esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study represents results of our efforts for optimization of catalyst composition from an economic point of view. Emphasis was placed on preparation of a suitable support able to stabilize small Au, Pd or Pd-Au nanoparticles from one side and to create an optimal contact structure between them and the support on the other. Alumina-supported ceria (30 wt.%) was prepared by impregnation or mechanical mixing and used as support of gold-based catalysts for CBO. Textural properties of mechanochemically mixed CeO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) in terms of surface area, pore volume and pore diameter affected positively the combustion activity and were selected for preparation of Pd-based and Pd-Au systems. Additionally, the favorable role of Y-doping of ceria in CeO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) mixed support on catalytic behavior of mono- and bimetallic Pd-Au catalysts was observed. It was related to improved mobility of oxygen in ceria surface layers. Superior activity exhibited Pd-based samples on alumina-supported ceria and particularly on alumina-supported Y-doped ceria. Redox transfer Pd\({}^{0}\leftrightarrow\) PdO, higher surface concentration of Pd and Ce\({}^{3+}\), evidenced by XPS analysis, as well as the presence of adsorbed superoxo species O\({}_{2}\)\({}^{-}\) close to the Ce\({}^{4+}\) ions registered in EPR spectra contributed to the best performance. The potential of these catalytic materials to meet stringent emission regulations in an economical and effective way was disclosed.  
T.T. and L.I. writing the manuscript; T.T. preparation of the samples; P.P. catalytic tests and TPR measurements; Y.K. EPR analyses; A.M.V. XPS analyses; L.F.L. morphology analyses; G.A. PXRD analyses. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Bulgarian National Science Fund grant number KIT-06-KOCT/4.  
This article is based upon work from COST Action INDAIRPOLLNET CA17136, supported by COST (European Cooperation in Science and Technology). The authors acknowledge technical support provided by Nunzio Galli.  
The authors declare no conflict of interest.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.2 Generalized Extreme Value Distribution (GEV)  
The parameter \(\alpha\) can be estimated using the \"approximation 1\", which represents a polynomial function presented by [PERSON] et.al [11], or using two new approximations of the polynomial or rational type, hereafter named \"approximation 2\" and \"approximation 3\".  
Thus, the parameter \(\alpha\) has the following approximate forms depending on \(\text{C}_{\text{s}}\):  
Approximation 1, polynomial function [11]:  
If \(\text{C}_{\text{s}}\leq 1.14\):  
\[\begin{array}[  
\[\gamma=\mu-\frac{\alpha}{|\alpha|}\cdot\frac{\sigma\cdot[1-\Gamma(1+\alpha)]}{\sqrt{ \Gamma(1+2\cdot\alpha)-\Gamma(1+\alpha)^{2}}} \tag{58}\]  
For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on \(\tau_{3}\):  
Approximation 1, polynomial form, presented in [PERSON] et al. [11] and [PERSON] [14]:  
\[\alpha=7.85890\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)+2.955 4\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)^{2} \tag{59}\]  
Approximation 2, also a polynomial function but more accurate, presented in [PERSON] et al. [11]:  
