esa-sceva/satcom-chunk-collection huggingface dataset:
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 4 Land Applications
### Floods  
Another immediate application of this technology over land is the detection of floods. CYGNSS data have been widely used for flood detection applications covering for the urgent need to quickly map which areas are inundated. This occurs particular due to the short revisit time of CYGNSS satellites and the sensitivity of GNSS-R signals to inundated surfaces, even beneath the vegetation cover. In [125], authors provided the very first analysis for this application through the analysis of the floods occurred from the 2017 Atlantic hurricane season, which led to significant flooding in many parts of the U.S. and the Caribbean. The authors demonstrated that the CYGNSS data provided clear signatures of surface saturation and inundation over land at higher spatio-temporal resolutions and shorter revisit times as compared to radiometers. In [125], the authors employed a simple threshold-based algorithm to estimate the area of land flooded by Hurricane Harvey in Texas, and by Hurricane Irma in Cuba. Following the first flood demonstration, the authors in [126] used CYGNSS data to monitor another flood event, this one being a typhoon event in China. The authors were able to confirm that the observed surface reflectivity and the flooded area were qualitatively consistent with the Global Precipitation Measurement (GPM)-derived precipitation and the SMAP/SMOS-generated brightness temperatures at circular polarization. Following those studies, the authors in [127] developed a flood inundation mapping algorithm that combined GNSS-R signals with topographical information. The authors pointed out that, because of the pseudo-random geographical distribution of GNSS-R specular points, it was not possible to obtain continuous high-resolution flood inundation maps. By combining topological indicators that indicate the probability of a certain area being flooded with GNSS-R, the authors derived a large-scale, high-resolution-flood inundation map. The validation was conducted using available Sentinel-1A SAR data during floods in Kerala (August 2018), and North India (August 2017). The results of the study indicated that the model developed exhibited a flooding accuracy from 60% to 80%. The study in [128] provided a humanitarian and disaster management perspective to flood assessments based on GNSS-R data. The manuscript highlighted the need to tailor flood assessment products to decision makers. Authors discussed the decision maker barriers to understanding and using flood inundation maps, either such as being outside of their expertise or because products do not provide the proper variables. Besides humanitarian considerations, the authors in [128] included in their methodology an artificial intelligence classification algorithm, based on k-means, to monitor flooding events and to characterize land changes due to flooding events. The methodology was evaluated towards the needs of the humanitarian sector by the use fa cognizant link (a translator) between the two worlds, i.e., technologist or scientist and decision-makers.
## 4 Results and Discussion
### Critical Analysis and Discussion
#### 4.4.2 The Effect of Streak  
An interesting finding of this study is that streak may not be as important a factor in determining the outcome of matches as offline, qualitative studies may suggest. However, there are several angles to consider here. Firstly, at a data level, the imputation requirement of streak in this study does have the potential to introduce bias, although this is partially accounted for by the sensitivity analysis carried out in Section 4.3. However, future studies should attempt to mitigate this via collecting data over a longer timeframe to account for the rate limits of the Riot API.  
The construct validity of what was computed is a more interesting point to consider. This study treats streak as an integer variable, which implicitly introduces the assumption that smaller/larger values of streak will have smaller/larger effects, respectively (i.e., that the actual numerical value of the streak matters). It could be the case, however, that the streak itself has more impact as a categorical variable (e.g., \"player is on a winning/losing streak\") or a binned variable (e.g., \"player has lost less than 3 matches in a row\", \"between 3 and 5 matches in a row\", etc.). A valuable addition to future work would be to account for the various possible constructs of streak and explore their relative importance.
