esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model and Data Description  
### WRF Model  
The Weather Research and Forecasting (WRF) model is an advanced mesoscale numerical weather prediction system designed to support both atmospheric research and operational forecasting applications [33]. Developed collaboratively by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and other partners, the WRF model features a highly modular, parallelized, and layered design. It employs a fully compressible, non-hydrostatic model and is equipped with a sophisticated 3D variational data assimilation system and a diverse array of internal parameterization schemes, enabling it to simulate meteorological processes across scales from tens of meters to thousands of kilometers. The fundamental equations of the WRF model are derived from the equations of motion, continuity, state, thermodynamics, and moisture. These are obtained by introducing potential energy and potential temperature into the foundational Navier-Stokes equations and applying terrain-following coordinate transformations. The WRF model utilizes an Arakawa C-grid for horizontal discretization, eta coordinates for vertical levels, and a Runge-Kutta algorithm for time integration.  
The WRF workflow, as shown in Figure 1, follows these main steps: (1) The terrestrial data and gridded meteorological data are processed through WRF Preprocessing System(WPS) to interpolate the data onto the grid points of the simulation domain specified by the user; (2) Then, the data are processed using the Initialization program (REAL) to interpolate the meteorological field data onto the vertical layers defined by the user, while also generating the boundary conditions and initial field data; (3) The WRF-ARW Solver (WRF) model is employed to perform time integration of the simulation, resulting in the final simulation output files; (4) Post-processing, analysis, and visualization operations are conducted on the simulation results.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 3 Results
### Effect of Agitation on Cellulase and Xylanase Production  
The influence of static and varying agitation speed on cellulase and xylanase production was examined and the result is presented in Figure 4. Highest xylanase production by the _Streptomyces_ strain was attained at agitation speed of 150, followed by speed of 100 (rpm), with respective activities of 4.02 \(\pm\) 0.02 and 2.44 \(\pm\) 0.02 (U/mL). Although the lowest production was recorded under static conditions, there was no significant difference between xylanase production at static (0) and 200 (rpm). A similar trend was observed for cellulase production, although the highest activity of 0.013 \(\pm\) 0 U/mL was attained at 100, followed by 0.007 \(\pm\) 0.001 U/mL at 150 (rpm); the lowest production of cellulase was also observed under static (0) condition (0.004 U/mL).  
Figure 4: Production of xylanase (**a**) and cellulase (**b**) under static and different agitation speed. Zero (0) speed represents static condition. Error bars represent standard deviation (SD) from the mean of triplicate tests.  
Figure 3: Temperature effect on xylanase (**a**) and cellulase (**b**) production. Error bars represent standard deviation (SD) from the mean of triplicate tests.
# Insulators and Defect Detection Based on the Improved Focal Loss Function
## 1 Introduction
[22] proposed a Cross Stage Partial Dense YOLO (CSPD-YOLO) model based on YOLO-v3 and the Cross Stage Partial Network. The CSPD-YOLO model uses the characteristic pyramid network and improved loss function to improve the accuracy of insulation fault detection. The average accuracy of the CSPD-YOLO model was 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, respectively. To sum up, in the existing methods and literature, the Focal loss function is introduced to solve the uneven distribution of positive and negative samples and difficult and easy samples. However, for the two weight factors in the Focal loss function, most of them adopt the method of empirical value. There are several problems in the results of training according to the empirical method. First of all, the existing methods determine the weights based on experience, and it is difficult to adjust dynamically according to the distribution ratio of positive and negative samples, and difficult and easy samples. Secondly, the accuracy of the model trained by the recommended empirical values may not be the optimal solution. Finally, training after multiple adjustments to the two weighting factors takes a lot of time and computational resources. In order to solve the above problems, this paper uses the YOLOv5 as the basic model, introduces the pre-trained weights trained on large-scale data sets for transfer learning, and uses the Focal loss function to replace the cross-entropy loss function. The two weighting factors in the Focal loss function are redesigned so that they can change dynamically based on the dataset. Finally, the training result of the original model is compared with the training result of the improved model. The improved detection model was also compared experimentally with Faster R-CNN, SSD, YOLOv3, and YOLOv4. The results show that the method proposed in this paper has high accuracy and short training time and can be used as a reference for the defect detection of transmission line insulators.
