esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Research Direction  
Satellite propagation channels have significant importance in optimizing the coverage, reliability, and capacity performance of satellite communication. Many works in the literature provide different models to be used for designing an opportune satellite channel model; however, despite this, many research issues remain open. Measurement campaigns are beneficial for the formulation of effective satellite channel models, evaluating the performance of their communication systems, and network planning. However, this type of studies are strongly dependent on regional environments. In fact, many works in the literature base their studies on different types of scenarios, such as urban, suburban, rural open areas, and so on, in order to capture the correct issues of the satellite propagation phenomena. Many other studies are based on stochastic approach, and propose satellite channel models based on the Markov chain approach in order to be able to capture adequately the different propagation characteristics of the satellite channel.  
In the last years, with the development of 6G technology and the new hierarchical architectures in which different wireless channels are contained, the studies on satellite channel models at different and always higher frequencies is of fundamental importance in order to guarantee the best performance for the users' applications [8; 63; 64]. Moreover, the birth of the new Cubesat technology represents another important motivation for continuing to analyze, study, and propose new channel models that are able to always boost network performance [65]. As shown in [66], the new frontier of satellite networks has progressed toward optical communications. In this work, the authors have investigated a robust power-allocation strategy for the downlink, taking into account the effects of the atmospheric impairments. Moreover, they have evaluated their proposal through simulative campaigns using experimental channel measurements from the ESA project on an optical satellite called ARTEMIS. Other studies [67; 68] show the use of non-orthogonal multiple access (NOMA) into an integrated satellite-terrestrial network in order to exploit the advantage of this technique for being able to provide the required QoS to the satellite  
Figure 8: Multi-state Markov channel model.  
Figure 9: PER discretization for multi-state Markov model.  
users and to guarantee better performance for the overall system. A key role in both satellite platforms and multi-layer architectures is played by scheduling schemes that determine an important improvement on the sytem performance, as shown in [69]. Also, massive multiple-input multiple-output (MIMO) is a promising new technology that is largely used and proposed in satellite scenarios, thanks to its capacity to enhance spectral efficiency, exploiting the slow-varying statistical channel state information (CSI) at the transmitter [70].
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 4 Result and Discussion
### PCA-PSO-LGBM Model Prediction Results
These results demonstrate that the PCA-PSO-LGBM model proposed in this paper effectively extracts information from shield excavation parameters and geological parameters, achieving precise predictions for the roll angle and pitch angle. Even if the prediction error of the roll angle of a few rings is greater than 0.20\({}^{\circ}\), it only accounts for 10-20% of the average roll angle, which is completely within the acceptable range of engineering errors. The significance of this work lies in the fact that all selected shield excavation parameters can be directly adjusted. Therefore, we can continuously try various combinations of excavation parameters based on the geological parameter information of unconstructed rings before shield excavation. By satisfying other requirements, we can achieve suitable values for the roll angle and pitch angle, ultimately determining an excellent combination of excavation parameters. This approach avoids the need for the continuous trial and adjustment of excavation parameters during the shield excavation process, significantly reducing construction risks. As the shield machine continuously advances, new datasets will be promptly added to the model's database. In this way, the hyperparameters of the model can be updated regularly to obtain more accurate prediction results. At the same time, data from other similar projects can be introduced as the training set to further reduce the model prediction error. The PCA-PSO-LGBM model was optimized, trained, and predicted on a personal laptop. The computer processor was AMD Ryzen 5, the memory was 16 GB, the system was Windows 10 (64-bit), and the Python version was 3.7.13. The hyperparameter optimization times for the roll angle and pitch angle were 104 s and 188 s, respectively. The model training and prediction time was only a few seconds, while the time for a single shield tunnelling cycle varies from several hours. Therefore, the model can be updated in a timely manner and provides quick feedback, showing good real-time applicability. Figure 11: Roll angle prediction results: (**a**) prediction performance on the training, validating, and testing set; (**b**) comparison between actual and predicted values in the testing set.