## 2 Minimum Transmission Range to \(2\)-Cover a Polygonal Region
**Theorem 1**: _Given a set \(S\) of \(n\) antennas and a polygonal region \(R\) with \(m\) vertices, \(\mathrm{MR}_{S}(R)\) can be calculated in \(\mathcal{O}(mn)\) time and space._  
**Proof**: Computing \(\mathrm{VD}_{2}(S)\) takes \(\mathcal{O}(n\log n)\) time since \(S\) is formed by \(n\) antennas on the plane [5]. The cardinality of set \(I=B(R)\cap\mathrm{VD}_{2}(S)\) is at most \(mn\) since \(\mathrm{VD}_{2}(S)\) has \(n\) faces and \(R\) has \(m\) edges. Adding all the vertices of \(\mathrm{VD}_{2}(S)\) that are interior to \(R\) to set \(I\) takes \(\mathcal{O}(nm)\) time. The region's vertices can be added to \(I\) in \(\mathcal{O}(m)\) time. For each intersection point \(q\in I\), \(\mathrm{MR}_{S}(q)\) is calculated in constant time using \(\mathrm{VD}_{2}(S)\) as it is the distance between \(q\) and its second closest antenna. The largest of these distances is \(\mathrm{MR}_{S}(R)\). Overall, the time complexity of this procedure is \(\mathcal{O}(mn)\). Regarding the space complexity, there is the need to store at most \(mn\) intersection points while \(\mathrm{VD}_{2}(S)\) can be stored in \(\mathcal{O}(n)\) space [5]. Consequently, this algorithm runs in \(\mathcal{O}(mn)\) time and space. **Corollary 1**: _Given a set \(S\) of \(n\) antennas and a convex polygonal region \(R\) with \(m\) vertices, \(\mathrm{MR}_{S}(R)\) can be calculated in \(\mathcal{O}(m+n\log n)\) time and \(\mathcal{O}(m+n)\) space._  
Note that, for a convex polygonal region \(R\), \(\mathrm{MR}_{S}(R)\) can be calculated in \(\mathcal{O}(m+n)\) time and space if \(\mathrm{VD}_{2}(S)\) has been previously built.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 4 Solution Approach
### Evaluation Logic Layer (_EVL_)
Dotted lines and nodes indicate the transition to further working principles in the causal structure leading to the fulfillment of an EVC. view describes the expected sequence of operations over time, breaking down processes into distinct intervals. It emphasizes dynamic transitions and captures system changes at different time scales (hours, days, months, etc.). The dependency-based view focuses on conditions and dependencies influencing the process without specific time steps. Properties are treated as quasi-stationary [73]. Transitions between states are acknowledged, but their dynamics are not emphasized, focusing instead on relationships between factors. Figure 6 compares these views. Both can be represented using directed edges between nodes in a Bayesian network but differ in interpretation. In the procedural view, nodes represent an entity evolving over time, while in the dependency-based view, nodes represent stable properties without temporal evolution. For example, the procedural view captures dynamic behavior of the state of a machine by showing its operability over time, while the dependency-based view focuses on dependencies of influencing factors without detailing temporal changes like the conditions required for a machine to be repaired. Distinguishing between these views helps in eliciting the a priori knowledge of PSS engineers regarding-eir causal relations. Figure 7 illustrates an example case of how functions and elements of the PSS concept from various domains and at different levels of granularity are causally chained together to achieve the EVC of _reliability in the starting operation of a car sharing vehicle_. The functions and PSS elements represented as boxes in Figure 7 serve as additional contextual background and are not part of the executable graph model. They provide context to the semantic framework, helping to grasp the semantic meaning covered by the graph model. Figure 6: Proceduraland dependency-based reasoning with a graphical representation and examples. Figure 7: Example of a functional structure with heterogeneous causal relationships (procedural in blue and dependency-based in black arrows) between characteristics of PSS elements and influences relevant for the EVC of starting the rental of a sharing vehicle in a car-sharing PSS. In order to make the evaluation logic executable, the vague behavior represented by different scenarios of the PSS concept must be formalized into a structured causal chain. Figure 8 illustrates how different causal relationships and scenarios are symbolically represented based on domain experts' understanding. The formalization of causal relationships requires two key aspects:  
1. The _qualitative and discrete description_ of causal relationships in terms of providing a symbolic representation of each PSS property, life-cycle factor, and purpose/risk;
2. The causal linkage of causally connected scenarios through _deterministic or probabilistic mapping_, showing the likelihood of one scenario leading to another.
## 2 Materials and Methods  
### Observational Forcing Datasets  
#### 2.1.1 Land Cover Map at 10m Resolution Derived from Sentinel-2  
The land cover classification (LCC) global map at 10m resolution was obtained from FROM-GLC10 [7]. The FROM-GLC10 LCC data is developed based on Sentinel-2 data in 2017 with Google Earth Engine, and the overall accuracy of this LCC validated against the circa 2015 validation sample is 73% [7]. The LCC data includes 10 classes (i.e., cropland, forest, grassland, shrubland, wetland, tundra, impervious surface, bare land, and snow/ice). The most advances of the FROM-GLC10 LCC map compared to previous Landsat series-based LCC products are that it provides more spatial detail, better distinguish the forest from shrub or grassland classes, and better performance in coastal areas [7].
# Design and Implementation of a Mixed IoT LPWAN Network Architecture
## 2 Related Technologies  
### Lpwan  
As already mentioned in the introduction section, Low Power Wide Area Networks are presented as an optimal technology for IoT in cases where great autonomy, long range, low cost and transfer of small data amounts [13]. Unlike WiFi, Bluetooth, ZigBee or NFC, it allows longer range links and scalability is greater since supports a higher number of devices. In comparison with cellular technologies such as 2G, 3G or 4G, the energy consumption is lower, which increases the autonomy, the cost of the device is smaller and the connectivity of new devices is simpler [14].  
