esa-sceva/satcom-chunk-collection huggingface dataset:
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### FoxS-GSC Route Service Operation
#### 3.7.2 Service Delivery  
In this stage, the FOXS-GSC congestion control process is carried out. A route request decision can be made by vehicles in two possible situations: first, route requests may occur periodically for congestion prevention and balancing traffic, for which the cloudlets use a congestion control algorithm; second, road information acquired by vehicles according to the previous step may verify congestion on a road to which the vehicle is traveling. In both situations, the vehicle in question requests a new route from the nearest cloudlet. The cloudlet that receives the request executes a Boltzmann probabilistic congestion control algorithm (presented in [15]), then answers by providing a new route to the requesting vehicle using the proposed communication protocol.  
However, for several reasons, the requested vehicle route message may not reach the nearest cloudlet, including network congestion causing packet loss or there being too few vehicles on the network to allow communication between the source and the destination. In light of this problem, when requesting a new route the vehicle starts a timer using the timestamp of the beacon. After reaching the time limit, the vehicle infers that the message has been lost. In this case, if the cloudlet is in coverage the message is sent again; otherwise, the vehicle performs the routing on its own using the local knowledge obtained in the step presented in [15]. Local vehicle routing is less effective than the routing performed by cloudlet, as vehicles have only restricted knowledge of the road traffic conditions in the scenario.
# Stress Behaviors at Rib-to-Floorbeam Weld and Cutout Details under Controlled Truck Loading
###### Abstract
It was found that the fatigue failure process of RF joints can be divided into four stages that fatigue cracks initiate from the weld end at the weld toe, then extended beyond the thickness of the floor beam, deflected upward and propagate through the rib wall, and finally propagate at an accelerated rate until failure. [PERSON] [22] studied the behavior of RF-W details in a steel-UHPC composite OSD and found that the RF-W detail presented an obvious out-of-plane bending deformation, which resulted from the torsion effect and Poisson's effect. Additionally, the application of bulkhead can greatly improve the fatigue performance of the RF-W details. As the laboratory tests or FEA may have difficulties to truly represent the real structures, connection details and boundary conditions in real bridges, field tests are widely  
Figure 1: Observed fatigue cracks at (**a**) RF-R; (**b**) RF-F; (**c**) RF-W; (**d**) Cutout. recognized as the most effective way to study the loading effects and fatigue performance of the OSD [23]. [PERSON] [24] carried out a field measurement through crawl tests and dynamic tests on an OSD. The work mainly presented the techniques used in the field monitoring and provided guidance on the instrumentation and field monitoring, with little report about the stress responses at various details. [PERSON] [25] estimated the residual fatigue life of an existing orthotropic box-girder bridge, using experimental data from the long-term monitoring of real traffic-induced effects. A method was proposed so that residual fatigue life and the real urgency of retrofitting interventions can be evaluated in operational conditions. [PERSON] [26] conducted field measurements on a lightweight composite bridge deck to characterize the contribution of a UHPC layer to the deck system, and strain responses at various details were measured with and without UHPC layers. The results showed that the UHPC layer influences the stress range of various details significantly. [PERSON] [27] simultaneously measured the stress time histories at the RF and Cutout details, considering two types of cutout geometry on adjacent floor beams under random traffic flows. The results indicated that the proposed new cutout geometry with large radius reduced the fatigue life at the RF detail, and hence was not suggested for rehabilitation of fatigue cracking at floor beam cutout. In this study, the stress measurement at RF and cutout details on an OSD is first carried out on a newly built long-span cable-stayed bridge under controlled truck loading. Then, finite element models are established with the truck loading in different transverse and longitudinal directions of the OSD panel. The results from the field tests and FEA are compared, as to investigate the stress behaviors at those details and clarify their inherent mechanism under the passage of trucks, which are the basis for proposing methods that could improve the fatigue performance of the details.
# Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
## 3 System Overview and Model
### Model of the Cache-Management Scheme
#### 3.2.3 Probability Distribution of AoI  
Denote \(P_{AoI}(\delta W)\) as the probability distribution function of AoI at the steady-state. It is defined as follows:  
\[P_{AoI}(\delta W)=P\big{\{}AoI\leq\delta W|X_{k}=\big{(}1^{+},w-j\big{)},1 \leq j\ w\big{\}}\text{ for any }\delta,\ 0\leq\delta\leq 1. \tag{4}\]  
So:  
\[P_{AoI}(\delta W)=\frac{\sum_{j=1}^{w}P\big{\{}AoI\leq\delta W|X_{k}=(1^{+},w -j)\big{\}}\pi_{1^{+},w-j}}{\sum_{j=1}^{w}\pi_{1^{+},w-j}} \tag{5}\]  
If in state \(X_{k}=(1^{+},w-j)\) at time instant \(t_{k}\), \(1\leq j\leq w\), the cached sample has been collected at a time instant \(t_{k-j}=t_{k}-js\), therefore, it is, for any \(j\), \(1\leq j\leq w\):  
\[P\big{\{}AoI\ \leq\ \delta W|X_{k}=\big{(}1^{+},w-j\big{)}\big{\}}=\begin{cases} 1&j\leq\lfloor\delta w\rfloor\\ \delta w-\lfloor\delta w\rfloor&\lfloor\delta w\rfloor<j\leq\lfloor\delta w \rfloor+1\\ 0&j>\lfloor\delta w\rfloor+1\end{cases} \tag{6}\]  
Finally:  
\[P_{AoI}(\delta ws)=\begin{cases}\frac{\delta w}{w\big{(}1-e^{-\lambda s} \big{)}+e^{-\lambda s}}&0\leq\delta<\frac{1}{w}\\ \frac{\delta w-e^{-\lambda s}(\delta w-1)}{w\big{(}1-e^{-\lambda s}\big{)}+e^ {-\lambda s}}&\frac{1}{w}\leq\delta\leq 1\end{cases} \tag{7}\]
###### Abstract  
A total of 21 samples were selected from a total of Jurassic coal-measure source rocks in the northern structural belt of the Kuqa Depression, in the Tarim Basin. By using a carbon-sulfur content analyzer, Rock Eval 7 rock pyrogy instrument and gas chromatography-mass spectrometry (GC-MS), the abundance, type, depositional environment, and source of organic matter are confirmed. The factors that control the development of coal-measure hydrocarbon source rocks are analyzed. The pyrolysis parameters of rocks, such as total organic carbon (TOC), hydrocarbon generating potential (S\({}_{1}\) + S\({}_{2}\)), hydrogen index (HI) and the highest pyrolysis peak temperature (T\({}_{\rm max}\)), show that good to excellent coal-measure source rocks constitute the majority, and the types of organic matter are predominantly type III and occasionally type II. Low S/C ratio, high Pr/Ph value, and high C\({}_{29}\) regular sterane levels suggest that the environment in which the coal-measure source rocks were deposited was oxidative, and the majority of the organic matter comes from higher terrestrial plants. In addition, the cross plot of isoprenoids with \(n\)-alkanes and the triangle diagrams of regular sterane also show that the primary source of organic matters in coal-measure source rocks is terrestrial higher plants. Because the shallow and turbulent water body is not easily stratified, the gammacerane content is low, which reflects the characteristics of low salinity. Combined with the cross plots of isoprenoids, it might be demonstrated that the freshwater environment is where the coal-measure source rocks were deposited. Comprehensive analysis shows that the oxidational and freshwater depositional environment is favorable to coal-measure source rock development.  