\[\begin{array}{l}\alpha=7.817740\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)} {\ln(3)}\right)+2.930462\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)} \right)^{2}+\\ 13.641492\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)^{3}+17.2 06675\cdot\left(\frac{2}{3+\tau_{3}}-\frac{\ln(2)}{\ln(3)}\right)^{4}\end{array} \tag{60}\]  
a better approximation, named in this section approximation 3, being a rational function:  
\[\alpha=\frac{0.283759107-1.669931462\cdot|\tau_{3}|}{1+0.441588375\cdot|\tau_ {3}|-0.071007671\cdot\tau_{3}^{2}+0.015634368\cdot|\tau_{3}|^{3}} \tag{61}\]  
\[\beta=\frac{\text{L}_{2}}{\Gamma(\alpha)\cdot(1-2^{-\alpha})} \tag{62}\]  
\[\gamma=\text{L}_{1}+\frac{\beta}{\alpha}\cdot[\Gamma(1+\alpha)-1] \tag{63}\]
## 3 Results
### Comparison between SPM and FLR of Subjects with BMI \(<\) 30 kg/m\({}^{2}\)  
The HKA of 24 lower extremities of subjects with BMI \(<\) 30 kg/m\({}^{2}\) were measured using SPM and FLRs. The FLRs' HKA had an absolute range of 166.4\({}^{\circ}\) (varus) to 195.1\({}^{\circ}\) (valgus); the SPM's HKA showed a range from 168.6\({}^{\circ}\) (varus) to 195.0\({}^{\circ}\) (valgus). Results show that the HKA of both methods highly correlate within this group ([PERSON]'s r = 0.93; \(p<\) 0.001, see Figure 3). The mean deviation (MD) of the HKA amounted to 2.4\({}^{\circ}\) (range 0.1-6.0\({}^{\circ}\)). The HKA's mean value of SPM (182.0\({}^{\circ}\)) was 1.6\({}^{\circ}\) higher (more towards valgus) than the one of the FLRs (180.4\({}^{\circ}\); see Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Overall** & **I** & **II** \\ \hline Inclusion criterion & & BMI \(<\) 30 kg/m\({}^{2}\) & BMI \(\geq\) 30 kg/m\({}^{2}\) \\ \hline \(n\) (participants) & 18 & 13 & 5 \\ \hline \(n\) (lower extremities) & 34 & 24 & 10 \\ \hline Height in cm (mean/SD/range) & 173/10/158–190 & 169/7/158–183 & 183/9/165–190 \\ \hline Weight in kg (mean/SD/range) & 79.5/24.6/43–120 & 66.4/14.4/43–85 & 110.8/13.4/85–120 \\ \hline BMI in kg/m\({}^{2}\) (mean/SD/range) & 25.5/5.8/15–34 & 22.6/4.2/15–29 & 32.4/1.6/30–34 \\ \hline \end{tabular}
\end{table}
Table 1: General data of the study population. cm: centimeters; SD: standard deviation; kg: kilogram; BMI: body mass index; m: meter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Group** & **I** & **II** & **Overall** \\ \hline \multicolumn{5}{c}{BMI \(<\) 30 kg/m\({}^{2}\)} & BMI \(\geq\) 30 kg/m\({}^{2}\) & \\ \hline \(n\) & 24 & & 10 & 34 \\ \hline method & FLR & SPM & FLR & SPM & FLR & SPM \\ \hline HKA range (\({}^{\circ}\)) & 166.4–195.1 & 168.6–195.2 & 177.0–183.8 & 182.5–190.0 & 166.4–195.1 & 168.6–195.2 \\ \hline HKA (mean \(\pm\) SD; \({}^{\circ}\)) & 180.4 \(\pm\) 5.9 & 182.0 \(\pm\) 6.4 & 180.3 \(\pm\) 2.2 & 185.8 \(\pm\) 2.7 & 180.4 \(\pm\) 5.1 & 183.1 \(\pm\) 5.8 \\ \hline mean deviation and range (MD/range; \({}^{\circ}\)) & 2.4/0.1–6.0 & 5.5/2.6–8.1 & 3.3/0.1–8.1 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of SPM and FLR measurements. \(n\): number of legs; FLR: full-leg radiograph; SPM: sonographic-assisted photographic measurement; HKA: hip–knee–ankle angle; SD: standard deviation.
## 3 Experimental Results and Analysis
### Implementation Details  
**Data Preprocessing**: To enhance the network's robustness and avoid overfitting, we employed data augmentation techniques such as random rotation, mirror flipping, and adjustments to image color, saturation, and contrast.  
