esa-sceva/satcom-chunk-collection huggingface dataset:
# A Single Wearable Sensor for Gait Analysis in Parkinson's Disease: A Preliminary Study
## 4 Results  
The evaluation of the proposed system was performed through a comparison in terms of walking bout and step between the results obtained with the developed algorithm and the video recording. Seven PD subjects were involved in the tasks of the experimental protocol both in the laboratory scenario and in the home scenario.  
In the laboratory scenario, the algorithm was validated through a comparison with the video recordings in terms of percentage error, sensitivity and specificity. From each walking bout the steps performed during the tasks carried out by the seven PD subjects were extracted. The results obtained by the algorithm were compared with those extracted from the recordings. Table 3 reported the results obtained for a subject during the task of the laboratory scenario. In this case we have individuated nine walking bouts for this PD subject and we compared the number of steps extracted by video with that obtained through the algorithm. For each walking bout we computed the percentage error and the sensitivity and specificity overall.  
Taking into account all seven PD subjects, we obtained a high accuracy of the proposed algorithm in terms of sensitivity and specificity, with values of 99.13% and 100%, respectively.  
To test and verify the algorithm outside a constrained environment, data on PD subjects were acquired in a domestic context, through continuous monitoring over 12 h. The subjects were equipped with the wearable device placed on their lower back and a GoPro for video recording the daily living activity, for a long-term evaluation of the proposed algorithm. The experimental results of the home scenario are shown in Figure 7. Bland-Altman plots showed agreement between the algorithm and video for bout (Figure 7a) and step (Figure 7b) count values. Figure 7 shows an average \(\pm\) Limits of Agreement (1.96*SD) of \(-\)3.29 \(\pm\) 14.03 and \(-\)1 \(\pm\) 24.74 for bout and step, respectively. Included in both plots is a point incorrectly identified by the algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Walking**} & \multirow{2}{*}{\begin{tabular}{c} **Duration of** \\ **Walking [s]** \\ \end{tabular} } & \begin{tabular}{c} **Number of** \\ **Steps from** \\ **Video Recording** \\ \end{tabular} & \
\begin{tabular}{c} **Steps Extracted** \\ **from the** \\ **Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Algorithm’s** \\ **Percentage** \\ **Error [\%]** \\ \end{tabular} & \multirow{2}{*}{**Sensitivity [\%]**} & \multirow{2}{*}{**Specificity [\%]**} \\ \hline
1 & 21.59 & 37 & 37 & 0 \\ \hline
2 & 10.78 & 19 & 19 & 0 \\ \hline
3 & 21.50 & 39 & 39 & 0 \\ \hline
4 & 23.69 & 40 & 40 & 0 \\ \hline
5 & 81.66 & 148 & 146 & 1.35 \\ \hline
6 & 75.16 & 132 & 130 &.52 \\ \hline
7 & 193.10 & 346 & 340 & 1.73 \\ \hline
8 & 39.96 & 68 & 67 & 1.47 \\ \hline
9 & 185.46 & 310 & 305 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Validation values for steps detection in each walking bout performed by a single subjects during the laboratory trial.
## 5 Computational Experiment  
The A comprehensive experiments are conducted; there are 23 test problem sizes \(n=\)[5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500]. Job processing time, release time and weights are generated uniformly on the interval [2, 15], [0, 50] and [1, 10], respectively. The maximum working time limit and maintenance time are \(L=\) {15, 30} and \(MT\) = 5. Additionally, the due date of each job was generated from a uniform distribution \(\left(r_{j}+p_{j}\right)+U[(1-\text{TF}-\text{R}/2)\overline{P},\ (1-\text{TF}+\text{R}/2) \overline{P}]\), where \(\overline{P}\) is the average processing time of all jobs, tardiness factor TF = {0.4, 0.6} and relative range factor of the due date R = {0.4, 0.6}. Ten instances were generated for each of the eight combinations of parameter values (L, TF, R), yielding 80 instances for each value of \(n\). Our MIP model is executed by IBM ILOG CPLEX Optimization Studio Version 12.7.1, and the proposed GA is coded in C++. All tests