esa-sceva/satcom-chunk-collection huggingface dataset:
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
###### Abstract  
Consider an evolving epidemic in which each person is either (S) susceptible and healthy; (E) exposed, contagious but asymptomatic; (I) infected, symptomatic, and quarantined; or (R) recovered, healthy, and susceptible. The inference problem, given (i) who is showing symptoms (I) and who is not (S, E, R) and (ii) the distribution of meetings among people each day, is to predict the number of infected people (state I) in future days (e.g., 1 through 20 days out into the future) for the purpose of planning resources (e.g., needles, medicine, staffing) and policy responses (e.g., masking). Each prediction horizon has different uses. For example, staffing may require forecasts of only a few days, while logistics (i.e., which supplies to order) may require a two- or three-week horizon. Our algorithm and system EpiInfer is a non-Markovian approach to forecasting infection rates. It is non-Markovian because it looks at infection rates over the past several days in order to make predictions about the future. In addition, it makes use of the following information: (i) the distribution of the number of meetings per person and (ii) the transition probabilities between states and uses those estimates to forecast future infection rates. In both simulated and real data, EpiInfer performs better than the standard (in epidemiology) differential equation approaches as well as general-purpose neural network approaches. Compared to ARIMA, EpiInfer is better starting with 6-day forecasts, while ARIMA is better for shorter forecast horizons. In fact, our operational recommendation would be to use ARIMA (1,1,1) for short predictions (5 days or less) and then EpiInfer thereafter. Doing so would reduce relative Root Mean Squared Error (RMSE) over any state of the art method by up to a factor of 4. Predictions of this accuracy could be useful for people, supply, and policy planning.  
epidemic modeling; compartmental models; SEIRS; forecasting; contact networks; parameter estimation 2025  
450
# A New Top-Mounted Shear-Hinge Structure Based on Modal Theory and Rubber-Pad Damping Theory
## 3 Finite-Element Modeling
### Material Properties, Constraints, and Load Settings  
The TMSH material is 60 Si2 Mn. According to the literature [17], the 60 Si2 Mn's modulus of elasticity is 201 GPa, its yield strength is 1293 MPa, and its fracture strength is 1430 MPa. A bilinear reinforced intrinsic structure model is used to define the mechanical parameters of the TMSH, and the material properties of the metal pad are the same as those of the TMSH. In the actual process of using rubber, its deformation is very small, and the displacement caused by the TMSH is much smaller than its size, so to simplify the model, a linear stress-strain curve is used instead of the superplastic rubber intrinsic model [30]. The specific parameters of the material are shown in Table 3, where the rubber has a natural hardness of 40.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**IRHD** & \(E_{b}\)**/MPa** & \(\kappa\) & \(E_{o}\)**/MPa** & \(n\) \\ \hline
70 & 7.34 & 0.53 & 1270 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Sensor parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material** & **Young’s Modulus/Pa** & **Poisson’s Ratio** &
\begin{tabular}{c} **Density** \\ **kg/m3** \\ \end{tabular} \\ \hline
60 Si2 Mn & \(2.01\times 10^{11}\) & 0.29 & 7850 \\ Rubber & \(1.5\times 10^{6}\) & 0.4995 & 1300 \\ \hline \end{tabular}
\end{table}
Table 3: Material parameters of TMSH.  
The TMSH is connected to the bolt sleeves cast in the floating slab using bolts. To facilitate the calculations, the strength of the TMSH is verified by default with intact TMSHs, i.e., the bolts are connected to the floating slab via a fixed connection. In the modal analysis of the TMSH, to simulate real contact in the field, the normal interaction property is defined as hard contact when setting the contact properties of each component to prevent the models from penetrating each other. Additionally, the tangential interaction is defined as a penalty function friction model, the friction coefficient between the metals is set to 0.2, and the friction coefficient between the metal and rubber is 0.7. In order to increase the model convergence, the enhanced Lagrangian algorithm is used for the vertical contact between the shear rod and the two bushings, and between the rubber and the metal (in order to save computational effort, the contact between the shear rod and the bushings is set as a fixed constraint during the strength check), and all the bottom surfaces are set as fixed constraints. According to the requirements of a city subway line, the shear-hinge bolt torque is 300 N\(\cdot\)m, and the TMSH M24 bolt diameter is 24 mm, according to the following formula [31]:  
\[F_{bolt}=\frac{M_{bolt}}{0.17d_{bolt}} \tag{5}\]  
where \(F_{bolt}\) is the bolt preload force, \(M_{bolt}\) is the bolt torque, \(d_{bolt}\) is the diameter of the bolt (calculated using Equation (5)), and the corresponding bolt preload is 73.529 kN. The TMSH loads and constraints are shown in Figure 6.
