esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Double-Beam System Traversed by a Moving Force
### Parametric Studies
#### 3.3.1 Speed Ratio  
Figure 14 shows the maximum dimensionless displacements of the primary beam (\(\overline{w}_{\rm 1,max}\)) and the secondary beam (\(\overline{w}_{\rm 2,max}\)) versus the speed ratio (\(\alpha\)) for different boundary conditions (\(s_{\rm e}\)) when \(s_{\rm e}=100\), \(s_{\rm m}=1\) and \(s_{\rm k}=1\). It can be found from the figure that the difference between \(\overline{w}_{\rm 1,max}\) and \(\overline{w}_{\rm 2,max}\) is smaller for looser boundary conditions.
## 3 Results and Discussion  
### Mortality Rate  
There was no increase in total cumulative mortality found between treated groups and the control. Among groups, mortality did not exceed 5% during the whole test. This result supports the finding from the previous study performed by [PERSON] et al. [27], who tested similar concentrations of tramadol hydrochloride (from 10 to 200 \(\upmu\)g/L) on zebrafish (_Danio rerio_) embryos and also on embryos and larvae of common carp (_Cyprinus arpio_). Likewise, [PERSON] et al. [26] did not record embryotoxicity of tramadol on zebrafish larvae. After 144 h of exposure (fish were exposed to tramadol concentrations from 24 to 6250 \(\upmu\)g/L), no adverse impact of the tested substance was observed. [PERSON] et al. [26] also did not notice any effect, even after treatment with a mixture of tramadol and antidepressant calidopram. However, in a test performed by [PERSON] et al. [12], where common carp embryos and larvae were exposed to a mixture of tramadol hydrochloride and naproxen sodium for a period of 32 days, increased mortality (50%) was already observed in the group of 200 \(\upmu\)g/L.  
Various authors have reported on the impact of tramadol on the behavior of aquatic organisms [14; 15; 26]. During our test, fish in all treated groups exhibited normal behavior. However, it is necessary to point out that monitoring of effects of tramadol on fish behavior was not the goal of our research, and thus no validated method on fish-behavior assessment was followed. [PERSON] et al. [26] monitored the swimming activity of zebrafish larvae and found out that a tramadol concentration of 320 \(\upmu\)g/L had a significant anxiolytic effect (hypoactivity during dark conditions). Even lower concentrations can affect the behavior of some other aquatic organisms. An environmentally relevant concentration of tramadol (1 \(\upmu\)g/L) caused crayfish to spend more time in shelters, and both their swimming velocity and swimming distance were affected [14; 15].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parent Ion** & **Product Ion** & **Collision Energy** & **Fragmentation Values** \\
**(m/z)** & **(m/z)** & **(V)** & **(V)** \\ \hline
264.5 & 58.1 & 36 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: LC/ESI/MS/MS analysis parameters for tramadol hydrochloride in water samples.
## 4 Methods and Results
### Neural Network Method  
#### 4.2.1 Basic Principle  
The BP (back propagation) neural network decreases the error between the output data and sample results by correcting weights of internal layers of existing learning samples, finally getting a relatively perfect neural network. This network is mainly composed of three parts (Figure 8). Part 1 is responsible for inputting original data for the next training; this part is called the input layer. Part 2 is the core of neural network, which is responsible for processing of input data and analyzing characteristics of data. However, specific operation of this process is kept within the network and this part is called the hidden layer. Part 3 is responsible for outputting data processed by the previous part and is called the output layer. In every part of the neural network, each layer is composed of several neural nodes and they are connected mutually into the neural network.  
The training process of the BP neural network is a transmission process of data among different neural nodes. After data input, data firstly transmits from the input layer to the output layer, during which each layer is endowed with certain weights. After the processed input data arrive at the output layer, they will be compared with the expected data and differences are calculated. If the error is in the preset permissible range, data can be output. At this moment, neural network training is finished. If error exceeds the permissible range, the error propagates from the input layer to the output layer, during which weights of each layer are modified continuously. After finishing the correction, the processed data are compared with the expected data again and their error is calculated. This process repeats until the error reaches the permissible range.  
