esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory
### Opto-Acoustic Method
#### 2.2.1 Michelson Interferometer  
Figure 2 illustrates the optical configuration of this method. The specimen is integrated in a Michelson interferometer as one of the end-mirrors with the film side facing the beam splitter and is oscillated with an acoustic transducer from the rear. The acoustic transducer is driven with a sinusoidal input in a frequency range of 2 kHz to 30 kHz. The resultant oscillatory motion of the film surface is detected as the corresponding optical path difference behind the beam splitter.  
To visualize the relative optical path length difference between the two arms, one of the end mirrors is slightly tilted. In this way, the relative phase of the optical intensity behind the beam splitter can be detected as parallel interferometric fringes as indicated by the insert in Figure 2. The fringe pattern is projected on a screen, and a digital camera is used to capture the pattern electronically. Here, the dark fringes represent the contour where the relative optical path length difference is totally destructive or the relative phase difference is an odd integer multiple of \(\pi\). When the specimen is oscillated with the acoustic transducer, the corresponding optical path varies in a sinusoidal fashion, causing the dark fringes to dither around the nominal position where the dark fringes are located when the transducer is off. Since the digital camera's frame rate is much lower than the transducer's oscillation frequency, the dithering lowers the fringe contrast. The present method utilizes the reduction in the fringe contrast to estimate the oscillation amplitude.  
In this frequency range, both the film and substrate behave as a rigid body because their elastic constants are so high that the acoustic phase velocities are high enough to make the wavelength at an audible frequency longer than their thicknesses. This means that if the interface has an elastic constant comparable to the film and substrate, the entire specimen should oscillate as a rigid body, as well. In other words, the oscillation amplitude of the rear and the front surface of the specimen is comparable or the transfer function of the rear-surface displacement to the front-surface displacement is unity. However, if the elastic constant of the interface is so low that the substrate-side to the film-side displacement transfer function is higher than unity, the film surface displacement becomes greater, as compared to the transducer's displacement. The preliminary study on the harmonic response of a thin-film specimen demonstrated a case where the low elastic constant of the interface makes the substrate-to-film displacement transfer function greater than unity (see Section 3.1 below).  
Figure 2: Experimental arrangement with a Michelson-type interferometer. The function generator (Gold Star Model FG 8002) sends sinusoidal input voltage to the acoustic transducer (magnetic type, Design Factory Dokodemo-Speaker 8297).
## 3 Security and Privacy Issues  
This section discusses: (i) which would be the possible threats to a wireless healthcare application without implementation of proper security; and (ii) privacy issues. Before discussing the security issues in wireless healthcare applications, it is worthwhile to assume the scale of deployment of healthcare applications using WMSNs. In this regards, we have considered three wireless healthcare scenarios, namely, a nursing home, in-home monitoring, and in-hospital monitoring, as shown in Figure 8.  
The wireless healthcare applications use medical sensors (_i.e._, as _per_ patient appropriateness) and environmental sensors (ES), mobile devices (_i.e._, PDA, laptop or iPhone), and more especially wireless communication (_i.e._, IEEE 802.11, IEEE 802.15.4, Bluetooth _etc._) protocols. Further, a back-end server is used for physiological healthcare information (PHI) storage, and for offline analysis of PHIs. According to the _nursing home scenario_ Figure 8 (left), medical sensors are placed on a patient's body and sense the physiological data of an individual and transmit it in a timely way to the PDA that may be held by a nurse. A nurse can query the patient's sensors and analyze the real-time patient data conditions. Later she can send patient data to the central server either using Internet or a wireless medium. Many ES are deployed in nursing homes that can form a wired or wireless network, sense the environmental parameters (e.g., ward temperature, humidity, _etc._) and transmit the data to either a nurse or a remote center. In addition, the environmental sensors may forward an alarm to the remote server in an emergency situation (e.g., supposing a severe condition is detected), should one occur. In the _home scenario_ (Figure 8, upper right), medical sensors are planted on a patient's body and capture the health data from an individual and transmit it in a timely fashion to a PDA held by a nurse or family member. In addition, environmental sensors are required when a patient is usually alone at home. The environmental sensors are placed at the corners of rooms, collecting the environmental conditions (e.g., room temperature, humidity, _etc._), and patient movement data. Later they automatically send collected environmental and patient abnormal conditions to the PDA, which is held by either a nurse or a responsible family member. The home local station can directly communicate with environmental sensors using Zigbee modules. To analyze the patient physiological data an application program will be implemented at the back-end network. In the _In-hospital scenario_ (Figure 8, bottom right), the same deployment and sensing scenario (_i.e._, as in the nursing home and homecare scenarios) is now applicable to the hospital environment, where groups of patients are temporarily monitored using a wireless medical sensor network by nurses or physicians using their PDAs; for more details the reader may refer to [56].  