**Training Details**: Our experiments were conducted using the PyTorch framework with CUDA version 11.0 on an Ubuntu 20 environment. We utilized an NVIDIA A40 with 46 GB of memory to accelerate model training. The AdamW optimizer was chosen with an initial learning rate of 0.0001, a decay rate of 1 \(\times\) 10\({}^{-8}\), \(\beta\)1 = 0.9, and \(\beta\)2 = 0.999. The batch size varies slightly across different steps: it is 4 for steps 1 and 2, and 2 for step 3. The epoch is set to 200. Details are shown in Table 1. In our large-format image semantic segmentation task, we chose binary cross-entropy as the loss function. The global feature extraction module and feature coding module were initialized with ResNet50 on ImageNet-1k as the initialization parameters. During network training, to conserve GPU memory, the parameters of the global feature extraction module remained fixed and were not updated. We employed a gradient accumulation strategy for image patch processing.  
**Metrics**: To evaluate our method's performance, we used four common metrics: Intersection over Union (IoU), precision, recall, and F1-score. These metrics are calculated based on true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) as follows:  
\[\text{IoU}=\text{TP}/(\text{TP}+\text{FP}+\text{FN}), \tag{8}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}), \tag{9}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}), \tag{10}\]  
\[\text{F1}-\text{score}=2\times(\text{Precision}\times\text{Recall})/(\text{ Precision}+\text{Recall}) \tag{11}\]
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 6 Discussion
Slot-based, DGS- based, Metamaterial-based, DRA-based, and transparent MIMO flexible antennas are reviewed in the article. The orientation of the individual elements with a low mutual coupling is very important in the MIMO implantation. Isolation can be improved by parasitic elements [66], meander lines [74], DGS [77; 81; 82], etc. Various studies have been carried out in the field of flexible antennas, especially in the fifth-generation technology. However, here we discuss some of the significant future directions in the flexible antenna design field. * Development of a novel flexible antenna for fifth generation wireless communication which involves the newly released sub-6 GHz bands which contribute to a high data rate, low latency and high-speed communication. * The gain of the flexible antenna is a major design consideration. Therefore, the flexible antenna design with an enhanced gain using various gain enhancement techniques can be conducted. * Flexible devices having less space integrate antenna that are small in volume. To achieve a good bandwidth while keeping the antenna volume low, is a challenge. Therefore, research in such a direction needs further investigation. * Antenna miniaturization using several techniques can be carried out in the specified frequency band since the flexible antennas for the abovesaid band can contribute to different applications, including IoT, mobile communication, and medical applications that need antennas to be highly flexible and compact and contribute to a high-speed communication. * Development of a flexible reconfigurable antenna has a future scope in the design of flexible antennas. * Wireless on-body communication utilize flexible antennas to be integrated onto different parts of the body. Therefore, a wearable antenna for body area networks is a major research area that needs attention. * Flexible implanted antenna designs can be conducted for various medical applications.
## 3 Test Results and Analysis
### Results of the CT
#### 3.3.2 Visualization of Pore Space Under CT Scanning  
Figure 7 shows the two-dimensional pore structure of the samples under different corrosion months, in which the blue part represents the pore area.  
Porosity is defined as the ratio of pore volume to the original sample volume. By performing nonlinear fitting of the porosity data points, we obtained a relationship curve between soaking months and porosity, as illustrated in Figure 8. Furthermore, the relationship between soaking time and porosity can be derived over time, with the specific expression being  
\[\varphi=14.90-3.87t+13.05t^{2}-6.95t^{3}+0.97t^{4} \tag{4}\]  
In this study, \(\varphi\) represents the porosity obtained from the CT scan of the sample at time \(t\). When the soaking time of the rock sample increased from 0 months to 2 months,  
Figure 8: Porosity fitting curves of samples under different soaking times.  
Figure 7: Two-dimensional pore reconstruction of the sample under different soaking times.  
the change rate of porosity reached 29.60%. These data indicate that the combination of brine and CO\({}_{2}\) significantly enhances the development of damaged pores during the dissolution phase. However, as the soaking time further extended, the porosity of the sandstone decreased rapidly, entering the precipitation stage, during which many pores become filled. Ultimately, the porosity increased slightly, suggesting that the effect of the combination of brine and CO\({}_{2}\) in the secondary dissolution stage on the development of damaged pores was relatively diminished.