Figure 6: Scatter plot of predicted TOC and measured TOC by \(\Delta\)LogR method.  
Figure 7: _Cont._
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Unit**} & \multicolumn{3}{c}{**BASE**} & \multicolumn{3}{c}{**TOP GCS**} & \multicolumn{3}{c}{**TOP LCS**} & \multicolumn{3}{c}{**SYSTEM**} \\ \cline{2-13}  & & **LE** & **ME** & \(p\) **(ES)** & **LE** & **ME** & \(p\) **(ES)** & **LE** & **ME** & \(p\) **(ES)** & **LE** & **ME** & \(p\) **(ES)** \\ \hline Trial & s & \(1.72\) & \(1.58\) & **<0.001** & \(1.72\) & \(1.58\) & **<0.001** & \(1.72\) & \(1.58\) & **<0.001** & \(1.72\) & \(1.58\) & **<0.001** \\ Duration & s & (1.47) & (0.14) & **(0.80)** & (0.47) & (0.14) & **(0.80)** & (0.47) & (0.14) & **(0.80)** & (0.47) & (0.14) & **(0.80)** \\ \hline \multirow{3}{*}{Phase I} & s & \(1.11\) & \(0.95\) & **<0.001** & \(1.13\) & \(0.99\) & **<0.001** & \(1.18\) & \(1.03\) & **<0.001** & \(1.12\) & \(0.98\) & **<0.001** \\  & & (0.42) & (0.24) & **(0.61)** & (0.41) & (0.20) & **(0.72)** & (0.37) & (0.11) & **(0.82)** & (0.41) & (0.22) & **(0.67)** \\  & \%time & (7.89) & (11.60) & **0.43)** & **0.02** & 65.18 & 61.99 & **<0.001** & \(66.68\) & **64.49 & **<0.001** & \(64.14\) & **(0.79)** & **<0.001** \\ \hline \multirow{3}{*}{Phase II} & s & \(0.61\) & \(0.56\) & **<0.001** & \(0.58\) & \(0.58\) & \(0.37\) & \(0.56\) & \(0.56\) & \(0.45\) & \(0.58\) & \(0.56\) & **0.04** \\  & (0.06) & (0.11) & **(0.60)** & (0.06) & (0.07) & (0.16) & (0.10) & (0.09) & (0.14) & (0.05) & (0.09) & **(0.37)** \\  & \%time & 36.91 & 40.56 & **0.02** & 34.82 & 38.01 & **<0.001** & \(33.32\) & \(35.51\) & **<0.001** & \(35.86\) & 39.21 & **<0.001** \\  & \%time & (7.89) & (11.66) & **(0.43)** & (7.49) & (6.19) & **(0.68)** & (6.24) & (4.17) & **(0.77)** & (8.49) & (7.82) & **(0.62)** \\ \hline \multirow{3}{*}{MD Pos} & m & \(-0.24\) & \(-0.25\) & \(0.89\) & \(-0.61\) & \(-0.63\) & **0.02** & \(-0.27\) & \(-0.29\) & \(0.13\) & \(-0.37\) & \(-0.39\) & \(0.07\) \\  & & (0.03) & (0.03) & (0.03) & (0.08) & (0.06) & **(0.43)** & (0.06) & (0.03) & (0.27) & (0.03) & (0.03) & (0.03) & (0.33) \\  & & \(-0.14\) & \(-0.15\) & \(0.30\) & \(-0.42\) & \(-0.46\) & **<0.001** & \(-0.19\) & \(-0.20\) & **0.02** & \(-0.12\) & \(-0.13\) & **0.005** \\  & (0.02) & (0.02) & (0.19) & (0.03) & (0.03) & (0.03) & **(0.67)** & (0.04) & (0.02) & **(0.41)** & (0.01) & (0.01) & **(0.53)** \\ \hline \multirow{3}{*}{TO Pos} & m & \(0.09\) & \(0.09\) & \(0.06\) & \(0.67\) & \(0.63\) & **<0.001** & \(0.08\) & \(0.09\) & \(0.05\) & \(0.30\) & \(0.27\) & **<0.001** \\  & (0.04) & (0.02) & (0.35) & (0.05) & (0.07) & **(0.75)** & (0.04) & (0.04) & (0.36) & (0.05) & (0.05
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Site Displacement Effects
#### 2.8.1 Solid Earth Tides  
The gravitational forces exerted by the sun and the moon, responsible for ocean tides, also influence the solid Earth, leading to both horizontal and vertical site displacements. These displacements are quantified through a spherical harmonics expansion characterized by Love and Shida numbers [63]. To achieve an accuracy level of 5 mm, it is adequate to consider only the second-degree tides and a height correction factor [64]. Following this approximation, the displacement vector \(\mathbf{\Delta r_{solid}}\) for a