esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
## 3 Results
Considering the Toronto test set, however, the MCC values of the HOG-SVM and FCN models were significantly higher than the value of the HOG-ELM model (0.826 and 0.821 vs. 0.664), which suffered from lower sensitivity and NPV due to the presence of small clusters of FN pixels inside the clusters of pixels representing the building footprints, as shown in Figure 6.  
Evaluating the aggregated metrics, the best values were achieved by the FCN-based classifier: the FCN-based building detection method exhibited good overall detection reliability. It was found that such an approach produces good-quality results in the different urban contexts considered, as demonstrated by the values of the F1 score (0.831), AUC (0.960), and MCC (0.834), which are fairly close to the ideal value of 1. Figures 7 and 8 illustrate the detection abilities of the three binary classifiers comparing ROC curves calculated on the output scores. The ROC curves of the SVM-based (blue lines), ELM-based (red lines), and FCN-based (orange line) classifiers plotted the dependency of the true-positive rate (sensitivity) on the false-positive rate (1--specificity) obtained at various thresholds. Globally, all the final models could successfully detect the building footprints within the study areas using only DSM patches as input, presenting good predictive ability with AUC \(>\) 0.8 [84]. The SVM-based classifier produced accurate predictions in Toronto (AUC = 0.96), fairly good predictions in Potsdam (AUC = 0.83), but minor predictions in Vaihingen (AUC = 0.78) patches. The ELM-based classifier performance varied according to the considered urban area: the prediction accuracy on the Potsdam test set slightly surpassed the SVM accuracy (AUC = 0.84 vs. 0.83), whereas the test results on Vaihingen and Toronto  
Figure 8: Receiver operating characteristic (ROC) curves of the three models for the different urban areas: (a) Vaihingen, (b) Potsdam, and (c) Toronto. Figure 7: Receiver operating characteristic (ROC) curves for the aggregated data, showing the relationship between true-positive and false-positive rates estimated by the three models compared to the ground truth. datasets were the poorest among the considered methods, albeit being still acceptable (AUC = 0.71 and 0.77). The FCN curves were close to the \"perfect\" classifier, with AUC values between 0.95 and 0.97 for each urban area.
## 3 Results and Discussion
### tortuosity-Minimum Throat Length Relationship
Overall, these findings highlight the contrasting pore-scale flow dynamics between sandstone and carbonate rocks, with sandstone exhibiting a more straightforward dependency of tortuosity on the minimum throat length. This is due to the intricate dependence of flow tortuosity on pore connectivity, topological order, and microscale anisotropy, factors not fully captured by porosity alone. Consequently, samples with identical effective porosities can have vastly different internal architectures, resulting in significantly varied flow path lengths. Therefore, while effective porosity filters out non-conductive pore spaces, it alone cannot represent the spatial complexity of flow paths characterized by flow tortuosity.
## 3 Robotic and Autonomous Inspection  
Robotic and Autonomous Systems (RAS) are also used for inspection and maintenance. They impress with their high levels of safety and efficiency and are mainly used for rolling stock or railway track maintenance [73; 74]. These systems can be equipped with diverse sensor technology and thus perform a wide variety of tasks. Of course, this also includes optical sensors, primarily cameras, and laser systems.  
Unmanned Ground Vehicle (UGV) and walking vehicles are vehicles that operate without a human on board. There are many different designs for this kind of carrier platform. These can operate completely autonomously but are often controlled remotely. An example would be ANYmal, which is used for the inspection of rail vehicles as it can easily move under trains and in cabins [75]. Felix [76], Railpod [77] and RIIS1005 [78] are just a few robots developed for use on railroad infrastructures. They were developed to fully automate the data quality of switches and crossings and track inspections. West Japan Railways has unveiled a \"humanoid robot\" to work on heavy machinery on its lines [79]. The prototype does not work autonomously but is controlled from a cockpit by a human. Its task is to help with the maintenance of the equipment. This is intended to increase safety for workers. Ref. [80] shows the use of an autonomous robot to inspect dangerous areas, for example railway tracks after a rockfall. It moves along the wires on the overhead line masts to check the condition of the tracks. Several of these robots can hang on steel cables at the same time and are independent of the weather; only the sensors used are affected by the weather.  
