esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Casting Mold Liquid Surface Area Change Threshold Verification  
Experimental analysis revealed that segmentation instability occurs due to factors such as casting lighting, steam, or other influencing factors. However, the change in casting mold liquid surface area during liquid aluminum leakage moments remains higher than during normal casting. Specifically, a 3 s time window is more effective at distinguishing liquid surface area changes between normal conditions and liquid aluminum leakage compared to 1 s or 2 s windows. Additionally, the response time is sufficiently sensitive for production personnel to take prompt action. Therefore, we choose the absolute value of the change in liquid surface area, \(\mid\Delta S\mid\), with a time window of 3 s as the judgment criterion for liquid aluminum leakage.  
Computing liquid surface area and changes for consecutive normal casting datasets from May 2023, along with abnormal casting datasets (related to liquid aluminum leakage) from March to May, resulted in a cumulative dataset of liquid surface area changes over 1856 s. Applying Equation (2) produced a calculated threshold of 1095 for changes in liquid surface area.  
This section aims to validate the effectiveness of the threshold for changes in liquid surface area of the casting mold in identifying liquid aluminum leakage. It utilizes datasets from June to August 2023, consisting of liquid aluminum leakage data and normal casting  
Figure 12: Comparison chart of the visualization model of liquid surface segmentation in the casting mold. (**a**) Original images. (**b**) Ground truth images. (**c**) DeepLabV3+ results. (**d**) UNet results. (**e**) PSPNet results. (**f**) Our results.  
data (the total number of datasets is 110, and the number of aluminum leakage datasets is 25), as the verification set to assess the validity of the calculated threshold.  
Here, any value exceeding the threshold in the dataset is considered abnormal. The computation results are presented in Table 5.  
The results show that the set threshold achieves an _Accuracy_ of 95.5%, indicating a strong capability to detect liquid aluminum leakage effectively. Due to limited aluminum leakage data and small sample size, the false negative rate and false positive rate are relatively elevated. This finding can be applied in practical production settings to further investigate the _FAR_ and _MAR_.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 3 Development of Rainfall Measurement Based on CML  
In the middle of the 20 th century, it was found that rainfall had a significant impact on the attenuation of electromagnetic waves, especially high-frequency electromagnetic waves, and it was proposed that the attenuation of electromagnetic waves could be used to retrieve rainfall intensity [37]. At the end of the 20 th century, the implementation of the Tropical Rainfall Measuring Mission (TRMM) project has promoted a series of experiments to retrieve DSD by using dedicated dual-frequency links [38]. However, it is not realistic to deploy such a network of microwave links just for rain measurement in a large area. Fortunately, since the beginning of the 21 st century, with the rapid growth number of  
Figure 2: Correlation between CML power total loss and rainfall intensity. mobile phone users, a variety of wireless base stations and microwave links have been widely deployed to build seamless WCNs. This forms the infrastructure foundation of the CML-based rainfall monitoring networks to timely observe rainfall with high temporal and spatial resolution. Each microwave link between two base stations is equivalent to a sensor node. Moreover, for the typical frequencies used by CML in WCNs, the coefficient \(b\) in Equation (1) is very close to 1, which means that there is a good linear relationship between rain-induced attenuation and rainfall intensity. In 2006, [PERSON] et al. [39] used CML-RSL data to retrieve the rainfall intensity in Israel for the first time. In 2013, [PERSON] et al. [40] used an unprecedented number of more than 2400 CMLs to reconstruct the rainfall map for the entirety of the Netherlands (35,500 km\({}^{2}\)). Subsequently, countries around the world established research teams and tried to explore the potential of CML in the field of environmental monitoring. Table 2 summarizes the countries that have achieved rainfall measurement by CML in chronological order.
## 4 Smart Grid Architecture
### Customer Domain  
The customer domain of the smart grid is where end users connect electricity to the distribution grid through smart meters. Smart meters track and control the flow of electricity to and from consumers and provide information about energy use and patterns. End users include commercial/building, industrial, and residential, as shown in Figure 9. The energy service interface (ESI), which provides secure communication between the utility and the consumer and the electric meter, is commonly referred to as the customer area boundary.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Participator** & **Functions** \\ \hline Substation & Controlling and monitoring systems within a substation \\ \hline Storage & Controlling a charging and discharging of an energy storage unit \\ \hline Distributed generation & Power sources located on the distributed side of the grid \\ \hline Measurement and control & Measuring, recording, and controlling to protect and optimize grid operation \\ \hline \hline \end{tabular}
\end{table}
Table 7: Distribution domain participators and their function.  
Figure 8: Distribution domain.  
