esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Manufacturing Design  
First, given the absence of historical data, the simulation-based framework was used to define some fundamental parameters of the manufacturing system. With the aim of reproducing the characteristics of the real system as much as possible, the processing time of each machine was modeled through a triangular probability distribution, where the minimum and maximum values differ by 5% from the average value. The simulation model was extremely useful in finding the work configuration capable of guaranteeing production continuity as much as possible and minimizing bottlenecks. After creating the simulation model, the Full Factorial Design technique as DOE was applied. In this case, four change factors and two levels for each factor were taken into consideration. Considering that 3 replications were carried out to account for some experimental errors, the campaign included a total of 48 experiments, since there are 16 possible combinations. All the data were then collected and entered into the Minitab software, to understand the relationship between the 4 change factors and 3 fundamental objectives: Flow Time, Level of machine utilization, Productivity. The aim was to understand how the 3 objectives vary as the 4 types of input vary. Table 1 shows the considered exchange factors and their levels.
## 2 Materials and Methods
### Methods for Estimating Distribution Parameters  
The present study utilizes the maximum likelihood method (MLM) to estimate the parameters of the six distributions (GAM, GEV, GUM, IGA, RAY, WEI), with the specific form of \(f(x;\theta)\) varying according to the chosen distribution [23,47]. The MLM method was chosen for parameter estimation due to its efficiency and accuracy in fitting the distributions to wind speed data [28]. MLM is a widely accepted approach, as it minimizes the mean squared error and ensures the best possible alignment between the data and the probability distribution function [31]. Given the challenge of accurately determining the parameters of distributions, MLM provides a reliable estimation compared to other methods [48].  
The general form of the maximum likelihood method (MLM) method for estimating the parameters \(\theta=(\theta_{1},\theta_{2},\ldots,\theta_{m})\) of a wind speed distribution is given as follows.  
For wind speed data \(\{x_{1},x_{2},\ldots,x_{n}\}\), the log-likelihood function is as follows:  
\[l(\theta)=\sum_{i=1}^{n}\text{log}f(x_{i};\theta) \tag{7}\]  
where \(f(x_{i};\theta)\) is the probability density function (PDF) of the respective distribution.  
The aim is to minimize the negative log-likelihood function, as follows:  
\[-l(\theta)=-\sum_{i=1}^{n}\text{log}f(x_{i};\theta) \tag{8}\]  
The optimal parameters \(\hat{\theta}\) are obtained by solving the following:  
\[\frac{\partial l(\theta)}{\partial\theta}=0 \tag{9}\]  
using numerical optimization techniques such as the limited-memory Broyden-Fletcher-Goldfarb-Shanno with box constraints (L-BFGS-B), Nelder-Mead, and Newton-Raphson methods [26,30,49,50]. Python scripts (version 3.9.12) were implemented to carry out the optimization and estimate the parameters.
## 3 Results
### The Accuracy of the Photogrammetric Intersection  
The photogrammetric intersection is a method of computing the coordinates of object points based on the spatially oriented stereo or multi-images referenced in the object reference system X, Y, Z. For example, the coordinates \(X_{B}\), \(Y_{B}\), \(Z_{\rm B}\) of the new object point B (Figures 1 and 2) are computed by solving four Equations (7) and (8) by the least-squares method: two equations for measured pixel coordinates of the projected point B on the left image (Figure 1) and two equations for projected point B on the right image (Figure 2). The computed spatial distance between points A (\(X_{A}\), \(Y_{A}\), \(Z_{A}\)) and B (\(X_{B}\), \(Y_{B}\), \(Z_{B}\)) is equal to \([(X_{B}-X_{A})^{2}+(Y_{B}-Y_{A})^{2}+(Z_{B}-Z_{A})^{2}]^{1/2}=0.210\) m. The distance between relevant points measured by tape is also equal to 0.210 m. So, in this case, the error of the photogrammetrically measured distance between two points A and B on the object is equal to 0.000 [m].  