Figure 8: Application scenarios for a nursing home, home care, and in-hospital.
## 3 Results and Discussion
### Comparisons of the EICP Method in This Work with Other Reported Materials  
In recent decades, researchers have explored various adsorbents for the effective removal of heavy metal ions from contaminated water. Common adsorbents include biological-based materials, agricultural waste, clay minerals, zeolite-based adsorbents, chitosan-based adsorbents, and nanomaterials. [PERSON], et al. [61] reported that the rice straw biochar adsorbed 1.79 meg/L of Zn, but its ability to adsorb multi-metal wastewater was unclear. [PERSON] et al. [62] investigated the natural zeolite adsorption capacity for Cr(VI) and found it to be lower than 0.2 mg/g. Although surfactant-modified zeolite enhanced Cr(VI) adsorption, the complex pre-treatment process limits its on-site applications. Similarly, the modified cross-linked magnetic chitosan-DAMO Schiff's base resin (CSMO) significantly enhanced the adsorption capacity for heavy metal ions like Ni\({}^{2+}\), compared to unmodified CSMO [63; 64]. However, such modification undoubtedly comes with increased costs. Table 4 compares the different adsorbents reported in the literature with the EICP method used in this study. No single adsorbent meets all criteria of being cost-effective, simple preparation, highly efficient for multiple metals at high concentration, and free from secondary pollution. In contrast, the EICP method used in this study has advantages, demonstrating a low cost and significant immobilization capabilities of multiple heavy metals contamination, especially for Zn\({}^{2+}\). Even though its effectiveness for  
Figure 14: FTIR results of precipitates in liquid batch tests.  
Ni\({}^{2+}\) and Cr(VI) is moderate and may require multiple treatment cycles, the EICP method remains a highly promising approach for practical applications.  
Figure 15: _Cont._  
According to the results above, the heavy metal immobilization mechanisms can be summarized as biomineralization, adsorption/complexation by organic matter from crude sword bean urease, and adsorption/complexation by enzyme-induced calcium carbonate. The adsorbents in this study are organic matter and calcium carbonate induced by the EICP method. The organic matter, derived from crude sword bean urease extracts, maintains its bioactivity for approximately 7 to 14 days but is not reusable [65]. The enzyme-induced calcium carbonate is another adsorbent. Although calcium carbonate is stable and does not degrade over time, its adsorption capacity is finite. Once its adsorption sites are saturated with heavy metals, it can no longer adsorb additional heavy metals [66]. While calcium carbonate lacks the reusability of materials such as zeolite or activated carbon, it still has a high price/performance ratio because the regeneration process often produces a lot of wastewater to pollute the environment. Additionally, the discarded calcium carbonate can be utilized in the building materials, further enhancing its economic and environmental value [67; 68; 69; 70].
# MC_MoveAbsolute(0) 4 th Order Real-Time Trajectory Generation Function Algorithm and Implementation
## 4 Generation of Profile
### Execution of Trajectory Profile Generation  
When the trajectory profile generation is executed (i.e., by the user) and output of the reference position trajectory has started, then the generation of snap profile is based on the following calcula-tions.  
Figure 5: Fourth order trajectory profiles in _point-to-point_ movement with profile times shown.  
When:  
\[start=\text{TRUE} \tag{55}\]  
Then:  
\[dd_{local}\gets dd9_{5} \tag{56}\]  
The new value of \(t_{16}\) is calculated based on Equation (52) and the resulting Equation (53). The variable \(start\) is set based on the algorithm shown in the following part of this subsection.  