The ESI, in turn, acts as a bridge to systems that focus on facilities such as the consumer energy management system (EMS) and building automation systems (BAS). The customer domain is further subdivided into the home (energy range \(\leq\) 20 kW), building/commercial (energy range 20-200 kW), and industrial (energy range \(\geq\) 200 kW) subdomains. This domain can also generate, store, and manage energy and connect plug-in vehicles. Within the consumer domain, two-way communication between the devices and the system takes place over a local area network (LAN) and a home area network (HAN). The customer domains share the interface between the service provider, operations, distribution, and business markets. The customer domain participants and their functions are summarized in Table 8.
## 5 Mechanism for Lithium-Ion Storage in Si-Based Anode Materials
### Structural Influence  
The structure of polymer-derived ceramics (PDCs) anode materials has a significant influence on their lithium-ion storage performance. Firstly, the microstructure of SiOC materials undergoes notable changes during lithium-ion intercalation and de-intercalation. [PERSON] and colleagues suggested that the mixed bond configurations (such as SiC\({}_{4}\), SiC\({}_{3}\)O, SiC\({}_{2}\)O\({}_{2}\), and SiO\({}_{3}\)) in SiOC materials are the primary lithium intercalation sites, and their presence significantly enhances the material's reversible capacity. Additionally, [PERSON] [31] and colleagues noted that the combined effect of the amorphous silicon oxycarbide phase and free carbon phase in SiOC materials enables high carbon-content SiOC materials to exhibit excellent cycling stability and performance under high-rate charge and discharge conditions [63]. Secondly, the pore structure and particle size of the material also significantly impact its electrochemical performance. [PERSON] [64] and colleagues indicated that the structure and porosity changes of PDCs materials during pyrolysis are influenced by temperature, precursor, and atmosphere, thereby affecting lithium-ion diffusion kinetics and battery energy efficiency. [PERSON] and colleagues suggested that the morphology of 3D Si/C/SiOC nanocomposites significantly impacts their electrochemical performance, and optimized nanostructures can significantly enhance lithium-ion storage capacity and cycling stability [65].  
In summary, the structure of polymer-derived ceramic anode materials plays a crucial role in the mechanism of lithium-ion storage. By regulating the microstructure, porosity, and particle size of the material, its electrochemical performance and cycling stability can be significantly enhanced.
# Comparing Approaches for Explaining DNN-Based Facial Expression Classifications
## 5 Results
### Comparing Explanations for the DNN and CNN  
In Figure 4, example explanations of the CNN using Grad-CAM (second row) and DNN (third row) can be seen. We decided to omit the explanations generated for the CNN using SHAP. These explanations were of a lower quality than those of Grad-CAM, since there was a lot of noise in the explanations with seemingly random pixels coloured. It should also be noted that explanations for the CNN generated by SHAP take dramatically longer to compute than those generated by Grad-CAM.  
Figure 3: Fidelity and accuracy scores per top \(n\) SHAP features for the frontal, half left and half right model. Also given is the proportion of the SHAP weight of the top \(n\) features with regard to the total weight of all features. All scores are calculated on the test set.  
To exemplify the textual explanation, the top left visual DNN explanation is accompanied by the following text:  
This person's emotion is classified as DISGUST. This classification is CORRECT. The following five features, listed from most important to less important, contributed for 36.6% to the decision:  
1. Left eye aspect ratio (ratio between eye width and eye height).
2. Angle from bottom mouth to left upper mouth.
3. Angle from left mouth corner to top of the mouth.
4. Distance between the centre of the left eye and the left inner eyebrow.
5. Left lower eye outer angle.  
For the geometric features explanations, we decided to visualize five features, which seems like the minimum given the fidelity scores of these explanations. This could easily be extended to show more features.
## 5 Impacts of Salinity on Soil
### Soil Microbial Function and Diversity  
In good conditions, 1 g of soil may incorporate 600 million bacteria appertaining to 15,000 or 20,000 distinct species. These amounts reduce to 1 million bacteria in desert soils comprising between 5000 and 8000 species [124]. This soil microbiota plays a vital role in preserving and/or improving the consistency of the soil by controlling the degradation of organic matter and the supply of nutrients, facilitating the production of macro-aggregates [125]. However, soil microbiota responsible for the formation and maintenance of soil aggregates are vulnerable to environmental changes because of their wide area to volume ratio, highly permeable cell membrane, and high turnover rates resulting in a quick reaction to nutritional supply variations [126].  