were conducted on a PC with an Intel Xeon E-2124 3.4 GHz CPU with 32 GB of RAM. To examine the performance of the proposed TISGA, we also proposed a basic GA without immigration and a GA with a random immigration strategy (GARI). For a fair comparison, all versions of the proposed GAs have \((2\times n)\) and 100 chromosomes for \((n\leq 50)\) and \((n>50)\), respectively, and their parameter settings mentioned above are the same. Each version of the GA algorithm is run five times repeatedly to obtain the best result for each instance, and the computational time limit is set to \((n\times 0.01)\) CPU seconds as the stopping criterion for each version of the GAs. First, we aim to show the efficiency of the proposed GAs. The proposed MIP model is built to find optimal solutions for small-sized problems since the considered problem here is NP-hard. Table 8 shows the average _TWT_ values (Ave_TWT_) and the number of optimal solutions found by each algorithm for small-sized problems. The results obtained by the GA algorithms that are worse (higher) than those obtained by the MIP model are presented in boldface. From Table 8, even with a small increment of \(n\) (\(n=15\)), it becomes impossible for the MIP model to reach optimal solutions within a reasonable computational time, where the computational time limit for the MIP model is set to 7200 s. Additionally, the three versions of the proposed GAs are almost equivalent when comparing the average _TWT_ value and Nopt. Regarding the average computational time shown in Table 9, note that the computational time limit of all versions of the GAs is the same as \((n\times 0.01)\) seconds. This experiment demonstrates that the MIP model is very expensive regarding the computational cost when \(n=15\). On the other hand, the GA performances are very reliable in finding the optimal solutions in less than 0.15 s. Therefore, this experiment justified that the development of GAs can reduce the computational effort without seriously losing solution quality.
## 3 LDPC Coding--Preliminaries  
The low-density parity check coding was initially proposed by [PERSON] in [19], and later recalled by [PERSON] [3; 20], and demonstrated that irregular LDPC codes can asymptotically approach the Shannon limit [21] of lossless transmission. In the last decade, LDPC codes have become very popular as a powerful error correction coding method with the possibility of algorithm parallelization that enables the development of hardware codecs with optimized algorithms and very high throughput [22; 23; 24; 25], typically for high-speed industrial communication standards [26; 27].  
The particular LDPC code is defined by its parity check matrix (PCM) \(\mathbf{H}\), which defines the specific parity checks involving the transmitted message bits. The important feature of PCM, which is its sparseness, allows LDPC codes to be iteratively decoded using low-complexity message passing algorithms [3]. Moreover, low-complexity encoding algorithms have also been well recognized [14]. Recent research in the field also involves nonbinary coding algorithms, as well as code design and implementation [28; 29; 30].
## 5 Discussion
### Influence on Energy Absorption  
Because of the isolation effect of the thyristor on the AC feeding current, the fault current during fault interruption is mainly the freewheeling current, given the energy stored in the smoothing reactor, whose path is depicted in Figure 21. The energy stored in the smoothing reactor can be obtained using Equation (50).  
\[E_{L}=1/2L_{dc}i_{fault}^{2}(t_{op}) \tag{50}\]  
where \(L_{dc}\) is the smoothing reactor; \(i_{fault}\big{(}t_{op}\big{)}\) is the fault current through the DC line while the DCCB operates, which can be obtained using Equation (9); and \(t_{0}\) is the time point of fault occurrences.  
\[i_{fault}(t_{op})=e^{-\frac{t_{op}-t_{0}}{t}}[U_{dc}\sqrt{\frac{C_{op}}{L_{op }}}\sin(\omega_{1}(t_{op}-t_{0}))-\frac{I_{dis0}\omega_{0}}{\omega_{1}}\sin( \omega_{1}(t_{op}-t_{0})-\theta)] \tag{51}\]  
Figure 21: Fault current path during fault interruption.  
Figure 20: DC line current with variables (**a**) \(R_{FCL}\) and (**b**) \(L_{FCL}\).  