Until:  
\[t_{exec}\leq t16_{1} \tag{57}\]  
Then:  
\[p_{1}=0 \tag{58}\]  
When:  
\[t_{exec}>t16_{1} \tag{59}\]  
Then:  
\[p_{1}=dd9_{5} \tag{60}\]  
where \(t_{exec}\) stands for the actual profile generation execution time. Calculations of \(p_{2}\ldots p_{16}\) values are based on the following conditions/equations:  
\[\begin{array}{c}If\,t_{exec}\leq t16_{2},\,p_{2}=0\\ If\,t_{exec}>t16_{2},\,p_{2}=dd9_{5}\end{array} \tag{61}\]  
Which gives the following algorithm:  
\[\begin{array}{c}\text{FOR}\,j=1\ldots 16,\\ If\,t_{exec}\leq t16_{j},\,p_{j}=0\\ If\,t_{exec}>t16_{j},\,p_{j}=dd9_{5}\end{array} \tag{62}\]  
\(p_{1}\ldots p_{16}\) are calculated iteratively in each time sample of algorithm execution. Then the actual value/shape of derivative of jerk signal is calculated with the following equation:  
\[\begin{array}{c}p_{d_{jerk}}=dir\left[\begin{array}{c}(p_{1}-p_{2}-p_{3}+ p_{4})+\ldots\\ \ldots+(-p_{5}+p_{6}+p_{7}-p_{8})+\ldots\\ \ldots+(-p_{9}+p_{10}+p_{11}-p_{12})+\ldots\\ \ldots+(p_{13}-p_{14}-p_{15}+p_{16})\end{array}\right]\end{array} \tag{63}\]  
where the \(dir\) variable stands for direction of movement. It is a two-valued variable:  
\[dir=\{1,-1\} \tag{64}\]  
calculated with Equations (65)-(68).  
When the previous trajectory profile generation is finalized, i.e., time of execution (\(t_{exec}\)) is greater than the sum of \(t_{final}\left[s\right]\) calculated with Equation (54) and some non-zero settling time (\(T_{set}\left[s\right]\)) has passed, then the system is ready for a new trajectory generation:  
\[ready=\text{TRUE} \tag{65}\]  
Determining direction of movement for Equation (63) starts with verifying the condition:  
\[dir_{calc,k}=(dir>0)\ AND\ (ready=\text{TRUE})AND\ NOT(enable=\text{TRUE}) \tag{66}\]Input \(EN=\) TRUE means that the user wishes for execution of the trajectory profile calculation (and profiled movement generation) and information about the readiness of the module for calculations is passed from the input variable \(ready\) to the variable \(ENstart\). The rising edge of the condition:  
\[(dir_{calc,k-1})OR(ENstart) \tag{67}\]  
enables determination of direction, i.e., calculation of variable \(dir\):  
\[IF\,dir_{k-1}\leq 0\,THEN\,dir_{k}=-1\,ELSE\,dir_{k}=1 \tag{68}\]  
Equation (68) is used when we want to switch operation mode of the movement, i.e., motor/servo is mounted in an opposite direction.
