esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 3 Results
### Frequency Analysis  
The maximal instantaneous discharges at Taunsa Barrage for floods of different return periods are indicated in Table 4 using the LN, Gumbel, and LP3 distributions, while Figure 6 depicts the discharge for each of those distributions. For the three distributions, the recorded discharge values are higher than those provided by [2]. The expected flood peaks using the LN distribution are larger than those obtained using the LP3 and Gumbel distributions for all return periods greater than 50 years. The K-S test results for Taunsa Barrage are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Station** & \multicolumn{5}{c}{**Taunsa Barrage**} \\ \hline _Return Period (years)_ & \(5\) & _10_ & _50_ & _100_ & _150_ \\ \hline LN & 3842 & 4422 & 5476 & 5862 & 6134 \\ Gumbel & 3923 & 4372 & 5360 & 5777 & 6021 \\ LP3 & 3861 & 4435 & 5299 & 5710 & 6110 \\ \hline \end{tabular}
\end{table}
Table 4: Maximum instantaneous discharge (m\({}^{3}\)s\({}^{-1}\)) according to the LN, Gumbel, and LP3 probability distributions at Taunsa Barrage for floods of various return periods.  
Figure 5: Damage assessment to the ‘crop/agricultural land’ and ‘built-up areas’ LULC categories for each subdivision of the district following the 2014 flood.  
The LP3 distribution was used to predict flood peaks at Taunsa Barrage based on the value of the K-S statistical test. For the return periods of 5-, 10-, 50-, 100-, and 150-year as well as for the 2014 flood, the instantaneous flows derived from the LP3 distribution were used as steady flow inputs into the HEC-RAS model. The first segment was downstream from lower Taunsa at some distance. The topography was comparatively precise compared to the upstream parts, while the second part was at the intersection of the Indus River. Both floods with 100- and 150-year return periods show higher water levels than the water levels that occurred during the 2014 flood (Figure 7).
## 4 Optimization Process
### 2D Optimization Stage
#### 4.3.1 Taguchi Method  
The Taguchi method [41] was originally intended for quality issues, and it has already been used for engineering before [42]. Unlike other simpler methods such as the factorial designs, the Taguchi method tests pair of combinations from all the possible combinations by a method where the parameters and levels are organized in orthogonal arrays that do not consider the interaction between the variables. By implementing the method, it can be computed how much each of the parameters affects the mean and the variance of the process and use it as a quality factor of the method itself.  
The steps of a regular Taguchi method are developed as follows:  
1. Determine the maximum or minimum target function. In this present paper, the goal was the minimization of the total pressure drop between the inlet of the mixer and the outlet.
2. Determine the variables involved, developed previously in this same section, along with the levels of each variable, as well as the levels imposed for each variable. These parameters are shown in Figure 8; they were geometrical parameters that determined the geometry, whereas the levels were selected by trial and error in order to avoid nonsense bodies.
3. Select the orthogonal array. The Taguchi methods usually provides an established set of orthogonal arrays that the user can use according to the number of variables and levels. In this case, the use of 13 variables with 4 levels each led to an orthogonal array of L64.
4. Evaluate the mathematical method and the computation of the variables to provide information about the quality of the process. In this case, once the method was performed, the influence of each of the variables was evaluated by calculating the corresponding sound/noise function for minimizing studies, which could be used for a second optimization round if needed:  
\[SN_{i}=-10\cdot\log\sum_{u=1}^{N_{i}}\frac{y_{u}^{2}}{N_{i}} \tag{9}\]  
where \(i\) is the experimental number, \(u\) is the trial number and \(N\) is the number of trials for each experiment.  
Figure 8: Control variables of the Taguchi optimization method.
## 6 Tools
### Network Simulators
#### 6.1.2 NS-2  
NS-2 [89] is a generic discrete-event network simulator that supports simulation of TCP, routing, and multicast protocols over wired and wireless (local and satellite) networks, mobile network simulations with node movement, protocols, and traffic connection. NS-2 has an extensive manual with information on topics like Routing, Packet forwarding, Applications, and Transport in the network. It also provides help for installation and application. Nam (Network Animator) is a tool that can be used for network visualization (including network topology and packet-level animation) with data from NS-2. A negative side of NS-2 is that, as mentioned by the authors, it can have bugs that affect results (some unreliability).
