esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Evaluation Metrics  
Average precision (_AP_) and average recall (_AR_) are used as the evaluation metrics in this paper. Average precision is the area under the PR curve, which is calculated by averaging the segmentation accuracy corresponding to the interval of Intersection over Union (_loU_) threshold [0.50:0.05:0.95], and average recall is the percentage ratio of the objects correctly detected as positive samples to all instance objects in the video frame. Higher values of _AP_ and _AR_ indicate better segmentation effect, and the relevant calculation formula is as follows:  
\[AP=\frac{TP}{TP+FP}\times 100\% \tag{14}\]  
\[AR=\frac{TP}{TP+FN}\times 100\% \tag{15}\]  
where \(TP\) is the number of objects correctly detected as positive, \(FP\) is the number of objects incorrectly detected as positive, and \(FN\) is the number of objects missed as positive. Compared with image instance segmentation, the IoU of video instance segmentation increases the calculation in the time domain. For example, given a video with \(F\) frames, by calculating the ratio of the intersection sum and the union sum of the predicted mask \(m_{f}^{P}\) and the true mask \(m_{f}^{T}\) of the same category in each frame, the IoU of a certain class of objects in the video can be obtained, and the MIoU can be obtained by calculating the average _loU_ of all classes. The formula for calculating _loU_ is as follows:  
\[IoU(P,T)=\frac{\sum_{f=1}^{F}\left|\begin{array}{c}m_{f}^{P}\cap m_{f}^{T} \end{array}\right|}{\sum_{f=1}^{F}\left|\begin{array}{c}m_{f}^{P}\cup m_{f}^{ T}\end{array}\right|} \tag{16}\]  
where \(f\) represents the total number of frames in the video, \(f\) represents the \(f\)th frame of the video, \(P\) and \(T\) refer to the predicted value and the true value of the instance, respectively, and m represents the instance of the current frame in the video.
## 3 Results and Discussion
### Regression Model  
PLSR, SVMR, RF, and ETR were used to model the regression. The calibration set was used to train the prediction of the TAs concentration in the soil model. Comparing the model predictions with the validation sets, it can be seen from Figure 5 that the four models all obtain a good accuracy. Model predictive power is estimated by the \(R^{2}\). The closer the value of \(R^{2}\) to 1, and closer the scatter plot of the measured value and predicted accuracy value for the 1:1 line. Among them, the ETR regression model shows the smallest deviation from the 1:1 line, and the degree of fitting is the highest. Model predictive accuracy estimated by the RMSE and RE (%). Most of the predictions are closely distributed around the 1:1 line, few predictions far away from the 1:1 line generated errors, indicating that the models are accurate. From the results of the model accuracy evaluation, the RMSE of the ETR (RMSE = 0.38) model is the lowest, RE (%) of the ETR (RE = 4.08%) model is also lower, indicating that the ETR prediction accuracy is optimal.  
From Table 3, it can be seen that the PLSR prediction is lowest, and the \(R^{2}\), RMSE, and RE (%) of the validation set are 0.71, 0.48, and 5.03, respectively. Meanwhile, the \(R^{2}\), RMSE, and RE (%) of the ETR validation set are 0.81, 0.38, and 4.08, respectively, which represent the best prediction results for prediction models. In summary, ETR has advantages in four models of model prediction power and prediction accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Preprocessing and Modeling** &
\begin{tabular}{c} **Characteristic Bands Wavelength (nm)** \\ **and Correlation Coefficients** \\ \end{tabular} & \multicolumn{3}{c}{**Validation**} \\ \cline{2-5}  & \(R^{2}_{\text{CV}}\) & \(RMSE_{\text{CV}}\) & \(RE_{\text{CV}}\) (\%) \\ \hline SNV+PLSR & & 0.49 & 0.63 & 6.63 \\ SNV+SVMR & & 0.56 & 0.58 & 6.14 \\ SNV+RF & & 0.53 & 0.60 & 6.31 \\ SNV+ETR & & 0.59 & 0.56 & 5.86 \\ \hline MSC+PLSR & & 0.07 & 0.76 & 7.88 \\ MSC +SVMR & 519 (0.50), 560.7 (\(-\)0.53), 564.0 (\(-\)0.52) & 0.23 & 0.69 & 7.22 \\ MSC +RF & & 0.25 & 0.68 & 7.15 \\ MSC +ETR & & 0.26 & 0.67 & 6.98 \\ \hline SD+PLSR & & 0.23 & 0.64 & 6.64 \\ SD +SVMR & 560.7 (0.53), 576.7 (0.52), 697.0 (0.58) & 0.36 & 0.58 & 6.09 \\ SD +RF & & 0.51 & 0.51 & 5.47 \\ SD +ETR & & 0.51 & 0.51 & 5.43 \\ \hline FD+PLSR & & 0.71 & 0.48 & 5.03 \\ FD +SVMR & & 0.78 & 0.42 & 4.50 \\ FD +RF & & 0.78 & 0.42 & 4.45 \\ FD +ETR & & 0.81 & 0.38 & 4.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of model regression based on the different preprocessing methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modeling Method** & \(R^{2}_{\text{CV}}\) & \(RMSE_{\text{CV}}\) & \(RE_{\text{CV}}\) (\%) \\ \hline PLSR & 0.71 & 0.48 & 5.03 \\ SVMR & 0.78 & 0.42 & 4.50 \\ RF & 0.78 & 0.42 & 4.45 \\ ETR & 0.81 & 0.38 & 4.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy validation of the different models.