It is evident that salinity decreases microbial activity, microbial modifications, biomass, and the structure of the microbial community [127; 128]. Furthermore, knowing the microbial group structure and its role in saline soil is a key ecological goal, as it will help to explain the biological regulatory processes for the nutritional cycles in saline soil [129]. Many other studies concentrate on the examination of the effects of salinity on microbial communities [130; 131]. Salinity has an adverse effect on quantity, distribution, structure, and functionalities of the microbiota [132]. Additionally, salinity was observed to lower soil microorganism's biomass and microbial growth rate [133; 134]. The disruption of the symbiotic relationship between plant and microorganisms is another damaging consequence of salinity. For instance, disruption in plant interaction with bacteria is induced by deterioration in proteins implicated in the anchoring and adsorption phases of the symbiotic interactions between bacteria and plant roots [135]. Salinity often has an adverse impact on biotransformation of the biogeochemical cycles like mineralization of N and C [129]. Microbes are engaged in nitrification, oxidation, ammonization, nutrient absorption, and other processes that contribute to soil organic matter nutrient cycling and degradation [136].  
Fungi are usually considered to be less salinity tolerant than prokaryotes. However, this claim is only because only prokaryotes are tolerant to extreme levels of salinity [137]. However, in fact, the total number of soil fungi is significantly reduced with increased levels of salinity. The total number of Actinobacteria and bacteria also decreases under salinity, but only at salinity levels above 5% [138]. The low osmotic potential characteristic of soil salinity declines hyphae production and spore germination, and induces modifications in the mycelium morphology [139]. Archaea is one of the microbial groups reported to increase its relative abundance in extremely saline ecosystems. However, the functional relevance of the association between plants and archaea is still unknown, since only a few reports of the association between archaea and plants (rice, maize cress, Abutilon, _Suada nudiflora_, and _Sporobolus_) have been established [140; 141].
## 4 Numerical Example
### Optimization Results  
Table 3 presents the tuning parameters and the processing time for every iteration found for objective functions OA1 and OA2, and mass ratios \(\mu=0.02\) and \(\mu=0.05\), by applying the WOA methodology proposed herein. Additionally, a comparison is made with the DEM and an ES process with precision to two decimal positions. It should be noted that DEM has proved to be effective in solving the tuning problem of TMDs and TMDIs under actual earthquake excitations [58; 59]; thus, it was selected to validate the WOA optimization results. Moreover, the work of [PERSON] et al. [58] demonstrated the feasibility of DEM over other conventional tuning methodologies considering harmonic loads [6], white noise processes [7; 8], and frequency domain analysis [9]. Similarly, Table 4 shows \(\zeta_{d}\) and \(f\) parameters derived from J1, J2, J3, and J4 approaches. As expected, the results computed by WOA and the other two comparison methodologies show correspondence. From Tables 3 and 4, it can be observed that some of the design variables approach the limits established previously in Equations (21) and (22). Such results are not realistic from a practical point of view, but they may be attributed to the impulsive nature of some of the seismic records used in the optimization process. However, in all cases the results are in excellent agreement with those computed through the DEM and the ES process, showing small differences from the third decimal position, which has no influence on the global response of the structural system. Furthermore, the numerical results reported by other works in which actual seismic records were used as excitation inputs [46; 58; 59; 65] exhibited a similar trend, since some of the optimal values are also close to the bound limits defined for the optimization process. On the other hand, WOA exhibits notable advantages against ES and DEM, among them, less computational cost avoiding operations, like mutation or crossover, and reductions up to 45% in the processing time, approximately (see Tables 3 and 4). Figure 4 illustrates the dispersion of optimal design values derived from the three optimization methodologies and the six objective functions used in this investigation.
## 3 Signal Level Compatibility Analysis  
### Analysis of the Effect of Interference on the Reception Performance of BeiDou  
According to the working principle of the BeiDou receiver, the interference signals in BeiDou receiver reception performance can be divided into four aspects: code tracking, carrier tracking, capture, and data demodulation. Signal-to-Noise Ratio (SNR) is an index used to measure the signal quality, generally used to indicate the quality of the received signal containing interference and noise, but in order to facilitate the analysis of the interference, it will be expressed in terms of the carrier-to-noise ratio, so we introduce the equivalent carrier-to-noise ratio for the analysis. The carrier tracking error and code tracking error are used to respond to the carrier tracking performance and code tracking performance. The BeiDou receiver is a device used to receive and process the signals of the BeiDou satellite navigation system. As shown in Figure 5, the BDS receiver mainly includes an antenna, an RF front-end module, an IF signal processing module, and a navigation demodulation module, and the receiver antenna turns the received satellite signals into digital IF signals after processing by the RF front-end, in which the signal processing module mainly consists of three stages: capture, tracking, and data demodulation.  
Figure 4: Power spectral density of 5G OFDM and FBMC waveforms.  
Figure 5: BeiDou receiver working principle.
