esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Glacier Mass Balance on River Runoff in the Typical Alpine Basin
## 2 Data and Methods
### Methods
#### 2.3.1 Production of DEM through InSAR Method
In this case, SARscape 5.6.2 software was used to select the SAR image from 12 February 2022, as the master image, and the SAR image from 24 February 2022, as the slave image for registration. Registration is a crucial step in InSAR data processing as it directly affects the accuracy of the final elevation values. Two S-1 SLC split products (master and slave) of the same sub-swath are co-registered using the orbits of the two products and a DEM. The one arc-second SRTM-C DEM was employed as a vertical reference [23]. Second, the process involves conjugate multiplication of the master and slave images to obtain an interferogram. Third, to suppress speckle noise in SAR images and reduce the impact of noise in low-coherence regions, multi-looking is performed on interferograms. Given an Incidence Angle = 39.794\({}^{\circ}\), PixelSpacingRg = 2.33, and PixelSpacingAz = 13.90, the ground range resolution is calculated as 9.7. The azimuth resolution is maintained at a consistent level with the ground range resolution after multi-looking, so the number of looks is set to 1 (Azimuth looks) \(\times\)5 (Range looks). As the number of looks increases, speckle noise gradually decreases, resulting in enhanced information smoothing effects. As the study area is located in a mountainous region, in order to improve the clarity of interferometric fringes and reduce decorrelation noise caused by spatial or temporal baselines, an adaptive Goldstein filter is applied for filtering. Next, to perform phase unwrapping, considering the presence of large areas of low coherence in mountainous glacial regions, the minimum-cost flow method is employed to achieve phase unwrapping. Additionally, to obtain more pixel information, the minimum coherence threshold for unwrapping is set to 0.2. Finally, terrain correction, also known as geometric correction, is the process of removing distortions and aberrations caused by the orbit, sensor, and Earth model from the raw data in radar co-ordinate system. This correction is performed using specific geometric correction methods. The corrected data are then transformed into a map projection, enabling individuals to understand and interpret the acquired geographic information more effectively. The result of the generated DEM compared with NASA DEM and ALOS DEM is shown in the Figure 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sensor** & **Acquisition** & **Pass** & **Polarisation** & **Beam Mode** \\ \hline Sentinel-1A & 12 February 2022 & Ascending & VV & IW \\ Sentinel-1A & 24 February 2022 & Ascending & VV & IW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SAR data used in this study.
## 4 Simulation Results
### Simulation Settings  
Considering a satellite network with \(M\) satellites and \(N\) users randomly distributed over a radius of 300 m. The number of satellites varies from 2 to 5 and the number of users varies from 10 to 40. We assume that each user has only one task that needs to be computed, and the user's bid for satellites is set from 8 to 14. The data size of the task is 100 kb to 1024 kb, and the execution result size of the task is 1 kb to 100 kb. Each task has different requirements for latency, so the tolerated latency of the task is from 0.5 s to 1 s. Since different user tasks have different execution characteristics, \(F_{i}\) is set to 200 to 2000 Mega cycles. The ask of satellites is 10 to 12. The computing capacity of the edge server on the satellites is 20 GHz and the maximum number of services is 15. The bandwidth \(B\) of satellites is 200 Khz [39].
###### Abstract  
This paper presents a time-to-digital converter (TDC) based on a field programmable gate array (FPGA) with a tapped delay line (TDL) architecture. This converter employs dual delay lines (DDLs) to enable real-time calibrations, and the proposed DDL-TDC measures the statistical distribution of delays to permit the calibration of nonuniform delay cells in FPGA-based TDC designs. DDLs are also used to set up alternate calibrations, thus enabling environmental effects to be immediately accounted for. Experimental results revealed that relative to a conventional TDL-TDC, the proposed DDL-TDC reduced the maximum differential nonlinearity by 26% and the integral nonlinearity by 30%. A root-mean-squared value of 32 ps was measured by inputting the constant delay source into the proposed DDL-TDC. The proposed scheme also maintained excellent linearity across a range of temperatures.  
