esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Interference of Extra Data Transfer Channel on Power Transfer
### Data Transfer Channel Analysis  
In the above section, we have learnt that the reduction of power transfer interference on data transfer can be achieved by reasonably setting some parameters. In this section, the output capacity of data transfer will be studied. When there is only data transfer in the system, the circuit is shown in Figure 7a, where the mutual inductance couplings on primary side coil and secondary side coil are represented by two controlled voltage sources \(j\omega Mi_{s}\) and \(-j\omega Mi_{p}\), respectively.  
According to Figure 7a, the following equations can be derived:  
\[\left\{\begin{array}{l}V_{d}=i_{s}(j\omega L_{s}+1/j\omega C_{s}+1/j\omega C_{ sd})-j\omega Mi_{p}\\ j\omega Mi_{s}=i_{p}\big{(}j\omega L_{p}+1/j\omega C_{p}\big{)}+V_{od1}\\ V_{od1}=i_{p}Z_{d}\end{array}\right. \tag{15}\]  
The transfer function from the input data carrier \(V_{d}\) to the output data carrier \(V_{od1}\) can be expressed as:  
\[G_{dd1}(\omega)=\frac{V_{od1}}{V_{d}}=\frac{j\omega MZ_{d}}{\omega^{2}M^{2}+Z _{d}Z_{sd}+\big{(}j\omega L_{p}+1/j\omega C_{p}\big{)}Z_{sd}} \tag{16}\]  
where \(Z_{sd}=j\omega L_{s}+1/j\omega C_{s}+1/j\omega C_{sd}\).  
As is mentioned before, \(L_{s}\), \(C_{sd}\) and \(L_{d}\), \(C_{d}\) resonate at data transfer frequency, so Equation (16) can be simplified as:  
\[G_{dd1}(\omega_{d})=\frac{j\omega_{d}{}^{3}C_{s}C_{p}MR_{d}}{\omega_{d}{}^{4} M^{2}C_{s}C_{p}+\omega_{d}{}^{2}L_{p}C_{p}-j\omega_{d}R_{d}C_{p}-1} \tag{17}\]  
Figure 7: The simplified data transfer circuit: (**a**) proposed data transfer circuit; (**b**) traditional data transfer circuit.  
Figure 7b is presented to compare the SNR of the proposed method (transferring data when power transfer is blocked) with the traditional method which transfers data when power is transferred. The following equations can be derived from Figure 7b:  
\[\left\{\begin{array}{c}V_{d}=i_{s}(j\omega L_{s}+1/j\omega C_{s})+i_{d}/j\omega C _{sd}-j\omega Mi_{p}\\ V_{d}=i_{eq}R_{eq}+i_{d}/j\omega C_{sd}\\ i_{d}=i_{s}+i_{eq}\\ j\omega Mi_{s}=i_{p}(j\omega L_{p}+1/j\omega C_{p})+V_{\alpha d2}\\ V_{od2}=i_{p}Z_{d}\end{array}\right. \tag{18}\]  
The transfer function from the input data carrier \(V_{d}\) to the output data carrier \(V_{od2}\) can be expressed as:  
\[G_{dd2}(\omega)=\frac{V_{od2}}{V_{d}}=\frac{j\omega^{2}MC_{sd}R_{eq}Z_{d}}{C_ {sd}R_{eq}\left(\omega^{3}M^{2}+Z_{d}Z_{rs}\omega+Z_{rp}Z_{rs}\omega\right)-j \left(Z_{d}+Z_{rp}\right)\left(R_{eq}+Z_{rs}\right)} \tag{19}\]  
where \(Z_{\text{rp}}=j\omega L_{\text{p}}+1/j\omega C_{\text{p}}\), \(Z_{rs}=j\omega L_{s}+1/j\omega C_{\text{s}}\).  
By comparing the Bode plots of Equations (17) and (19), the data transfer capacity of the proposed method and the traditional method can be compared, and this will be presented in the following section.
