esa-sceva/satcom-chunk-collection huggingface dataset:
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 2 Methods
### Changes in Displacement Time Series
#### 2.2.1 Offset Detection
\[\sigma^{k}=\sqrt{\frac{\sum_{i=1}^{N}(y_{t}^{k}-\bar{y}^{k})^{2}}{N-1}} \tag{5}\]  
where \(y^{k}\) is the subset of \(N\) observations of the difference series \(\
abla^{d}\gamma_{t}^{k}\) that lie between the 5% and 95% quantiles, and \(\sigma^{k}\) is the standard deviation of the difference series. For the online detector our assumption is that the standard deviation remains the same for all new observations added to the time series. Assuming the noise in the displacement time series is i.i.d. Gaussian, the detection problem now becomes a matter of calculating the Student's \(t\)-statistic and testing for statistical significance at a given threshold, here set to 0.95. Since we want to perform an online test with new incoming data points we use the form of the test statistic for unequal sample sizes but similar variance [36]. \[t_{stat}=\frac{\
abla^{d}\gamma_{t}^{k}-\bar{y}^{k}}{\sqrt{\frac{(n-1)(\sigma ^{k})^{2}+(N-1)(\sigma^{k})^{2}}{n+N-2}}}\sqrt{\frac{1}{n}+\frac{1}{N}} \tag{6}\]  
We perform a test with each new point one at a time, \(n=1\). And so the \(t_{stat}\) equation reduces to:  
\[t_{stat}=\frac{\
abla^{d}\gamma_{t}^{k}-\bar{y}^{k}}{\sigma^{k}\sqrt{1+\frac{ 1}{N}}} \tag{7}\]  
\(\bar{y}^{k}\) is the mean of the difference series, which we assume remains constant. Since differencing removes all trends from the displacement time series and assuming noise in the displacement series is Gaussian and drifts in the series are small, then \(\bar{y}^{k}\) is small compared to the noise in the data. Computationally this is now much more efficient because the denominator is also a constant, making the \(t_{stat}\) calculation a vectorised operation on \(\gamma_{t}^{k}\). Points that pass the significance test at the 95% confidence level are labelled as candidate change points in the time series. In order to increase our confidence and reduce false positives in the detections, we perform this step on the first, second and third difference time series (\(k=1,2,3\) respectively), and take the intersection of points detected by all three series independently as the confirmed offset change point (Figure 3).
# Acceptance Model for Mobile Building Information Modeling (BIM)
## 5 Model Validation
### Validation of the Proposed Model
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Latent Constructs** & \
\begin{tabular}{c} **Observed** \\ **Indictators** \\ \end{tabular} & **Factor Loading** & \(t\)**-Value** &
\begin{tabular}{c} **Composite** \\ **Reliability** \\ \end{tabular} & **AVE** \\ \hline  & TQ 1 & 0.837 & - & & \\  & TQ 2 & 0.883 & 11.795 & & \\  & TQ 3 & 0.897 & 12.117 & & \\  & TQ 4 & 0.905 & 12.326 & & \\
**Tool Quality (TQ)** & TQ 5 & 0.813 & 10.253 & 0.906 & 0.493 \\  & TQ 6 & 0.788 & 9.767 & & \\  & TQ 7 & 0.863 & 11.315 & & \\  & TQ 8 & 0.833 & 10.664 & & \\  & TQ 9 & 0.786 & 9.726 & & \\ \hline  & PE 1 & 0.723 & 8.593 & & \\  & PE 2 & 0.472 & - & & \\
**Personal Efficacy (PE)** & PE 3 & 0.855 & 5.014 & 0.865 & 0.571 \\  & PE 4 & 0.83 & 4.962 & & \\  & PE 5 & 0.936 & 5.16 & & \\  & PE 6 & 0.904 & 5.108 & & \\ \hline  & BC 1 & 0.949 & - & & \\
**Behavior Control (BC)** & BC 2 & 0.925 & 14.711 & 0.856 & 0.667 \\  & BC 3 & 0.750 & 9.994 & & \\ \hline
**Perceived Ease of Use** & EOU 1 & 0.898 & - & & \\
**(EOU)** & EOU 2 & 0.887 & 13.068 & 0.675 & 0.409 \\  & EOU 3 & 0.885 & 13.004 & & \\ \hline  & U 1 & 0.919 & - & & \\  & U 2 & 0.938 & 17.488 & & \\
**Perceived Usefulness** & U 3 & 0.888 & 14.768 & & \\
**(U)** & U 4 & 0.882 & 14.527 & 0.904 & 0.535 \\  & U 5 & 0.899 & 15.297 & & \\  & U 6 & 0.722 & 9.495 & & \\  & U 7 & 0.895 & 15.113 & & \\ \hline
**Individual Intention to** & IIA 1 & 0.971 & - & & \\
**Accept Mobile-BIM** & IIA 2 & 0.848 & 13.025 & 0.800 & 0.572 \\
**(IIA)** & IIA 3 & 0.805 & 11.648 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of CFA. As expected, hypothesis H1a and H2a were supported (\(\gamma\) = 0.666 and \(\gamma\) = 0.42, respectively). Increased tool quality and personal efficacy were associated with increased perceived ease of use. Behavior control was not very supported (\(\gamma\) = 0.123). The tool quality and personal efficacy had an effect on perceived usefulness. H1b and H2b were supported (\(\gamma\) = 0.424 and \(\gamma\) = 0.45, respectively). Perceived ease of use appeared to be a significant determinant of perceived usefulness; H6a was supported (\(\beta\) = 0.405). In addition, perceived usefulness had a significant effect on individual intention in accepting a mobile BIM; H8b was supported (\(\beta\) = 0.631). Therefore, tool quality and personal efficacy exhibited a relatively strong influence on mobile BIM acceptance, and behavior control had relatively weak influence. H1a, H2a, and H3a explained 58.6% of the variance in the perceived ease of use. H1b, H2b, and H6a explained 71.6% of the variance in perceived usefulness. H8b explained 43.7% of the variance in individual intention to accept a mobile BIM. The final proposed model is shown in Figure 4, and the direct, indirect, and total effects of each construct are summarized in Table 11.
## 3 Modulation Techniques
### Binary Phase-Shift Keying  
BPSK is the simplest form of PSK where the carrier phase represents only two-phase states. BER of BPSK is given by:  
\[\mathrm{P_{BPSK}}=\frac{1}{2}\mathrm{erf}\left(\sqrt{\frac{E_{b}}{N_{0}}}\right) \tag{9}\]
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 5 Applications
### Material Science
#### 5.3.1 Semiconductor Industry
Depth profiling studies along the main axis of TSVs, as well as chemical composition analysis on the surface of cross-sectioned TSV structures, revealed a gradient in the carbon (C) content and indicated that the filling concept significantly deviates from common Damascene electroplating processes. The quantitative analysis revealed a C content that is similar to 1.5 times higher at the TSV top surface compared to its bottom. For a more comprehensive, understanding of the underlying mechanisms, further studies would be necessary [173]. The next study conducted by IR fs-LMS was done on electrochemically deposited Sn/Ag solder bumps, other important interconnects in the microchip industry [174]. Similar to the previously mentioned structures, the purity of the material, including the quantification of incorporated organic impurities, is of concern. Considerably improved analysis of this system was demonstrated by applying UV fs-ablation indicating that IR fs-ablation is less suitable for analysing low melting materials like Sn [81]. This study provided the first report on 3D-resolved chemical composition analysis on industrially high relevant Sn/Ag solder bumps. The 3D chemical imaging of the bumps was obtained by combining the 2 Dbinning approach along each layer of the entire bump (about 50 \(\upmu\)m in diameter) and the depth profiling method in the depth direction (10 \(\upmu\)m) (Figure 14). The quantification of the Ag content was found in very good agreement with ICP-MS studies, but in addition to the ICP-MS, which provides only bulk information, fs-LMS had provided micrometre level spatial resolution. In addition, the Ag heterogeneity, an undesired feature in solder bumps, was detected. The most relevant contaminants found in the solder bumps resulting from the plating process were O, C, and S, and their 3D distribution in the analysed volume was also determined [159]. Elaboration of matrix-matched RSC values for Sn and Ag from a certified reference material (BCS-CRM No. 347, BAS Ltd., Coulby Newham, UK) allowed for accurate quantification of the metallic phases of the alloy and also showed severe depletion of Ag on the surface. The studies contribute to the understanding of the SnAg plating process and the presented procedure can be important in additive performance screening. Figure 14: Signal intensity distribution of Sn, Ag, and the most relevant organic contaminants C, O, and S within the surface and early bulk region of the SnAg solder bump analysed by the 2D layer binning approach. Top row: depth profiles, bottom panels: 3D chemical imaging prepared on several elements. Differences in the alloy homogeneity, SnAg, and the spatial distribution of individual organic plating bath constituents (C, O, S) are apparent. Data have been normalized to the maximum signal intensity for this representation. Reprinted with permission from [81].