The energy absorbed by \(R_{FCL}\) can be expressed as  
\[E_{R}=\int{(U_{R}^{2}/R_{FCL})dt}=\int{((R_{FCL}i_{fault})^{2}/R_{FCL})dt} \tag{52}\]  
Then, the energy absorbed in the MOA of the DCCB can be expressed as  
\[E_{M}=E_{L}-E_{R} \tag{53}\]  
Figure 20b displays that a large smoothing reactor will lead to a small \(i_{fault}\big{(}t_{op}\big{)}\). Thus, the energy stored in the smoothing reactor will decrease, thereby indicating a reduced energy consumed in the MOA and decreased time spent in the fault current interruption. Moreover, the fault detection time can influence the energy stored in the smoothing reactor in a positive relationship. Thus, determining the energy absorbed in \(R_{FCL}\) must consider the effect of inductance and fault detection time to reduce stress on the MOA of the DCCB. The simulation result presented in Figure 22a shows that a large \(R_{FCL}\) results in minimal energy absorption in the MOA of the DCCB given the low fault current level at the DCCB operation time point and shared energy absorption. Similarly, a large \(L_{FCL}\) denotes a minimal energy absorbed in the MOA because an increase in \(L_{FCL}\) leads to a low current and energy stored in \(L_{dc}\) when the main breaker opens. Overall, \(R_{FCL}\) and \(L_{FCL}\) can limit the fault current to a relatively low level to reduce the energy absorbed in the MOA of the DCCB. However, a balance between the DCCB and EFCLC is observed in terms of energy consumption during the fault current-limiting period and fault current extinguishment stage. Therefore, the tradeoff between FCL performance and additional cost on the snubber circuit and cooling system of the EFCLC must be considered.
## 3 Interpolation Algorithm
### Transfer Function Approximation of Bernoulli Beam
Given that  
* the roots of a polynomial change continuously as its coefficients vary continuously;
* the sensitivity of the poles (see Equation (11)) is a continuous function of \(\zeta\);
* the diameters of the circles \(C_{j1}\) and \(C_{j2}\) are continuous functions of \(\zeta\), based on Equations (12) and (13)  
the following limits hold:  
\[\lim_{\zeta\to\zeta_{i}}p_{j\zeta}=p_{j1},\qquad\lim_{\zeta\to\zeta_{i}} \lambda_{j\zeta}=\lambda_{j1}.\]  
Here, \(\zeta\) approaches \(\zeta_{i}\) from the direction of \(\zeta_{i+1}\). The limit represents finer sampling, as increasingly finer discretization causes the poles and their sensitivities to become progressively closer. Based on the limits above and Equation (12), the diameter of the circles \(C_{j1}\) and \(C_{j2}\) tends to zero as \(\zeta\) approaches \(\zeta_{i}\). Since no two poles can occupy the same location, there must exist a sampling such that all connected \(D\) regions are disjoined from each other. It follows that the regions \(D_{j},j=1,2,3\ldots n\), formed by the union of overlapping circles corresponding to the same pole across sampled values of \(\zeta\), vary continuously as well. Therefore, under sufficiently fine sampling, each \(D_{j}\) remains connected, and disjointness between different \(D\) regions can be maintained, ensuring reliable pole matching. Note that it does not matter whether we use hyperbolic or Euclidean geometry, since they have the same topology [25]. The intuition behind this is that the sensitivity of the poles determines their possible locations as the parameter varies from its initial to final value. Each pole must remain within a certain region--specifically, within a defined circle \(C_{j1}\) at the starting value and within another circle \(C_{j2}\) at the ending value of the parameter. This overlap indicates that the two different estimates are consistent. We assume the model does not vary so drastically that the region \(D_{j}\) becomes disconnected. If all the overlapping pairs of circles are disjoint, then we have identified the correct pole matching. If this criterion is met, then the sampling of the system is sufficient; otherwise, the sampling needs to be finer.
# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 1 Introduction
Because of the strong time dependence of seismic data, the Bi-LSTM network can learn the feature of history information and future information at the same time, so as to better recognize the seismic phase automatically. Therefore, the Bi-LSTM network is used to construct the automatic phase recognition model to realize the automatic phase recognition with high precision and low misdetection rate.
# Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna
## 1 Introduction
Alternatively, in the DRA antenna, a higher gain can be achieved by exciting the higher mode of the DRA, and this leads to the simplicity of the design. The previous work on enhancing bandwidth has been proposed by implementing a conformal strip feed with dual TM mode [25] and mushroom-shaped DR excited by a conformal trapezoidal patch [26]. It can be seen that the DRA exhibits a 59% and 65% impedance bandwidth. Yet, the proposed method in this paper has shown the simplest method in improving the bandwidth without any complicated structure. The paper presents a triband CP rectangular DR antenna fed by a coaxial cable to a double stub strip on the DRA side. Using this enthusiasm, CP for broadband has been accomplished without any multi-layering or any complicated trimming of the DRA. The impact of the parasitic and the external stub present a novel approach for generating a wide impedance bandwidth and operates in a higher mode. The degenerate mode pair TE\({}_{1\partial 1}^{y}\) and higher order TE\({}_{2\partial 3}^{y}\) has been excited to achieve CP and enhance the antenna gain. The return losses, AR, radiation patterns, and gain have been simulated and measured. The simulated results are in close agreement with those obtained from the measurement.