# Helix-Free Ferroelectric Liquid Crystals: Electro Optics and Possible Applications
###### Abstract  
This is a review of results from studying ferroelectric liquid crystals (FLCs) of a new type developed for fast low-voltage displays and light modulators. These materials are helix-free FLCs, which are characterized by spatially periodic deformation of smectic layers and a small value of spontaneous polarization (less than 50 nC/cm\({}^{2}\)). The FLC director is reoriented due to the motion of solitons at the transition to the Maxwellian mechanism of energy dissipation. A theoretical model is proposed for describing the FLC deformation and director reorientation. The frequency and field dependences of the optical response time are studied experimentally for modulation of light transmission, scattering, and phase delay with a high rate. The hysteresis-free nature and smooth dependence of the optical response on the external electric field in the frequency range up to 6 kHz is demonstrated, as well as bistable light scattering with memorization of an optical state for a time exceeding the switching time by up to 6 orders of magnitude. Due to the spatially inhomogeneous light phase delay, the ability of a laser beam to cause interference is effectively suppressed. The fastest FLCs under study are compatible with 3D, FLC on Silicon (FLCoS), and Field Sequential Colors (FSC) technologies.  
liquid crystal; ferroelectric; helix-free; soliton; transparent mode; light scattering; speckle suppression +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussion
### Station Performance Analysis
#### 4.3.1 Event Processing Distribution  
Event processing was unevenly distributed across stations:  
* Station 207: Highest event volume (63,798 events)
* Station 208: Second highest (60,281 events)
* Station 203: Among bottlenecks, processed 55,921 events
* Station 204: Lowest among active stations (44,746 events)
# Cell Therapy in the Treatment of Coronary Heart Disease
## 4 Pluripotent Stem Cells for Cardiac Cell Therapy  
### Embryonic Stem Cells  
Embryonic stem cells (ESCs) come from the inner cell mass of the blastocyst during early embryonic development. ESCs are pluripotent, which means they can differentiate into all three primary germ layers: ectoderm, endoderm, and mesoderm [107]. Under carefully selected culture conditions, ESCs can differentiate into various types of adult somatic cells, making them an exciting therapeutic option for the treatment of coronary artery disease. However, several limitations decrease the feasibility of ESCs in cell therapy. Firstly, undifferentiated pluripotent stem cells possess oncogenic potential and may result in the formation of tumors in recipients upon transplantation. Secondly, ESCs are allogeneic cells that the body may reject following transplantation. Finally, ESCs' use is restricted by the legislation of many countries due to various moral and ethical issues [108; 109].  
During the initial stages of researching the potential use of ESCs transplantation in treating CHD, there was a belief that they could differentiate into cardiomyocytes within the cardiac tissue microenvironment [110]. However, this assumption was later disproven when undifferentiated ESCs were intramyocardially injected, leading to the formation of teratomas [111]. In later stages, researchers directed their focus to differentiating ESCs toward cardiomyocyte generation and transplanting the resulting cells into the myocardium [112]. Multiple experimental studies demonstrated the participation of cardiac derivatives from human and mouse ESCs in the restoration of cardiac tissue in experimental animals [113; 114; 115; 116]. The ESCORT clinical trial demonstrated the short-term safety of transplantation of human cardiac ESC-derived cells in patients with severe left ventricular dysfunction after myocardial infarction. No tumors or arrhythmias were detected during the follow-up period, and three out of six patients developed clinically asymptomatic allolimmunization [117].