The Unmanned Aerial Vehicle (UAV) is a flight platform in which various sensors can be integrated. It is externally controlled but can also fly autonomously. UAVs are easy to operate and are able to fly and cover large areas quickly. There is no need to enter  
Figure 10: Result of the Sector Profile Scanner (SPS) from Fraunhofer IPM. The 3D image is available immediately after the train has passed and can be analyzed for geometrical irregularities.  
the danger zone of the track. The disadvantages are its limited battery life and loading capacity, dependency on good weather conditions, and risk of dropping [81]. Nordic Unmanned ASA offers an UAV specialized for track/catenary/infrastructure inspection, surveillance, and safety [82]. The UAV is combined with an UGV. Ref. [83] uses a UAV equipped with cameras to inspect railway catenary. Insulators are automatically detected using the image data and AI. Insulators were identified from a distance of 20 m in such a way that the condition of the insulator is recognizable (Figure 11). Fraunhofer IPM is working on the autonomous inspection of infrastructure (in this case bridges) using optical sensors. Both UAVs and UGVs are used. The systems use cameras and laser scanners and employ computer-assisted evaluation and analysis processes using machine learning and artificial intelligence [84; 85; 86].  
An overview of fields of application for robotic and autonomous inspection systems can be found in [81; 87].
# A Short-Term Forecasting Method of Inter-Frequency Clock Bias for GPS Block IIF Satellites
###### Abstract
Based on these, a high-precision IFCB short-term prediction model was established for the GPS Block IIF satellites to handle the problem of overestimation of the parameter to be estimated in multi-frequency PPP. We started with the triple-frequency uncombined PPP model that takes the IFCB into account. Subsequently, the estimation approach of the IFCB was introduced, and some important features were highlighted. Afterward, a short-term forecast model was constructed on the basis of analyzing the time-varying characteristics of the IFCB. Finally, a variety of evaluation methods were used to verify the feasibility of forecasting the IFCB, and the main points and the conclusions are summarized.
# Using Plastic Waste in a Circular Economy Approach to Improve the Properties of Bituminous Binders
## 2 Materials and Methods
### Methods  
#### 2.2.1 Thermal Pyrolysis of HDPE in a Reactor/Separator Unit  
As mentioned before, pyrolysis has several advantages, namely environmental, arising from the fact that it takes place in an oxygen-free atmosphere; therefore, it does not produce dioxins and has no carbon monoxide and carbon dioxide emissions [44].  
Different reactors can be used in pyrolysis, but the reaction system used in this research was the one developed at DL--DEQ--IST, depicted in Figure 1a, which also includes its schematic diagram (Figure 1b). In this thermal cracking system or thermal pyrolysis (which implies a relatively low cost, deviating from the trend referred to in [11]), the production of waves represents the most significant fraction (from 40% to 92% [32]), with temperature and reaction time being two determining parameters in terms of the quantity available in each \"batch\". In general, this amount of wax is higher the lower the maximum temperature used; however, as the pyrolysis temperature increases, softer waxes tend to be produced. As mentioned in [34], for temperatures in the range of 430-500 \({}^{\circ}\)C, the waxes obtained in this system can represent 40-80% of the initial mass of the plastic waste. So, as the properties and yield of the wax produced are dependent on the pyrolysis conditions, several pyrolysis temperatures were tested, and to maximize the waxes' production, a set-point of 470 \({}^{\circ}\)C was chosen in this research (the temperature profile in the reactor had an average of 405 \({}^{\circ}\)C, for 90 min). The waxes produced under these conditions provided the best results in a preliminary study to evaluate their influence on the mixture with several types of bitumen. In this case, the mean percentage obtained in the wax production was \(\approx\)92%, and the liquid fraction (oil) was around 4%.
## 4 Results
### Factor Identification by eGA-DT
#### 4.5.3 Model Comparison  
1. The performances under different bus stops  
Considering the transitivity of bus bunching, later bus stops may have more fluctuating headway. Therefore, the model performance of eGA-DT, DT, and ET on all bus stops of route 113, except the initial and terminal stops, was analyzed, as presented in Figures 11 and 12. The MAE was utilized to evaluate the model performance. It could be clearly seen from Figure 11 that eGA-DT outperformed other models at all bus stops, especially compared to ET. Compared to the single DT model, the average reduction in MAE by eGA-DT at each bus stop was about 5%, as shown in Figure 12.  