station at position \(\mathbf{r_{r}}\) can be expressed in the ECEF frame as \[\mathbf{\Delta r_{\text{solid}}}=\sum_{j=\mathscr{K}_{j},\zeta}\frac{GM_{j} \left\|\mathbf{r_{r}}\right\|^{4}}{GM}\left\|\mathbf{R_{j}}\right\|^{3}\left\{ \left[3l_{2}\left(\mathbf{\tilde{R_{j}}}\cdot\mathbf{\hat{r_{f}}}\right) \right]\mathbf{\tilde{R_{j}}}+\left[3\left(\frac{h_{2}}{2}-l_{2}\right)\left( \mathbf{\tilde{R_{j}}}\cdot\mathbf{\hat{r_{f}}}\right)^{2}-\frac{h_{2}}{2} \right]\mathbf{\hat{r_{f}}}\right\}\\ +\left[-0.025\sin(\varphi)\cos(\varphi)\sin(\theta_{g}+\lambda) \mathbf{\hat{r_{f}}}\right] \tag{23}\]  
where \(GM\) is the earth's gravitational parameter and \(\mathbf{\hat{r_{f}}}\) denotes the geocentric unit vector in the direction of \(\mathbf{r_{r}}\), while \(GM_{j}\) represents the gravitational parameter and \(\mathbf{R_{j}}\) represents the geocentric position with the corresponding unit vector \(\mathbf{\hat{R_{j}}}\) for the sun when \(j=\mathscr{K}\) and the moon when \(j=\mathscr{L}\). The nominal second-degree Love and Shida numbers \((l_{2},h_{2})\) can be approximated as \((0.609,0.085)\).  
The height correction, the last term in (23), depends on the station latitude \(\varphi\), longitude \(\lambda\), and the Greenwich mean sidereal time \(\theta_{g}\). More harmonics and more accurate Love and Shida number values may be considered for high-precision applications [65]. As elaborated in Section 2.6.1, the sun and moon positions can be calculated from the planetary ephemerides or analytical formulas.  
The magnitude of the displacement vector in (23) can reach 30 cm and 5 cm in the vertical and horizontal directions, respectively. This vector consists of a permanent component at the decimeter level, which is latitude-dependent, and a periodic component [7; 63]. While the periodic component may be averaged out over long static datasets, it remains crucial to account for solid Earth tides due to the permanent component.
# Systems Engineering: Availability and Reliability
###### Abstract  
Current trends in Industry 4.0 are largely related to issues of reliability and availability. As a result of these trends and engineering systems' complexity, research and development needs now refer to new solutions in the integration of intelligent machines or systems, with emphasis on changes in production processes aimed at increasing production efficiency or equipment reliability. The emergence of innovative technologies and new business models based on innovation, cooperation networks, and the enhancement of endogenous resources is assumed to be a strong contribution to the development of competitive economies all around the world. Innovation and engineering, focused on sustainability, reliability, and availability of resources, have a key role in this context. The scope of this Special Issue is closely associated with that of the ICIE'2020 conference. This conference and the journal's Special Issue aims to present the current innovations and engineering achievements of top world scientists and industrial practitioners in the thematic areas related to:  
* Reliability and risk assessment;
* Innovations in maintenance strategies;
* Production process scheduling, management and maintenance;
* Systems analysis, simulation, design and modeling.
