esa-sceva/satcom-chunk-collection huggingface dataset:
# Inkjet-Printed and Paper-Based Electrochemical Sensors
## 1 Introduction
As a result of the prototype produced by this particular group, it became apparent that paper-based sensors can indeed be fabricated using simple processes that yield disposable devices for point-of-care diagnostics. Following this publication, a number of other groups demonstrated the ability to pattern paper-based substrates using hydrophobic materials. In addition to photolithography [8; 9], other methods capable of producing paper-based microfluidic devices include plasma treatment [10], wax printing [11; 12], inkjet printing [13; 14; 15; 16; 17; 18; 19], and screen printing [5; 11; 12; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Among these patterning methods, wax printing has been the most frequently used method to develop microfluidic paper-based analytic devices (\(\upmu\)PADs) [11; 12; 22; 29]. Since the emergence of paper-based sensors, various sensing mechanisms have been utilized [30]. The most popular methods demonstrated in literature include colorimetric [8; 11], chemiluminescence [31], chemiresistive [32; 33; 34], and electrochemical [15; 17; 18; 35; 36; 37] techniques. Each of these sensing mechanisms have been employed extensively to develop paper-based sensors. Although the various sensing mechanisms have their respective advantages and disadvantages, electrochemical sensing remains the most common and most effective method for paper-based devices due to its accuracy, reliability, quantitative results, and ability to distinguish between multiple analytes in a single sample. These sensors are called electrochemical paper-based analytical devices, or simply ePADs, a term coined by the [PERSON] group in 2012 [21]. Given the substantial growth in the fields of paper-based analytic devices and printed electronics, this review article is intended to focus on the key components of each field that support the fabrication of low-cost point-of-care sensors. The collision of these two fields combines significant advantages and is paving the way towards inkjet-printed paper-based electrochemical devices for chemical and biological sensing. Such devices are low-cost, easy to manufacture, and disposable while maintaining high accuracy and sensitivity. Applications of these devices include on-site patient diagnostics, water and air pollution monitoring, cancer screening, and much more.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna
### ECC Derivation
#### 2.2.1 ECC Based on S-Parameters  
In practical scenarios for which radiation patterns are not readily available, the ECC can be approximated using the S-parameters of the antenna system. This assumes that the antenna is both lossless and exhibits high radiation efficiency. The ECC is then calculated using (7), which considers the mutual reflection coefficients of the two-port network:  
\[\rho_{e}=\frac{\left|S_{11}^{*}S_{12}+S_{21}^{*}S_{22}\right|^{2}}{\left(1- \left|S_{11}\right|^{2}-\left|S_{21}\right|^{2}\right)\left(1-\left|S_{22} \right|^{2}-\left|S_{12}\right|^{2}\right)} \tag{7}\]  
where  
* \(S_{ij}\) is the S-parameters of the two-port network;
* \(S_{11}\) and \(S_{22}\) are the reflection coefficients of ports 1 and 2;
* \(S_{12}\) and \(S_{21}\) are the transmission coefficients between the ports.  
The expression is valid when the total system loss is negligible and mutual coupling is not excessive. If MIMO performance is good, the ECC values are typically less than 0.5 and preferably below 0.1 for high-speed applications such as 5G and WLAN systems [12; 13].  
The S-parameter-based method is widely used for both simulations and measurements because it is simple and efficient. The method requires only scattering parameters, which can be easily obtained using a vector network analyzer (VNA) or full-wave electromagnetic simulation tools. This is especially useful during the initial design phase and when comparing multiple designs. However, the accuracy of the method is inherently limited by the assumptions made. The method does not consider the real radiation characteristics of antenna and may yield misleading results when significant losses or strong mutual coupling are in play. Furthermore, the method neglects the effects of polarization and angular coverage, both of which are critical in terms of MIMO performance.
## 3 Simulation Experiment
### EMD_CS_ST Algorithm Application
After applying this method, the \(SNR\) is increased to 27.170 dB, the signal standard deviation is the smallest, and the correlation coefficient is the largest. Transient non-stationary characteristics of the MS signal are fully preserved compared to the other three methods.