# Effect of Thermo-Mechanical Processing on the Corrosion Behavior of Fe-30 Mn-5 Al-0.5C TWIP Steel
## 2 Materials and Methods
XPS surface analysis was conducted using a PHI 5000 VersaProbe II (Physical Electronics Inc., Chanhassen, MN, USA) spectrophotometer with an Al K\({}_{\alpha}\) source (_l_rv = 1486.6 eV), a working residual pressure below \(10^{-9}\) torr, a high-voltage excitation of 15 kV, a power of 25 W and a take-off angle of 45\({}^{\circ}\) with respect to the sample surface. The high resolution XPS used a CLAM2 hemispherical electron analyzer with a non-monochromatic Mg K\({}_{\alpha}\) X-ray source operated at 300 W, with a pass energy of 23.5 eV and at energy step size of 0.2 eV/step. The Fe 2p\({}_{3/2}\), Al 2p, Mn 2p, O 1s, and C 1s areas were analyzed. The calibration was done on Ag standard substrate, using independent measurements for the Ag 3d\({}_{5/2}\) peak (368.3 eV). A Gaussian-Lorentzian mixed function was used to fit the data, after a Shirley background subtraction [28]. The binding energy reference used was adventitious C, with 285.0 eV for C 1s peak. Figure 1: Micrograph of an as-cast Fe\(-\)30 Mn\(-\)5 Al\(-\)0.5C twinning-induced plasticity (TWIP) steel specimen.
# A Novel Carrier Tracking Approach for GPS Signals Based on Gauss-Hermite Kalman Filter
## 5 Simulation Setup and Experiment Results
### Static Weak Signal Environment Scenario  
A scenario of receiving a weak signal in a stationary GPS receiver was simulated. The GPS simulator emulated the controlled, attenuated GPS signals as weak signals. In this scenario the signal power of the satellite PRN 27 remained constant at about 42 dB-Hz until 9 s, and then it decreased by 2.5 dB/s. The minimum power reached 26 dB-Hz in 15 s and was maintained at this level until 34 s. Then, it increased by 2.5 dB/s until 40 s, where it was restored to its original value, before finally remaining constant until 50 s. To compare the sensitivity of the tracking systems, we obtained the \(C/N_{0}\) estimates during the tracking in each algorithm. Figure 3 shows the \(C/N_{0}\) estimates for the three methods.  
As can be seen in the figure, the EKF and UKF algorithms could only follow the actual \(C/N_{0}\) value when the signal power was high. When \(C/N_{0}\) reached 26 dB-Hz, the deviation of the \(C/N_{0}\) estimates from the actual value was larger than 4 dB-Hz, especially after 15 s of tracking. In contrast, the GHKF algorithm still correctly estimated the \(C/N_{0}\) parameter until the end of the test. Figure 4 shows the output of the prompt channel correlator, \(I_{p}\), for a period of 10 s (from 20 to 30 s), when the \(C/N_{0}\) value was below the level of 27 dB-Hz. From the figure, we can see that the EKF-based tracking incorrectly decoded bits of the navigation message; hence, the bit synchronization failed, i.e., there was data bit inversion during the tracking. In this case, the tracking system was unable to serve tracking during the above time interval. Unlike EKF and UKF tracking methods, the GHKF method continued correctly decoding data navigation bits until the end of experiment.  
Figure 4: Data navigation bits from 20 to 30 s, at \(C/N_{0}=26\) dB-Hz.  
Figure 3: The \(C/N_{0}\) estimates.  
Thus, these results demonstrate that the proposed GHKF tracking algorithm had better performance and improved the tracking sensitivity and capability under weak signal conditions as compared with the EKF- and UKF-based tracking methods.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 5 Experimental Evaluation and Discussion
### Global Reputation Table Evaluation
Even if networks were perfectly normalized (returning identical anomaly scores for each hostname), statistical differences between their outputs would exist and be erroneously corrected by the norm-all algorithm. The pairwise algorithm utilized 1.2 million hostnames (18% of total) and performed on percentage% with the average algorithm. The pairwise algorithm could have improved upon the average algorithm by performing normalization and thus removing spurious calibration differences. On the other hand, it could have decreased performance by removing meaningful calibration differences, or by making an inaccurate estimate of the normalization parameters due to noisy data. The positive and negative effects have canceled out to leave the performance of pairwise and average nearly equivalent. As can be observed in the next section, there are benefits to using pairwise normalization over a simple average, but the positive effect manifests mainly for the worst-performing networks. This improvement was likely diluted in the mostly unchanged performance of typical networks and did not manifest in the global reputation table of AUC scores discussed above, as is clear in following results. Figure 7: Global reputation table receiver operating characteristic (ROC) curve comparison on the biggest 100 networks.