NB-IoT is a LPWAN technology standardized by the 3 GPP (3 rd generation partnership project) which uses licensed frequency bands assigned to mobile telephony operators and takes advantage of its already deployed infrastructure. Although it is based on a simplified version of the LTE protocol, still requires more energy than other LPWAN solutions because the synchronous communication, QoS handling and orthogonal frequency division multiple access (OFDMA). Despite it has the highest throughput and lowest latency, additional drawbacks are the lowest range and not all suburban areas benefit from LTE coverage [15].  
The LPWAN technologies which are currently receiving the most attention from the industry and the research community are Sigfox and LoRa. Both operate in ISM radio bands (Industrial Scientific Medical). In the ISM bands it is possible to operate without a license provided that the restrictions of radiated power, bandwidth and transmission cycle are met. The band most used in Europe by these technologies is the 868 MHz, defined in the ECC 70-03 [16]. Regulation for this spectrum segment allows an ERP (Effective Radiated Power) of 14 dBm at 27 dBm and an operating cycle below of 1% or 10%. This implies that the same device is not authorized to emit beyond 1% every hour on each frequency, that is to say 36 s, which depending on the duration of the transmission of the message, can suppose a maximum of 6 messages per hour, that is, a message every 10 min.
## 2 Experimental Program
### Methods
#### 2.3.2 Variable Temperature  
The carbonation reaction is an exothermic reaction, and an increase in temperature will accelerate the diffusion rate of CO\({}_{2}\) and enhance the speed of movement between ions.  
For general chemical reactions, the reaction rate increases by 2-3 times for every 10 \({}^{\circ}\)C increase in temperature. In the carbonation reaction of concrete, researchers from various countries have different opinions on the influence of temperature on concrete carbonation. Some researchers think that the carbonation reaction is not sensitive to temperature change at a certain humidity because carbon dioxide diffusion is affected by humidity; some researchers believe that when the temperature is between 20 and 40 \({}^{\circ}\)C, the influence of temperature on the carbonation rate is not obvious. Some studies have also shown that when the relative humidity is 75% and the temperature is increased from 20 to 40 \({}^{\circ}\)C, the carbonation reaction will be greatly intensified. To study the effect of GNP on temperature changes on the carbonation depth of concrete, the experimental setting temperature was 25-45 \({}^{\circ}\)C, relative humidity was 65%, and CO\({}_{2}\) concentration was 6%. According to the empirical formula of Talukdar.S [33], the relationship between the temperature and carbonation time is as follows:  
\[T(t)=25+(20/35)t \tag{2}\]  
where \(T(t)\) is the experimental temperature (\({}^{\circ}\)C) and \(t\) is the carbonation time (days).  
By measuring the carbonation depth of GC0 and GC0.05 at 7, 14, 21, 28, and 35 days, the impact of temperature change on the carbonation depth of concrete was studied.
## 3 An Automated Framework for DSM
### Data Preprocessing
#### 3.1.3 Integration  
The integration of data is the organic formation of links between different types of data such as monitoring data, geometric data, and spatial information, extending the dimensionality of the data. This allows the original static geometric model to be extended to a dynamic model overlaid with monitoring data, which extends the presentation of monitoring data into three dimensions, making it clearer and more realistic, greatly improving visualization, and enhancing easy management.  
The first step involves parsing the model generated using Building Information Modeling (BIM) and decomposing the overall model into several submodels (dam sections, monitoring instruments, etc.), which are the basis for integrating the monitoring data with the model. Managers can access all the data of a submodel by indexing them in the BIM model based on the identifier, which is unique. The model information is stored in an object ('uuid': '2B90C7C4-572C-4263-8966-E48367E6C5D3','name': 'TC2',...). In the data, the most important feature is the ID and sensor number of the submodel, so the parsing process mainly involves the extraction of the 'uuid' and 'name' attribute values of the object. However, with many monitoring sensors, automatically mapping the instruments to the submodels is crucial. Thus, the sensor number is assigned to the submodel during the creation of the model to achieve the association. There are two keywords in the table--ELEMENTID' and 'DCODE'. The first keyword refers to the number of the sensor model in the whole model, and the second keyword is associated with the 'DCODE' of the sensor information table. The tables in the database are related to each other by keywords to form a linked whole between the tables, as shown in Figure 2.