## 4 Results and Discussion
### Performance Overview  
The analysis reveals that the **Temporal Convolutional Network (TCN)** consistently outperforms other models across all configurations. As summarized in Table 20, TCN achieves the highest scores in accuracy, precision, recall, and F1 Score, reaching up to **0.9770** in the four Sensors Non-overlapping setup. This superior performance is attributed to TCN's ability to capture temporal dependencies effectively through dilated convolutions, making it particularly well-suited for handling multi-sensor data.  
Recurrent models like **LSTM** and **GRU** also show strong performance on the test set, particularly in configurations with overlapping data and multiple sensors. For instance, LSTM achieves a test accuracy of **0.9760** in the four-sensor 50%-overlapping setup, closely following TCN and demonstrating its effectiveness in capturing long-term temporal patterns.  
Traditional models such as **Random Forest (RF)** and **Support Vector Classifier (SVC)** perform adequately but do not match the performance of deep learning models. For example, RF achieves a test accuracy of **0.9753** in the four-sensor non-overlapping configuration,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configuration** &
\begin{tabular}{c} **Best** \\ **Model** \\ \end{tabular} & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline two sensors Non-overlapping & TCN & 0.9757 & 0.9757 & 0.9757 & 0.9757 \\ \hline two-sensor 50\%-overlapping & TCN & 0.9765 & 0.9765 & 0.9765 & 0.9765 \\ \hline four-sensor non-overlapping & TCN & 0.9770 & 0.9770 & 0.9770 & 0.9770 \\ \hline
4 Sensor 50\%-overlapping & TCN & 0.9762 & 0.9762 & 0.9762 & 0.9762 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Overall Performance Comparison of Models Based on Test Set Metrics.  
while SVC reaches up to **0.9745** in the two-sensor 50%-overlapping setup. This highlights their limitations in handling complex temporal dependencies inherent in ITS applications.
## 1 Introduction
The PE has the advantages of simple calculation and high sensitivity to signal change, which can be employed to evaluate the denoising effect [22; 23]. Nevertheless, the phenomena of excessive noise reduction should be avoided, as it causes the details to be lost in the signal. Thus, the reconstructed signal must maintain a certain similarity with original signal, which can be described by cross-correlation coefficient. Therefore, regularization parameter optimization method is proposed based on permutation entropy and cross-correlation coefficient in this paper. It not only will be able to ensure the greatest degree of suppression noise, but also can improve the similarity between the signal after noise reduction and the original signal, which can avoid the problem of excessive denoising. In order to verify the rationality and feasibility of the proposed method, it is used to analyze the numerical simulation and the actual fault vibration signal of rolling bearings in the bearing test rig and the fan in metallurgical industry. The rest of the paper is organized as follows. In Section 2, the basic ideas of convex 1-D second-order total variation denoising method are introduced. The simulation signal analysis is described in Section 3. The fault bearing data from bearing test rig is analyzed in Section 4. The industrial measurement signal analysis of faulty bearing in fan is illustrated in Section 5. The final conclusions are given in Section 6.
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 2 The Importance of Supervising and Modeling the Maintenance Process  
Dependability refers to the ability to provide trustworthy services within a time period. According to international standards [31; 32; 33], it can be characterized as a set of properties describing the availability of the object and influencing it:  
* Reliability;
* Maintainability;
* Providing support.  
The term dependability is used only for a non-numeric general description.  
Focusing on maintainability, it can be concluded that it is the ability of the object to maintain or restore, under given operating conditions, a state in which it can perform the required functions, assuming that the service is carried out under established conditions with the observance of established procedures and means [34,35].  