## 5 Controllable Queuing Model
### Action Space for Resource Reallocation  
The control policy for the two-slice model dictates the necessary actions to adjust the size of the 1-slice in response to a signal from the controller. This adjustment is only applicable to states within subset \(\mathcal{S}_{\delta}\), where resource reallocation is required. The action space can be divided into four cases, based on the ratio of users transmitting traffic and waiting in buffers for each slice. If both buffers are either free or occupied, no reallocation occurs upon receiving a signal. However, if one buffer is free while the other is occupied, resource reallocation can be initiated. Therefore, the set of action space (\(\mathcal{A}_{\mathbf{s}}\)) can be defined as follows:  
\[\mathcal{A}_{\mathbf{s}}=\begin{cases}\{m,\dots,n_{1}\},&\mathbf{s}\in \mathcal{S}_{\delta}^{11}(m),\\ \{m,\dots,N-n_{2}\},&\mathbf{s}\in\mathcal{S}_{\delta}^{12}(m),\\ \{N-n_{2},\dots,m\},&\mathbf{s}\in\mathcal{S}_{\delta}^{21}(m),\\ \{n_{1},\dots,m\},&\mathbf{s}\in\mathcal{S}_{\delta}^{22}(m),\\ \emptyset,&\mathbf{s}\
otin\mathcal{S}_{\delta}(m).\end{cases} \tag{3}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Description** & **Transition Rate** & **Condition on \(\mathbf{s}\)** & **State \(\mathbf{s}^{\prime}\)** \\ \hline Request from 1-user has arrived & \(\lambda_{1}\) & \(n_{1}+1\leq R_{1}\) & \((m,n_{1}+1,n_{2})\) \\ Request from 2-user has arrived & \(\lambda_{2}\) & \(n_{2}+1\leq R_{2}\) & \((m,n_{1},n_{2}+1)\) \\ Traffic from 1-user has been transmitted & \(\frac{n_{1}}{N}C\mu_{1}\) & \(m>0,\;\;n_{1}>0\) & \((m,n_{1}-1,n_{2})\) \\ Traffic from 2-user has been transmitted & \(\frac{N-m}{N}C\mu_{2}\) & \(N-m>0,\;\;n_{2}>0\) & \((m,n_{1},n_{2}-1)\) \\
1-User has abandoned 1-buffer & \((n_{1}-m)\varepsilon_{1}\) & \(n_{1}\geq m\) & \((m,n_{1}-1,n_{2})\) \\
2-User has abandoned 2-buffer & \((n_{2}-N+m)\varepsilon_{2}\) & \(n_{2}\geq N-m\) & \((m,n_{1},n_{2}-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transition rates related to users.
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## 4 Experiments and Results  
The outbreak was set to begin on 1 October 2013 and it was simulated until 30 April 2014 for a total of 212 days, keeping in mind the timeline of the actual outbreak. This 7-month period provided sufficient time for the outbreak to subside.  
The infectious humans and mosquitoes were introduced in Tahiti in the simulation. The system has two input parameters which were varied to test the intervention strategies:  
1. **Ratio of mosquito-human population (_m_):** The ratio of mosquito-human population was used to estimate the initial vector population, such that total mosquito population is \(m\) times the total human population on each island. The base ratio was chosen as 3.
2. **Transit network load (_load_):** The network shown in Figure 3 represents the daily average of the maximum passengers that travel from one island to another. Since all the flights or ferries may not be full everyday, the simulation was executed by changing the load from 1 (maximum) to {0.75, 0.50, 0.25, 0.10}.
## 3 Proposed System Design
### Modelling of DC-Link Capacitor  
For the estimate, the VSI DC-link capacitor, the harmonic content present in the AC supply is reflected on the DC side as a prior harmonic in the three-phase supply system. The minimum and the maximum speeds for the BLDC motor can be calculated through the fundamental frequencies of the VSI. These two frequencies \(\omega_{max}\) and \(\omega_{min}\) are further used to calculate the dc link capacitance value which would satisfy the operation of the solar panel when working below the reduced level of irradiance. The highest capacitor value is chosen for designing a DC-link capacitor.  