Keywords:field-programmable gate array (FPGA); time-to-digital converter (TDC); tapped-delay line (TDL); dual delay lines (DDL); run-time calibration; differential non-linearity (DNL) +
Footnote †: journal: Applied
# Bisector-Based Tracking of In Plane Subpixel Translations and Rotations
## 4 Results
### Numerical Simulation  
We created a sequence of frames with a rhombus that rotated 2 degrees in steps of 0.01 degrees with reference to a point, and we applied the proposed minimization technique to track the object. We assumed that we were able to obtain an edge binary image in the image-processing stage, and we have only isolated two nonparallel segments from the border of the object at each frame of the sequence. In Figure 4, we show a processed frame of the sequence (see Visualization 1) where both segments, the theoretical and the computed intersection point, the rotation centre, the computed bisector, and the orientation angle are plotted.  
Figure 4: Segments (white) extracted from a rhombus and the obtained bisector (pink line), its orientation and the intersection point (pink cross) together with the theoretical intersection (green cross) and rotation centre (red circle).  
Figure 3: Flow chart for the algorithm.  
We have compared the computed intersections and orientations with the theoretical ones. They show a good correlation, with \(p\)-values \(<\)0.05 in all parameters and Pearson coefficients near one (0.99). This way, we have denoted the linear dependence existing between the computed and the theoretical location vector parameters. In Figure 5, we represent the quotient between them, computed at each frame of the sequence. Note that all are near one. The MSE resulting from the linear least-squared fittings (computed vs. theoretical ones) is 0.0010 for \(x_{c}\) (crosses), 0.0028 for \(y_{c}\) (squares), and 0.0005 for \(\beta\) (circles). With the standard deviation, we can set that the mean subpixel accuracy determining the position and the angle is to be 0.03 px and 0.02 degrees, respectively.
## 2 Research Methodology
### Research Questions  
The role of research questions (RQs) is to select and classify publications by quantifying the presence of main concepts in the current literature. Prominent use and the application of machine learning technologies for predictive maintenance in smart manufacturing have been investigated. Therefore, a cluster of ML techniques, its application, and main results are discussed. The six RQs are as follows:  
**RQ1.How many articles cover the use of machine learning for predictive maintenance in smart manufacturing?**  
The amount of interest in this area, from 2013 to 2019 (early included), was illustrated by quantifying the sources of literature in the field and assuming the publication rate as an indicator, in order to estimate the trend.  
**RQ2.What type of research is being conducted in this area of ML techniques for PdM in SM?**  
The maturity level was formulated. It is assumed that simulation without an industrial test bed may be indicative of a developing effort. On the contrary, complete maturity would exhibit a larger number of studies where solution results have been commented.  
**RQ3.What type of contributions are resulting from publications?**  
An effort was made to understand the specific challenges by analysing the obtained output in publications and by classifying the output in terms of contribution. This question may further help in identifying gaps or trends in the research field. It was assumed that early research efforts may focus on architecture, model, framework, process, or methodologies, whereas more mature research may propose tools.  
**RQ4.What class of equipment is a candidate to be mapped for smart manufacturing, and what is the relation between equipment and type of data under investigation?**  
The specific research approaches that support challenges for manufacturing industries were identified. Therefore, by classifying industrial equipment and components, it is possible to provide consistency of ML in handling degradation and fault prediction, estimation of remaining useful life (RUL), detection quality, performance and health monitoring, and reliability and risk analysis based on different types of data sets.  
**RQ5.What machine learning approaches are the most frequently applied for PdM and for which tasks are they widely used?**  
The most prominent ML technologies for PdM in SM were identified as well as the specific tests for which they were used. Thus, the latest advancements in relevant areas are highlighted and commented on.  
**RQ6.Which type of ML approach shows the best performance in PdM?**  
Successful performances in ML for PDM in SM were reviewed in order to suggest future (incoming) research directions.
## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario  
This section describes the experimental validation of the proposed energy management system built on top of the JFML-IoT framework. The aim is not to evaluate the framework itself, but to validate the performance and responsiveness of a real fuzzy control solution deployed in a realistic but controlled smart building environment.  
To this end, we selected a representative office room and instrumented it with real sensors and actuators to simulate common building dynamics, including variable occupancy, lighting, and indoor air conditions. This environment enables repeatable experiments and clear evaluation criteria without the complexity of full-building variability. Importantly, this setup prioritizes occupant comfort while ensuring energy-efficient responses, consistent with the expert-driven philosophy described in the introduction.  
The primary objectives of this case study are threefold: (i) to confirm the technical feasibility of deploying fuzzy logic controllers on embedded systems using the existing JFML-IoT library; (ii) to demonstrate the scalability and modularity of the system through the integration of multiple sensors and actuators distributed throughout the monitored space; and (iii) to assess whether the system's behavior aligns with the expectations of domain experts in terms of occupant comfort and energy efficiency. By utilizing the FML, the fuzzy control logic is defined in a transparent and interpretable format, ensuring reusability and consistency with expert-defined knowledge.  
To illustrate these capabilities, we begin by describing the physical deployment scenario, including the number and distribution of sensors and actuators within the environment (Section 5.1). We then present the technical implementation of these components within the JFML-IoT framework, detailing how input and output variables are instantiated and mapped to the corresponding hardware interfaces (Section 5.4). Next, we describe the design of the fuzzy rule-based system (FRBS) through collaboration with a panel of domain experts, covering the definition of linguistic variables, terms, and fuzzy rules (Section 5.2). This is followed by the encoding of the FRBS in FML, in accordance with the IEEE Std 1855-2016 specification (Section 5.3). Several test scenarios are then evaluated to assess the system's behavior under diverse environmental conditions and to validate its control effectiveness (Section 5.5). Finally, Section 5.6 provides a summary of the overall system behavior across all scenarios and discusses its runtime performance on embedded platforms, with insights into inference latency and resource usage.
## 3 Results and Discussion  
### Characteristics of Sugar Beet Leaves before Ensiling  
The properties of SBL before ensiling are summarised in Table 1. The water content of SBL was seen to be as high as 81.55%, which is consistent with the report of [PERSON] et al. (2012). The additive increased the total solids of the substrates in direct proportion to the substrate to additive ratio, as was also reported during the ensiling of maize (2018). A high concentration of WSC (mainly glucose) was also observed in the substrate before ensiling, an indication that SBL can be easily ensiled (2018). The crude protein content of SBL in this study was less than the 22.8% reported by [PERSON] (2018). The crude fibre content was higher than the 9.9%TS reported by [PERSON] and [PERSON] (2018). Differences in crude protein and crude fibre content could be a result of a difference in varieties, growing conditions, etc. Cellulose content was seen to be higher than hemicellulose content, as is common with most lignocellulosic biomass (2018), and falls within the range reported by [PERSON] et al. (2018) for SBL. TS and VS content of SBL before ensiling were consistent with the values reported by [PERSON] et al. (2018) and [PERSON] et al. (2018). The pH value of the leaves before ensiling was 4.98, which is lower than the 7.87 previously reported by [PERSON] et al. (2018), probably due to a higher concentration of lactic acid arising from handling technique and time difference between harvest and ensiling in the laboratory. The presence of lactic acid and acetic acid is an indication that initial fermentation started in the substrate before ensiling (2018).  
The carbon/nitrogen ratio of the SBL in our study is comparable with the 12.0 reported by [PERSON] and [PERSON] (2018) but lower than the 14.0 reported by [PERSON] et al. (2018). The ash content of SBL was above the range of 9.6 to 19.5%TS reported by some authors depending on species and maturity (2018), but was consistent with the report of [PERSON] et al. (2018) who obtained an ash content value of 3.2%FM. In terms of fresh matter, the ash content for the control, A50:1, B150:1, and C500:1 silages were 2.94, 3.16, 3.10, and 3.26%FM, respectively.