## 4 Algorithm for Energy and Fuel Consumption Minimization
### Dynamic Programming (DP)  
DP is an optimization method that minimizes an objective function by evaluating all possible control sequences. Every state transition of the system involves a cost. The optimal control sequence is built by joining the specific state's sequence that obtains the least total cost. The cost of each state transition is defined by an objective function. Although DP needs a long computational time, it finds the global minimum in an optimization process.  
Bellman's Principle of Optimality [40; 41] is the theoretical support for Dynamic Programming. This principle states that all the portions of an optimal trajectory are, by themselves, optimal trajectories [42]; _i.e._, for a given state, the optimal rule for the following transitions does not depend on the rule applied in the previous transitions.  
In [43], a typical optimization problem is solved by using DP. The requirement is to find the input sequence or optimal decision sequence that minimizes the following objective function:  
\[J=\sum_{k=0}^{N}q_{k}(x(k),u(k)) \tag{1}\]  
where, \(x\) is the state variable; \(u\) is the decision variable; and \(k\) is the stage.  
This cost value depends on the initial time \(i\), the initial state \(x(i)\) and the input variable \(u\) in the interval \([i,N]\), but the cost does not depend on any previous states. For the trajectories that in instant \(i\) are in state \(x(i)\), the optimal cost depends only on the initial state \(x(i)\) and the initial time \(i\):  
\[J_{i}^{*}=\min_{u}\{\sum_{k=i}^{N}q_{k}[x(k),u(k)]\}=J_{i}^{*}[x(i)] \tag{2}\]  
Then, Bellman's Principle of Optimality indicates that:  
\[J_{i}^{*}=\min_{u(i)}\{q_{i}[x(i),u(i)]+J_{i+}^{*}[x(i+1)]\} \tag{3}\]  
Additionally, \(x(i+1)\) depends only on \(x(i)\) and \(u(i)\). Therefore, when we minimize with respect to \(u(i)\), the optimal cost in \(i\) will depend only on \(x(i)\) and \(i\). So, using iterative application of Equation (3) and collecting the results from the final state back to the initial state (backward recursion), the optimal input sequence u can be found. The aforementioned problem can be solved using either backward or forward recursion. The results obtained by both methods should be the same.
# Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
## 5 Analysis Results
### Technical-Related Time Delays  
Table 6 presents the results of the regression analysis concerning both the occurrence and the duration of delayed time attributed to technical factors. Similar to the findings for human-related factors, the coefficient estimates in both models exhibit considerable similarity. The probability of delayed time occurrence is elevated when events are associated with high-potential incidents (3.848 in the Two-stage model; 3.969 in the ZINB model). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Training Data**} & \multicolumn{3}{c}{**Testing Data**} \\ \cline{2-7}  & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** & \(\mathbf{R^{2}}\) & **MSE** & **RMSE** \\ \hline Two-stage & 0.355 & 12.114 & 3.481 & 0.311 & 13.012 & 3.607 \\ ZINB & 0.389 & 11.478 & 3.388 & 0.342 & 11.503 & 3.392 \\ ANN & 0.446 & 10.426 & 3.229 & 0.373 & 10.808 & 3.288 \\ ANN/TWO & 25.6\% & \(-\)13.9\% & \(-\)7.2\% & 19.9\% & \(-\)16.9\% & \(-\)8.8\% \\ ANN/ZINB & 14.7\% & \(-\)9.2\% & \(-\)4.7\% & 9.1\% & \(-\)6.0\% & \(-\)3.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model performance comparison for the human-related factors. However, the duration of time delay was not found to be statistically significant in the negative binomial part. In instances of derailment accidents, there is an increased likelihood of prolonged delayed time (0.021 in ZINB). Additionally, when the accident involves fire, the duration of delayed time tends to increase. The analysis reveals that accidents associated with signal, telecommunication, rail, or structural facilities are less likely to experience time delays, as evidenced by the negative signs of all coefficient estimates in the logit part. Furthermore, the duration of time delay is less likely to be prolonged in cases involving signal or communication facilities (\(-\)0.290 in the Two-stage model and \(-\)0.236 in the ZINB model) and rail or structural facilities (\(-\)0.166 in the Two-stage model). Conversely, accidents linked to electric power facilities tend to exhibit an increase in the duration of time delay (0.151 in the Two-stage model and 0.185 in the ZINB model). Accidents involving personnel other than crew and control staff tend to decrease the likelihood of prolonged time delays. Specifically, in incidents related to conventional trains in railroad accidents, there is a statistically significant likelihood of an increase in the temporal duration of delay (0.422 in the Two-stage model and 0.441 in the ZINB model). This aligns with the interpretation of the model output for human-related factors. The analysis also indicates that incidents occurring on the main line, between railroad stations, consistently result in an elevated probability of extending the duration of time delay, as evidenced by positive signs in the negative binomial part on both models.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 2 System Model
### Receiver Model Description  
The UE is responsible for several synchronization and demodulation processes of the received waveform to determine the physical cell identity (PCI), and decodes the MIB and then the DCI in the PDCCH. Furthermore, the UE uses the DCI to decode the downlink shared channel (DL-SCH) and finally recover the transport block (TB) information.  