## 2 Dynamic Compression Test at the High Strain Rate
### Test Results and Analysis  
#### 2.3.1 Test Results at Strain Rate \(1\times 10^{3}\) s\({}^{-1}\)  
The results of the dynamic compression tests under the strain rate at \(1\times 10^{3}\) s\({}^{-1}\) are shown in Table 3. The average strain rate at the stable stage is approximately \(1.3\times 10^{3}\) s\({}^{-1}\)  
It can be seen that the maximum strain reaches 79% at strain rate\(4.6\times 10^{3}\) s\({}^{-1}\), and the average compression rate of the specimen reaches 96.73%, which is 2.95% higher than that at the strain rate of \(1.3\times 10^{3}\) s\({}^{-1}\), and 2% higher than that of the strain rate of \(2.0\times 10^{3}\) s\({}^{-1}\). It can be seen that the impact velocity increases, the strain rate is significantly enhanced. The specimen reaches the densification after the plateau stage.  
To obtain the macroscopic mechanical property of aluminum honeycomb structure at strain rate of \(4.6\times 10^{3}\) s\({}^{-1}\), the average value of the strain rate curve and stress-strain curve from the two tests are obtained after interpolation.  
There are slight deviations of each test result at the same strain rate. The reason is because the thickness of aluminum foil varies during processing, and the boundary difference of the specimen when fabricated by wire-cutting.
## 5 Experiments
### Comparison of Different Models' Results
#### 5.2.2 Results Comparison  
In order to ensure the scientific comparison of the experiments, five sets of experiments were conducted and the accuracy was judged based on the mean. The NWP Information design contrast experiment was added or not added from the short-term and mediumlongterm power prediction tasks. The evaluation metrics consisted of MSE and MAE. The results obtained are shown in Table 1.  
* It can be seen that the introduction of NWP auxiliary information significantly improves the accuracy of power prediction. For short-term prediction tasks, the average prediction accuracy of introducing the NWP model is MSE = 0.0651. The average prediction accuracy of the model without introducing NWP data is MSE = 0.0678, meaning it improved prediction accuracy by 5%. For long-term prediction tasks, the average prediction accuracy of introducing the NWP model is MSE = 0.5470. The average prediction accuracy of the model without introducing NWP data is MSE = 1.1896. The prediction accuracy was improved by 117%. Therefore, introduction of NWP data can not only improve the accuracy of short-term predictions, but also significantly improve prediction accuracy in medium- to long-term tasks.
* In addition, when comparing the prediction accuracy of different models, the proposed G-Former model has the best prediction performance in both short-term and medium- to long-term prediction tasks. For short-term tasks, the accuracy was improved by about 8%. For medium to long-term tasks, the accuracy was improved by about 11%. This also proves the superiority of the proposed scheme.
# An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
## 2 Related Work
However, most studies have focused on the development of an efficient overlay structure to reduce startup delay or increase playback continuity. In LayerP2P, startup delay is reduced by using a hierarchical structure of peers and a tit-for-tat algorithm [29]. A novel scheme to increase data redundancy across the system by synchronizing peers' playback positions with a media server has also been proposed [30]. However, few studies to support the time-shifted viewing in a P2P live streaming structure have been conducted. Furthermore, to the best of our knowledge, no study to control the ratio of caching and prefetching areas in each peer's buffer has been proposed.
## 3 Implementation Methodology
### Data Pre-Processing  
The number of raw data points for the Zhongli, Changhua, and Fengshan monitoring stations includes 91,672, 94,453, and 94,145, respectively. The analysis of these readings begins with a crucial phase - data preprocessing. Various preprocessing operations precede the learning phase. At any particular time, one invalid variable will not affect the whole data group, and thus it will just be either marked blank or, where available, replaced by a value sourced from the CWB, without eliminating the full row. The missing values are treated by imputation to recover the corresponding values. Given the lack of spatial proximity of the readings to the original monitoring stations, the missing values are imputed for relative humidity, temperature, and rainfall, without using wind speed or wind direction. The next imputation process used the \(k\)-NN algorithm to substitute the rest of the invalid or missing data that did not qualify for the previous imputation process. Note that the percentage of missing values is lower than 1.3% in all three-station datasets.  