## 5 Conclusions  
This paper presents the probabilistic fatigue life prediction of bridge cables based on multiscaling and mesoscopic fracture mechanics. According to the methodologies and assumptions adopted in this paper, the following conclusions are drawn.  
Figure 10: (**a–c**) probabilistic density functions of \(\sigma_{m}\); (**d–f**) probabilistic density functions of \(\sigma_{\Delta}\); (**g–i**) probabilistic density functions of \(\sigma_{m}\sigma_{\Delta}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial Number of Cables**} & \multicolumn{2}{c}{\(\sigma_{m}\sigma_{\Delta}\) (**MPa2)**} & \multicolumn{2}{c}{**\(T\) (year)**} \\  & \(\mu_{\mathbf{In}}\) & \(\xi_{\mathbf{In}}\) & \(\mu\) & \(\sigma\) \\ \hline J13 & 4858.193 & 0.716 & 29.11 & 10.32 \\ J12 & 4059.391 & 0.716 & 34.85 & 13.15 \\ J11 & 3175.808 & 0.428 & 44.54 & 17.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicted fatigue lives of cables.  
1. Fatigue crack growth in stay cables is a multiscale process, influenced not only by the initial defects, material properties, and also by some global parameters, such as cable length, mean stress, longitudinal position and vehicle loads, _etc._ According to the FE analysis, long cables near bridge piers, pylons and mid-span may be more prone to fatigue than the others, and transversal positions of vehicles may influence the cable force, which calls for a more comprehensive vehicle load model including lane occupation.
2. The fatigue crack growth, on the other hand, abounds with uncertainties, so that a probabilistic analysis approach is proposed, which is based on a probabilistic vehicle load model, finite element analysis and multiscaling and mesoscopic fracture mechanics. The proposed uncertain parameters, with their probabilistic properties, are defined and a demonstration study is made.
3. According to the probabilistic FE analyses, the mean lives of the three cables are ranging from 29.11 to 44.54 years; however, the standard deviation of fatigue lives of the three cables are considerable, indicating that there are high possibilities for the cables to have a shorter life than designed.  
Support from the Natural Science Foundation of Jiangsu under Grant No. BK20130023, Jiangsu Transportation Department under Grant No. 2014Y02 and the Graduate Research Innovation Project of Jiangsu Province under Grant No.KYLX15-0087 is gratefully acknowledged. [PERSON] collected and processed the data, made the calculation, initiated the manuscript draft preparation, reviewed and approved the final draft. [PERSON] conceptualized and designed the study, analyzed the data, revised the manuscript, reviewed and approved the final draft. [PERSON] assisted in background literature search, prepared the tables, reviewed and approved the final draft.  
The authors declare no conflict of interest.