Following the signal transmission by the gNB, the UE performs the first step to acquire DL synchronization, corresponding to a cell-search procedure firstly detecting the PSS, which is allocated to 127 subcarriers of the first symbol within each SSB [30], performing  
Figure 2: Spatial scattering scene.  
time-offset compensation. Once both the reference PSS sequence and the received waveform are aligned, the UE also obtains one of the 336 possible SSS sequences (\(N_{ID}^{1}\)) for an effective PCI computation [30; 31]. After having computed the PCI, the UE determines the frequency-domain position of the DM-RS candidates in the PBCH and performs both channel and additive-noise estimations [30]. This previously described procedure is repeated for every SSBs, and these channel estimations are used to compute the most suitable beam pair, based on the proposed BSAE algorithm. Then, the UE proceeds with the demodulation and decoding of the PBCH [30; 32] to successfully acquire the MIB message [33], where a combination of system frame number (SFN), half radio frame bit, and the frequency domain offset between SSB and the overall RB grid in number of subcarriers (\(k_{SSB}\)) parameters allow the acquisition of radio-frame synchronization and slot timing, completing the cell-search procedure. Subsequently, the UE proceeds by trying to demodulate the PDCCH and blindly search for DCI-format messages to get both the frequency and time resources of the PDSCH [33]. Finally, the UE performs the PDSCH decoding.
# Planning-Level Optimisation of Headway Regularity
## 4 Case Study
### Evaluation of Headway Regularity (Phase III)  
The evaluation of headway regularity was carried out for different combinations of primary headway disturbance, based on the simulations carried out in the second phase (Figure 5). In Figures 6 and 7, the hypothetical primary headway disturbance is given on the \(x\)-axis and the realised PRDM values are presented on the \(y\)-axis. It is evident from the graphs that the primary headway disturbance has a linear effect on the PRDM, and that an improvement in the degree of regularity was achieved during peak as well as off-peak hours. The optimisation effects also depend on the line direction and the observation period. The greatest improvements in headway regularity are achieved during the peak period when the following headway is the shortest and the number of boarding passengers per stop is the greatest (Figures 6 and 7).
## 6 Results and Discussion
### Single Pedestrian HSI Analysis
(**a**) Forces over the entirety of the bridge, (**b**) Forces corresponding to four strides at mid-span. Figure 8: Sagittal Plane Angular Displacements at Mid-Span. HSI (Green solid line) and rigid surface (Black dotted line). **(a)** Head, **(b)** Upperarm, **(c)** Forearm, **(d)** Trunk, **(e)** Hip, **(f)** Thigh, **(g)** Shank and **(h)** Foot. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Head** & **Upper Arm** & **Forearm** & **Trunk** & **Hip** & **Thigh** & **Shank** & **Foot** \\ \hline RMSE \(\theta_{i}\) [\%] & 6.27 & 0.99 & 0.97 & 14.37 & 12.74 & 2.58 & 1.32 & 1.48 \\ RMSE \(\varphi_{i}\) [\%] & 24.93 & 36.87 & 37.09 & 3.13 & 4.18 & 129.19 & 54.64 & 50.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Differences angular displacements during human gait between 9.5 and 10.5 s.  