The fundamental output frequency of VSI corresponding to the maximum and minimum pumping speed requirement is:  
\[\begin{array}{ll}\omega_{max}&=2\pi f_{max}=\frac{2\pi N_{max}P}{120}\\ &=2\pi\times 1500\times\frac{6}{120}=471.20\ \text{rad/s}\end{array} \tag{12}\]  
\[\begin{array}{ll}\omega_{min}&=2\pi f_{min}=\frac{2\pi N_{min}P}{120}\\ &=2\pi\times 1000\times\frac{6}{120}=314.15\ \text{rad/s}\end{array} \tag{13}\]  
where \(f_{max}\) is the maximum fundamental frequency in Hz, \(f_{min}\) is the minimum fundamental frequency in Hz, and P is the number of poles of the BLDC motor.  
The corresponding capacitor can be calculated as:  
\[\begin{array}{ll}C_{0\ max}&=\frac{l_{0}}{6}\times\omega_{max}\times\Delta V_{ out}\\ &=\frac{2.5}{6}\times 471.2\times 24\times 0.05=736.8\ \upmu\text{F}\end{array} \tag{14}\]  
\[\begin{array}{ll}C_{0\ min}&=\frac{l_{0}}{6}\times\omega_{max}\times\Delta V _{out}\\ &=\frac{2.5}{6}\times 314.15\times 24\times 0.05=1105.27\ \upmu\text{F}\end{array} \tag{15}\]  
where \(\Delta V_{out}\) is the amount of the maximum permissible ripple voltage. Finally, \(C_{0\ min}\) is chosen to construct the DC link capacitor.
## 4 Experimental Verification of Control Algorithm
### Closed-Loop Sweep Experiment  
In the closed-loop sweep experiment, the input sweep signal was set as shown in Equation (1), the amplitude was set to 1 mrad/s, and the frequency range was set to 0.01 Hz-200 Hz. The closed  
Figure 12: Frequency domain analysis of guidance absolute error curve.  
Figure 13: Absolute value of sinusoidal guidance error curve.  
loop sweep curve of pitch axis and the frequency characteristic curve of control system are shown in Figures 14 and 15.  
According to the frequency characteristic curves shown in Figures 14 and 15, the closed-loop bandwidth of the proposed algorithm is \(\sim\)9.85 Hz, slightly higher than that of the digital PID, \(\sim\)9.05 Hz. The closed-loop bandwidth using the Ref. [22] algorithm is the smallest, \(\sim\)8.7 Hz. Therefore, the dynamic performance of the proposed chatter-free algorithm is much better than that of the previously described chatter-reduced algorithm [22]. The resonance peak of the proposed algorithm is \(\sim\)1.41 dB, which is much smaller than that of the previous algorithm [22] of \(\sim\)3.87 dB. The resonance peak of the digital PID is the largest, \(\sim\)8.05 dB. The smaller the resonance peak is, the better the stability of the system.  
Figure 14: Closed-loop sweep curve of pitch axis.  
Figure 15: Contrast curve of the frequency characteristics of the control system.
## 3 Results
### Univariate Statistical Analysis  
After normalization, Student's \(T\)-test was performed between Control and All Cancer classes and identified 44 out of 250 VOCs with \(p\)-value < 0.05. Additionally, the \(T\)-test found 37 VOCs with \(p\)-value < 0.05 when applied between the Cancer Week 1 and Week 3 sample classes. These VOCs are identified and listed in Table S1 with their corresponding name, retention time and \(p\)-values. After adjusting \(p\)-values for FDR, 18 VOCs with \(p\)-value < 0.05 were found between Control and Cancer (italicized in Table S1) and 3 VOCs were identified with \(p\)-value < 0.05 between Week 1 and Week 3 (underlined in Table S1). Of the 44 VOCs identified with \(p\)-value < 0.05 between Control and Weeks 1-3, 37 features were downregulated and 7 upregulated. With regards to the 37 significant VOCs between Cancer Week 1 and Week 3, 24 were downregulated and 13 upregulated. Downregulated features were more significant than upregulated ones for both comparisons. Hierarchical heatmaps were generated using statistically significant VOCs between each comparison  
Figure 1: Illustration of murine tumor injection, mouse urine sample collection, sample treatment and analysis via solid phase microextraction coupled to gas chromatography-quadrupole time-of-flight mass spectrometry to identify volatile organic compound (VOC) biomarkers for breast cancer and tumor progression.  