# Acoustic and Aerodynamic Coupling during
## 3 Results and Discussion
### Spectral Analysis of the Radiated Sound Pressure  
Figure 5 depicts the SPL for the realistic and simplified VT replicas that were measured with the experimental setup for each subject. Each of the spectra shows peaks corresponding to the fundamental frequency, the corresponding harmonics and the broadband sound, which is the part of the spectrum without the energy of the harmonics.  
Figure 5: SPL of the sound emitted by vocal fold replicas coupled with MRI-based VT replicas from six subjects. Realistic geometries (blue curve) and simplified geometries with a concentric straight tube approximation (red curve) are shown for each individual. The formant frequencies are marked with vertical lines. The applied detection method for all formants is based on inverse filtering, as described in Section 2.3.  
The comparison of the spectra of the realistic and the simplified replicas reveals that the SPL is changed by the simplification for all subjects, but to a different extent. Whereas the broadband sound level of the simplified replicas remains on a comparable level with their realistic counterpart for subject 2, subject 3, and subject 4, the broadband sound level of the simplified subject 1, subject 5, and subject 6 replicas is reduced. Hence, on average the simplified replicas exhibit a smaller broadband sound level than the realistic ones. This reduced broadband level correlates with a smaller volume flow rate or subglottal pressure in the experiments for these simplified VTs. As a result thereof, the global turbulence intensity may be expected smaller, thus creating less turbulence-induced broadband sound.  
Investigations on the influence of a geometry simplification on the radiation using models with pure acoustic excitation have so far led to the result that the influence up to a frequency of 5 kHz is perceptually not relevant [18; 52]. However, since we found that the broadband sound was changed for the simplified replica for three out of six subjects, it can be assumed that this presumption is only conditionally valid, although it has to be mentioned that the studies [18; 52] only analyzed the transfer function of the VT. While there is proof of negligible influence of the simplification on the acoustic resonance behavior [18; 52], there may be a significant change of the flow field that creates spectral differences.  
The spectral response of the mean VT is displayed in Figure 6 in addition to the spectra of the simplified VT replicas. The spectra show different levels of broadband sound, which correlate with differences of 115 L/min in the required flow rates for the different simplified VT replicas. The spectrum of the mean VT replica is in the range of the replicas showing a higher broadband sound. As the required volume flow rate of the mean VT replica is also in the range of these replicas, the shift of the broadband sound of the mean VT to a higher level in comparison to the average of the broadband sound levels can be attributed to the higher volume flow rate.
###### Abstract
[16] studied the flow field around a gurney flap with this model placed in the front part of the flap to produce a counter-current wall jet. They found a 15% drag reduction. [PERSON] et al. [17] also utilized this model to improve the aerodynamic performance of a flat-plate wing and discovered that the outboard wall jet can increase both the lift and the rolling moment by over 30%. [PERSON] et al. [18] employed this model to achieve plasma circulation control for an elliptic airfoil and pointed out that the plasma control efficiency for lift augmentation was superior to the conventional jet control method. These examples show that this model has become a prevalent approach for the simulation of plasma actuators. Therefore, the model is adopted in the current research. However, the application of the plasma actuators in manipulating fluctuating wind load needs to be further studied based on the model. Also, the mechanism of plasma actuators to reduce fluctuating wind load on the surface of objects is still unclear. Thus, it is urgent to strengthen this research. The current study can be considered as a continuation of the authors' previous work [19] on plasma actuators. The previous work experimentally and numerically demonstrated the variation of mean pressure and peak negative pressure on the roof centerline under the action of plasma actuators, but did not investigate the effect of the actuators on fluctuating pressure. Compared with the previous work, the innovation of this study is the exploration of the influence and control mechanisms of plasma actuators on fluctuating wind load, such as fluctuating pressure. Moreover, based on the temporal evolution of the instantaneous vorticity field, this study reveals how SDBD actuation interacts with the shedding vortices. The next section introduces the modeling and numerical methods. The grid independence study and the validation of the numerical model are performed in the following section. In the fourth section, the results and discussions in absence and presence of approaching flow are presented regarding the change of mean and fluctuating wind loads, as well as for the flow field under the influence of three actuator arrangements. The final section presents a summary and conclusion.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
## 3 System Design
### Monitoring Approaches  
Monitoring was created on the peer leader to monitor data flow and nodes status. This monitoring was performed by relying on active and passive approaches. Active monitoring was approached by pinging the aimed nodeID in the IPFS network (Figure 7). It ran every minute to obtain status information from all nodes. This ping was not just sending Internet Control Message Protocol (ICMP) packets to the destination node but must go through several additional stages.  