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
## 3 Application and Results
### Application Results
#### 3.4.3. Relation between \(S_{s}\) and Pipe Replacement Cost
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Critical Path**} & \multirow{2}{*}{**Net Inflow**} & \multicolumn{2}{c}{**System**} & \multicolumn{2}{c}{**Pipeline**} & \multicolumn{2}{c}{**Total Rank Sum**} \\  & **(m\({}^{3}\)/d) (Rank)** & & **Serviceability** & **Rank (b)** & **Replacement Cost** & **Rank (c)** & **(a) + (b)** \\  & & & **(s\({}_{\omega}\), \%)** & & **(Thousand USD)** & & **(b) + (c)** & **+ (c)** \\ \hline RES1 Pipeline & 316,645 & 2 & 89.04 & 2 & 10,372 & 8 & 10 & 12 \\ RES2 Pipeline & 732,455 & 1 & 92.69 & 1 & 21,039 & 9 & 10 & 11 \\ RES3 Pipeline & 316,645 & 2 & 86.12 & 5 & 8176 & 7 & 12 & 14 \\ RES4 Pipeline & 194,551 & 5 & 85.2 & 9 & 3327 & 6 & 15 & 20 \\ RES5 Pipeline & 34,582 & 8 & 85.55 & 6 & 1893 & 1 & 7 & 15 \\ RES6 Pipeline & 34,582 & 8 & 85.27 & 8 & 2695 & 3 & 11 & 19 \\ RES7 Pipeline & 96,905 & 7 & 88.27 & 4 & 2748 & 4 & 8 & 15 \\ RES8 Pipeline & 211,363 & 4 & 88.43 & 3 & 3142 & 5 & 8 & 12 \\ RES9 Pipeline & 102,036 & 6 & 85.33 & 7 & 2166 & 2 & 9 & 15 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comprehensive Assessment of System Serviceability (\(S_{s}\)), Cost of Earthquake Resistance, and Supply. All in all, as shown in Table 5, an earthquake resistance strategy for each pipeline should be determined by comprehensively considering the reliability of the water supply in the system, the amount of supply in each section, and the cost of implementing earthquake resistance. The reliability of the water supply is largely affected by the physical characteristics (pipe diameter distribution, pipe length, and connectivity) of the target section as well as the water quantity demand of the route, which has a direct influence. However, since the supply performance index \(S_{s}\) for each route defines the ratio of the available supply to the total demand, in order to compare the reliability of water supply between routes, the total demand for each route must be considered separately. In addition, the construction costs associated with installing earthquake-resistant pipe increase as the pipe diameter increases, but the reliability of the seismic reinforcement also increases with pipe diameter at a very good rate. Therefore, it is necessary to establish a seismic reinforcement strategy in consideration of economic feasibility and in accordance with the distribution of pipe diameters in a given pipeline.