## 3 Results and Discussion of the Research
Although the presented technical solution does not allow such a significant relative rotation of the wheels on the same axle relatively to each other as it is in the case of using independently rotating wheels described above, it does combine advantages of the traditional wheelset with wheels pressed on an axle with advantages of independently rotating part of the wheel flange, which reduces negative effects related with rail vehicle running through curves with small radii [29]. Clarification of the features of the movement of wheels with various designs on the rail with two-point contact points taking into account slippage in the main contact, made it possible to specify the following:  
* The level of slippage in the main contact of the perspective wheel design insignificantly influences the value of slippage speed in its flange contact. For the traditional wheel design, this effect is more significant
* The results of the calculations confirm the possibility of a significant reduction of the slip rate in the flange contact for the perspective wheel design compared with the traditional wheel design. This gives reason to expect a reduction of the corresponding friction forces power in these contacts and a reduction of the resistance to move the rail vehicles if they were equipped with wheels with the perspective wheel design, especially when they move in curved sections of a railway track  
In further research, a great effort is to proceed in the study by creating a model of the presented technical solution of the wheel with the perspective wheel design in MBS software [30; 31; 32; 33]. A created model will allow us to compare dynamic properties of a wheelset or a rail vehicle equipped with wheels with the traditional wheel design with dynamic properties of a rail vehicle, whose wheels the perspective wheel design will be used on. In MBS software, it will also be possible to define the real geometry of a track, including track irregularities [34], to change appropriate parameters, such as running speed, radius diameters, etc. In this way, it will be possible to verify the achieved presented results. We suppose, the calculation of the contact surface of the wheel will be different, because for the two-point contact, i.e., the wheel tread and the flange, a relative motion of these two contact points will occur. The objective of the research will be to investigate, whether it will be possible, and with what reliability, we can apply known algorithms for calculation of the wheel/rail contact [35; 36; 37]. Furthermore, the research will continue in terms of studying of structural properties of the perspective wheel design. It is obvious, the distribution of stress, as well as heat in the wheel with the perspective design, will be significantly different in comparison with the wheel with the traditional design [38; 39; 40]. Undoubtedly, attention should also be paid to the issues of traffic safety of rail vehicles with wheels of the perspective constructive scheme [41].
# The Importance of Incorporating Denitrification in the Assessment of Groundwater Vulnerability
## 1 Introduction  
Groundwater pollution represents one of the main concerns all over the world, especially for those regions where agriculture is the primary activity. This is mainly due to the high dependence of all human assets (agricultural, industrial, municipals, etc.) on groundwater resources, that could make them highly susceptible to external pollution [1]. The intensive use of groundwater resources,indeed, influences not only water availability but also groundwater quality, bringing to its progressive deterioration over time [2].  
One of the most common pollutants, generally associated with groundwater degradation, is surely nitrate [3]. The European Commission [4] established that a nitrate concentration higher than 25 mg/L is enough to consider water resources at risk of pollution, and 50 mg/L of nitrate is fixed as the maximum value not to be exceeded in many regions. Intensive agricultural practices, together with domestic wastewater disposal, industrial discharge and urban activities have been identified as the main sources of nitrate in groundwater [5; 6; 7; 8]. Generally, the alluvial plains are the areas more characterized by intensive agricultural activities and accordingly they are often affected by groundwater nitrate pollution [9; 10; 11; 12].  
When the assessment of nitrate pollution in groundwater has to be done, the chemical-biological processes of contaminant attenuation are often not properly considered. In an agricultural environment, it is possible to distinguish between different nitrogen (N) inputs/outputs of the system. Among the inputs, fertilizer/manure application, atmospheric deposition and nitrification are considered, while among the outputs, denitrification and surface run-off can be recognized as the main removal processes [13]. Considering the output processes, the degradation of nitrate through denitrification can potentially reduce the N levels in both deep and shallow aquifers [14]. Denitrification relates to the dissimilatory reduction, done by aerobic bacteria, of the ionic nitrogen oxides (NO\({}_{3}\)\({}^{-}\) and NO\({}_{2}\)\({}^{-}\)) to the gaseous oxides (NO and N\({}_{2}\)O), which may be further reduced to dinitrogen (N\({}_{2}\)). This process is emphasized in shallow aquifers influenced by surface water recharge, where denitrification could efficiently attenuate NO\({}_{3}\)\({}^{-}\) concentration in groundwater. Factors like the dissolved organic carbon content in the soil, the presence of peat layers (that strongly enhance reducing condition) [15], the vegetation type and the soil/aquifer temperature, can directly influence the denitrification rate making it highly variable in time and space [16; 17].
# Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
## 4 Emerging Solutions and State-of-the-Art Approaches
### Federated Learning and Edge AI  
Federated learning (FL) has emerged as a powerful technique for collaborative model training across distributed, resource-constrained devices without requiring raw data to leave each node [29]. In a typical FL setup, each device locally trains a model using its own dataset and periodically sends only model updates or gradients to a central server, which aggregates and updates a global model [27]. This architecture can significantly reduce the bandwidth burden associated with traditional centralized training and helps preserve privacy, as personal data rarely traverses the network in raw form [9]. However, many challenges remain, including dealing with heterogeneous hardware, unbalanced data distributions, and asynchronous update cycles.  