Figure 7: Forces Differences (RMSE) between HSI and Rigid Surface. The differences in the angular displacements presented in Table 8 indicate significant variations in the gait kinematics owing to the bridge vibrations. Specifically, the RMSE \(\varphi_{i}\) differences are greater in the lower and upper extremities (Figure 9f-h). It was observed that the abduction motion of the lower limbs (Thigh, Shank, and foot) and arms (upper and forearm) increased, that is, the lateral body sway was increased to ensure stability. This is consistent with observations made by [PERSON] et. al. (2009) [PERSON] et al. (2009) in human experimental trials on an oscillating treadmill, where they recorded that the lateral step width generally increased as a consequence of vibration ([PERSON] et al., 2018). Figure 10 shows the instantaneous natural frequency and damping ratio of the coupled system during pedestrian transit over the structure. It can be seen that, as suggested by several studies, the effect of anthropic loads generates a change in the dynamic properties of the coupled system ([PERSON] et al., 2018), decreasing the modal frequency and increasing the damping of the pedestrian structure system under the effects of HSI. The instantaneous frequency decreases as the pedestrian advances along the structure, reaching a minimum value when the pedestrian is at mid-span of the bridge, with a difference of 1.98% and 2.42% for the vertical and lateral directions, respectively. Similarly, the damping increases by 3.5% in the vertical direction and by 1.93% in the lateral direction. Figure 10: Instantaneous Natural Frequency and Damping Ratio of Coupled System. (**a**) First vertical mode, (**b**) First lateral mode. Figure 9: Frontal Plane Angular Displacements at Mid-Span. HSI (Green solid line) and rigid surface (Black dotted line). (**a**) Head, (**b**) Upperarm, (**c**) Forearm, (**d**) Trunk, (**e**) Hip, (**f**) Thigh, (**g**) Shank and (**h**) Foot.
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 2 Beamforming
The comparative description of analog and digital beamforming is depicted in Table 3, where \(\uparrow\) denotes high and \(\downarrow\) denotes low. Figure 3: Digital beamforming.
# A Fast and Accurate Method for Computing the Microwave Heating of Moving Objects
## 1 Introduction
Moreover, this method is also too time-consuming because too many physical fields are involved. The computation times of heating frozen potatoes for 6 minutes with these two approaches are 550 hours and 96 hours, respectively. Recently, [PERSON] et al. proposed a continuous algorithm by using the arbitrary Lagrangian-Eulerian (ALE) method [16], in which the computational mesh moves in a prescribed way and deforms according to the variation of a free surface for ensuring the continuous rotation of the heated sample and the turntable. Moreover, the dielectric properties of samples are updated in real-time just like in actual microwave heating. This simulation model has been validated by their experiments. However, it took about 6.24 hours to simulate a small rotating sample (40 mm \(\times\) 50 mm \(\times\) 10 mm) being heated for a short time (12 s). Obviously, for larger samples being heated for longer time, the mesh will increase dramatically and the computation time will be intolerable. In this paper, a fast and accurate numerical method is shown to simulate microwave heating that simultaneously couples electromagnetic field, thermal field, and temperature-dependent moving objects. During our whole simulation, the oven cavity is rotated instead of the potato, naturally avoiding the temperature inheritance errors provoked by the mesh-mismatch inside the moving objects. More importantly, by contrast with most discrete methods which update the dielectric parameters of the heated samples only when they move to a new position or even turn a circle, in our simulations the electromagnetic field and the heat transfer are two-way coupled and solved each time, like these parameters being updated in a real-time way. To evaluate the efficiency and accuracy of our method, a 3-D fine element method (FEM)-based microwave oven model with rotating turntables is developed. Compared with the ALE method, one of the most accurate methods for computing the microwave heating of moving objects till now, our method takes only about 11% of the calculation time with almost the same accuracy for the same computation model. Moreover, using our method the effects of discrete angles under different rotation speeds are also studied and this showed that the slower rotational speed is, the smaller the rotation angle is needed to ensure the accuracy of the simulation. Finally, we also systematically investigate the influence of the rotational speed of the turntable on the microwave heating of two different shaped samples (i.e., cuboid and cylinder). The results show that a widely used speed in domestic microwave ovens, 5 rpm, is indeed a good choice for improving the temperature uniformity with high energy efficiency. Our method may be helpful in optimizing other parameters of microwave heating, especially with heat-sensitive materials, fast and accurately.