Another definition describes maintainability as a property of a technical object, which is measured by the Maintainability function. The Maintainability function represents the probability of restoring the object to a working condition within a given time frame.  
\[M(t)=P(DMT\leq t)=\int_{0}^{t}m(\tau)d\tau \tag{1}\]  
where the following variables are defined as follows:  
* _DMT_--the duration of the maintenance task;
* _m_(\(\tau\))--is a function of the probability density of the duration of the maintenance task.  
Maintainability programming consists of a set of activities aimed at endowing a technical object and its environment with features that facilitate maintenance to the greatest extent possible. We distinguish actions on the technical object itself and actions on elements of the object's surroundings [36]. These activities are carried out during the following:  
* Designing a technical facility;
* Designing the technique and organization of servicing an object with a shaped susceptibility to servicing.  
The implementation of the second type of activities (in the vicinity of the facility) is essential, as their absence could hinder the effective use of the maintainability features built into the object during the design phase. Throughout the entire life cycle of a technical object, maintenance techniques and their organizational structure should be continuously improved [20,32,37,38].
## 4 Results and Discussions
Figure 8 shows that the proposed Cu-DNNGRU has a training time of 13.35 ms, which is significantly less than the training time of Cu-LSTMNN, Cu-BLSTM, and Cu-GRU, which consume the training times of 31.24 ms and 24.72 ms, and 17.6 ms, respectively, as pictorially elaborated in Figure 9.  
Figure 8: ROC curve analysis. Figure 9: Testing time analysis. The proposed Cu-DNNGRU is further compared with some state-of-the-art DL classifiers from the literature. The core objective of this extended performance evaluation is to obtain a comprehensive analytical idea regarding the performance of the proposed framework with its competitive algorithms. The performance comparison is conducted on the core performance parameters, i.e., accuracy, precision, recall, and F1-score. Table 7 summarizes this comparison where it can be transparently witnessed that the proposed Cu-DNNGRU has accomplished outstanding performance by outclassing some well-known benchmarked classifiers.
# Space Weather Effects on Satellites
## 4 Results  
Instead of focusing on the Starlink launch on 2022-02-03, as the majority of the previous research noted above, we prefer to consider all SpaceX launches in the period 2019-2022, since the information on the timing and number of satellites still in orbit is freely available. The aim of our analysis is to identify the geomagnetic disturbances which are well known to endanger the satellite performance. We start by identifying the value of the Dst index (as one proxy for the GS strength) at the time of the launches. A focus is then put on the cases occurring during the strongest geomagnetic disturbances. We consider the conditions not only before and after the launch time but also co-temporal with the launch. The event on 2022-02-03 is depicted with a different symbol in the plots for comparative purposes.
# A Study of 5G Edge-Central Core Network Split Options
## 3 Edge-Central Split Models
### Local Offload Split Option  
The local offload split option presumes that only one UPF, part of the data path, is present in the edge network, similarly to the architecture proposed by ETSI MEC. The rest of the control plane including decision elements and subscription profile are stored centrally. The UPF may handle only the locally offloaded data traffic or also forward the data traffic to the central side. For both options, a very large amount of the signaling in the control plane is passed over the backhaul, including the complete access and mobility management messages, the complete UE session management as well as the control messages from the SMF to the local offload UPF.  
Such a split alternative is viable for network deployments where there is a reduced trust into the edge node as the full control plane is centralized such as edge nodes on premises of subscribers or third parties with no trust relationship. Furthermore, it could be potentially used for use cases where the delay and the capacity of the signaling does not impact the end-to-end services, although the backhaul could have a very large delay such as in-aircraft communication or live content acquisition using nomadic networks.  
Figure 3: The 5G core network split options.
