clustering suggests that, among the variables analyzed, storage time emerges as the primary discriminating factor [50].
## 4 Discussion
### Adsorption of Ticagrelor and Rivaroxaban  
CytoSorb(r) has demonstrated the ability to efficiently adsorb certain lipophilic, protein-bound drugs, including ticagrelor and rivaroxaban, both of which have molecular weights within the effective adsorption range (~522 Da for ticagrelor and ~436 Da for rivaroxaban). Ticagrelor: this P2Y\({}_{12}\) receptor antagonist is highly lipophilic and undergoes hepatic metabolism, with its active metabolite also contributing to platelet inhibition. Adsorption occurs primarily via hydrophobic interactions with the polymer surface, reducing systemic drug levels and potentially reversing its antiplatelet effect in critical bleeding situations. Rivaroxaban: as a direct factor Xa inhibitor, rivaroxaban is partially protein-bound (~90%) and demonstrates moderate hydrophobicity. It is effectively removed by CytoSorb through a combination of hydrophobic and \(\pi\)-\(\pi\) interactions with the aromatic structures of the polymer adsorbent, facilitating drug clearance and mitigating the anticoagulant effect in cases of life-threatening hemorrhage [29].
# tRNA and tsRNA: From Heterogeneity to Multifaceted Regulators
## 3 Chemical Modifications: Guardians of tRNA Integrity and Function
### RNA Modification on tRNA
#### 3.1.3 N1-Methylguanosine (m\({}^{1}\)G)  
m\({}^{1}\)G modification usually occurs on position G9 of the D loop and G37 of the anti-codon stem-loop of tRNA [73]. In yeast, m\({}^{1}\)G9 was methylated by Tm10, a S-adenosyl-L-methionine-dependent tRNA methyltransferase [74], while m\({}^{1}\)G 37 was methylated by TmD in _Escherichia coli_ (_E. coli_) and TRM5 in eukaryotes and archaea [75]. Interestingly, primary m\({}^{1}\)G methylation at the anticodon position of 37 in tRNA\({}^{\text{PRO}}\) is necessary for adding the modification on wobble position U34, which is of great importance to maintain reading-frame accuracy during translation, suggesting a hierarchical ranking of different RNA modifications on tRNA. m\({}^{1}\)G37 itself also plays a key function in preventing ribosomal frameshift errors and enhancing translation efficiency and fidelity during protein synthesis procedures [76; 77]. The mutation of _TRMT5_ in humans abolishes its function to methylate tRNA at G37 and causes a series of diseases, such as hereditary spastic parapresis [78], neuropathy syndrome [79], combined oxidative phosphorylation deficiency 26 (COXPD26) [80], idiopathic non-cirrhotic portal hypertension, and hepatopulmonary syndrome [81], etc., which are associated with the impaired translation efficiency and fidelity in mitochondrial proteins that depend on the correct methylation of mt-tRNA m\({}^{1}\)G37 by TRMT5. Instead, the upregulation of TRMT5 in human cells is associated with tumor progression. Although the regulatory mechanism is not clear, it is reported that TRMT5 was increased in hepatocellular carcinoma (HCC) and correlated with poor prognoses. This might be associated with TRMT5-mediated enhanced tRNA function in translational control [82].  
Notably, besides the mutation of TRMT5 affecting the m\({}^{1}\)G methylation levels of tRNA, mutations on tRNA genes also have important roles in regulating tRNA function in translation. For instance, the m.4295A\(>\)G mutation of tRNA\({}^{\text{IE}}\) introduces the base alteration of positions 37A to 37G and methylates to m\({}^{1}\)G 37 on tRNA\({}^{\text{IE}}\) by TRMT5, which further affects the structure and function of tRNA\({}^{\text{IE}}\) to impair mitochondrial function and leads to a deafness phenotype [83]. Another example is the m.4435A\(>\)G mutation of mitochondrial DNA, which introduces an m\({}^{1}\)G 37 modification into tRNA\({}^{\text{Met}}\). The incorporation of m\({}^{1}\)G 37 in tRNA\({}^{\text{Met}}\) increases the melting temperature and electrophoretic mobility of tRNA\({}^{\text{Met}}\) while decreasing the aminoacylation and steady-state levels of tRNA\({}^{\text{Met}}\), consequently affecting the translation efficiency of mitochondrial proteins [84]. These studies highlight the importance of appropriate tRNA modification levels, as neither hypomodification nor hypermodification on tRNA could maintain the biological function of tRNA.