In the second example, the distance between points C and D is computed (Figures 1 and 2). The coordinates of points C and D were computed in the same way as those of point B. The computed spatial distance between photogrammetrically determined points C (\(x_{\rm C}\), \(y_{\rm C}\), \(z_{\rm C}\)) and D (\(x_{\rm D}\), \(y_{\rm D}\), \(z_{\rm D}\)) is equal \([(X_{D}-X_{C})^{2}+(Y_{D}-Y_{C})^{2}+(Z_{D}-Z_{C})^{2}]^{1/2}=0.989\) m. The distance between these points measured by tape equals 0.990 m. In this experiment, an error of the photogrammetrically measured distance between two points C and D on the object equals 1 mm. For clarity, the aforementioned results are summarized in Table 2.  
Figure 4: Determined camera locations L-left and R-right with 12 control points deployed on the test object.
## 3 Results  
Of the 54 subjects who met the inclusion criteria, 51 were women, and the subjects had a mean age of 53 years (SD = 6). The sociodemographic characteristics of the sample are shown in Table 1. The static SVV test measure showed a mean MAE of 2.41 (SD = 1.36), while the RFT showed a mean MAE of 10.82 (SD = 10.16). Taking the normal values of both tests as reference, 19 subjects (35.2%) presented an altered SVV, while 32 subjects (59.3%) presented an elevated RFT. In total, 16 subjects (29.6%) presented good visual verticality measured with both tests, 6 subjects (11.1%) presented an exclusive alteration in the SVV test, 19 subjects (35.2%) presented an exclusive alteration in the RFT, and 13 subjects (24.1%) showed high values in the two tests. In other words, 38 subjects with FMS (70.4%) presented some alteration in visual verticality perception. Table 2 shows the correlations between the MAE measured with the visual verticality perception tests and the PROMs. As can be seen, the MAE-SVV test showed medium but statistically significant correlations with numerous PROMs such as FIQ, PCS, DHI, and ABC-16 (Figure 4). However, the MAE-RFT values did not present any significant correlation. Table 3 shows the correlations between the MAE measured with the VV perception tests and the static and dynamic balance measures. As can be seen, the MAE-RFT showed medium but statistically significant correlations with the JAEN subscales when the base of support was reduced and during walking with eyes open (Figure 4), and a low but significant correlation with the mean of the anterior-posterior position of the CoP in the test with eyes open of the static posturography. However, the values of the SVV test did not present any significant correlation. \begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & **Frequency** & **\%** & **Mean** & **SD** \\ \hline Gender & Female & 51 & 94.40 & \\ \hline  & Male & 3 & 5.60 & \\ \hline Civil Status & Single & 2 & 3.70 & \\ \hline  & Married & 39 & 72.20 & \\ \hline  & Divorced & 10 & 18.50 & \\ \hline  & Widower & 3 & 5.60 & \\ \hline Education level & No Education & 2 & 3.70 & \\ \hline  & Primary & 17 & 31.50 & \\ \hline  & Secondary & 24 & 44.40 & \\ \hline  & University & 11 & 20.40 & \\ \hline Employment status & Active & 18 & 33.30 & \\ \hline  & Sick Leave & 10 & 18.50 & \\ \hline  & Unemployed & 7 & 13.00 & \\ \hline  & Housewife & 4 & 7.40 & \\ \hline  & Retired & 15 & 27.80 & \\ \hline Fibromyalgiagia Impact & & & \\ Questionnaire FIQ (0–100) & Mild (<50) & 7 & 13 & \\ \hline  & Moderate (50–75) & 29 & 53.7 & \\ \hline  & Severe (>75) & 18 & 33.3 & \\ \hline SVV category & Healthy & 35 & 64.8 & \\ \hline  & Altered SVV & & \\  & (>2.5 degrees) & 19 & 35.2 & \\ \hline RFT category & Healthy & 22 & 40.7 & \\ \hline  & Altered RFT & & \\  & (>4.5 degrees) & 32 & 59.3 & \\ \hline Age (years) & & & 53.00 & 6.00 \\ \hline Weight (kg) & & & 77.00 & 16.00 \\ \hline Height (cm) & & & 161.00 & 6.00 \\ \hline Body Mass Index (kg/m\({}^{2}\)) & & & 30.00 & 7.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sociodemographic characteristics of the sample.