## 3 Results  
### Properties of the Activated Carbon  
Analysis of surface elements was performed on original (before the acid ash) and fresh (after the acid ash) activated carbon, with the results shown in Table 1. The contaminants of activated carbon were significantly removed by acid washing, as shown by the increased carbon content. As revealed in Figure 2, SEM imaging also revealed that acid washing removed impurities, giving rise to a pore structure with a seemingly smoother surface. The pore structure characteristics of the original activated carbon and fresh activated carbon are presented in Table 2. Fresh activated carbon exhibited a slightly larger specific surface area and total pore volume compared to original activated carbon; this may be an effect of other chemicals, such as oxygen, being removed during the acid washing process. However, the acid washing seems to have little effect on the pore size of activated carbon. The mean pore diameters of the original and fresh activated carbon indicate that a vast majority of the pores fall into the range of micropores. The point of zero charge, which is the pH at which the net surface charge is zero, of fresh activated carbon was found to be about 6.7, as revealed in Figure 3. The surface charge of activated carbon depends on the solution pH and its point of zero charge, the activated carbon surface is positively-charged at pH lower than the point of zero charge and vice versa [26]. Surface functional groups on activated carbon affect the surface charges and adsorption properties of activated carbon. Boehm titration of fresh activated carbon revealed that most of the acidic functional groups are lactone and phenolic groups, followed by the carboxylic group. The total number of the basic functional groups was calculated to be 0.207 mmol/g, which is approximately equal to the total number of acidic functional groups, confirming the previously observed point of zero charge of 6.7.  
Figure 1: Experimental setup for ultrasonic regeneration of exhausted activated carbon.  
Figure 3: Point of zero charge titration for fresh activated carbon.  
Figure 2: SEM images of original, fresh and regenerated activated carbon at 1000\(\times\) magnification.
## 3 Positioning System Experimental Platform
### WinBus
#### 3.1.4 Inertial Measurement Unit (IMU)  
The IMU uses three-direction gyroscopes and three-direction accelerometers to measure the three-axis angular velocity and acceleration changes. It uses these data to calculate the pose of the object. The IMU is usually mounted on the object's center of gravity to ensure correct calculations. Some IMUs integrate other sensors to extend its functions, such as magnetometers or barometers, and to calculate bearing and altitude. The WinBus integrates the IMU to increase the reliability of the positioning system.
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 2 Analysis of Robot Structure and Steering Radius
### Relation between Driving Force and Slip Rate  
When describing the skidding and slipping characteristics of a robot, it is necessary to assume that each point of the track cannot be stretched. That is, no deformation will occur. If the speed of each point on the track connected to the ground is the same, then the shear displacement of each point increases linearly. The relationship between \(x\) and \(j\) is shown in Figure 2. The expression \(j\) for the shear displacement of the track is given as follows:  
\[j=ix \tag{3}\]  
Figure 2: Skid and slip analysis of robot track.  
where \(i\) is the skid or slip rate, and \(x\) is the distance between the track in contact with the ground and the endpoint of the track.  
The formation of the track shear force is related to the shear displacement, so the distribution of the shear stress along the contact length can be determined. When the slip rate is given, the total driving force generated by the track can be obtained from the relationship between shear stress and shear displacement under the track, which can be calculated using the following formula:  
\[F=b\int_{0}^{L}\tau dx=b\int_{0}^{L}\left(c+p\tan\phi\right)(1-e^{-j/k})dx \tag{4}\]  
where \(b\) is track width, \(\tau\) is shear stress, \(p\) is the normal pressure, \(k\) is the shear displacement parameter, \(c\) is the ground cohesion, \(\phi\) is the internal shear resistance angle, and \(L\) is the wheelbase of the robot.  
It can be seen from the above formula that the driving force of the track is related to the normal pressure distribution on the track contact surface. For a uniform normal pressure distribution, \(p=G/bL\), the total driving force of the track is determined using the following formula.  
\[F=bcL+G\tan\phi\left[1-\frac{k}{iL}(1-e^{-iL/k})\right] \tag{5}\]
## 4 Results
### Prediction Model  
The medical records are classified as 70% for training date set and 30% for testing data set. For example, of E11--type 2 diabetes mellitus, 3154 records are classified as training data and 1351 records as testing data. This study aims to predict the medical records which are judged as deducted since the medical service is regarded as \"wasted and unnecessary\" by the audit committee. However, the deducted medical records are with a low rate of 5.22% of all records in Pu-Chi Hospital. A small number of deducted records vs.. large number of approved records result in low accuracy in data training since deducted records are with a low probability. The synthetic minority oversampling technique (SMOTE) is therefore used to increase the percentage of deducted cases [31]. In the case of E11--type 2 diabetes mellitus, the AUC is enhanced to 0.782 from 0.677 after applying the SMOTE procedure.  