## 5 Conclusions  
In this paper, a single-link flexible manipulator was studied. The partial differential equations of the flexible manipulator were established first, and then the fuzzy backstepping control algorithm was designed, and the algorithm was combined with the RBF neural network. The Lyapunov function was designed, and the stability of the system was proved. Finally, Simulink was used to build a simulation platform, and three different groups of external interference were simulated using the proposed algorithm and compared with the RBF neural network boundary control method and the RBF neural network inversion control method. From the two dimensions of displacement error and velocity error, the two parameters of stable convergence equilibrium position and stable frequency were quantified and compared. The effect of vibration suppression was roughly analyzed by the trend of the curves. The simulation results show that the RBF neural network fuzzy backstepping control method is superior to the two control algorithms the vibration is well suppressed, and the whole system converges to an equilibrium position with less error.  
Future research can consider expanding to three dimension to study the vibration suppression of the manipulators, and adding practical experiments to verify the algo  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Control Method**} & \multicolumn{2}{c}{**Displacement Error**} & \multicolumn{2}{c}{**Velocity Error**} \\ \cline{2-5}  & **Steady Displacement Error (mm)** & **Stable Frequency (Hz)** & **Steady Velocity Error (mm/s)** & **Stable Frequency (Hz)** \\ \hline RBF boundary control & 0.02289 & Approximately equal to 0 & 0.0232 & 0.56 \\ RBF backstepping control & 0.09486 & Approximately equal to 0 & 0.2173 & 0.714 \\ RBF fuzzy backstepping control & 0.00646 & 0 & 0.0354 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the data when the manipulator is under the \(w_{3}\) interference.  
Figure 9: Velocity error of the manipulator under the \(w_{3}\) interference.  
rithm, besides adding a variety of complex vibration types, to make the simulation and experimental environment more realistic.  
Conceptualization, Q.Z.; methodology, Z.W.; validation, W.M.; investigation, P.Z.; writing Z.W. and J.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (62073239).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Conclusions and Future Work
The second set of experiments on real datasets and the third set of experiments on datasets from video-surveillance systems showed that the proposed algorithm using WOA (FS-WOA) was superior to the latest algorithms. Part of our future work involves addressing the need for high computational power by developing faster optimizers and exploring the handling of categorical data and other data formats. Additionally, the measurement tool in this paper is limited to evaluating the imputation process itself using MSE. Future evaluations should include the downstream processes, such as classification or other main tasks, to ensure that the imputation method has a real impact on further analysis. Methodology, M.M.E., K.E. and A.H.A. ; Software, M.M.E. ; Formal analysis, M.M.E., A.H.A. and A.T.; Writing--original draft, M.M.E. and A.H.A. ; Writing--review & editing, M.M.E. and A.T.; Project administration, K.E. All authors have read and agreed to the published version of the manuscript. This research was conducted within the framework of the project \"SAIL: SustAlnable Lifecycle of Intelligent SocioTechnical Systems\" (grant no. NW21-059B). SAIL is receiving funding from the program \"Netzwerke 2021\", an initiative of the Ministry of Culture and Science of the State of North Rhine-Westphalia. The sole responsibility for the content of this publication lies with the authors. The authors declare no conflicts of interest.
###### Abstract  
Coal-grout composites were fabricated in this study using the jet grouting (JG) technique to enhance coal mass in underground conditions. To evaluate the mechanical properties of the created coal-grout composite, its unconfined compressive strength (UCS) needed to be tested. A mathematical model is required to elucidate the unknown nonlinear relationship between the UCS and the influencing variables. In this study, six computational intelligence techniques using machine learning (ML) algorithms were used to develop the mathematical models, which includes back-propagation neural network (BPNN), random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR). In addition, the hyper-parameters in these typical algorithms (e.g., the hidden layers in BPNN, the gamma in SVM, and the number of neighbor samples in KNN) were tuned by the recently developed beetle antennae search algorithm (BAS). To prepare the dataset for these ML models, three types of cementitious grout and three types of chemical grout were mixed with coal powders extracted from the Guobei coalmine, Anhui Province, China to create coal-grout composites. In total, 405 coal-grout specimens in total were extracted and tested. Several variables such as grout types, coal-grout ratio, and curing time were chosen as input parameters, while UCS was the output of these models. The results show that coal-chemical grout composites had higher strength in the short-term, while the coal-cementitious grout composites could achieve stable and high strength in the long term. BPNN, DT, and SVM outperform the others in terms of predicting the UCS of the coal-grout composites. The outstanding performance of the optimum ML algorithms for strength prediction facilitates JG parameter design in practice and could be the benchmark for the wider application of ML methods in JG engineering for coal improvement.  
jet grouting; coal-grout composite; hybrid machine learning models; beetle antennae search algorithm +
Footnote †: journal: Applied Sciences