Keywords:coal-measure source rock; biomarkers; depositional environment; source of organic matter +
Footnote †: journal: Applied Sciences
# Investigating BIM Implementation Barriers and Issues in Pakistan Using ISM Approach
## 3 Results and Discussions
### ISM Methodology
#### 3.7.2 Step Two: Reachability Matrix  
The W,X,Y,Z values in SSIM were replaced by 0 and 1 as mentioned in Table 8 [43]. The values in the reachability matrix were put as per the mentioned substitution rule. E.g., if (B1, B12) in the SSIM matrix is Y, then (B1, B12) in the reachability matrix would be 1. A preliminary matrix was established demonstrating the interrelationships among each of the 12 barriers as shown in Table 9. Further, the final reachability matrix was generated manually via excel as shown in Table 10. In Table 10, entry 1 shows incorporating transitivity. For instance, (B1, B5) is 1, which explains that there is an indirect relationship amongst barriers B1 (Lack of government regulation about BIM) and B5 (The top management do not support change).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **B12** & **B11** & **B10** & **B9** & **B8** & **B7** & **B6** & **B5** & **B4** & **B3** & **B2** & **B1** \\ \hline
**B1** & Y & Z & Z & Z & X & X & Z & X & Y & Y & Y & - \\
**B2** & Y & X & Z & Z & Y & Z & Y & W & Y & Z & - & \\
**B3** & W & X & Z & X & Z & Y & Y & X & X & - & & \\
**B4** & X & X & Y & X & W & W & W & W & - & & & \\
**B5** & W & X & Y & X & W & Z & W & - & & & & \\
**B6** & Y & X & X & X & Y & W & - & & & & & \\
**B7** & Y & W & Z & Z & X & - & & & & & & \\
**B8** & Y & X & Z & Z & - & & & & & & & \\
**B9** & W & Z & Z & - & & & & & & & & \\
**B10** & Z & Z & - & & & & & & & & & \\
**B11** & W & - & & & & & & & & & & \\
**B12** & - & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Structural Self-Interaction Matrix (SSIM) for barriers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SSIM** & **REACHABILITY MATRIX** \\ \hline
**(i,j)** & **(i,j)** & **(j,i)** \\ \hline
**W** & 1 & 0 \\
**X** & 0 & 1 \\
**Y** & 1 & 1 \\
**Z** & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Rule adopted for substitution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **B1** & **B2** & **B3** & **B4** & **B5** & **B6** & **B7** & **B8** & **B9** & **B10** & **B11** & **B12** \\ \hline
**B1** & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 \\
**B2** & 0 & 1 & 0 & 1 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 \\
**B3** & 0 & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 1 \\
**B4** & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 1 \\
**B5** & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 \\
**B6** & 0 & 1 & 1 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 \\
**B7** & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\
**B8** & 1 & 1 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 0 & 1 \\
**B9** & 0 & 0 & 1 & 1 & 1 & 1 & 0 & 0 & 1 & 0 & 1 & 1 \\
**B10** & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 0 & 1 & 0 & 0 \\
**B11** & 0 & 1 & 1 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 1 \\
**B12** & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Initial Reachability Matrix.
## 5 Experiments and Discussions  
### Experiment Design  
The experiment was conducted in Thousand Island Lake, Zhejiang Province, China. Following preliminary investigation, the northwestern lake area of Thousand Island Lake was selected because it is not a primary navigation channel and exhibits fewer background noise interferences. The majority of the experiment area has a depth of approximately 40 m, with a maximum depth of approximately 60 m. Figure 7 illustrates the experiment environment.  
The experimental apparatus primarily consisted of a constant-speed winch, an RHCA-14 hydrophone, a signal acquisition card, a dedicated hydrophone power supply, and a PC, as partially depicted in Figure 8a. The experimental sketch is illustrated in Figure 8b. The hydrophone exhibited several parameters, which were as is shown in Table 3.  
Figure 8: Experiment design. (**a**) Experiment equipment, (**b**) experiment sketch.  
Figure 6: Vortex distribution. (**a**) Smooth skin, (**b**) riblet skin, (**c**) placoid-scale skin.  
Figure 7: Thousand Island Lake experiment area.  