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 3 Results
### An Example of EQ Case Analysis
Compared with other published studies on this T\(\ddot{\text{u}}\)rkiewicz EQ case, we found that the anomalies indicated by arrows in Figures 6e and 7e coincide with those identified in Figure 6 by [PERSON] et al. [41]. These anomalies, with similar timing and position observed in other studies using multiple parameters, could confirm the reliability of the anomaly observed by CSEs to a certain extent. Nonetheless, further synchronous evidence from ground-based observations or other observation equipment is imperative to conclusively establish their correlation with seismic precursors.
## 4 Discussion
One thing that should be emphasised is the variability in the evaluation methods used in the different studies to evaluate papilla deficiency. For example, [PERSON] et al. [12] used an intraoral scanner (Trios TM, 3-Shape, Copenhagen, Denmark) to assess fillings and increase in volume of the papillae [12]. For their research, [PERSON] et al. [13,14] used a standardised photographic device at the outset and in each session in both their studies and analysed the photographs using the program Adobe Photoshop CS5 (Adobe System Inc., San Jose, CA, USA). [PERSON] and [PERSON] [11] used a modified occlusal stent (made of transparent acrylic resin) to measure interdental papilla deficiency. To evaluate photographs, the authors used ImageJ software (National Institute of Health, Bethesda, MD, USA). [PERSON] et al. [15] took a photograph at the start of each session and used the software program ImageJ (National Institute of Health, Bethesda, MD, USA) to calculate the variations in height. In terms of pain assessment and side effects after HA injection, most articles made little reference [23]. Generally, pain was attributed to the hygroscopic nature of HA, which causes vascular compression and partial occlusion of neighbouring blood vessels. No side effects were reported in any patient within 6 months of the injection. In the study by [PERSON] et al. [23], following the initial injection of HA, pain was classified as moderate (VAS: 50-60 points). In contrast, [PERSON] and [PERSON] [11] used the Numerical Pain Rating Scale (NPRS) to assess patients' discomfort. Post-injection pain was lowest in the 1% group and highest in the 5% group. However, the perception of pain decreased in all groups between the first and third injections. Adverse effects, probably due to the injection, were also reported in three patients. Two patients had severe pain and swelling of the lip after the second injection. In another patient, a painless granuloma of approximately 6 mm diameter was observed above the mucogingival junction after the first injection. The granuloma persisted for more than 4 weeks. The advantages of this technique lie in the ease of use, a fast learning curve, and its few side effects (especially in cases of failure where the original defect will not be increased). This cannot be generalised to other surgical techniques. On the other hand, its effectiveness is limited to small defects (less than 1 mm).
# One-Dimensional Consolidation of Multi-Layered Unsaturated Soil with Impeded Drainage Boundaries
###### Abstract  
In geotechnical engineering, the consolidation of unsaturated soil is a common issue of great interest. Considering the multi-layered property and impeded drainage boundary condition of the soil stratum in real engineering, this study aimed to develop a general semi-analytical solution for assessing the one-dimensional (1D) consolidation behavior of multi-layered unsaturated soil that is subjected to a general impeded drainage boundary condition and a time-dependent loading. To achieve the final solution, the proposed consolidation system is firstly decoupled and solved in the Laplace domain. Then, the semi-analytical solutions for the excess pore-air pressure and excess pore-water pressures as well as the soil settlement are formulated. The Crump method is employed to provide their final results in the time domain. The correctness of the derived solutions was verified against the available analytical and numerical solutions, and excellent agreements were found for the two comparisons. Moreover, two studied examples are presented to illustrate the 1D consolidation behavior of multi-layered unsaturated soil and the influences stemming from the impeded drainage parameters are discussed.  