## 2 Related Work
### Change-Proneness Metrics  
Metrics are measures of software engineering attributes, and several research studies have explored the use of source code metrics as independent variables when predicting change-proneness [3]. Most of the studies [5,15] have only used the Chidamber and Kemerer (C&K) metrics: weighted methods per class (WMC), depth of inheritance tree (DIT), number of children (NOC), coupling between object classes (CBO), response for a class (RFC), and lack of cohesion in methods (LCOM) [16]. These studies reveal that the C&K metrics are good predictors of change-proneness [5,15]. [PERSON] and [PERSON] investigated various metrics (product, process, and developer-related factors) to predict change-proneness and observed that the models created using developer-related factors outperformed the other models that were built using product and process metrics [4]. [PERSON] and [PERSON] explored the effectiveness of a set of source code metrics in predicting change-proneness based on different features of the software, such as size, coupling, cohesion, and inheritance [17]. In addition to this study, they examined the relationship between object-oriented metrics and change proneness and found that this relationship occurred and machine learning models performed better than the statistical methods [18]. [PERSON] et al. used ten source code metrics and performed FS techniques to determine the best metrics with an objective to increase the prediction accuracy of predicting change-proneness. They found that these metrics are able to predict change-proneness accurately [19]. [PERSON] et al. employed 62 different object-oriented metrics, involving 20 coupling metrics, 7 size metrics, 17 inheritance metrics, and 18 cohesion metrics on 102 Java systems to predict change-proneness [20]. The results obtained from this study indicate that size metrics achieved high prediction accuracy, whereas coupling and cohesion metrics produced low prediction accuracy, and inheritance metrics attained poor performance to predict change-proneness [20]. From these various studies, a clear association between object-oriented metrics and change proneness emerges and it is safe to conclude that these metrics can be used as predictors for change-proneness.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
## 4 Experiment Analysis and Results
### Influence of Flood on L4 for GLONASS Satellites
#### 4.2.2 Influence of Flood on GLONASS L4 Combination Observation  
The influence of flood on GLONASS L4 combination observation is analyzed, and the results are presented in Figure 12. The blue and red lines denote the results of L4 residuals on DOY 194 and DOY 202, respectively. Four satellites including R03, R05, R18, and R24 are selected to illustrate this phenomenon in this experiment. From Figure 12, it is obvious that the residuals of L4 of all these four satellites on DOY 202 (with the flood) perform larger than that of DOY 194 (without flood). The RMS of all these series is given at the bottom of each subplot to further demonstrate the influence of flood on L4. For example, it can be seen that the RMS of the R18 satellite on DOY 194 is only 0.48 cm. However, the RMS increased to 0.85 cm during the flood on DOY 202. The increase rate of RMS of L4 residuals on the R18 satellite is approximately 77.95%. Moreover, this trend can also be found according to the range of 90% CI. In addition, it should be noted that the same phenomenon can also be found in other GLONASS satellites.  
Figure 11: Sky plot of the GLONASS R24 satellites on DOY 193, DOY 194, and DOY 201, DOY 202, 2021.  
In order to further analyze the influence of flood on L4 of GLONASS satellites, the RMS increase rates of all other GLONASS satellites are presented in Figure 13. It can be seen that the residuals of L4 of all these satellites on DOY 202 perform obviously larger than that of DOY 199. The average RMS increase rate can reach approximately 43.65% for all satellites. In addition, the maximum increase rate is R19 satellite, and it is approximately 79.84% compared with DOY 199, 2021. Although the increase rate of the same satellites is relatively small, such as R04 satellite, it is mainly because the flood is small in the starting stage of this satellite.  
Figure 12: Residuals of L4 on R03, R05, R18, and R24 satellites. The blue line denotes the residuals of L4 collected on DOY 194 (without flood).  
Figure 13: RMS increase rate of L4 residuals on DOY 202 compared with DOY 194 for GLONASS satellites.  
Based on the above analysis, it can be concluded that the influence of flood on L4 observation is obvious for GLONASS satellites, and the residuals of L4 perform a significant increase during the flood. This phenomenon is consistent with GPS satellites. Thus, the influence of flood on L4 combination observation should be considered in the related applications.
# An Efficient Library for Reliability Block Diagram Evaluation
## 2 Reliability Block Diagrams
### Quantitative Evaluation Using RBDs  
In this section, we recall the mathematical formulas used to quantitatively evaluate the probability that a block is correctly operating, i.e., its state is equal to _success_, by using the RBD formalism. More specifically, we firstly introduce the general formulas that can be always used; afterwards we present simplified formulas that can be used if and only if all components inside a block are equal, i.e., they have the same probability of being in _success_ state.  
Let \(p_{i}\) be the probability that the state of the \(i\)-th component is equal to _success_, and let \(q_{i}=1-p_{i}\) be the probability that the state of the \(i\)-th component is equal to _failure_.  
Since a singleton block is composed by only one component, its probability of being in _success_ state \(p_{singleton}\) is trivially equal to the probability of being in _success_ state of its sole component \(p\).