During the experiment, the constant-speed winch raised three types of cylindrical shells with varying skin (smooth, riblet, and placoid scales) at a fixed speed (0.833 m/s). The experiment process in Figure 9a, and different skin objects are shown in Figure 9b.  
In the experiment, the flow noise signal was recorded by hydrophones and subsequently collected by data acquisition cards. These signals were then converted into a digital format within the acquisition card before being stored on the PC. To ensure accuracy and mitigate potential errors, multiple measurements were performed during the experiment, as illustrated in Figure 10.
## 2 Materials and Methods  
ETNA-AF-Europe (Clinicaltrials.gov: NCT02944019) is a multinational, multicentre, post-authorisation, observational study conducted in 825 sites in 10 European countries (Austria, Belgium, Germany, Ireland, Italy, The Netherlands, Portugal, Spain, Switzerland, and UK) [14,15].  
The observational plan of this post-authorisation observational study was agreed in close collaboration with the European Medicines Agency (EMA) and has been reported previously [15]. The study was approved by the institutional review boards and Independent Ethics Committees for all participating centres in compliance with the Declaration of Helsinki and Guidelines for Good Pharmacoepidemiological Practice (GPP). All participants provided written informed consent.  
One-year follow-up outcomes of the first 13,092 prospectively enrolled patients treated with edoxaban, have been published previously [16]. The present sub-analysis categorised patients into \(\leq\)60 kg, >60-\(\leq\)80 kg (reference weight group), >80-\(\leq\)100 kg and >100 kg groups. In the absence of universally defined categories of body weight, the limits were based on the threshold for dose adjustments of oral anticoagulants (60 kg) and on the basis of a grouping according to 20 kg ranges (60-80 and 80-100 kg).  
In addition, patients were also analysed according to their body mass index; patients were classified, as commonly done, as underweight (<18.5 kg/m\({}^{2}\)), normal weight (\(\geq\)18.5<25 kg/m\({}^{2}\)), overweight (\(\geq\)25-\(\sim\)30 kg/m\({}^{2}\)), Class 1 obesity (\(\geq\)30-\(<\)35 kg/m\({}^{2}\)) and Class 2 + 3 obesity (\(\geq\) 35 kg/m\({}^{2}\)).  
Baseline characteristics are summarised descriptively as frequencies (\(n\) and percentage), mean value \(\pm\) standard deviation (SD) rounded to integer, or median [interquartile range (IQR)]. Standardised differences of weight groups versus >60-\(\leq\)80 kg group are reported for the baseline demographics and clinical characteristics.  
There was no specific definition for frailty; it was left to the discretion of the physician to categorise a patient as frail (clinical evaluation).  
Based on the reported events, annualised event rates (percentage per patient-year) are reported for the safety and effectiveness outcomes. Hazard ratios versus the reference group (>60-\(\leq\)80 kg) are reported both for unadjusted data (univariate Cox proportional hazard model) and for data adjusted for eGFR and CHA\({}_{2}\)DS\({}_{2}\)-VASc score (multivariate Cox proportional hazard model).  
In the univariate analysis, age, eGFR and CHA\({}_{2}\)DS\({}_{2}\)-VASc were always amongst the top predictors for all analysed outcomes. Age is included in the CHA\({}_{2}\)DS\({}_{2}\)-VASc score therefore, eGFR and CHA\({}_{2}\)DS\({}_{2}\)-VASc were selected for adjustment.  
All analyses were done using SAS version 9.4 for windows 10 (64-bit version). Univariate and multivariate Cox proportional hazard models were fitted by SAS proc PHREG.