# A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation
## 4 Synthetic Data Test  
To evaluate the performance of the proposed algorithm, taking RESNET as an example, we designed two synthetic models as shown in Figure 3. The differences between the two models lie in the distinct electrical resistivity contrasts at a depth of 100 m and in local regions near the Earth's surface. In model I, the probing depth on the left side is limited due to the shielding effect of the conductivity layer. Conventional algorithms are affected by inversion model discrepancies near the shallow low-velocity zone (Figure S2), whereas the predictions of deep learning models closely resemble the probing depth derived from the true model. The true model is derived from one-dimensional slices, thus exhibiting abrupt changes in probing depth in localized areas devoid of conductivity layers. The uncertainty of the 1D inverted model near the surface may result in reduced probing depth (Figure S2). Such discrepancies are also governed by different inversion parameters, such as regularization parameters.  
In model II, when the shallow low-velocity layer is absent, the probing depths obtained by both deep learning and conventional methods closely align. To test the noise resilience of different algorithms, we introduced random Gaussian noise of 10% to the synthetic observations. This process was repeated 100 times for probing depth prediction. Interestingly, conventional algorithms demonstrated superior noise resistance. However, despite this, the prediction deviations of deep learning remained within an acceptable range. The uncertainty introduced by the conventional inverted model led to discrepancies in prediction far exceeding the uncertainty introduced by noise. These results demonstrate the effectiveness of the DL predictions. We also found that ResNet demonstrates good noise resistance when trained with noisy data (Figure S3).  
Figure 2: Prediction of testing dataset. (**a**) Joint distributions of label and prediction. (**b**) Joint distributions of label and late-time IEF. (**c**) Joint distributions of prediction and the late-time IEF.  
In Table 1, we compared the performance of different deep learning models by evaluating the test dataset's Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), as well as cross-entropy loss. The loss evolution curves of the three functions are shown in Figure S1. Overall, all three models performed well, with RNN performing the best and RESNET performing relatively worse.
## 3 State of the Art
### Graph-Based Design Languages
From this high-level central data model, different interfaces generate domain-specific models, such as geometry models or simulation models (CAD, CFD, FEM, etc.) fully automated with no user interaction. One main advantage of this central data model consists in the information content, which is not limited to geometry definition, but can also include abstract information concerning physical loads, materials, functions, or other  
Figure 7: Application of a graph-based design language (compare Rudolph [44]). aspects. By this approach, it is possible to address a multitude of programs in engineering product development processes. The central benefit of graph-based requirements management based on UML is the consistent interconnection between structural, geometric, functional, and physical information. One important advantage in this project is the availability of the \"DesignCompiler43\". This compiler is developed by the IILS mbH ([[http://www.iils.de](http://www.iils.de)]([http://www.iils.de](http://www.iils.de))) in cooperation with the University of Stuttgart, which is one of the cooperation partners in this project. DesignCompiler43 is an eclipse-based software tool for the compilation of design languages formulated in UML. The design compiler can process design languages on all abstraction levels to the full detail of the simulation models. This leads to a fully automated process within a unified framework. The work on DesignCompiler43 is ongoing, but many important functionalities are already available in the current version. The application of a graph-based design language and the usage of UML diagrams in this process is explained in Figure 8.  
As stated above, the main elements of a graph-based design language are vocabulary and rules. Vocabulary are UML-based components, e.g., the Gearbox itself or one of its shafts (Shaft). Rules are UML model transformations: these show the input status (only car) on the left side (LHS) and the output state on the right side (LHS--Gearbox with connected Shaft--see Figure 1). The engineer creates the UML model, which compiles the program into a design graph, from which, for example, the geometry of the product can be derived. Vocabulary and rules are linked in a so-called \"production system\" (UML activity diagram). Through the design compiler, the rules are executed to instantiate the class/vocabulary. This compilation process builds the central data model. From this central data model,  
Figure 8: Application of a graph-based design language and usage of Unified Modelling Language (UML) diagrams. different interfaces generate domain-specific, generic models, such as geometry models or simulation models. This application of graph-based languages allows for the automatic generation and evaluation of multiple product variants and is currently being introduced in several industrial companies.