## 4 Applications of Piezoelectric Materials in Analytical Chemistry
### Piezoelectric Crystal Detector for Water  
A wide range of materials can be used to make water vapor detectors [112]. Since 1964, hygroscopic crystal has been used as a water detector. With its high selectivity and long life, this device can detect ppm water in 30 s. [PERSON] et al. have created a radio sonic element having a film of SiOx deposited over a piezoelectric crystal. Between 15 and 95 percent relative humidity, there was no hysteresis. Several studies have shown that piezoelectric crystal detectors can also be used to determine the amount of water in the Martian atmosphere. The atmosphere on the surface of Mars is different compared to that on Earth. Mariner flyby showed that the Martian atmosphere consists of 80% of carbon dioxide, with a total pressure of 6-8 mbar. There is evidence that water vapor pressure in the atmosphere of Mars ranges from 50 mm to 0.9 mm. Under these adverse conditions, [PERSON]'s piezoelectric sorption hygrometers have been used to measure water vapor concentrations. Water was measured under various experimental conditions (temperature, pressure, etc.) [106; 107; 108]. Water measurement is an indicator that describes the level of hydration and water binding in a certain substance. Therefore, measures of water activity have great potential for technological use. In 2021 [PERSON] et al. provided a technique for measuring water activity using a piezoelectric quartz crystal sensor covered with a porous layer of alumina. By injecting the air with the appropriate humidity in the vessel containing the resonator, it was possible to evaluate how the piezoelectric crystal's frequency varies with respect to the relative humidity. The observed dependence was changed into the water sorption isotherm on the alumina. The water adsorption onto the pores wall of anodic oxide is enhanced by the presence of sulfate ions, which are the hydrated ions [113]. In order to address the global issue of sustainable access to water, [PERSON] et al., in 2021, developed an amino acid-based detector that is capable of detecting pipe leaks, Figure 22. The polycrystalline system uses the correlation between the piezoelectric voltage and the leak-induced vibration to detect pipeline degradation. The voltage constant and the piezoelectric strain for this device are 60 mV m/N and 0.9 pC/N, respectively. Compared to the PVDF patches, the crystal sensor of glycine has substantially higher sensitivity [114].
# COX2-Inhibitory and Cytotoxic Activities of Phytoconstituents of _Matricaria chamomilla_ L.
## 2 Materials and Methods
### Fluorometric COX2 Inhibitor Screening Assay  
To validate the in silico results from virtual drug screening, we performed an in vitro COX2 inhibitor assay (BioVision Incorporate Berlin, Germany [41]) according to the manufacturer's instructions. The assay is based on the fluorometric detection of prostaglandin G2, which is produced by the conversion of arachidonic acid by the catalytic activity of COX2. Selected phytochemicals of chamomile tea (apigenin, \(\beta\)-amyrin, \(\beta\)-eudesmol, \(\beta\)-sitosterol, daucosterol, farnesol, and myricetin) were chosen based on their LBE values from virtual drug screening. These compounds were tested for their COX2 inhibition in vitro at concentrations of 0.1 \(\mu\)M and 1 \(\mu\)M.
## 2 System Model and Channel Model
### System Model  
An FL system with IRS is considered as shown in Figure 1, where an IRS, composed of \(N\) reflective elements, deployed in the system to assist the communication between a single-antenna BS and \(K\) single-antenna UDs. UDs and reflective elements are indexed by \(\mathcal{K}=\{1,\ldots,K\}\) and \(\mathcal{N}=\{1,\ldots,N\}\). In the FL system, after receiving the global model from the BS, UDs train it to obtain the local models using the local dataset. Then, the local models are sent to BS to be aggregated into a new global model. This process is repeated until the accuracy of the learning model is reached. Specifically, the process can be divided  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Definition** & **Symbol** & **Definition** \\ \hline \(K\) & Number of UDs & \(N\) & Number of IRS elements \\ \hline \(\mathcal{K}\) & Set of UDs & \(\mathcal{N}\) & Set of IRS elements \\ \hline \(\tau_{k}^{l},e_{k}^{l}\) & The time and energy consumed on local training of the \(k\)-th UD & \(\tau_{k}^{u},e_{k}^{u}\) & The time and energy consumed on the model transmission of the \(k\)-th UD \\ \hline \(L\) & Number of local iteration & \(\alpha_{k}\) & Effective capacitance coefficient of the \(k\)-th UD’s computing chipset \\ \hline \(c_{k}\) & Number of CPU cycles for the \(k\)-th UD to process one sample data & \(D_{k}\) & Training data set of the \(k\)-th UD \\ \hline \(f_{k}\) & CPU frequency variable of the \(k\)-th UD for local training & \(f_{min},f_{max}\) & The minimum and maximum computation capacity of UDs \\ \hline \(p_{k}\) & The transmission power variable of the \(k\)-th UD for the model transmission & \(p_{max}\) & The max transmission power of UDs \\ \hline \(b\) & Bandwidth of each UD & \(h_{d,k},h_{r,k},g_{k}\) & Channel from the \(k\)-th UD to IRS and from IRS to BS \\ \hline \(\sigma^{2}\) & The noise power spectral density & \(\varepsilon_{d,k},\varepsilon_{a,k}\) & Estimation error of direct channel and cascaded channel \\ \hline \(R_{k}\) & The transmission rate of the \(k\)-th UD & \(\
u_{k}\) & The transmission target rate of the \(k\)-th UD \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key symbols.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **Meaning** & **Acronym** & **Meaning** \\ \hline ML & Machine learning & FL & Federated learning \\ \hline UD & User device & BS & Base station \\ \hline CSI & Channel state information & IRS & Intelligent reflector surfaces \\ \hline SDR & Semi-definite relaxation & CCCP & Constrained concave convex procedure \\ \hline CCDF & Complementary cumulative distribution function & CSCG & Circularly symmetric complex Gaussian \\ \hline MINLP & Mixed integer non-linear programming & QCQP & Quadratically constrained quadratic program \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acronyms.  