## 3 Results  
### Spectral Separability Analysis  
The analysis of spectral separability based on normalized median spectral-temporal signatures provided valuable insights into the similarity patterns among the land cover classes. The resulting distance matrix (Table 2) illustrates the pairwise distances between the classes, calculated using the Euclidean distance metric.  
The analysis based on the Euclidean distance metric revealed clear relationships among the land cover classes. Specifically, classes VEG and HZ demonstrated the highest similarity. Classes NPS and AL showed moderate similarity to each other but were distant from classes VEG, HZ, and MAA, with the latter displaying less separability from the VEG and HZ cluster. NPS and AL were distinctly separated from classes containing various types of tree vegetation (Figure 8).  
The cluster analysis generated three levels of distinction for hazelnut grove discrimination. At the first level, a cluster separated NPS_AL from MAA_VEG_HZ. At the second level, another cluster distinguished MAA from VEG_HZ, and at the final level, a separate cluster isolated VEG from HZ. As a result, to train the different models, all observations for each cluster node were selectively filtered. Initially, AL and NPS were separated from  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **VEG** & **HZ** & **MAA** & **AL** & **NPS** \\ \hline VEG & 0 & 3.4 & 5.7 & 9.5 & 8.9 \\ HZ & 0 & 5.7 & 8.0 & 8.5 \\ MAA & & 0 & 6.4 & 7.6 \\ AL & & & 0 & 6.8 \\ NPS & & & & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Matrix of Euclidean distances on the normalized spectral–temporal signatures of different land covers.  
Figure 7: Workflow for characterization of different hazelnut areas using unsupervised K-means Clustering.  
MAA, VEG, and HZ (RF1). Subsequently, MAA was separated from VEG and HZ (RF2). Finally, all VEG observations were separated from HZ (RF3).
# Anomaly Identification of Wind Turbine Yaw System Based on Two-Stage Attention-Informer Algorithm
## 2 Preparatory Knowledge  
### Basic Mechanism of Yaw System  
Yaw is the process by which the nacelle of a wind turbine adjusts horizontally to face the wind, ensuring that the rotor always faces the direction of the wind. From a control perspective, the yaw system is divided into passive and active types. The passive yaw system utilizes the torque generated by the wind acting on the turbine blades to automatically align the wind turbine with the wind direction through aerodynamic effects. When the wind direction changes, the wind force on the blades will produce a rotating torque, driving the entire wind turbine to change direction. Active yaw is the use of electric or hydraulic drive to complete the yaw mode of wind action. At present, most of the yaw drive structures of wind turbines are horizontal axes, active types, and electric-driven rolling yaws [22; 23]. The yaw system is usually located at the top of the engine room of the wind turbine, near the wind wheel and the tower, mainly composed of yaw gear rings, yaw motors including electromagnetic brakes, yaw calipers, yaw pinions, yaw reducers, and other parts. During power generation, the yaw system coordinates with the wind turbine's control system to keep the rotor constantly facing the wind, maximizing the utilization of wind energy, and improving the power generation efficiency of the wind turbine. When the wind and storm exceed the cutting wind speed, the unit stops, and the load of the unit is reduced by yawing to ensure the safety of the unit.
# Diverse Roles of Previous Years' Water Conditions in Gross Primary Productivity in China
## 2 Materials and Methods
### Research Data and Processing  
#### 2.2.1 Remote Sensing Data  
The scarcity and relatively short duration of field experiments (i.e., the carbon flux data obtained by eddy-covariance flux towers (compiled in FLUXNET)) make it difficult to obtain a full picture of how vegetation responds to climate [47; 48], and MODIS-GPP data with high temporal-spatial resolution is always calculated on remote sensing data. Thus, in our research, to characterize the level of vegetative productivity, this study chose MOD17A2H GPP for the period from 2001 to 2016; the product is a cumulative 8-day composite of values with 500 m pixel size based on the radiation use efficiency concept ([[http://search.earthdata.nasa.gov/](http://search.earthdata.nasa.gov/)]([http://search.earthdata.nasa.gov/](http://search.earthdata.nasa.gov/))). Before the analysis of remote sensing data, the original 8-day GPP data were preprocessed by the MODIS Reprojection Tool (MRT), which included image mosaic and projection transformation. After that, we calculated the annual GPP based on these 8-day GPP products. To compare the relative changes in vegetation, we detrended the GPP data by using a linear regression model to eliminate the external factors such as the CO2 fertilizer effect and the influence of vegetation factors such as forest age. Specifically, we fitted the original GPP data from 2001 to 2016 to get their linear trends and then minus their trends to get GPP data without trends.