## 2 System Model  
### Transmitter and Channel  
The payload and channel of the system model consist of four building blocks, namely the input multiplexer (IMUX), high-power amplifier (HPA), output multiplexer (OMUX), and channel, as illustrated in Figure 1.  
The IMUX \(\mathbb{M}\) composes \(N\) analog code-division multiple access (CDMA) waveforms \(c_{n}(t)=\sum_{m=-\infty}^{+\infty}c_{n}[m]d_{n}[m]p_{n}(t-mT_{\text{c},n})\) to one composite signal  
\[x(t) =h_{\text{IMUX}}(t)*\left(\mathbb{M}\{c_{1}(t),c_{2}(t),...,c_{N} (t)|\mathbf{p},\mathbf{\phi},\mathbf{f}^{\Delta}\}e^{|\omega_{0}t}\right) \tag{1a}\] \[=h_{\text{IMUX}}(t)*\left(\left(\sum_{n=1}^{N}c_{n}(t)\sqrt{P_{ n}}e^{i\phi_{n}}e^{j2\pi f_{n}^{\Delta}t}+x_{\text{IM}}(t)\right)e^{i\omega_{0}t}\right) \tag{1b}\]  
modulated onto a joint carrier with the angular frequency \(\omega_{0}=2\pi f_{0}\), where \(c_{n}[m]\in\{+1,-1\}\) denotes the binary spreading code with a code rate of \(f_{\text{c},n}=1/T_{\text{c},n}\), \(d_{n}[m]\in\{+1,-1\}\) denotes the navigation data, and \(p_{n}(t)\) denotes the pulse shape of the \(n\)-th CDMA waveform. Furthermore, \(f_{n}^{\Delta}=f_{0}-f_{n}\) denotes the frequency offset of the  
Figure 1: System model of transmitters and channels.  
\(n\)-th CDMA waveform to the carrier frequency. In the case of the GAL E1 band, the first three CDMA waveforms are the BOC-modulated PRS and the composite binary offset carrier (CBOC)-modulated E1B and E1C signals. The fourth CDMA waveform was the potential E1D signal generated with a frequency offset. Possible imperfections in the digital-to-analog conversion may be captured by the filter \(h_{\text{IMUX}}(t)\). However, in the following, an all-pass IMUX was assumed. The multiplexed pass-band signal \(x(t)\) was then fed to an HPA which amplifies the signal to a desired output power level. In this study, we constrain ourselves to an idealized, memoryless HPA model such that the resulting amplified signal is  
\[y(t)=A(|x(t)|)e^{i(\Phi(|x(t)|)+\text{atan2}(\text{Im}\{x(t)\},\text{Re}\{x(t) \}))} \tag{2}\]  
where \(A(|x(t)|)\) denotes the AM/AM mapping function and \(\Phi(|x(t)|)\) denotes the AM/PM mapping function [17]. The amplified signal \(y(t)\) is then passed to the OMUX which facilitates a band limitation, which can be modeled with a filter such that the signal radiated from the satellite can be expressed as  
\[z(t)=h_{\text{OMUX}}(t,\kappa)*y(t) \tag{3}\]  
where \(\kappa\in[0,1]\) denotes the payload distortion parameter, which controls the degree of filtering distortion introduced by the OMUX. Section 2.3 describes the implementation of a scalable distortion OMUX filter \(h_{\text{OMUX}}(t,\kappa)\) used in the numerical evaluations. After OMUX filtering, the satellite contribution  
\[z_{\text{C}}(t)=\gamma z(t-\tau_{0}) \tag{4}\]  
is formed by the channel which delays the signal by \(\tau_{0}\) and attenuates the signals mainly due to path loss, which is captured by a constant complex scalar \(\gamma\).