Beyond federated learning, the broader concept of edge AI seeks to partition intelligence tasks among end devices, edge servers, and the cloud in a hierarchical fashion [76]. Low-latency tasks or privacy-sensitive computations are handled locally, while more computationally demanding operations can be offloaded to powerful edge servers when necessary [60]. This flexible approach acknowledges the heterogeneity of device capabilities within an IoT ecosystem and allows for more complex collaborative scenarios [61]. For instance, certain data might be partially processed on a microcontroller and then encrypted and forwarded to an edge server for refined analysis [63]. The design of such distributed architectures involves careful orchestration of resource scheduling, data caching, and robust communication protocols [9].
# A Survey of Machine Learning Methods for Time Series Prediction
## 6 M5 and M6 Forecasting Competitions
### M5 Forecasting Competition
#### 6.1.2 M5 Uncertainty Competition  
The Uncertainty competition [91] tasked participants with forecasting the distribution of realized values, requiring predictions of nine quantiles (0.005, 0.025, 0.165, 0.250, 0.500, 0.750, 0.835, 0.975, and 0.995). The Weighted Scaled Pinball Loss function was used to evaluate the performance. Although this competition was less popular, attracting 892 teams, the reliance on LightGBM models remained consistent, with four of the top five submissions incorporating it in their frameworks. Brief insights from the top five models include the following:  
1. First Place: utilized 126 LightGBM models, one for each quantile and aggregation level;Second Place: combined recursive LightGBM models, statistical methods, and simple time series forecasting techniques;  
Third Place: employed a hybrid approach integrating LightGBM and neural networks;  
Fourth Place: used two LSTM-based neural networks;  
Fifth Place: implemented 280 LightGBM models in a comprehensive ensemble.  
A Monte Carlo simulation, used by the sixth-place team, was the only top-50 method not involving LightGBM, XGBoost, or neural networks. The findings from the Uncertainty competition mirrored those of the Accuracy competition, reaffirming the dominance of LightGBM and the importance of model combination. A notable observation was the stark contrast in participant expertise: while the Accuracy competition was won by an undergraduate student with limited knowledge in the domain of retail sales, the Uncertainty competition was dominated by Kaggle masters and grandmasters with strong statistical backgrounds.
## Materials and Methods
### Radiographic Analysis Using CBCT  
To measure the alteration of the alveolar ridge height and width, consecutive CBCT images were acquired at baseline (before extraction) and at 4 months after ridge preservation. All CBCT images were acquired in identical conditions, and all of the radiographic measurements and analyses were performed by one examiner ([PERSON]-S.S.) using InVivoDental software, Version 5.0 (Anatomage Inc., San Jose, CA, USA). All measurements were performed based on imaginary lines parallel and perpendicular to the vertical and horizontal reference lines previously described in the literature [29]. The buccal and lingual ridge heights were measured from the most apical point of the alveolar socket to the height of the crest (Figure 5a). The ridge width was calculated at 1, 3, and 5 mm below the highest crest in the coronal plane (Figure 5b). The data points and lines were determined on the baseline CBCT images and duplicated onto the 4-month follow-up CBCT to accurately quantify the height and width of the alveolar ridge (Figure 6).  
Figure 4: Volumetric analysis of hard tissue. The three-dimensional cone beam computed tomography (CBCT) images were taken at baseline (**a**) and at 4 months after ridge preservation (**c**). The regions of the postextraction socket/ridge preservation sites were determined. The cropped preoperative hard tissue (**b**) and postoperative hard tissue (**d**) volumes were measured in cubic mm (mm\({}^{3}\)) via InVivoDental software, Version 5.0 (Anatomage Inc., San Jose, CA, USA).  
Figure 5: Schematic drawings of radiographic landmarks for measurement of the height and width of the alveolar ridge on CBCT images. The vertical reference line passed through the middle of the alveolar socket from the apex, and the horizontal reference line was drawn perpendicular to the vertical line. (**a**) The buccal and lingual bone heights were measured based on these reference lines relative to the marginal crest and root apex. (**b**) The horizontal ridge width was recorded at 1, 3, and 5 mm from the highest marginal crest.  
Figure 6: Cross-sectional images of the baseline and 4 months after ridge preservation. (**a–c**) Preoperative CBCT images of Groups A, B, and C, respectively. (**d–f**) Four months after extraction and ridge preservation for the Bio-Oss collagen, demineralized dentin matrix (DDM), and recombinant human bone morphogenetic protein-2 (rhBMP-2)/DDM groups. The buccal bone height (BBH), lingual bone height (LBH) and horizontal ridge width at 1, 3, and 5 mm from the alveolar crest (RW-1, RW-3, RW-5) were measured from the reference lines shown in Figure 5.