and are shown in Figure 2a,b. The heatmap corresponding to Control vs. Weeks 1-3, shows low intraclass variation and high interclass variation (Figure 2a). Figure 2b shows many VOCs are downregulated in Week 3, indicating the concentration of these VOCs decreases as cancer progresses. The heatmap displays high intraclass variation for VOCs with \(p<0.05\) detected in Week 2, indicating some mice progressed faster than others, or possibly some mice had undergone tumor-induced osteolysis and some had not yet [30], but no imaging or invasive studies were undertaken during intermediate time points, so this is not confirmed. In Figure 2, abbreviations for VOCs that show high statistical significance and are utilized for further analyses are indicated.
## 4 Materials and Methods
### In Vitro and In Vivo TPS Assays  
For the in vitro TPS assay, the recombinant protein with 6 His-tag was cloned into the destination vector pDEST17 and expressed using the _E. coli_ BL21 pLysS strain. A cell pellet from 5 mL of isopropyl \(\beta\)-D-1-thiogalactopyranoside (IPTG)-induced culture was passed through a His spin trap (GE Healthcare) to obtain the cell lysate. The assay was carried out by mixing 250 uL of 2\(\times\) reaction buffer (50 mM HEPES (pH 7.4), 200 mM KCl, 15 mM MgCl\({}_{2}\), 10% glycerol, 10 mM DTT) with 50 ug of cell lysate, 5 ug of the substrate (GPP, FPP, NPP, and GGPP) and massed up to 500 uL with 25 mM HEPES (pH 7.4) in an inert glass bottle. Then, 250 uL of hexane was added on top slowly and the reaction bottle was sealed with parafilm. After an incubation at 30 \({}^{\circ}\)C for 2 h, the reaction mixture was vortexed for 1 min and centrifuged at 1200 rpm for 30 min. The hexane layer was then transferred to a fresh GC bottle and subjected to GC-MS analysis.  
For the in vivo assay, A. tumefaciens cultures harbouring plasmids 35 Spro:CsTPS, 35 Spro:HMGR, and silencing suppressor 35 Spro:p19 were pelleted and resuspended in MMA (10 mM MES, 10 mM MgCl\({}_{2}\), and 100 uM acetosyringone) solution to OD\({}_{600}=1\). The solutions were then mixed or infiltrated separately into _N. benthamiana_ leaves using a 1 mL syringe, and 2-3 infiltrated leaves were excised after 3 d and incubated with 500 uL hexane for 1 hr. The homogenized samples were then centrifuged for 10 min at 13,000 revolutions per min (rpm). The hexane layer was transferred to glass vials and analyzed using GC-MS (7890A with 5975C inert mass selective detector, Agilent Technologies, Santa Clara, CA, USA). Then, 2 uL samples were injected, and separation was achieved with a temperature program of 50 \({}^{\circ}\)C for 1 min, increased at a rate of 8 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C and held for 5 min, on a 30 m HP-5 MS column or CP-Chirasil Dex CB column (25 m \(\times\) 0.25 mm, 0.25 um film thickness) (Agilent Technologies, Santa Clara, CA, USA). The compounds were identified by comparison with the mass spectra reference library NIST MS 2014. The data were processed by MSD ChemStation Data Analysis (Agilent Technologies). The enantiomeric identity of linalool was confirmed by comparison with the GC data of (S)-linalool and (R)-linalool standards.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 3 Materials and Methods
### Data Analysis  
The nature of the manual method for adjusting CF during measurements resulted in sudden and very short amplitude changes or artifacts due to inconsistent pressure application between CF steps. Therefore, it was necessary to preprocess the data before performing the analysis. Processing was carried out using Python in Jupyter Notebooks [61]. Figure 4 shows the processing flowchart.  