into three steps, which are \"local model computation\", \"local model transmission\", and \"global model aggregation and broadcast\" [19; 20]. The details are as follows:
## 2 Accuracy of Predictive Methods and What Is Required
### Introduction  
The state-of-the-art thus suggests it to be difficult to build models that have a high accuracy and high reliability (no significant outliers) and currently there are few methods satisfying this requirement. As we have argued not all cases require accurate predictions, whereas reliability (few outliers, toxic is toxic, order of boiling points is correct, etc.) is obviously always a relevant item. So the question is can we have more accurate and reliable methods that produce results 'on the fly' so the chemist designing the next experiment or the engineer designing a process has quick access to high quality while screening options. As elucidated in the previous sections there will be no single generic solution. From the discussion in the above, it will be evident that for certain classes of problems the quality of data is more problematic and consequently predictions might be less certain (AMES test, log K\({}_{\text{ow}}\)). For organometallics, we will remain largely dependent on either _ab initio_ methods or, when its properties can be linked to activity, with descriptor based QSAR methods. The limitations of the former have been referred to above. _Ab initio_ methods are applicable to a limited range of properties but remain indispensable for molecules for which we cannot develop appropriate QSPR or GC methods. For organometallics a key property is reactivity. A good tutorial review with many appropriate references was provided by [PERSON] and [PERSON] [18].
Self-Organizing Map (SOM) is another pixel clustering method with structural stability. Thus, the SOM based method [25] has shown better results but, due to its winner-takes-all strategy in the learning process, the fatal irreversible loss of intensity information could cause the failed extraction of appendix when the shape has long oval pattern. Fuzzy C-Means (FCM) based algorithm with semi-dynamic control of initialization process was almost perfect in extracting inflamed appendix if exists [26]. However, in terms of object identification, previous studies only reported if the software could locate the target if the inflamed appendix were given. Such previous works used 6 mm single cutoff standard to extract the target according to the clinical experts' suggestions, but recent report suggests a ternary classification of appendix diameter (negative, equivocal, and positive) with cut-offs at 6 and 8 mm and showed better clinical classification result [11]. Thus, for practical applications, an accurate automatic localization of the appendix region is more important than finding inflamed appendix having a certain size. Thus, in this paper, we propose a robust segmentation algorithm to locate the inflamed appendix from ultrasound that has two articulating points from previous studies. 1. We compare the location of target object with human expert's drawing from the same ultrasonographic images as tried in other medical tasks [19; 27]. By doing so, we want our segmentation algorithm acts like human expert;
2. We propose Double-layered Outlier Rejection Fuzzy C-Means (DORFCM) as the quantization algorithm that mitigates the outlier sensitivity of FCM. FCM based segmentation has shown many successful applications in many image analysis domains [27; 28; 29; 30; 31]; however, its sensitivity to noise and outliers have been criticized [28]. To mitigate such claimed deficiency, there are numerous modified versions of FCM such as having weighted features in consideration [32; 33], using alternative distance measure [34], and outlier rejection [35]. In this paper, we adopt outlier rejection strategy in detecting inflamed appendix for the robustness of segmentation. Furthermore, we have double layered learning structure used in [36] for the stable learning. Thus, our proposed method in this paper is a hybrid FCM learning method with outlier rejection strategy and double layered learning structure to make the inflamed appendix detection to be more robust in quality.