## 3 Results
### Shear elastic modulus  
Tendon stiffness was reported in nine studies [58; 59; 60; 61; 62; 63; 65; 67; 68]; eight studies examined one muscle (i.e., medial gastrocnemius [58; 59; 60; 62; 65; 67; 68]), and one study examined one muscle but two protocols (i.e., medial gastrocnemius [63]), leading to a total of 10 data points. Three data points showed a significant decrease from three studies [59; 60; 62], whereas seven showed no significant changes from six studies [58; 61; 63; 65; 67; 68]. A meta-analysis revealed a significant overall small to moderate effect with no heterogeneity (\(d=-0.30\); 95% CI \(=-0.52\) to \(-0.08\); \(p=0.01\); \(l^{2}=0\)%; Figure 4D).  
Thirteen studies reported the shear elastic modulus [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]; three studies examined one muscle (i.e., medial gastrocnemius [33; 37; 41]), three studies examined two muscles (i.e., medial/lateral gastrocnemius [32; 34; 38]), two studies examined three muscles (i.e., soleus and medial/lateral gastrocnemius [36], or biceps femoris, semitendinosus, and semimmembransous [35]), two studies examined medial gastrocnemius with a different protocol (i.e., 10-/30-/90-s interval [44], or 0-/30-s interval [39]), one study examined three different regions (i.e., proximal/middle/distal parts) of two muscles (i.e., medial/lateral gastrocnemius) [40], one study examined three muscles (i.e., rectus femoris and vastus medialis/lateralis) with three different intensities (i.e., 120%/100%/80% intensity [43]), and  
Figure 4: Forest plots of the effect sizes and 95% confidence intervals of acute static stretching-induced changes in fascicle length (**A**), pennation angle (**B**), muscle stiffness (**C**), tendon stiffness (**D**), and shear elastic modulus (**E**). Note that the red diamond marker indicates a significant overall effect size. Abbreviations: low-intensity (LI); high-intensity (HI); constant angle (CA); constant torque (CT); middle/distal/central region of muscle (m/1/c); soleus (SOL); medial gastrocnemius (MG); lateral gastrocnemius (LG); rectus femoris (RF); vastus medialis (VM); vastus lateralis (VL); semitendinos (ST); semimembranosus (SM); biceps femoris (BF) [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49].  
one study examined three muscles but with more measurement sites (i.e., soleus, lateral gastrocnemius, central/distal medial gastrocnemius) [42], and the raw values for the soleus reported in one study [42] were not available. This led to the attainment of a total of 38 shear elastic modulus data points from the 13 studies, and the shear elastic modulus significantly decreased in 27 out of the 38 data points. A meta-analysis revealed a significant overall moderate to large effect with low to moderate heterogeneity (\(d=-0.59\); 95% CI = \(-0.74\) to \(-0.44\); \(p<0.001\); \(I^{2}=46\%\); Figure 4E).
## 4 Results and Discussion
### Micro-Analysis
#### 4.5.3 Tg  
Figure 19 shows the TG/DTG curves of RPSG. As can be seen from Figure 19a,b, the specimens have three main regions of mass loss corresponding to three temperature ranges: (1) room temperature \(\sim\) 200 \({}^{\circ}\)C, (2) 200-600 \({}^{\circ}\)C and (3) 600-1000 \({}^{\circ}\)C. Higher temperature results are not covered in this paper. The mass loss of the specimens below 200 \({}^{\circ}\)C is mainly the loss of free water adsorbed in the voids, while 200 to 600 \({}^{\circ}\)C is mainly the loss of bound water, corresponding to the dehydroxylation process of the hydration products NASH and CASH [72]. The mass loss of specimen RP-S30-M1.3N6 in this range is greater than that of specimen RP-S0-M1.3N6. This is because the incorporation of slag increased the internal hydrated gel content of the specimens, and more free water was adsorbed on the surface of the gel pores, while slag promoted the generation of CASH and NASH gels (corroborating the analysis in Figure 17). The mass loss of the specimen at 600-1000 \({}^{\circ}\)C is mainly the loss of calcium carbonate, which is closely related to the carbonation resistance of the cementitious material [15]. The mass loss rate of specimen RP-S30-M1.3N6 in this range is less than that of specimen RP-S0-M1.3N6, indicating that slag can improve the carbonation resistance of RPSG.  
Figure 18: FTIR results of RPSG with different slag contents.