## 5 Results and Discussion
### Advantages of the Methodology  
The main advantages of the method proposed are related to the flexibility in data processing and output:  
* Due to the popularity and ease of use of the Python programming language, it is relatively easy to increase the acceptable attack tree formats by determining the attribute identifiers used within that tool's XML format. Utilizing an \"if-then\" statement within the lister function would allow for the conversion of these attack trees as well.
* The configuration of version two allows for a relatively small set of changes which would mean a single JESS expert system could read the.dat files for multiple attack trees. As the data are already in a separate file, it would be possible to select multiple.dat files to minimize consumedstorage space on a device. This was not pursued by the authors due to a lack of necessity in modern systems.
* Finally, there are several points where data can be configured by the user, and it will be accepted throughout. It is possible to set the attribute values either before translation in ATTop, after translation from ATTop and before input to ATES, after output from ATES by manually editing the data files, or inside the JESS program created by ATES. The most efficient and accurate method is to either modify the final JESS.dat files or the XML files after being output from ATTop.
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 2 Data and Methods
### Constellation Design and Data Simulation
The orbit of satellites is obtained from the Satellite Tool Kit software. The satellite clock is obtained from the IGS precise clock products. The new GNSS satellites and LEO satellites will use clock offsets of old satellites randomly. With the position of the satellite and receiver, the geometric distance between the receiver and satellites can be calculated. The dry part of tropospheric delays was calculated by empirical models--the Saastamoinen model in this study [31]. The phase center offsets (PCOs) and variations (PCVs) and tidal loading can be calculated according to the existing models [32]. The ionospheric delay was computed based on the mapping function and total electron content (TEC) provided by CODE's global ionosphere maps (GIM) [33]. The code bias of each frequency is simulated using the multi-GNSS differential code bias (DCB) products provided by the German aerospace center. The phase delay at each frequency is assumed to be small floating-point constants. The noise was simulated as white noise, with the standard deviations (STDs) of 0.3 m and 0.003 m for the code and phase observations, respectively. To investigate the contribution of multi-frequency LEO observations to the PPP ambiguity resolution, the LEO observations were simulated at three frequencies: L1 at 1575.42 MHz, L2 at 1227.6 MHz, and L5 at 1176.45 MHz, which is consistent with the current GPS signals.
## 4 Results
### Sediment Export Estimation Using SDR Model
#### 4.7.4 Sediment Export Estimation of Predicted LULC under Scenario III
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Sediment Export**} & \multicolumn{2}{c}{**Mean Rainfall Erosivity**} \\ \cline{2-4}  & **Total (tons)** & **Average (tons/km\({}^{2}\))** & **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\))** \\ \hline
2020 & 43,130.50 & 48.39 & 2854.33 \\
2021 & 37,330.55 & 41.88 & 2490.34 \\
2022 & 34,520.48 & 38.73 & 2239.93 \\
2023 & 46,451.16 & 52.11 & 2711.93 \\
2024 & 47,447.38 & 53.23 & 2601.06 \\
2025 & 59,526.42 & 66.78 & 2992.66 \\
2026 & 76,068.33 & 85.34 & 3130.43 \\
2027 & 59,092.34 & 66.30 & 2456.18 \\
2028 & 67,376.26 & 75.59 & 2588.82 \\
2029 & 74,740.75 & 83.85 & 2640.18 \\ \hline
**Average** & **54,568.42** & **61.22** & **2670.59** \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Estimation of sediment export between 2020 and 2029 under Scenario III. Figure 9: Relationship between mean rainfall erosivity and average sediment export under Scenario III.
## 3 Concepts, Models and Technologies
### Intelligent Edge Computing (IEC)  
The IEC, or its origin in _Edge Computing_, as shown in Figure 2 and [7], serves as a local source for data processing, storage, and computational needs within a broader cloud computing strategy. The most comprehensive definition of IEC is Edge Computing with enhanced intelligence in data handling, applications and communication representing a natural evolution of network and cloud computing architectures. So, to simplify notation, the term IEC is adopted.  