## 3 Experimental Methods
### The Application of Fuzzy Control
#### 3.2.3 Defuzzication Design  
After fuzzication, defuzzication is the next step to convert the results back to explicit values for further application. In general, the commonly used defuzzication methods include Center of Gravity Defuzzification, Center of Sum Defuzzification and Middle of Maxima Defuzzification. In this work, the Distributed Gravity Defuzzication is taken. As shown in Equation (8), the variables, \(n\), \(y_{j}\), and \(\mu_{c}\left(y_{j}\right)\) indicate the quantization levels, the \(j\)th quantization value and membership value of \(y_{j}\) in the fuzzy set (\(C\)). Furthermore, \(y_{cog}\) is the output of the fuzzy controller.  
\[y_{cog}=\frac{\sum\limits_{j=1}^{n}\mu_{c}(y_{j})\times y_{j}}{\sum\limits_{i =1}^{m}\mu_{c}(y_{j})} \tag{8}\]  
Figures 10 and 11 are two trialing results generated by our model. Here, \(R_{V}=5\) k\(\Omega\) when the ambient light intensity is 497 Lux and the digital variable resistance \(D_{n}=128\); while \(R_{V}=1.015\) k\(\Omega\) when ambient light intensity is 989 Lux and the digital variable resistance \(D_{n}=26\). Table 3 shows some data of \(R_{V}\), \(D_{n}\) and \(Lux\) obtained in our experiments.  
Figure 12 illustrates the characteristic curves of fuzzy controller inputs and outputs after fuzzication. To simplify programming on microcontrollers, we further divided the curve into seven domains. The equation for each domain is listed below: (here, \(m\) = slope, \(Lux_{n}\) = ambient light intensity, \(Lux_{0}\) = initial ambient light intensity, \(D_{n0}\) = initial value of the digital variable resistance).  
\[PWM=m\times(Lux_{n}-Lux_{0})+PWM_{0} \tag{9}\]Figure 11: When \(Lux\) is 989 Lux, \(D_{n}\) is 26.  
Figure 10: When \(Lux\) is 497 Lux, \(D_{n}\) is 128.
## 3 Results and Discussion
### Risk Evaluation
#### 3.2.2 Comprehensive Evaluation of Soil Heavy Metal Pollution
The contents of As, Cu, Zn, Ni, and Cr were relatively high not only in the surrounding areas of the slag field, but also in the garden and dry land, indicating that As, Cu, Zn, Ni, and Cr in the garden and dry land are affected by other factors in addition to the slag field. It is hypothesized that this could be due to the differences in land use types, fertilization, and agricultural production methods since there are no other industrial and mining pollution sources in the affected area [18]. Agricultural irrigation using surface water can affect the content of heavy metals in the topsoil of the region. Figure 2: Spatial distribution of heavy metals in soil. According to the comprehensive pollution evaluation on the spatial distribution of soil heavy metals, the pollution degree was divided into 5 grades (Figure 3). The comprehensive pollution index of spatial distribution was consistent with the results of spatial interpolation distribution of soil heavy metals (Figure 2). The topsoil in the majority of northeast and southwest of the study area showed the grade of No Pollution or Slight Pollution. The pollution index near the central and northern part of the slag field, the National Highway 108 and the lower reaches of the Lima River was relatively high. The central part of the study area showed Slight Pollution and the degree of pollution showed a trend of gradually increasing from the center of the study area to the surrounding areas. From the center to the northeast or southwest, the degree of pollution was lighter, and from the center to the northwest or southeast, the degree of pollution was more serious. This is related to the distance from the slag field, soil physical and chemical properties, regional ecological climate, land use types, etc. The accumulation of heavy metals in the soil threatens the environmental quality and requires further vigilance. Overall, the comprehensive pollution index of heavy metals in the topsoil of the study area was between 0.59 and 3.69, and the heavily polluted sites were distributed in the northwest (site #3, 27, 28, 30, 34, 46, 50, 61, 62 points, Figure 2) and southeast (site #59 points, Figure 2) of the study area. Sites with No Pollution or Slight Pollution were mainly distributed in forest and areas with vegetation coverage, while sites with Moderate Pollution or Heavy Pollution were mainly distributed in crop planting areas and areas near the slag fields. The zoning of soil heavy metal pollution in the affected areas of slag field can facilitate risk assessment and regional management, and control of heavy metal pollution in soils.