one-dimensional consolidation; multi-layered unsaturated soil; semi-analytical solution; impeded drainage boundary; Laplace transform +
Footnote †: journal: Applied Sciences
## 2 Equipment Group Maintenance Planning Model Based on Service Age Regression Model
### Optimization Objective
#### 2.2.3 Constraints in the Maintenance Task  
Similar to the maintenance cost, the maintenance hours of equipment are also related to the maintenance level of equipment. The calculation process of maintenance hours is as follows:  
Maintenance time \(t\) associated with \(f_{ij}\) maintenance level is  
\[t\big{(}f_{ij}\big{)}=\begin{cases}t\Big{(}f_{ij}^{\max-1}\Big{)}\times f_{ij }/f_{ij}^{\max-1}&f_{ij}\
eq f_{ij}^{\max}\\ 0&f_{ij}=f_{ij}^{\max}\end{cases} \tag{11}\]  
The maintenance time of subsystem \(i\) in the equipment system is  
\[T_{i}=\sum_{i=1}^{S}\sum_{j=1}^{N_{i}}\Big{(}t_{ij}^{0}+t\big{(}f_{ij}\big{)} \Big{)} \tag{12}\]  
The virtual age rollback factor is determined by the ratio of the invested maintenance cost to the replacement maintenance cost. Given the component maintenance grade \(f\), the virtual age rollback factor can be expressed as  
\[A(k)=1-\left(\frac{c\big{(}f_{ij}\big{)}}{c\Big{(}f_{ij}^{\max}\Big{)}}\right) ^{m_{ij}} \tag{13}\]  
where \(m_{ij}\) is the virtual age improvement coefficient, which is determined according to the method based on the virtual age and remaining life of the system. The remaining life of components can be expressed as  
\[T_{MRL}=E\big{(}T-V_{ij}(k)\mid T>V_{ij}(k)\big{)}=\int_{V_{ij}}^{\infty}R(t) dt/R\big{(}V_{ij}(k)\big{)} \tag{14}\]  
Then, the virtual age improvement coefficient of components can be expressed as  
\[m_{ij}\big{(}V_{ij}(k)\big{)}=V_{ij}(k)/T_{MRL}=V_{ij}(k)\times R\big{(}V_{ ij}(k)\big{)}/\int_{V_{ij}}^{\infty}R(t)dt \tag{15}\]In summary, the constraints considered in this paper include maintenance level, maximum maintenance time and minimum mission satisfied capability.
## 4 Performance Evaluation
### Directivity Pattern Performance  
Figure 9 shows an experimental setup for evaluating the performance of the directivity patterns. A stereo microphone array was placed with 3-cm spacing, and one loudspeaker was located at 60\({}^{\circ}\) from the center of the microphone array at a distance of 1.5 m. The white noise at a sampling rate of 48 kHz was played out via loudspeaker and recorded by the microphone array. The recorded signal was processed by both the DS beamformer and the proposed method. After that, the recorded and processed white noise signals were all played through stereo loudspeakers that were configured according to International Telecommunication Union Radiocommunication Sector (ITU-R) Recommendation BS.775-1 [28]. Then, a dummy head [29] was rotated from 0\({}^{\circ}\) to 350\({}^{\circ}\) at 10\({}^{\circ}\) steps to measure the directivity patterns.  
Figure 10 compares the directivity patterns of the original source with those obtained by the DS beamformer and the proposed method in three different environments (anechoic, convolutive, and real reverberant room). As shown in Figure 10a, the directivity for the original signal was towards 0\({}^{\circ}\) in the anechoic environment, while the actual directivity was set to 60\({}^{\circ}\). However, the directivities of the signals processed by the conventional and proposed methods were approximately 30\({}^{\circ}\), which was the same angle of the loudspeakers against the dummy head. Consequently, we concluded that the proposed and conventional methods significantly enhanced the originally recorded signal.  
Figure 10: Comparison of directivity pattern of the original source with those obtained by the DS beamformer and the proposed method in three different environments: (**a**) anechoic; (**b**) convolutive; and (**c**) real reverberant room.  
Figure 9: Experimental setup for evaluating the directivity patterns.  
Next, when comparing the directivity in convolutive and real reverberant environments, it was clear that the proposed method could locate the sound source to approximately 30\({}^{\circ}\), while the DS beamformer failed to do so. This was because the simulated and real reverberation limited the stereophonic image enhancement of the DS beamformer. However, the proposed method was not affected by the reverberation, due to the frequency-dependent direction estimation and panning.
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 4 Performance Analysis
### Dynamic Analysis  
The calculation of the structural response under random loads is crucial in the process of instrument structural design [32; 33]. A random vibration analysis was conducted on the support structure components of the three-band multispectral camera using finite element software. The random vibration test data were based on the National Military Standards of the People's Republic of China [34]. The power spectral density is shown in Figure 12, and the random vibration response results are presented in Table 4.  
The analysis indicates that the amplification factor is 1.42, and the maximum response occurs in the \(Z\)-direction. This demonstrates that the structure exhibits good mechanical performance under random vibration conditions, performs stably overall, and meets the design requirements.