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
## 3 Design of Golay Complementary Coded Radar
### The Network Clock Module  
During the multiple observation point detection process, the accurate network clock module is required to avoid mutual interference between radars which need to be activated in their own time-slices; that is, by setting a time margin and executing wireless clock synchronization, the mutual interference between radars can be prevented effectively. Here, as shown in Figure 2d, the network clock module is designed to include a SimpleLink Sub-1 GHz module based on Texas Instruments' CC1310 chip, a counter and a network clock controller in FPGA. A counter in FPGA begins to count at an interval of 1 microsecond after power-on and plays the role of the local network clock for each radar. For different radars, the corresponding counter (called the network clock) has a different initial offset and clock drift. Assuming that the counter's value of the first radar joining the network is \(m_{1}\), the counters' values of the remaining radars can be expressed as \(m_{n}=m_{1}+S_{n}+T_{n}\), where \(n\) means the radar IDs, \(S_{n}\) represents the clock drift and \(T_{n}\) represents the initial offset of non-simultaneous start-up. \(m_{1}\) is set as the time base for all radars to realize the TDM.  
\(S_{n}\) can be expressed as \(S_{n}=p^{*}m_{1}\), where \(p\) represents the frequency tolerance of the radar's main oscillator. For the worst case, a crystal oscillator with \(\pm\)10 ppm frequency tolerance might introduce a 20 microsecond error within 1 s. As the human respiration detection algorithm requires 16 s of sampling echoes for one detection, the minimum time margin for \(S_{n}\) is 320 us. The measurement process of \(T_{n}\) can be regarded as wireless clock synchronization between radars, and all radars' local network clocks need to be set to \(m_{1}\) after the synchronization. Here, the CC1310 SimpleLink Sub-1 Ghz module deployed on each radar is used for wireless clock synchronization. The chip provides a set of timers and taggers for radio operation. By sending and receiving the synchronization request twice, the modules can measure the differences of the local network clocks between two radars via a two-way synchronization algorithm [28], where the algorithm is similar to the Network Time Protocol. In order to avoid mutual interference between radars, the CC1310 synchronization module is activated only once for each detection. Therefore, \(T_{n}\) can be measured between the first radar (its local network clock is \(m_{1}\)) and other radars in turn. In addition, the measurement of the CC1310 has an error of \(\pm\)10 us, which should be added into the time margin. In practical terms, the time margin of the network clock module is set as \(\sim\)15 ms, which is longer than the above requirements (see Section 4). Thus, the clock drift and measurement error would not make the radar network clock invalid.