## 4 Data Collection and Correlation Analysis
### Selection of DMUs  
In this research, 17 European countries were selected as DMUs. Table 1 shows the list of 17 DMUs to be investigated in this research. They include Austria, Belgium, the Czech Republic, Finland, France, Germany, Greece, Hungary, Italy, the The Netherlands, Norway, Poland, Portugal, Spain, Sweden, the United Kingdom, and Switzerland. In DEA, these countries are termed DMUs, and each has been assigned a DMU code.
## 3 Parameter Design Method of TBM Tunnel ASRBs
### Analysis and Design Optimization of ASRB Parameters  
Based on the engineering geological conditions, the geological stress field conditions, and the relevant construction design parameters (e.g., tunnel burial depth, excavation diameter, etc.) of the project area, the influence of the ASRB design parameters on the stress release and energy dissipation effects, as well as the correlation between the parameters of ASRBs, can be obtained based on the previous studies.  
The analysis needs to be conducted from two perspectives: sensitivity analysis and correlation analysis. The sensitivity analysis aims to evaluate the impact of each parameter related to ASRBs on the stress release and energy dissipation effects. The effect of a given parameter can be demonstrated by calculating the magnitude of range as shown in Equation (11), where \(R\) is the range, \(N_{max}\) is the maximum value of the test scheme indicator when the parameter is varied, and \(N_{min}\) is the minimum value of the test scheme indicator. By comparing the range of stress release effects at different parameter values, the priority of ASRB parameter adjustments can be determined.  
\[R=N_{max}-N_{min} \tag{11}\]  
The correlation analysis aims to identify and examine the relationship between each ASRB parameter and the stress release and energy dissipation effects. This analysis aids in defining a strategy to optimize and adjust ASRB parameters. Ultimately, this approach facilitates the improvement of the existing design scheme or the suggestion of a more practical layout for ASRBs.
## 4 Results and Discussion  
### Spray Process  
Figures 4 and 5 shows photographs of the diesel oil wall shape developing over time. The spray mist head diverged before hitting the wall and the front of the body showed a more obvious diffusion phenomenon. A \"mist\" appeared on the edge of the sprayed head [19]. At 0.4 ms, after the beginning of the injection, the obvious oil beam can already be seen. The diesel oil beam is sucked with the surrounding spray penetration length is 22.0 mm, the spray radius is 3.33 mm, and the oil beam angle is 6\({}^{\circ}\). At 0.8 ms after the initial injection, the spray penetration length is 40 mm, the spray radius is 9.3 mm, and the oil beam angle is 6\({}^{\circ}\). After two measurements, it was found that the cone angle of the oil beam do not change obviously. The results agree with the conclusion that the change of cone angle is small at high injection pressure. At this point, the phenomenon of diesel spray hitting the wall has occurred. The spray radius increases obviously in the spray stage after the measurement. Authors found that the injection pressure changes in spray cone angle were mainly affected by injection pressure [20] and air entrainment around the spray bundle. The injection pressure and the environment is unchanged in this test, so in the entire injector spray process the spray of diesel cone angle is unaltered significantly. Late in the fuel injection, due to the influence of gas on the spray oil bundle dominance, by the air entrainment effect, spray cone angle slightly increased. According to previous studies, the spray body was vertically challenged to form a circular wall expansion area, wall friction oil mist rise, the middle part of the sunken and the overall showing a \"blanket\" shape.  
\begin{table}
\begin{tabular}{c c c} \hline Fuel injector model & LISA \\ Evaporation model & &
\begin{tabular}{c} Frossling model \\ O’Rourke droplet collision model \\ sequential \\ Turbulence model \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Physical model.  
Figure 4: Morphology evolution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{C_{SI}}\) & \(\mathbf{C_{S2}}\) & \(\mathbf{C_{S3}}\) & \(\mathbf{C_{\mu}}\) & \(\mathbf{n_{0}}\) & \(\mathbf{\beta}\) \\ \hline
1.42 & 1.68 & \(-1.0\) & 0.0845 & 4.38 & 0.012 \\ \hline \hline \end{tabular}
\end{table}
Table 3: k-\(\varepsilon\) model parameters.  
The observation shows that there is obvious thinning area of oil mist around the outer wall of the diesel mist layer, and the area of the thin diesel mist becomes larger gradually with the passage of time. The area of the oil mist layer remains stable. There is the result of friction and air resistance received by wall-running oil bundles during wall expansion and intense entrainment with air. From diesel spray vertical wall test results, there is a clear characteristic that the fog injection contact wall and its development along the wall are symmetrical distribution. The wall is the center of the oil spray and the radial diffusion along the wall surface and the formation of oil film and phenomenon of oil mist wall climbing.