Similar to Fog Computing, IEC is positioned closer to the edge in the layered model and is primarily designed for applications requiring real-time responses, low latency and connectivity to IoT devices [8; 9]. Many times, edge nodes [10] are often implemented on high-end IoT devices, such as boards equipped with NVIDIA GPUs (high-performance) or from other manufacturers, as well as on desktop computers and laptops.  
These nodes do not necessarily depend on traditional servers, which are typically required to provide high computational power and moderate storage capacity but are often constrained by cost considerations, [10; 11]. The degree of maturity and security in these implementations has advanced significantly, enabling the adoption of more sophisticated models, depending on the specific use case.  
Finally, given the extensive literature on cloud over the past fifteen years, the text simplifies the discussion by omitting a redefinition of Cloud Computing, focusing on pertinent topics related to the use case.
## 2 Materials and Methods
### Degranulation Assay  
As a rat basophilic leukemia cell line, the RBL-2H3 cell line is used as a model for mast cells. RBL-2H3 cells exhibit phenotypes of mucosal mast cells and are recognized as a brilliant tool for investigating the regulation of mast cell responses [29]. Thus, the RBL-2H3 cell line was used in this test to investigate the potential effect of R-PE on the degranulation of mast cells. \(\beta\)-hexosaminidase was used as a marker to monitor the degranulation of mast cell [30]. The \(\beta\)-hexosaminidase detection method was modified from reference [31]. RBL-2H3 cells were seeded at 1 \(\times\) 10\({}^{5}\) cells per well in 24-well cell culture plates (Corning, New York, NY, USA). Cells were first treated with 1 uM calcium ionophore A23187 (Sigma-Aldrich, St. Louis, MO, USA) followed by the addition of differing dosages of R-PE (0 (negative control), 1.25, 2.5, 5, 10, and 20 ug/mL) and incubated for 2 h. Concurrently, cells that were incubated with 100 uM quercetin (Sigma-Aldrich, St. Louis, MO, USA) were used as a positive control. The methodology used for \(\beta\)-hexosaminidase quantification was obtained from previously published studies [32; 33]. After treatment, the supernatant (30 uL) from each well was collected and transferred to a new 96-well plate prior to the addition of 50 uL of 4-nitrophenyl N-acetyl-\(\beta\)-D-glucosamine (NP-GlcNAc, 1.3 mg/mL in citrate buffer (pH 4.5), Sigma-Aldrich, St. Louis, MO, USA). The plate was left to stay at 37\({}^{\circ}\)C for 1 h, and the reaction was terminated by the addition of 80 uL of 0.5 M NaOH. The plate was then analyzed for any formation of p-nitropolate using a spectrophotometer (Jasco, Halifax, NS, Canada) at a wavelength of 405 nm. Cells left from the original culture plate were used in an MTT assay to examine cell viability after treatments.
# A Multi-Degree of Freedom Tuned Mass Damper Design for Vibration Mitigation of a Suspension Bridge
## 5 Experimental Verification
### Hammer Tests of TMDs  
The structure of 2 DOFs TMDs with adjustable natural frequencies is presented in Figure 16. Each TMD consists a mass block, two pedestals, and eight flexible beams with adjusters. For each flexible beam, one side is fixed on the pedestal by bolts and the other side is connected to the mass block. Though changing the positions of the adjuster, the effective length of the flexible beam can be adjusted, then tunable stiffness of the TMD is realized. The adjuster not only improves the tuning ability of TMD, but also compensates for the errors. The errors include the machining error and the calculation error which is caused by ignoring the prestress of flexible beams (Equations (39) and (40)). Thus, the adjuster is an indispensable part of the TMDs.  
Based on Equations (39) and (40), the natural frequencies of 2 DOFs TMDs are determined by the dimensions of the flexible beams. In order to improve the tuning ability at runtime, the TMDs are designed with tunable \(L_{1}\) and \(L_{2}\) which is the effective length of the flexible beams (Figure 16).  