## 5 Example Analysis
### Analysis of Icing Prediction Method Result  
#### 5.3.1 Example 1: Data Preprocessing Method  
To verify the effectiveness of the data preprocessing method, a comparison experimental group is set up, 20 sets of continuous winter meteorological data are input, and _Robust STL-BTSF_ and _BTSF_ are tested for alignment. The distance distribution results are shown in Figure 9. In addition, after verifying the superiority of IBWO above, the results oficing prediction obtained by two methods based on the IBWO-LSSVR model are shown in Figure 10, respectively. The comparison of data preprocessing methods is shown in Table 5.  
As shown in Figure 9, the alignment results of the two methods are displayed. Compared with _BTSF_, _Robust STL-BTSF_ has the highest average value of the orthogonal feature distance, which preserves the maximum information of the data, indicating that _Robust STL-BTSF_ achieves the best alignment of the data features. Analyzing Figure 10 and Table 6, _Robust STL-BTSF_ improves by 20.5% in running time. In terms of _MSE_, there are problems such as insufficient feature quantity extraction and low pre-prediction accuracy  
Figure 10: Comparison of data preprocessing methods.  
Figure 9: Distance distribution number.  
under \(BTSF\) processing. In summary, after verifying the superiority of _Robust STL-BTSF_ in the processing of meteorological data with small samples and short time series, all the following sections use this method to analyze and preprocess meteorological data.
## 4 Results
### Model Accuracy Verification
#### 4.2.1 Data Resource  
The \"2013-2016 National Survey of Changes in Nutritional Health Status\" was used to validate the accuracy of the model. The data contain a \"24-h dietary recall nutrient intake sum analysis file\" and a \"24-h dietary recall food weight and nutrient ingredient file\" with the information of 24 nutrients, including Energy, Water, Protein, Lipid Fat, Sugars Total, Calcium Ca, Phosphorus P, Iron Fe, VitaminB1, VitaminB2, Nicotinic, Vitamin C, Saturated Fat, Cholesterol, Vitamin E alpha TE, Sodium Na, VitaminB6, Magnesium, Dietary Fiber, Potassium K, Equivalent, VitaminB12, Zinc Zn, and VitaminD2D3.  
* 24 h dietary recall nutrient intake sum analysis file (sum_nutrients_24 hH--total 2602 data entries. This file includes the data of the total nutrient intake in a single 24 h dietary recall survey.
* 24 h dietary recall food weight and nutrient ingredient file (food_wt_and_nutrients)--totaling 113,824 data entries. This file includes the data of the sum of nutrients for each individual dish, food, health product, etc.
## 5 Conclusions  
This study investigates the effects of boron oxide (B\({}_{2}\)O\({}_{3}\)) on the properties of DLP-formed silica (SiO\({}_{2}\)) ceramics, focusing on how boron oxide content influences the microstructure, phase composition, shrinkage rate, porosity, and room temperature flexural strength of the SiO\({}_{2}\)-based samples. The main conclusions are as follows:  
1. Thermal gravimetric differential thermal analysis (TG-DSC) revealed that the actual melting temperature of boron oxide is 424.61 \({}^{\circ}\)C. A high-temperature sintering curve for the ceramic shell was established, providing essential conditions for subsequent silica ceramic sample preparation.
2. As boron oxide content increases, the shrinkage rate of the silica ceramic samples significantly rises, with the XY plane shrinkage increasing from 4.57% to 16.40%, and the Z-axis shrinkage from 6.25% to 17.03%. The trends for porosity, bulk density, and flexural strength first increase and then decrease: maximum porosity reaches 36.17%, minimum is 23.27%; maximum bulk density is 1.78 g/cm\({}^{3}\), minimum is 1.49 g/cm\({}^{3}\); flexural strength peaks at 15.39 MPa, with a minimum of 7.63 MPa. Ultimately, a boron oxide content of 3 Wt% resulted in a porosity of 23.27% and a flexural strength of 15.39 MPa, meeting the requirements for silica-based ceramic shells.  