# Responses of Water Use Efficiency to Drought in Southwest China
## 5 Conclusions  
We analyzed the temporal and spatial patterns of WUE and its responses to drought in Southwest China using the MODIS data products (GPP and ET) and the scPDSI drought index. From 2000 to 2017, the mean annual WUE differed among broad vegetation types, with forests having the largest WUE (3.25 gC kg\({}^{-1}\)H\({}_{2}\)O), followed by shrublands (2.00 gC kg\({}^{-1}\)H\({}_{2}\)O), croplands (1.76 gC kg\({}^{-1}\)H\({}_{2}\)O), and grasslands (1.04 gC kg\({}^{-1}\)H\({}_{2}\)O). Annual WUE was higher in the Sichuan basin and Yunnan and lower in Guizhou and western Sichuan Plateau. Annual WUE showed a gradual downward trend from 2000 to 2017. The WUE was sensitive to drought, and overall, drought had an enhancement effect on WUE. However, the responses of WUE to drought varied with vegetation type and drought severity. WUE was the most sensitive to drought over croplands, and a slight water deficit was likely to reduce cropland WUE. The drought resistance of grasslands was relatively weak because of its shallow root system; mild drought could increase its WUE while moderate and severe drought could reduce its WUE. The adaptability of forests and shrublands to drought was relatively strong; moderate drought could increase their WUE, and only severe drought could reduce their WUE. Our findings can help us better understand plant water use strategy and inform ecosystem water management.  
Conceptualization, T.X.; methodology, J.X.; software, H.F.; validation, X.H.; formal analysis, K.M.; investigation, L.S.; resources, S.L.; data curation, Y.Y.; writing--original draft preparation, J.Z.; writing--review and editing, J.X. and T.X.; supervision, T.X.; project administration, S.L.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDA20100101), National Natural Science Foundation of China (41671335, 91647104) and project supported by State Key Laboratory of Earth Surface Processes and Resource Ecology (2017-KF-16).  
Annual GPP and ET data with 1-km resolution were both obtained from MODIS products ([[http://www.nttsg.umt.edu](http://www.nttsg.umt.edu)]([http://www.nttsg.umt.edu](http://www.nttsg.umt.edu))). The scPDSI data product is available from the University of East Anglia (UEA) ([[http://www.crtu.uea.ac.uk/](http://www.crtu.uea.ac.uk/)]([http://www.crtu.uea.ac.uk/](http://www.crtu.uea.ac.uk/))).  
The authors declare no conflict of interest.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Roasting Effects
#### 3.2.3 Roasting Analysis  
The SEM results of the fly ash samples after roasting are shown in Figure 5a-c. Figure 5a,b shows the roasted fly ash, with indistinct particle edges and particles bonded together by sodium carbonate. The sodium carbonate disrupts the vitreous structure within the fly ash, compromising its particle integrity and enhancing the reactivity for the subsequent leaching process.  
Additionally, the XRD analysis in Figure 1c reveals that the primary phases of the fly ash before roasting are mullite, quartz, and a small quantity of iron-rich microspheres. After roasting with sodium carbonate, the XRD analysis in Figure 5c shows that nepheline and magnetite phases were formed. Furthermore, with an R\({}_{\text{fs}}\) of 1:2 in conventional roasting, residual sodium carbonate was observed. Therefore, the possible pathway for the reaction in the sodium carbonate roasting process is as follows:  
\[\text{Na}_{2}\text{CO}_{3}\rightarrow\text{Na}_{2}\text{O}+\text{CO}_{2}\uparrow \tag{1}\]  
Figure 5: SEM results of (**a**) sodium carbonate-roasted fly ash (50 \(\upmu\)m), and (**b**) sodium carbonate-roasted fly ash (10 \(\upmu\)m); XRD results of (**c**) sodium carbonate-roasted fly ash.  