## 5 Numerical Results and Discussion
### Performance Analysis
#### 5.1.2 Performance Analysis of PU2 Packets  
Figure 2 depicts the variation trends of PU2 packets' blocking rate \(B_{P2}\), loss rate \(L_{P2}\), and throughput \(T_{P2}\) under the proposed communication failure and repair mechanism.  
As shown in Figure 2, if the communication failure rate \(f\) rises, PU2 packets' blocking rate \(B_{P2}\) and loss rate \(L_{P2}\) increase while throughput \(T_{P2}\) decreases. When the repair rate \(r\) increases, \(B_{P2}\) and \(L_{P2}\) decrease while \(T_{P2}\) increases. When \(f\) increases, the probability of the system state being failure rises, and the low-speed transmission of PU packets reduces the channel's idle time, so the probability of the newly arrived PU2 packets being blocked increases. If the system state fails, the channel preemption behavior of PU1 packets to PU2 packets becomes frequent, and the probability of PU2 packets being lost increases accordingly. Due to the increased blocking and loss rates, the amount of PU2 packets that is transmitted completely decreases. When \(r\) increases, the possibility of the system maintaining a normal state increases, and the high-speed transmission of PU1 and PU2 packets makes the channel more free to meet the newly arrived PU2 packets, so the blocking rate reduces. If the system state is normal and PU1 packets will not preempt the channel of PU2 packets, the probability of PU2 packets being preempted decreases. Therefore, the probability of PU2 packets being lost decreases, and more PU2 packets are transmitted successfully.  
From Figure 2, we can find that as PU1 packets' arrival rate \(\alpha_{11}\) increases, PU2 packets' blocking rate \(B_{P2}\) and loss rate \(L_{P2}\) increase while throughput \(T_{P2}\) decreases. As PU2 packets' arrival rate \(\alpha_{12}\) increases, \(B_{P2}\), \(L_{P2}\), and \(T_{P2}\) all increase. When \(\alpha_{11}\) increases, the probability that the channels are occupied by PU1 packets increases, so the probability that those newly arrived PU2 packets are unable to enter into the channel increases. Meanwhile, when the system is in the failure state, the preemption behavior of PU1 packets to PU2 packets increases, resulting in more lost PU2 packets, and the amount of PU2 packets  
Figure 2: The performance change trends of PU2 packets: (**a**) the change trend in \(B_{P2}\); (**b**) the change trend in \(L_{P2}\); (**c**) the change trend in \(T_{P2}\).  
transmitted completely decreases. Because at most one packet can exist in the channel at a time, when \(\alpha_{12}\) increases, the number of PU2 packets blocked grows, and more PU2 packets entering the channel encounter PU1 packets and lose channel access right; therefore, \(B_{P2}\) increases. According to our parameter setting, \(\alpha_{12}\) has a larger increase compared with \(B_{P2}\) and \(L_{P2}\), so after performance calculation, \(T_{P2}\) also shows an increasing trend.
## 5 Discussion
### Failure Modes  
The deformation of the sliding surface in Model 1 during the excavation needs to be discussed further. Figure 13 displays a map of the y-direction displacement during the excavation, with the red area representing right displacement and the blue area representing left displacement.  
The shape of the potential sliding surface changed at the beginning of the second excavation (Figure 13a). The y-direction displacement of the rock mass below the new outcrop also changed. When the third step was excavated, Boundary 1 was formed (Figure 13b), causing the rock mass on both sides to squeeze. This reflects the release of elastic deformation energy in the deep rock mass. Boundary 1 tends to become stable as the excavation continues. Zone 1 is developing towards the new exposed line of the potential sliding surface (Figure 13b-i). Zone 1 divided the rock mass into three parts when the excavation was finished (Figure 13i). A shear dislocation occurs between Zone 1 and Zone 2 (Figure 13i), forming a shear boundary as Boundary 2. Additionally, a banded area in Zone 3 is formed by the tension Boundary 3. This process explains the formation of the banded-shaped area.  