Then, input and target data are normalized to eliminate potential biases; thus, variable significance won't be affected by their ranges or their units. All raw data values are normalized to the range of [0, 1].  
Inputs with a higher scale than others will tend to dominate the measurement and are consequently given greater priority. Normalization not only improves the model learning rate, but also supports \(k\)-NN algorithm performance because the imputation is decided by the distance measure.
## 3 Control Algorithm
### Mpcc  
To effectively address the implicated operation constraints within the three-level inverter, the objective of the MPCC scheme is involved in the applied vector choosing and neutral-point voltage balance.  
After transforming the stator voltage equation in (2), the differential equation of the \(d\)-\(q\)-axes current can be obtained as  
\[\frac{\mathrm{d}}{\mathrm{d}t}\left[\begin{array}{c}i_{d}\\ i_{q}\end{array}\right]=\left[\begin{array}{cc}-R/L_{d}&\omega_{e}L_{q}/L_{d }\\ -\omega_{e}L_{d}/L_{q}&-R/L_{q}\end{array}\right]\left[\begin{array}{c}i_{d} \\ i_{q}\end{array}\right]+\left[\begin{array}{c}u_{d}/L_{d}\\ \left(u_{q}-\omega_{e}\psi_{f}\right)/L_{q}\end{array}\right] \tag{2}\]  
The currents at the \((k+1)\)th, namely, \([i_{d}(k+1)\) and \(i_{q}(k+1)]\), are able to be predicted by utilizing the forward Euler discretization equation as  
\[\left[\begin{array}{c}i_{d}(k+1)\\ i_{q}(k+1)\end{array}\right]=\left[\begin{array}{cc}1-RT_{s}/L_{d}&\omega_{e}T _{s}L_{q}/L_{d}\\ 1-RT_{s}/L_{q}&-\omega_{e}T_{s}L_{d}/L_{q}\end{array}\right]\left[\begin{array} []{c}i_{d}(k)\\ i_{q}(k)\end{array}\right]+\left[\begin{array}{c}T_{s}/L_{d}&0\\ 0&T_{s}/L_{q}\end{array}\right]\left[\begin{array}{c}u_{d}(k)\\ u_{q}(k)\end{array}\right]+\left[\begin{array}{c}0\\ -\psi_{f}\omega_{e}T_{s}/L_{q}\end{array}\right] \tag{3}\]  
where \(T_{s}\) donates the sampling period; \((k)\) and \((k+1)\) represent the components measured at the \((k)\)th and predicted at the \((k+1)\)th, respectively.  
Specifically, at the \((k)\)th sampling period, the stator voltages used in (3) can be calculated as  
\[\left[\begin{array}{c}u_{d}(k)\\ u_{q}(k)\end{array}\right]=\left[\begin{array}{cc}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{array}\right]\left[\begin{array}{c}u_{a}(k)\\ u_{\beta}(k)\end{array}\right] \tag{4}\]  
with  
\[\left[\begin{array}{c}u_{a}(k)\\ u_{\beta}(k)\end{array}\right]=\left[\begin{array}{cc}2/3&-1/2&-1/2\\ 0&\sqrt{3}/3&-\sqrt{3}/3\end{array}\right]\left[\begin{array}{c}u_{a}(k)\\ u_{b}(k)\\ u_{c}(k)\end{array}\right] \tag{5}\]  
where \(u_{x}(k)\) are the three-phase voltages at the \((k)\)th sampling period, \(u_{x}(k)=(S_{x}+1)\times U_{dc}/2\), \(x\)\(\in\)\(\{a,\)\(b,\)\(c\), \(U_{dc}\) is the dc-bus voltage, \(S_{x}\) refers to the switch states of the three-phase bridge arms, as can be seen in Figure 4, and \(S_{x}\)\(\in\)\(\{-1,\)\(0,\)\(1\}\); \(u_{a}(k)\) and \(u_{\beta}(k)\) represent the \(\alpha\beta\)-axes voltages, respectively; \(\theta\) is rotor position angle.  