## 3 Preliminaries
### Collaborative Charging Problem  
The energy consumption rates of different nodes are assumed to be varied, as are the requesting nodes during the operation of the network. Scheduling the vehicle and drones appropriately is a complex collaborative problem in the proposed model. Note that while the vehicle charges some nodes, drones can charge other nodes simultaneously. Allocating charging tasks to satisfy all nodes with charging requests is a key issue in the proposed scheme. Moreover, charging as many nodes as possible within the designated time is the main goal of this work. Therefore, the charging problem can be expressed as follows:  
Maximize:  
\[\sum_{s_{i}\in S}c_{i} \tag{6}\]  
Subject to:  
\[c_{i}=\begin{cases}1,\tau_{arrive}^{s_{i}}\leq L_{s_{i}}\\ 0,\tau_{arrive}^{s_{i}}>L_{s_{i}}\end{cases} \tag{7}\]  
\[dist(s_{j},s_{i})\leq\frac{e_{\max\_d}-(e_{\max\_sn}-\phi_{\mathit{sn}}-\tau_ {arrive}^{s_{i}}r_{k})}{r_{d}},\text{ for }\forall s_{j}\in S_{out\_d},\exists s_{i}\in S _{v} \tag{8}\]  
\[dist(s_{j},s_{o(0)})\leq\frac{e_{\max\_d}-(e_{\max\_sn}-\phi_{\mathit{sn}})}{ 2r_{d}},\text{ }\forall s_{j}\in S_{in\_d} \tag{9}\]  
As mentioned earlier, the scheduling objective is presented by Formula (6), and the constraints are listed in Formulas (7)-(9). Constraint (8) indicates that the distance between some segments of each charging path of the vehicle and a node charged by one drone cannot exceed the maximum flight distance. Constraint (9) indicates that the distance between the base station and a node charged by one drone starting from the base station cannot exceed half of the maximum flight distance since the drones can fly back to the base station directly after finishing their assigned charging task.
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 4 Conclusions and Outlook to the Airborne Application
### Outlook to the Airborne Instrument  
For airborne EC measurements using our planar cell setup, we developed a low air drag concept for an open-path EC laser hygrometer to be used on the outside of the airplane fuselage. Figure 7 shows an explosion view of the design of this airborne EC instrument. The hygrometer is designed around the optics assembly (1) containing the statically validated optical setup, which is covered (6) and can be continuously purged with dry air to reduce the effects of parasitic absorption [25]. The temperature and pressure probe consists of a heated pitot-static tube combined with a thermocouple and a PT100 for fast and accurate temperature measurements. The pitot-static tube is mounted inside the optical multipass cell such that the tube passes through the \"hole\" at the center of the beam pattern visible in Figures 3a and 4. This placement enables static pressure measurements inside the optical measurement plane. CFD simulations were used to optimize aerodynamic fairing (3) and to minimize the pressure and velocity differences in the optical plane compared to the free air flow. The inlet of the fairing is heatable and made of aluminum to prevent icing and to reduce absorption. The hygrometer is designed for an average indicated airspeed of 75 m/s, and the current length of the pylon (5) and the baseplate are optimized for the Cessna F406 research aircraft of the TU-Braunschweig.  
**Author Contributions:** Conceptualization, V.E., F.W. and A.L.; methodology, V.E.; formal analysis, F.W., J.N. and V.E.; data curation, F.W. and H.B.; writing--original draft preparation, F.W.; writing--review and editing, A.L., V.E., J.N. and H.B.; visualization, F.W.; supervision, V.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 7: Explosion view of the design for the airborne dTDLAS hygrometer.  
Data Availability Statement: The data of the instrument comparison are available from the authors of PTB upon request.
## 2 Materials and Methods
### Procedure
#### 2.3.1 Self-Registered Outcomes  
The patients were asked about the neck pain intensity, the self-perceived neck disability and the impact of headache, and these outcomes were registered with VAS, NDI and HIT-6.  
The intensity of pain was registered using a 10 cm VAS line, in which 0 was considered \"no pain\" and 10 was considered \"worst pain imaginable\". VAS has been shown to be valid and reliable to assess pain intensity in patients with neck pain, with excellent test-retest reliability values (intraclass correlation coefficient (ICC) = 0.94 [37, 38, 39, 40].  
Neck disability was assessed with NDI. This questionnaire consists of 10 items about pain, personal care, lifting, reading, headache, concentration, work, driving, sleeping, and recreation, and the total score ranges from 0 to 50. Higher scores indicate greater disability [41, 42, 43]. The NDI shows psychometric properties and excellent test-retest reliability values: ICC = 0.97 [44].  
The impact of the headache was assessed with the short-form HIT-6. This questionnaire consists of 6 items about pain, social participation, general activity, vitality, intellectual activity, and biological suffering, and the total score ranges from 36 to 78. Higher scoresindicate greater impact [45]. The internal consistency of HIT-6 was good ([PERSON]'s alpha 0.89) and the test-retest reliability was excellent: ICC = 0.78 to 0.90 [46].