In hammer tests, the TMDs are excited by an INV9311 impact hammer, and the acceleration responses are recorded by a PSV-500-1D scanning laser vibrometer. The experimental results are shown in Figure 17. The figure shows the experimental acceleration responses of the designed 2 DOFs TMDs: (a) when \(L_{1}\) = 48.5 mm, \(L_{2}\) = 55.5 mm, the first three order frequency of TMD1 is 4.37 Hz, 10.08 Hz, and 65.81 Hz; (b) when \(L_{1}\) = 37.5 mm, \(L_{2}\) = 40 mm, the first three order frequency of TMD2 is 6.21 Hz, 9.47 Hz, and 72.19 Hz. Comparing with the first two order frequency, the third order frequency of TMDs is a relatively large value, which is far beyond the bandwidth (0-15 Hz) we are considering. Therefore, the 2 DOFs TMDs meet the design requirements and verify the validity of the theoretical model.  
Figure 16: Prototype of the 2 DOFs TMDs.  
Figure 17: Experimental acceleration responses of the designed TMDs. (**a**) TMD1; (**b**) TMD2.
## 3 Results
### Data Analysis  
#### 3.4.1 Diagnostic Performance of ADC Values to Differentiate MIBC from NMIBC  
ADC values were significantly lower in MIBC than in NMIBC in all studies (Table 2). [PERSON] et al. demonstrated significant differences in ADC values between Ta and T1 disease and between T1 and T2 disease [14]. Four studies provided the sensitivity and specificity of ADC values to diagnose MIBC. Cutoff ADC values were determined using the point closest to the upper-left corner of the ROC curves in four studies [16, 17, 20, 26]. The median (range) sensitivity, specificity, and AUC for the diagnosis of MIBC were 73.5% (68.8-90.0%), 79.9% (66.7-84.4%), and 0.762 (0.730-0.884), respectively. [PERSON] et al. showed that FROC parameters yielded a significantly higher specificity (85%) than ADC values (67%) for distinguishing MIBC from NMIBC [26]. The performance of VI-RADS to diagnose  
Figure 4: Risk of bias and applicability concerns summary: review authors’ judgments about each domain for included studies according to revised quality assessment of diagnostic accuracy study 2 (QUADAS-2).  
MIBC improved from 88% to 90% in diagnostic accuracy and from 0.859 to 0.931 in the AUC when combined with FROC parameters [26].  
.4.2 Diagnostic Performance of ADC Values to Differentiate High-Grade Disease from Low-Grade Disease  
The ADC values were significantly lower in high-grade disease than in low-grade disease in all four eligible studies (Table 3). Two other studies demonstrated significant differences in ADC values between diseases with histological grades 1, 2, and 3. The median (range) sensitivity, specificity, and AUC to diagnose high-grade disease were 75.0% (73.0-76.5%), 95.8% (76.2-100%), and 0.902 (0.804-0.906), respectively, among the three eligible studies.
###### Abstract  
This study presents a new method to track driver's facial states, such as head pose and eye-blinking in the real-time basis. Since a driver in the natural driving condition moves his head in diverse ways and his face is often occluded by his hand or the wheel, it should be a great challenge for the standard face models. Among many, Active Appearance Model (AAM), and Active Shape Model (ASM) are two favored face models. We have extended Discriminative Bayesian ASM by incorporating the extreme pose cases, called it Pose Extended--Active Shape model (PE-ASM). Two face databases (DB) are used for the comparison purpose: one is the Boston University face DB and the other is our custom-made driving DB. Our evaluation indicates that PE-ASM outperforms ASM and AAM in terms of the face fitting against extreme poses. Using this model, we can estimate the driver's head pose, as well as eye-blinking, by adding respective processes. Two HMMs are trained to model temporal behaviors of these two facial features, and consequently the system can make inference by enumerating these HMM states whether the driver is drowsy or not. Result suggests that it can be used as a driver drowsiness detector in the commercial car where the visual conditions are very diverse and often tough to deal with.  