## 3 Results
### Baselines  
We employ a comprehensive set of baseline methods to benchmark the performance of HMAGNN across various dimensions. The baselines include traditional heuristic methods, embedding-based approaches, and GNN methods. For traditional heuristic methods, we employed three models. The Common Neighbors (CN) measures the number of shared neighbors between two nodes. The Adamic-Adar (AA) assigns weights to shared neighbors based on their degrees [47]. The Resource Allocation (RA) allocates a resource value between nodes according to shared neighbors. Additionally, the Heuristic Learning Method MF is a heuristic learning approach that leverages graph structural features, as mentioned in [48]. For Embedding-Based methods, there are two main approaches. Node2 Vec is an embedding-based method that learns continuous representations of nodes in the graph. MLP utilizes a neural network with multiple layers to capture non-linear relationships in the graph. In addition, we compare our method to four GNN-Based Methods. The GCN (Graph Convolutional Network) is a standard GNN that operates on graph-structured data. The GraphSAGE (Graph Sample and Aggregation) Aggregates features from a node's local neighborhood using sampling. SEAL (Structural Edge Attribute Learning) is a GNN that incorporates structural features of subgraphs for enhanced performance. The Neo is a GNN-based method that, similar to SEAL, learns both node representations and structural features for improved link prediction [49]. These baselines collectively cover a spectrum of methods, from classical heuristics to advanced embedding and GNN techniques. Comparing the performance of HMAGNN against these baselines allows us to assess its effectiveness in capturing complex relationships and structural features for link prediction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **OGB-PPA** & **OGB-DDI** & **OGB-Collab** & **OGB-Citation2** & **Jujube** \\ \hline Nodes & 576,289 & 4267 & 235,868 & 2,927,963 & 2118 \\ Edges & 30,326,273 & 1,334,889 & 1,285,465 & 30,561,187 & 43,417 \\ Features & 50 & 232 & 128 & 128 & 32 \\ Training & 403,402 & 3413 & 216,998 & 2,869,403 & 1624 \\ Validation & 115,258 & 427 & 9435 & 29,280 & 247 \\ Test & 57,629 & 427 & 9435 & 29,280 & 247 \\ Metric & Hits@100 & Hits@20 & Hits@100 & MRR & Hits@20 \\ \hline \end{tabular}
\end{table}
Table 1: Statistics and evaluation metrics of link prediction datasets.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### Access Network
#### 3.2.2 LTE  
This access network also supports SDN. It consists of the evolved packet core (EPC) entities and some eNodeBs (eNBs) that are connected to each other through independent OF switches. The EPC entities and the eNBs have no OF support, which means that the controller cannot modify their behavior. As in the previous case, the amount of OF switches rises according to the number of EPC entities and eNBs that are deployed. Therefore, the simplest topology would comprise two OF switches, one for the interconnection of EPC entities and the other one to aggregate the traffic from the eNBs.
## 3 Magnetic Circuit Design and Theoretical Modeling
### Theoretical Models  
In order to calculate the bearing capacity of a permanent magnet thrust bearing theoretically, the first step is to construct the theoretical model of the bearing. A large body of research has demonstrated that the equivalent magnetic circuit method is an effective and reliable approach for analyzing the magnetic field distribution and magnetic force in permanent magnet bearings [22]. Through the equivalent magnetic circuit model, the mathematical expression of the axial magnetic force in permanent magnet bearings can be established. From this, parameters such as axial stiffness and other mechanical properties of the bearing can be derived.  
The core idea behind the equivalent magnetic circuit method is to draw an analogy between the magnetic parameters, such as magnetic flux, magnetic potential, and magnetic resistance, in the permanent magnets and the elements of an electrical circuit--specifically, electric potential, current, and resistance. By simplifying the magnetic circuit in this way, the method allows for efficient calculations with high accuracy [23]. This approach not only streamlines the process of calculating the magnetic forces but also provides a practical and precise means of evaluating the performance of permanent magnet thrust bearings.  
The \(N\)-layer nested \(N\)-pole crossover permanent magnet thrust bearing contains \(N/2\) layers of the rotor and \(N/2\) layers of the stator, and each layer, in turn, contains \(N\) rings of permanent magnets with opposite magnetization directions. The two adjacent layers of permanent magnets generate four layers of suction and three layers of repulsion when generating small axial displacements \(e\), ignoring the force between the non-adjacent permanent magnets. In the modeling, in order to improve the computational efficiency, the magnetic leakage of the two end surfaces of the permanent magnet bearings and the outermost layer is ignored, the magnetic leakage of the rotor and stator air gap parts are ignored, and the central axis is also taken as a benign magnetically conductive material, and its magnetic resistance is ignored. The \(N\)-layer nested \(N\)-pole crossover permanent magnet thrust bearing model constructed by the equivalent magnetic circuit method is shown in Figure 8, which includes the \(N\)-layer cross-nested structure of the permanent magnet permeability and the \(N\)-1-layer air gap permeability.  