Not applicable.  
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# 2 CA-R2: A Hybrid MAC Protocol for Machine-Type Communications
## 6 Simulation Experiments and Results
### Delivery Time of the Complete Set (One Packet per Station)  
Additional measurements were taken to compare 2 CA-R\({}^{2}\) and DCF. To this end, another simulation scenario was implemented using the same conditions as the ones used in the previous simulations. The difference was that in this case, each one of the \(n\) devices generated only one packet, which they tried to transmit at the beginning of the simulation.  
Figure 12: Mean access delay.  
Figure 13: Percentage of dropped data packets.  
The metric of performance was the time that the system took to transfer the whole set of \(n\) packets to the access point.  
Regarding the 2 CA-R\({}^{2}\) mechanism, in Figure 14 we can see that after 20 devices, it can deliver the entire set of packets in less time than DCF, reaching a difference of more than 100 ms for 256 devices. As an example of the importance of this result, let us consider a situation where the time to deliver data packets is limited to 250 ms. As depicted in Figure 14 (see the dashed red line), approximately 145 stations can transmit their data packet using DCF, whereas in the same time, approximately 203 stations are able to transmit using 2 CA-R\({}^{2}\). That is, at this point, 2 CA-R\({}^{2}\) achieves an improvement of roughly 40% with respect to DCF. As can be seen in the figure, larger gains are expected as the red line moves further up. This result emphasizes the importance of developing more efficient MAC algorithms able to support the communication needs of the highly populated IoT networks that we envision to appear in the near future.
## 2 Related Work
A position-finding strategy based on fuzzy logic and the received signal strength indication from BLE beacons and the geometric distance between the current beacon and the fingerprint point was suggested. Based on their findings, the fuzzy logic type-2 fingerprinting approach might be used in conjunction with BLE beacons to provide accurate indoor positioning. With a mean localization error of 0.43 meters and a mean navigation accuracy of 98.21%, the algorithm performed well. In [39], the authors presented a new method for avoiding obstacles using image depth and fuzzy control principles. Their method uses fuzzy logic to precisely alert the VI user of possible impediments so that they may avoid them without the user needing any prior knowledge of the surroundings. For a summary of the current state of research on the topic, readers might read the most up-to-date surveys [41; 42; 43; 44]. They display a broad variety of indoor and outdoor wearable and assistive devices-based navigation approaches and provide an analytical analysis of each, pointing out its benefits and shortcomings. The authors furthermore provide a taxonomy of wearable and assistive technologies that are grounded in both qualitative and quantitative criteria for assessment. It is clear that every strategy has its benefits and drawbacks, and that no one strategy is sufficient. Here, after examining a wide range of technologies, we found that the tradeoff between accuracy and the cost of creating and implementing an indoor navigation system was a significant barrier to its widespread adoption. In this research, we build on this insight by proposing a unique system that combines sensor-based approaches with soft computing technologies to gather enough information to provide an accurate indoor navigation solution for people with VI. The primary purpose of this study is to provide neutrosophic logic modifications to the widely utilized fuzzy-based indoor object detection. Like their fuzzy analogs, neutrosophic systems might benefit from the insight of human operators. The membership grade in Fuzzy Sets (FS) is utilized to deal with uncertainty, but in neutrosophic sets (NS), the truth, indeterminacy, and falsity membership grades are treated as separate entities [13]. The next section describes the suggested method.