pose extended-active shape model; driver drowsiness detection; head pose estimation; eye-blink; head nodding; hidden Markov model +
Footnote †: journal: Applied Sciences
## 2 USV Dynamic Models
### Wave Model  
The wave model adopts the three-dimensional Gerstner model given in [17], and its expression is as follows:  
Figure 2: Thrust generated by the two padded thrusters.  
\[\left\{\begin{aligned} x=x_{0}-\sum_{i=1}^{N}q_{i}A_{i}\cos \theta_{i}\sin[k_{i}(x_{0}\cos\theta_{i}+y_{0}\sin\theta_{i})-\omega t+\varphi _{i}]\\ y=y_{0}-\sum_{i=1}^{N}q_{i}A_{i}\sin\theta_{i}\sin[k_{i}(x_{0} \cos\theta_{i}+y_{0}\sin\theta_{i})-\omega t+\varphi_{i}]\\ z=z_{0}-\sum_{i=1}^{N}A_{i}\cos[k_{i}(x_{0}\cos\theta_{i}+y_{0} \sin\theta_{i})-\omega t+\varphi_{i}]\end{aligned}\right. \tag{20}\]  
where \((x_{0},y_{0},z_{0})\) is the initial position of the particle; \(q_{i}\), \(A_{i}\), \(\theta_{i}\), \(k_{i}\), \(\omega_{i}\), and \(\varphi_{i}\), respectively, represent the steepness, amplitude, directional angle, wave number, angular frequency, and initial phase of the \(i\)th unit wave. The main energy of waves is concentrated near the peak frequency \(\omega_{0}\), and the average wave direction angle \(\theta_{m}\) is considered for the wave direction.  
The wave spectrum adopts the Bretschneider spectrum:  
\[S_{\text{B}}(\omega)=\frac{1.25\omega_{0}^{4}\overline{H_{\text{s}}^{2}}}{4 \omega^{5}}\exp\left[-\frac{5}{4}\Big{(}\frac{\omega_{0}}{\omega}\Big{)}^{4}\right] \tag{21}\]  
where \(\overline{H_{\text{s}}}\) is the significant wave height. Wave gain \(K_{H}=\overline{H_{\text{s}}}/H_{\text{s}}\) is introduced, where \(H_{\text{s}}\) is the significant wave height of a single parameter PM spectrum, described as follows:  
\[S(\omega)=A/\omega^{5}\cdot\exp(-B/\omega^{4}) \tag{22}\]  
where \(A=8.1\times 10^{-3}g^{2}\) is a constant, \(g\) is the acceleration of gravity, and \(B=3.11/H_{\text{s}}^{2}\). \(H_{\text{s}}\) and \(\omega_{0}\) satisfy the following expression:  
\[H_{\text{s}}=0.162g/\omega_{0}^{2} \tag{23}\]  
Thus, Equation (21) can be expressed as  
\[S_{\text{B}}(\omega)=(K_{H})^{2}\frac{8.1\times 10^{-3}g^{2}}{\omega^{5}}\exp \left[-\frac{5}{4}\Big{(}\frac{\omega_{0}}{\omega}\Big{)}^{4}\right] \tag{24}\]  
The relationship between unit wave amplitude \(A_{i}\) and \(S_{B}\) is as follows:  
\[A_{i}=\sqrt{2S_{\text{B}}(\omega_{i})\Delta\omega_{i}} \tag{25}\]  
where \(\Delta\omega_{i}\) represents the \(i\)-th angular frequency interval. At this point, the relationship between wave amplitude \(A_{i}\), wave gain \(K_{H}\), and peak frequency \(\omega_{0}\) has been established. Due to the wave period \(T_{0}=2\pi/\omega_{0}\), the wave model can ultimately be determined using wave gain \(K_{H}\), wave period \(T_{0}\), and average wave direction \(\theta_{m}\). The wave force vector \(\tau_{\text{wave}}\) is detailed in Algorithm 1 in [17].