## 8 Discussion and Further Required Research
### Applicability of the Proposed Approach
Given the numerical modeling results for the theoretical lakes (Section 5.1) and the results produced for the Chehalis Lake test (Section 6), it is concluded that the proposed approach is applicable for lakes that are flanked by steep unstable slopes, for which failures with volumes ranging from thousands to a few million cubic meters can be expected. However, this assertion requires validation with additional historical case studies from a variety of mountain regions, as well as fiords with basin characteristics similar to alpine lakes. Extending the dataset to other mountain regions for which good data are available (e.g., Canadian Cordillera or a select part of the Andes) to newly formed glacial water bodies [39] and artificial reservoirs would also improve the analysis and results.
# Noise Prediction Using Machine Learning with Measurements Analysis
## 2 Materials
### Methods  
#### 2.2.1 Feature Extraction  
We derived and extracted features from the Leq time data for model training. The continuity of the noise generation process, which is affected by working days and working time, resulted in the input vector \(X_{t}\) containing temporal features; the output variable \(Y_{t+1}\) is the Leq value for the next minute. In this experiment, we chose to include days in a week, hours in a day, whether the day was a holiday, whether it was a Saturday or a Sunday, and the previous one minute or two minutes of historical noise frequency data for sensor 8. Thus, a total of 21-dimensional features were input for training (as shown in Table 1).
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 7 Performance Evaluation
This highlights the importance of studying networking protocols under different fading conditions. Notably, the AMPAD exhibited an approximately 0.13 higher DAR performance compared with the other approaches under intensive fading conditions (\(K=1\)). At high \(K\) values (reliable links), both the AD and ETX demonstrated comparable performance to the AMPAD, while the HOP exhibited inferior behavior. Hence, one of the strengths of the AMPAD is its versatility in coping with different fading conditions. Figure 10: DAR vs. Rice factor \(K\). Lower values of \(K\) indicate less reliable links. All approaches performed better with increasing \(K\) values. Figure 9: DAR over time under normal traffic and traffic burst, with 0.75 packets per second during the first 6 h and 48 packets per second during last 2 h. Monitoring turned off after 4 h. The performance of all approaches degraded during bursts.
## 4 Adaptive \(\alpha\)-Value Computation  
When a multi-robot exploration process is going to run under communication constrained conditions, choosing between _only exploring_ or _exploring preserving connectivity level_ is a crucial decision. The first choice would be suitable when connectivity is out of the question, or it is impossible for a robot to keep connected and explore at once. In such a case, connectivity does not play any role in the decision-making process. On the contrary, the second choice is suitable when it is necessary to interleave high-performance exploration (minimising the total exploration time) and acceptable connectivity level (avoiding robot isolation as much as possible).  
To this end, the human operator is let to use his application field expertise in order to influence the robot decision--defining a criterion to balance the importance of both objectives--by merely setting a parameter before the exploration starts.  
Therefore, since \(\alpha\) and \(\beta\) parameters determine the behaviour of the robots concerning target selection, two questions come up: (i) How can the value of those parameters be defined in order to ensure the applicability of the human-operator criterion along the exploration process? (ii) Should these values be adapted during the exploration process?  
Henceforth, the task selection framework and the human-operator criterion are formalised. Besides, several proofs to demonstrate the existence and correctness of an adaptive \(\alpha\)-value that makes the robots behave following the criterion mentioned above are conducted.