## 4 Bigram and Traditional Results
### Advanced Leak Detection in Water Networks
#### 4.4.1 Bigram Document Analysis  
The treemap with the main concepts has been examined in the bigram analysis as shown in Figure 13, revealing that leak detection is mainly applied to water distribution networks and leak detection. Among the highlighted algorithms, support vector machine, artificial neural networks, and Decision Trees have been identified. Furthermore, four main groups have been observed in the thematic map analysis. The first group, in light blue, is related to machine learning methods that have been used for leak detection. However, close to this group, another group has been identified, which considers research related to sensors associated with leak detection. In the emerging themes quadrant, two groups are also observed. The first group is associated with Bayesian techniques used for tackling leak identification and localization, while the second group connects leaks with operational matters. The latter point is considered crucial, given that such models should ultimately be employed for decision-making. The operational aspect is deemed the most suitable area for conducting these tasks. Lastly, when analyzing the conceptual structure map and the dendrogram, as shown in Figure 14, two major groups are observed. The blue group encompasses everything related to operational conditions, leak detection, and risk assessment, all of which are linked to decision-making support. However, the primary focus of the main group is the utilization of machine learning techniques for constructing leak detection models.  
Figure 13: Tree and thematic map for a leak detection topic.
# Coseismic Stress Change and Viscoelastic Relaxation after the 2008 Great Sichuan Earthquake
## 3 Results
### Cumulative Stress Changes on Faults  
Furthermore, the stress tensor was projected on each mainshock source fault, and the stress changes were calculated using the source fault parameters listed in Table 3. Table 4 lists the cumulative stress changes with the viscoelastic relaxation effect on each mainshock source fault. S1 indicates the stress changes induced by all the previous earthquakes. S2 refers to the stress changes caused by both previous earthquakes and this event with the viscoelastic relaxation effect. Table 4 implies that the stress accumulation before this earthquake played a role in increasing the stress of the earthquake and promoting the stress accumulation process in the focal area. However, after the large earthquake, the stress state in the focal area was basically unloaded by this main earthquake. The Yushuearthquake occurred far from the Longmenshan fault zone and was not obviously affected by the stress of the previous Wenchuan earthquake. Otherwise, because the Kangding Ms 5.8 earthquake occurred after the Kangding Ms 6.3 earthquake, tectonic stress might also accumulate to an adjacent degree; thus, even if a small part of the stress was unloaded in the focal area, the subsequent Ms 5.8 earthquake still occurred.  
The stress changes imposed on the active faults in the study area are shown in Figure 8. The northern and southern segments of the Longmenshan fault, the southern segment of the Xianshuihe fault, the east Kunlun fault, the northern segment of the Huya fault, and the Huayingshan fault are obviously affected by the stress increases caused by these mainshocks. Continuing forward in time, the postseismic stress change fields between 2013 (Figure 8c) and 2020 (Figure 8e) are slightly different. That is, after 2013, although western Sichuan was in a deep stress shadow, most of the northern and southern segments of the Longmenshan fault and Xianshuihe fault were in the stress concentration area. These results have certain reference significance for risk analysis associated with these earthquakes in the future.
## 2 Materials and Methods
### Instrumentation  
Kinematic data were collected using an eight-camera infrared 3D motion capture system (Vicon T40, Oxford Metrics, UK) at a sampling rate of 100 Hz. Forty infrared retroreflective markers, each with a diameter of 14.0 mm, were attached bilaterally to both lower extremities to define hip, knee, and ankle joints by using a plug-in gait marker set (Figure 1). GRFs were measured with a split-belt fully instrumented treadmill (Bertec Corporation, Columbus, OH, USA) at a sampling rate of 1000 Hz. The 3D kinematic and GRF data were synchronized using the Vicon system. A heart rate (HR) monitor (V800, Polar Electro-OY, Kempele, Finland) was attached to each participant's chest to continuously monitor their HR during the entire fatiguing run procedure. Blood lactate concentration (BLa) was collected at different time points that were determined from the capillary blood sampled from the right fingertip and assessed with a Biosen C-line glucose analyzer (EKF Diagnostics, Magdeburg, Germany). Specifically, after cleaning with a sterile alcohol swab, a finger pick capillary puncture was performed and approximately 10 \(\upmu\)L of sampled whole blood was aspirated into an EKF Diagnostics glucose and lactate analyzer [23]. Four measurement time points were obtained [21; 24]: upon arrival at the laboratory (pre-), immediately after running (post-0 min), 4 min after running (post-4 min), and 9 min after running (post-9 min).