## 6 Conclusions  
Because it is difficult to extract fault features from rolling element signals, a novel feature extraction method called KMVMD-PGMCKD is proposed for bearing rolling elements. The KMVMD provides a new mode number selection method to improve the signal processing effect of VMD. The PGMCKD provides a new parameter optimization method to improve the signal processing effect of MCKD. Through the experimental analysis under different working conditions, the following conclusions are obtained:  
1. The kurtosis mean has a suitable effect on the selection of mode number, which avoids the phenomenon of over decomposition or under decomposition and provides a suitable foundation for further processing;
2. The power spectrum entropy is used as the evaluation criterion for the selection of parameters in MCKD. The difference between the actual and theoretical conditions is considered. The L and the T are adaptively selected, which improves the effect of MCKD on the enhancement of weak fault features;
3. The combination of improved VMD and MCKD fully suppresses the noise interference on the fault features, highlights the periodic impact component, and solves difficult fault feature problems for bearing rolling elements. The effectiveness of the KMVMD-PGMCKD is also verified by experiments;
4. The signal processing effect of KMVMD-PGMCKD is not affected by the variable working condition. The experiment shows that KMVMD-PGMCKD has a strong generalization ability under the variable working condition.  
Methodology, W.L. and H.C.; validation, Y.L. and L.Z.; resources, L.Y.; data curation, J.M.; writing--original draft preparation, W.L., Y.L. and L.L; writing--review and editing, W.D.; project administration, L.L.; funding acquisition, W.D. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Foundation for Civil Aviation University of China (2020 KYQD123).  
Not applicable.  
**Data Availability Statement:** [[https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website](https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website)]([https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website](https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website)) (accessed on 10 May 2020).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: The obtained results under variable working conditions.
## 2 Materials and Methods
### Classifier Evaluation  
The classifier performance was evaluated using a leave-one-out cross-validation scheme where each test subject's signals were classified with a classifier that was trained using the signals from all the other test subjects. This method has been previously used in other physical activity classification studies to reduce overfitting errors [29; 31].  
Sensitivity (also called recall or true positive rate) was chosen as a statistical measure to evaluate classification performance during feature selection. Sensitivity shows the proportion of true positives classified (True_positives) in relation to correct or real ones (Real_positives), i.e., true positives that are correctly identified [32], and it is calculated as follows:  
\[\begin{array}{c}\text{Sensitivity = True\_positives/Real\_positives =}\\ \text{True\_positives/(True\_positives + False\_Negatives).}\end{array} \tag{2}\]  
Classification results were evaluated using F1-score (also called F-score or F-measure), which is calculated as harmonic mean of precision and sensitivity [27], using the following formulas:  
\[\begin{array}{c}\text{Precision = True\_positives/Predicted\_positives =}\\ \text{True\_positives/(True\_positives + False\_positives),}\end{array} \tag{3}\]  
\[\begin{array}{c}\text{F1-score = (2\cdot Sensitivity\cdot Precision)/(Sensitivity + Precision).}\end{array} \tag{4}\]  
While evaluating the results with different window lengths, sampling frequencies and number of features, F1-scores were calculated separately for each activity type. Additionally, an average F1-score for different parameter combinations was found as a means of the activity type F1-scores.  
A paired \(t\)-test (\(p<0.05\)) was used to find statistical differences between the classification F1-scores of different activity types and averages while using different window lengths and sampling frequencies.
## 5 Discussion  
The proposed control strategy ensures stable trajectory tracking under indoor disturbances, as demonstrated by the experimental results. In fact, the findings overcome several limitations found in conventional control methods. Studies [17; 18] highlight that, while classical PID controllers are simple and widely used, they are unable to handle the nonlinear dynamics of the quadrotor due to their sensitivity to parameter tuning. As a result, they suffer from significant overshoot and long settling times during takeoff and aggressive maneuvers.  
The study in [19] took a different approach but reported a similar limitation. It identified the problem of chattering caused by the discontinuous switching function, which results in degraded performance. To address this, the authors in [20] proposed using a continuous saturation function; however, this introduced filtering delays and increased implementation complexity.  
In contrast, the backstepping cascade structure with EKF delivers smooth control action, effectively handles system nonlinearities, and maintains tracking accuracy without inducing overshoot or requiring excessive tuning effort. These advantages are clearly confirmed by the experimental results presented above.