\[\text{xAl}_{2}\text{O}_{3}\text{-}\text{ySiO}_{2}+\text{nNa}_{2}\text{O}\to \text{nNaAlSiO}_{4} \tag{2}\]  
\[\text{SiO}_{2}+\text{Na}_{2}\text{O}\to\text{Na}_{2}\text{SiO}_{3} \tag{3}\]
## 3 Base Model Design
A reduced bolt pretension force is applied first to establish a smooth contact relationship between the bolt and the surrounding, and then the full pretension is applied according to the design value. The bilinear kinematic constitutive model was adopted for the stress-strain curve of the steel plate, defining the stress strengthening of 0.01 E. According to the material property tests, the material properties were determined. The [PERSON] yield criterion was adopted for the calculation of the model, and Poisson's ratio was 0.3. The high-strength bolt in the test was used in the model. The schematic diagram of the base model is shown in Figure 16. The dimensional design of the parametric model was based on the prototype structure regarding the relevant codes and regulations in China, and three sets of parametric models were considered, with a total of nine different models. The various parameters are the length of the energy-consuming region \(e\), the different steel strength combinations, and the length of the replaceable link section \(e^{\prime}\). The models for each series are shown in Table 3.  
Figure 15: Bolt connection design of link. In the table, \(e\) and \(e^{\prime}\) are the length of the energy-consuming region and the length of the replaceable link section, respectively, and \(\rho\) is the length ratio of the link. As the frame column and beam do not require high ductility, Q460- and Q690-grade steel were used. To reduce the cross-sectional area of the members, the bolts were selected from M22, 10.9-grade high-strength bolts, and the other members were made of Q345-grade steel. In this paper, solid units were selected to establish the bolt model. The preload was applied according to China's steel design standard, M22 with grade 10.9 should be used with a preload of 190 kN, and it was applied in steps to the center of the bolt rod. The horizontal loading method was displacement-controlled loading, the Z-axis positive load was applied at the beam end by the coupled action surface until the structure was damaged, and the cyclic loading was applied to study the seismic performance of the structure. The model boundary conditions are shown in Figure 17. The loading protocol was referred to China's seismic test regulations [21] and the loading protocol is shown in Figure 18.  
The high-strength eccentrically braced steel frame structure with replaceable link consumed seismic energy through plastic deformation, while the non-energy-consuming elements such as frame columns, beams, and braces were still in the elastic phase. For the numerical analysis in this paper, the structural damage was determined by the following phenomena that occurred: (1) Stress in the link reaching ultimate strength. (2) The maximum story drift of the structure greater than 1/20. (3) Large plastic deformation of members other than links, bolts, and end plates due to local buckling.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 3 Outdoor Measurements and Wireless Link Characterization  
Since a SN is intended to operate as part of a big network of other SNs, the energy consumption is considerably affected by the wireless channel condition and the distance between the nodes. Therefore, a realistic model for PL is of interest in order to estimate the received power at the receiver and determine the communication range for a given reliability requirement. To this end, in the following measurements a pair of Sub-1 GHz CC1310 wireless microcontroller unit (WMCU) LaunchPad development kits, operating at 915 MHz, and tow laptops running the SmartRF Studio application [55] are used as  
Figure 6: Estimated versus measured SN’s energy per measurement.  
depicted in Figure 7. Starting at a 1 m distance, the three following metrics (i.e., \(PER,\ BER,\) and \(RSSI_{avg}\)) are collected in steps of 5 or 10 meters by keeping the transmitter at the same place and moving the receiver in order to evaluate the radio link quality. The packet error rate (PER) is given by  
\[PER=100\ \frac{N_{PNOK}+N_{PLOST}}{N_{P}}, \tag{7}\]  
where \(N_{PNOK}\) represents the number of packets received in error, \(N_{PLOST}\) is the number of completely lost packets (i.e., the receiver knows that it has to receive a given number of packets), and \(N_{P}\) represents the total number of packets, which is in this case 400.  
The BER is also taken into account and estimated using  
\[BER=100\left(1-\left(1-\frac{PER}{100}\right)^{\frac{1}{N}}\right), \tag{8}\]  
where \(N\) is the number of bits per packet.  
Lastly, the RSSI is also considered and calculated using  
\[RSSI_{avg}=\sum_{i=1}^{N_{P}}\frac{RSSI_{i}}{N_{P}}, \tag{9}\]  
where \(RSSI_{i}\) is the received signal strength indicator of the \(i^{th}\) packet.