Furthermore, the cost function, which combines the absolute errors between the commands and prediction values in terms of the stator current, can be constructed as  
\[g=\left|i_{d}^{ref}(k+1)-i_{d}(k+1)\right|+\left|i_{q}^{ref}(k+1)-i_{q}(k+1)\right| \tag{6}\]  
Figure 3: A diagram of the proposed sensorless control algorithm.  
where \(i_{u}\)_ref_(\(k+1\)) and \(i_{u}\)_ref_(\(k+1\)) are given as the \(d\)-\(q\)-axes current commands at the \((k+1)\)th. To keep balance between the two current errors, a weighting factor is not utilized in the cost function.
# Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis
## 4 Materials and Methods
### Neurophysiological Evaluation and Clinical Characteristic  
The neuropsychological evaluation was modified for the Chinese population based on the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACIMS) test. The modified MACIMS has been previously validated for assessing cognition in MS [16]. The battery consists of five cognitive domains commonly impaired in MS: the SDMT and PASAT (information processing speed/working memory), the BVMT-R (visual memory), the JLOT (spatial processing), the TMT (executive function), and VFT (language ability). We substituted the AVLT-Chinese Edition for the California Verbal Learning Test-Second Edition (verbal memory).  
The raw score obtained from each test was converted into a standardized z-score based on the mean scores and SD of the HC participants. Patients who exhibited impairment in at least two or more cognitive domains (scoring at least 1.5 SDs below the mean test value of HCs) were classified as CI [1].  
Fourteen patients (35.9%) with MS in our study were classified as CI. MS patients with CI had a lower level of education and higher EDSS, HAMA and HAMD scores than MS patients with CP, but there were no significant differences in age, onset age, disease duration, or annualized relapse rate between the two groups (Table 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Healthy Control (_n = 29_)** & **Patients with MS (_n = 39_)** & \(p\) \\ \hline Age, year & 37.79 \(\pm\) 10.29 & 38.26 \(\pm\) 9.05 & 0.84 \\ Sex, F/M & 17/12 & 23/16 & 0.98 \\ Years of education & 12.10 \(\pm\) 4.03 & 11.90 \(\pm\) 3.68 & 0.83 \\ Disease duration, month & NA & 92.33 \(\pm\) 71.54 & NA \\ Annualized relapse rate & NA & 0.54 \(\pm\) 0.46 & NA \\ EDSS score & NA & 2.24 \(\pm\) 1.58 & NA \\ HAMA & 4.10 \(\pm\) 3.19 & 8.33 \(\pm\) 5.33 & \textless{}0.01 \\ HAMD & 2.00 (0.00–3.50) & 6.00 (3.00–10.00) & \textless{}0.01 \\ \hline \hline \end{tabular} Normally-distributed data are the means \(\pm\) SD and analyzed by the independent-samples _t_-test. Non-normally-distributed parameters are the median (interquartile range) and analyzed by the Mann-Whitney _U_-test. Abbreviations: EDSS = Expanded Disability Status Scale; HAMA = Hamilton Anxiety Scale; HAMD = Hamilton Depression Scale; NA = not applicable; F = female; M = male.  
\end{table}
Table 5: Demographic and clinical characteristics in the MS group and healthy controls.  
Correcting for education level, EDSS, HAMA and HAMD scores, MS patients with CI exhibited worse performance on the Auditory Verbal Learning Test (AVLT), Verbal Fluency Test (VFT), Brief Visuospatial Memory Test (BVMT), and PASAT 3 seconds (PASAT-3\") tests, compared to the HC group, and worse performance on the AVLT, VFT, PASAT-3\", BVMT, and Trail making test (TMT) compared with CP patients. These differences were statistically significant (Table 7).