## 2 Literature Review and Research Objectives
### Research Objectives  
The overall objective of this research is to investigate the impact of factors affecting flood hazard identification and assess flood resilience using Ayutthaya Province in Thailand (located within the floodplain area) as a case study. Specific objectives were to assess: (a) the impact of climatic and hydrological factors on flood hazard identification using the analytical hierarchy process (AHP) and (b) the city's flood resilience in Ayutthaya, Thailand, through the modified UNDRR indicators. The following research questions were addressed in this study: (i) How do climatic and hydrological factors affect flood-mapping-based hazard identification? (ii) What are the main barriers and motivators for enhancing flood resilience according to the modified UNDRR indicators? Ultimately, the results of this study are expected to provide the necessary knowledge and supportive information for policymakers and related stakeholders to actively promote and foster action to improve flood resilience at the local scale, especially in the identified floodplain areas. Moreover, flood resilience analysis through the modified UNDRR indicators provides the required baseline data for consideration prior to formulating strategies for management and a reduction in flood risk.
# Spin-Related Micro-Photoluminescence in Fe\({}^{3+}\) Doped ZnSe Nanoribbons
## 4 Conclusions  
In summary, we synthesized high quality Fe\({}^{3+}\) doped ZnSe nanoribbons by the CVD method, which is confirmed by SEM images, XRD patterns, micro-Raman spectrum and PL spectra. Micro-photoluminescence spectra of an individual Fe\({}^{3+}\) doped ZnSe nanoribbon have been measured, which show three emission peaks: near bandedge emission peak at 467 nm and two Fe\({}^{3+}\)\(d\)-\(d\) transition emission peaks at 553 nm (D1) and 630 nm (D2), respectively. The power-dependent PL spectra demonstrate that bound exciton emission in near band edge is dominated at low excited power, while it shows that EMP and EHP component emission characteristics with excited power increasing. The emission peak at 553 nm is assigned to \({}^{4}\)T\({}_{2}\) (G) \(\rightarrow\)\({}^{6}\)A\({}_{1}\) (S) \(d\)-\(d\) transition and the peak at 630 nm to \({}^{4}\)T\({}_{1}\) (G) \(\rightarrow\)\({}^{6}\)A\({}_{1}\) (S) of Fe\({}^{3+}\), in which \(p\)-\(d\) hybridization plays an important role in the transition. Excited power-dependent PL spectra show that alio-valent doping has a great impact on the formation and emission of EMP in a diluted magnetic semiconductor. Temperature-dependent PL spectra further confirm the conclusions. Temperature-dependent lifetime measurements clarify the exciton and phonon interaction and show a break point in the curve of lifetime versus temperature, which may be related to a magnetic phase transition.  
This work was supported by the National Natural Science Foundation of China (No. 11004009), the 973 Project of China (No. 2014 CB920903), the Special Key Project for the National Strong Magnetic Field Center at Huazhong University of Science and Technology (HUST) (No. 2015 KF03) and the Renovation Team Fund (BIT-2011).  
[PERSON] performed the experiment and wrote the paper; [PERSON] participated in part of the experiment measurements; [PERSON], [PERSON], [PERSON], and [PERSON] revised the manuscript and participated in part of the experiment measurements; [PERSON] provided the facilities and discussed the results; [PERSON], [PERSON] and [PERSON] participated in the discussions of the results; [PERSON] supervised the work and led the discussions.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Adsorption Performance
#### 3.2.4 Kinetic Analysis
The observed temperature dependence further supports the endothermic nature of the process, consistent with thermodynamic findings and the XDA-1G resin's high surface area, oxygen-containing functionalities, and hierarchical porosity characterized by FTIR, XPS, and BET analyses.