Figure 7: Four-layer nested four-pole crossover bearing magnetic circuits. The arrows in the diagram represent the direction of the magnetic lines of force.
# Hierarchical Coordinated Predictive Control of Multiagent Systems for Process Industries
## Appendix C Governing Equations of the Microwave Heating Process  
The solid heat transfer model of microwave heating couples the electromagnetic field model and solid heat transfer model, where the electromagnetic field model can be expressed through the following Maxwell equations  
\[\
abla\times\mathbf{E}^{\text{EM}}=-\mu\frac{\partial\mathbf{H}^{\text{EM} }}{\partial t}-\mathbf{M}^{\text{EM}} \tag{1}\] \[\
abla\times\mathbf{H}^{\text{EM}}=\varepsilon\frac{\partial\mathbf{E}^{ \text{EM}}}{\partial t}+\mathbf{J}^{\text{EM}}\] (2) \[\
abla\cdot\mathbf{D}^{\text{EM}}=\rho_{\text{e}}\] (3) \[\
abla\cdot\mathbf{B}^{\text{EM}}=0 \tag{4}\]  
where \(\mathbf{E}^{\text{EM}}\) is the electric field strength, \(\mathbf{H}^{\text{EM}}\) is the magnetic field strength, \(\mathbf{M}^{\text{EM}}\) is the magnetic current density, \(\mathbf{J}^{\text{EM}}\) is the current density, \(\mathbf{D}^{\text{EM}}\) is the potential shift vector, \(\mathbf{B}^{\text{EM}}\) is the magnetic induction strength, \(\mu\) is the magnetic permeability, \(\varepsilon\) is the relative permittivity and \(\rho_{\text{e}}\) is the electric charge density. The electromagnetic power loss inside the material is as follows  
\[P_{\text{abs}}=\left.\pi\!f\!\varepsilon_{0}\varepsilon^{\prime \prime}\right|\mathbf{E}^{\text{EM}}\Big{|}^{2} \tag{5}\]  
where \(P_{\text{abs}}\) is the electromagnetic power loss, \(f\) is the microwave frequency, \(\varepsilon_{0}\) is the permittivity in vacuum and \(\varepsilon^{\prime\prime}\) is the loss factor. The energy generated by the electromagnetic loss is the heat source inside the solid material. The heat transfer model is expressed as follows  
\[\rho\text{C}_{\text{P}}\frac{\partial T}{\partial t}=\kappa\bigg{(}\frac{ \partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y^{2}}+\frac{ \partial^{2}T}{\partial z^{2}}\bigg{)}+P_{\text{abs}} \tag{6}\]where \(\rho\) is the material density, \(C_{\rm p}\) is the constant pressure heat capacity, \(T\) is the temperature and \(\kappa\) is the thermal conductivity. The boundary and initial conditions of the microwave heating process are given by  
\[\mathbf{n}\cdot(\kappa\
abla T) =h(T_{\infty}-T) \tag{12}\] \[\mathbf{n}\cdot(\kappa\
abla T) =\varphi_{\rm se}\sigma_{\rm sb}(T_{\infty}^{4}-T^{4})\] (13) \[T(0) =T_{0} \tag{14}\]  
where \(n\) is the normal vector at the material boundary, \(h\) is the convective heat transfer coefficient, \(\varphi_{\rm se}\) is the surface emissivity of the material, \(\sigma_{\rm sb}=5.6703\times 10^{-8}\;\;{\rm W/m^{2}\cdot K^{4}}\) is Stefan-Boltzmann constant, \(T_{\infty}\) is the external temperature and \(T_{0}\) is the temperature without microwave heating, i.e., the initial temperature.
# Impact of Audio Delay and Quality in Network Music Performance +
## 6 Discussion  
Below, we discuss the statistically significant results for the QoE against audio delay (scenario A) and the QoE against quality (scenario B) and then provide some guidance on setting up future experiments based on our experience.