The displacement cloud map in Figure 14 shows the Y-direction movement of the sliding surface after failure. Boundary 2 runs through the sliding surface, dividing the original Zone 3 (Figure 13i) into upper and lower parts. This suggests that the rock mass of the fourth step experienced relative displacement and may result in a crack. Eventually, Boundary 3 is likely to develop a pull crack on the right side of the excavated slope, indicating a tendency for the right boundary of the sliding body to crack during failure. Boundary 1 disappears as elastic deformation energy is released after failure.  
Figure 13: Y-direction displacement of the sliding surface in Model 1 during excavation of the 2 nd to 10 th steps (the red area represents the right displacement of the y-direction component of the rock mass, and the blue area represents the left displacement).
###### Abstract  
Traceability, namely the ability to access information about a product and its movement across all stages of the supply chain, has been emerged as a key criterion of a product's quality and safety. Managing fresh products, such as fruits and vegetables, is a particularly complicated task, since they are perishable with short shelf lives and are vulnerable to environmental conditions. This makes traceability of fresh produce very significant. The present study provides a brief overview of the relative literature on fresh produce traceability systems. It was concluded that the commercially available traceability systems usually neither cover the entire length of the supply chain nor rely on open and transparent interoperability standards. Therefore, a user-friendly open access traceability system is proposed for the development of an integrated solution for traceability and agro-logistics of fresh products, focusing on interoperability and data sharing. Various Internet of Things technologies are incorporated and connected to the web, while an android-based platform enables the monitoring of the quality of fruits and vegetables throughout the whole agri-food supply chain, starting from the field level to the consumer and back to the field. The applicability of the system, named AgroTRACE, is further extended to waste management, which constitutes an important aspect of a circular economy.  
traceability; food safety; IoT; event capturing; integrated information system; information management; transaction support; data sharing; real-time communication; interoperability +
Footnote †: journal: Applied Sciences
# The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation
## 2 Literature Review  
### Assessment of Emergency Shelter Demand  
The first step to understanding the location selection of emergency shelters is to analyze the temporal and spatial distributions of shelter demand; here, the difficulty lies in figuring out the size and distribution of the urban population [12; 13]. Traditional geospatial data, such as land cover data, land use data, and light emission data, have been widely used to describe the population distribution [14; 15]. With the in-depth development of big data applications, new methods for the determination of the population distribution have been applied in some studies. [PERSON] et al. [12] proposed an approach for the examination of the distributions of daytime and nighttime populations at the plot scale via the use of point-of-interest (POI) data. A common method used in earthquake shelter demand forecasting is to set input parameters (such as risk, vulnerability, and exposure) in combination with different variables to estimate demand [16]. For a tsunami disaster, the time that is required for residents to seek shelter, the inundation depth and flow velocity, and the evacuation speed are the main factors that are used for the estimation of the scale and spatial distribution of the affected population [17]. In fact, the analysis of shelter demand and capacity is very complicated and is easily affected by external conditions. For example, the instability of the evacuation network and the restriction of the path capacity will lead to changes in both the demand capacity and the ideal shelter location [18]. In addition, the mobility of the population within the city also causes greater uncertainty for the assessment of emergency shelter demand, and the different functions between the districts of (mega) cities have also caused large differences in the number and distributions of daytime and nighttime populations. To obtain the most accurate population distribution information for the assessment of casualties and shelter demand and the formulation of evacuation plans, [PERSON] et al. [19] used city maps as auxiliary information. Based on different downscaling techniques, they constructed a population vulnerability analysis method for night disaster scenarios. To meet the needs of all-weather disaster prevention, [PERSON] and [PERSON] [20] used high-resolution aerial images, census data, and high-precision land use data to evaluate day and night disaster scenes and to analyze the supply and demand of shelters. Some studies have highlighted that needs should be differentiated according to different situations. In an emergency evacuation scenario, considering the complexity of the evacuation behavior and nearby sheltering, the demand can be the maximum number of people during the day and night. In short-term to long-term evacuation scenarios, the demand can be the number of people that are present at night, because sufficient disaster relief operations can be carried out at this time [12; 21].