When the ping command was executed, the node would find the nodeID in DHT first, then establish a connection to that node. Henceforth, it sent the ICMP packet and waited for the pong response. If the destination node was offline, then the pong produced an error; otherwise, it received a reply and finally printed out round trip time (RTT) for its latency. The pong result was formed to a JavaScript Object Notation (JSON) file and displayed on a web page by a JQuery function. NodeJS was used as our HTTP server, and the index page would update the status every 20 s.  
Moreover, our passive approach utilized a data collector watcher that monitored each time the latest files were sent from the instrument's PC to the site server. It then uploaded the file to the IPFS via the IPFS-Cluster automatically. This process generated a log file based on the instrument's name that contained information of the metadata file, such as filename, timestamp, file size, block hash, instrument's name, and total size. Additional synchronization of all log files was conducted between the site and central server every minute using Rsync. Updated log files on the main server would be retrieved by JQuery via HTTP and displayed on the website (Figure 8).  
Figure 6: The Sequence of how the file was stored.  
Figure 8: Flowchart of data monitoring by a passive approach.  
Figure 7: Flowchart of node status monitoring by the active approach.
## 3 Experimental Setup of the CDM Transmission System
The separation of the received C-band and L-band signals was also achieved through an OBM, and another C+L band integrated WSS was followed to demultiplex the modulated signals. The demultiplexed C-band and L-band signals were then sent to the C-band and L-band OTUs, respectively, to conduct coherent detection and perform a real-time bit error rate (BER) calculation. In our field trial, experimental results such as bit error rate, optical power, and optical spectrum could be obtained in real time from the network management system. Since the performance of our experimental system remained relatively stable during the test, the experimental results were directly obtained after the system was stabilized. The 800 Gb/s coherent OTUs utilized in the field trial belong to the ZTE S3F-E series optical transmission equipment, specifically the LB4 TF model optical line board. The OTN transmission system was installed on a ZXONE 19700 platform, which was manufactured by ZTE Corporation in Shenzhen, China. And the network management system was a ZTE ElasticNet UME system. The OTN transmission equipment and the network management system are all placed in the Jinxiu Chuan data center as depicted in Figure 2. We conducted the experiment on-site at the Jinxiu Chuan data center. Figure 2: Experimental setup of the real-time CDM field trial using 800 Gb/s OTN transceivers over deployed MCF cable.
## 4 Identification Method for SRRV Deviation Using Wheel-Rail Force Trend Component
### The Mapping Relationship Between SRRV Deviation and Wheel-Rail Force Trend Component
#### 4.2.2 Negative SRRV Deviation  
Figure 11 presents the 7 th-order trend component of wheel-rail vertical force and lateral force on the switch rail side when the vehicle passes through turnouts with varying SRRV deviations. The figure indicates that with the increase in negative SRRV deviation, the peak magnitude of the 7 th-order trend component of wheel-rail vertical force gradually increases, and the location of the peak shifts forward, the oscillation amplitude of the 7 th-order trend component of wheel-rail lateral force gradually increases, and the wavelength gradually decreases.