First, it was verified that there were no missing data in the time series, confirmed by an exhaustive analysis of the data sequences. This ensured signal integrity and continuity throughout the entire measurement period. An adaptive threshold-based artifact detection algorithm was used to identify and remove signal segments containing obvious artifacts. An amplitude threshold of 20% above the signal's mean was established to detect anomalous peaks. Then, 500 ms segments around the detected artifacts were removed. Finally, the deleted segments were replaced using linear interpolation to maintain signal continuity. Before signal filtering, a median filter with a 5-sample window was applied to smooth  
Figure 4: Dataset processing flowchart.  
the signal and reduce unwanted high-frequency noise. This preliminary step helped to prepare the signal for more efficient filtering and eliminated isolated peaks that could affect subsequent analyses. For the \(P_{SQI}\) analysis, the raw MW-PPG signal is processed using a fourth-order analogue Butterworth low-pass filter with a 10 Hz cutoff, which helps to eliminate noise while maintaining AC and DC components. For all other analyses, the signal is bandpassed from 0.5 to 10 Hz to extract the AC component and preserve pulse shapes using the same type of filter.  
The data were analyzed using the windowing method to reduce the computational burden and improve the clarity of the analysis. For this, non-overlapping windows of 10 s were used to capture the dynamics of several signal pulses effectively. This specific window size helps to ensure that each window captures sufficient physiological data to reflect meaningful trends while avoiding redundancy that could complicate the analysis. Additionally, using non-overlapping windows minimizes the influence of transient CF variations, thereby improving the distinction between true physiological signals and artifacts. This approach optimizes data processing and enhances the accuracy of interpreting short-term physiological changes. The signals were normalized to standardize the scales of the recordings and facilitate comparative analyses: the AC component of the PPG measurements was adjusted to a range of \(-\)1 to 1, while the CF signal was normalized from 0 to 1. On this scale, a value of 0 represents the minimum CF, and 1 indicates the maximum CF reached during the measurement for each data record.
## 2 Experimental Section
### Sample Preparation  
The mix preparation was carried out according to the standard EN 14016-2 [35]. The reference MOC cement composites (R-MOC) were prepared from MgO powder, MgCl\({}_{2}\) solution, and silica sand. MgO powder, MgCl\({}_{2}\) solution, and EPP aggregate were used for the production of composite-labelled EPP-MOC. Mixture with EPP aggregate and inner hydrophobic admixtures was named EPP-MOC-IH, as inner hydrophobic agents were used Ligastar CA 800 (calcium stearate) and Ligaphob N 90 (sodium oleate). These additives were provided by Excel Mix Cz, Ltd., Velim, Czech Republic. The hydrophobic admixtures were applied in the amount of 1 g of stearate and 2 g of oleate to 100 g of MgO powder. Stearate in the material reacts with water to form a gel that seals the structure and prevents moisture penetration. The interaction between the MgO powder and sodium oleate molecules can be expressed as van der Waals forces and hydrophobic forces [36].  
The mixture proportions of MOC cement composites are summarized in Table 3. The fresh mixtures were casted in 40 mm \(\times\) 40 mm \(\times\) 160 mm prism molds, 70 mm \(\times\) 70 mm cubic molds and in a circular mold having a diameter of 100 mm and a height of 20 mm. Samples were immediately after casting covered with polyethylene sheets, after 24 h demolded and then air-cured for next 27 days at a temperature of \(23\pm 2\)\({}^{\circ}\)C and relative humidity of \(45\pm 5\%\). Examples of prepared cubic samples are displayed in Figure 3.  
The R-MOC and EPP-MOC specimens intended for the measurement of water- and water vapor transport properties were coated with boiled linseed oil, which might affect the resulting diffusion and sorption properties of the material. The boiled linseed oil is used in the construction industry as a protective layer for wood and concrete elements, plasters, etc. It forms a thin layer on the concrete surface and clogs its pores, avoiding the penetration of water and chemical solutions into the pore structure [37]. Samples with boiled linseed oil surface treatment were labelled as R-MOC-LO and EPP-MOC-LO.  
Figure 3: Tested samples: R-MOC (**a**); EPP-MOC-IH (**b**); EPP-MOC (**c**).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & **Mass (g**) & & \\ \hline
**Mixture** & **Caustic Magnesite** & **MgCl\({}_{2}\) Solution** & **Sand** & **EPP** & **Calcium Stearate** & **Sodium Oleate** \\ \hline R-MOC & 450 & 500 & 1350 & – & – & – \\ EPP-MOC & 450 & 500 & – & 21.8 & – & – \\ EPP-MOC-IH & 450 & 500 & – & 21.8 & 4.5 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of MOC cement composite mixtures.