Table 2 shows the five factors that may led to weight deduction obtained from the results of regression analysis. (1) Object (data of doctors' judgement and examinations), (2) D19 (primary diagnostic code), (3) D20--d23 (secondary diagnostic code), (4) p4, (codes of drugs and examinations) and (5) price (total weight). For text-based information such as \"object\", research team transfer it into binary variable and put into the regression model for prediction. Moreover, D19 (primary diagnostic code) and D20--d23 (secondary diagnostic code) were used to observe whether a disease that has a great possibility of being deducted (far higher than the deduction rate of outpatient cases at Puli Christian Hospital) is relevant to weight deduction. The price (total weight) was utilized to observe whether the denial of a medical claim is related to the amount involved in the medical claim. Lastly, p4 (codes of drugs and examinations) was employed to see whether a drug has a high denial rate of medical claims.  
After the SMOTE was used and the prediction model was trained, the 30% data set was tested. The regression coefficients were obtained, where positive/negative refers to deducted or not, respectively, for example E11 in Figure 11.  
The factors that led to medical claim denial were selected for logistic regression modeling. The model training results are shown: the area under the curve (AUC) of the model is large enough for both training set and testing set. This indicates that the discrimination power of the ROC curve was enough to be adopted.  
Figure 10: Topic deducted (approved/denied) distribution (E11).  
In Figure 12, the E11 recall increased from 0.167 to 0.417, which shows that the proposed prediction model improved 25% of deducted cases.
## 3 Results
### Measured Outcomes  
Basis for all measurements is the defining moment when the recordings begin. In most cases, it was when perturbing force started affecting a subject. However, [PERSON] et al. [21] and [PERSON] et al. [24] decided to start measurements when the perturbation device started its motion, i.e., before any force started affecting a subject.  
In all publications, the authors chose to measure ground reaction forces. In all but one paper [24], ground reaction forces were measured using two platforms. The recording frequency varied between 100 and 2000 Hz, and was different in each paper: 100 Hz [21], 480 Hz [15], 500 Hz [5; 23], 600 Hz [25], 1000 Hz [20], and 2000 Hz [19].  
Vicon [20; 25], Optitrack [19] and Motus [5; 23] systems, set to 120 Hz and 250 Hz, respectively, for Vicon, 200 Hz-Optitrack, 60 Hz-Motus were used to collect kinematic data in six works [5; 19; 20; 23; 25].  
[PERSON] et al. [24] used two accelerometers. One was placed on the subject, the other on the pendulum. A pendulum accelerometer (model 208 CO3, PCB Piezotronics Inc, USA) was attached to the pendulum, and its signal determined the timing of pendulum release and impact. A participant-worn accelerometer (model 1356a16, PCB Piezotronics Inc. Inc, USA) was attached to the participant's dorsal surface at the LSS1 level, to detect temporal pelvic motion events.  
[PERSON] et al. [21] used multi-purpose rehabilitation frame (MRF). The MRF was used to generate perturbations for a standing subject. It consists of two 2-dof and two 1-dof rotational joints, two vertical support rods and a bracing system. Subjects stood with each foot on an aluminum block that constrained the position and orientation of their feet. Torque impulses, delivered by two hydraulic motors through a bracing system which was put around the subjects' pelvis, were used to induce perturbations.  
Many authors [19; 20; 24; 25] chose to measure lower-extremity muscle activity. All of them, with the exception of [PERSON] et al. [20], chose to measure activity on two lower limbs. The activity of the tibialis anterior (TA) muscle was recorded in each paper except [PERSON] et al. [25]. The authors additionally chose muscles such as: GMed, RF, ST, IL, GMax, AM [20]; GMed, TFL, ADD [25]; MG, RF, BF, GMed, EO, RA, ES [24]. In contrast, only [PERSON] et al. [20] included mechanomyogram data beside the EMG signal. The frequency of data collection was 2000 Hz [19; 20], 1500 Hz [25], and 1000 Hz [24].  
Ankle responses were included in publications by [PERSON] et al. [15], [PERSON] et al. [20], and [PERSON] et al. [19]. The knee joint was omitted by most authors and was only included in the study by [PERSON] et al. [20]. The pelvis and hip joints were mentioned in three reports [15; 20; 24]. CoP and CoM movements were the most frequently analyzed, as they were considered in four publications [5; 20; 24; 25].