2. The performances under different bus routes  
There are various levels of bus routes with different characteristics, including regular, main, express, and branch routes. Eight routes from different types were selected, and eGA-DT, DT, and ET were conducted for comparison, as shown in Table 5. The results clearly indicate that eGA-DT performed better for all types of bus routes. Particularly, the eGA-DT performed best for branch routes with 23.54 in MAE. In general, eGA-DT outperformed DT and ET with an average reduction of 26% and 43% in MAE, respectively.  
The data were divided into four types of periods, including morning peak time on weekdays (7:00-9:00), evening peak time on weekdays (17:30-19:30), off-peak time on weekdays, and rest days. Results in Table 6 show the MAE of the model for four different types of periods. It could be clearly seen that the MAE in peak periods was larger than in off-peak periods, probably because of the severer fluctuation of headway affected by unexpected passenger flow and traffic conditions. Through the performance of different models, eGA-DT outperformed others with an average reduction of 7.3% in MAE during peak periods and 10% during off-peak periods.
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
###### Abstract  
The potential use of wastewater sludge as a biosorbent for the removal of various metals and metalloids from aqueous solutions was investigated. The sludge was immobilized in a rod shape with chitosan to improve sorption capacity and solid-liquid separation ability. An optimal condition for the production of rod-shaped chitosan-immobilized sludge (RCS) was determined by considering the biosorbent production potential and As(V) removal efficiency. The optimal sludge and chitosan contents and RCS thickness were 6.0%, 4.0%, and 0.2-0.3 mm, respectively. The anion removal performance of RCS was investigated for As(V), Cr(VI), and Mn(VII), and the cation removal performance was investigated for Cd(II). Pseudo-first-order and pseudo-second-order models adequately explained the kinetic data for the RCS, while the Langmuir and Freundlich models explained the equilibrium data for the RCS. These results showed that RCS has a higher adsorption capacity for anions than for cations. The results also indicated that electrostatic attraction or ion exchange is the main mechanism for metal/metalloid removal by RCS, except for the case of Mn(VII) where an adsorption-coupled reduction mechanism may be suggested.  
Keywords:sewage sludge; biosorption; chitosan; arsenic; mechanism Article
## Active Debris Removal
### ADR Mechanisms  
The Institute investigates several technologies for ADR, namely gecko inspired adhesives [56], robotic arms and grippers [57], and tether systems [58; 59]. In this section, an overview is given on the tether system research activities. The use of a net to capture the target is a promising solution because the net does not need any specific interface to capture the target. However, the control and stabilization of the target after capture proves to be much more ambitious. Thus, it is required to perform a fast stabilization of the captured target to avoid roll up/collision of the flexible connected objects.  
Since 2002, Airbus DS (Bremen, Germany) has been developing a Net Capture System (NCS). The In Flight Demonstration (IFD) of the NCS is planned for 2018, as part of the _RemoveDebris_ Mission [60]. The IFD marks an essential step in the development chain of the whole system towards an operational usage.  
However, in addition to the validation of the mechanism, issues regarding the control of the system needs to be resolved. Therefore, the Institute develops in cooperation with Airbus a software tool (Tether Dynamics Toolbox--TDT). This Matlab/Simulink Toolbox provides the capability to analyze a ADR mission (with flexible link) starting from the initial capture (including the stabilization phase) until the de-orbiting of the ADR system [61].  
To validate the simulation environment in 2017, the Synchronized Position Hold, Engage, Reorient, Experimental Satellites (SPHERES) Tether Demo experimental study have been carried out. More than 16 different test scenarios had been pre-defined to gain experimental data for the validation of the tether dynamics simulation. The results are yet to be published.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem  
The dispersal problem is defined in the context of deploying a many-in-one satellite system, where a constellation is launched as a single unit and sequentially separates until full deployment. This formulation imposes no restrictions on satellite size or quantity but operates under the following assumptions:  
1. Satellites are cube-shaped;
2. Satellites are identical in terms of external surface properties, mass, and shape;
3. Constellation orbit is circular or quasi-circular;
4. Constellation size follows \(2^{x}\), where \(x\) is a positive real integer;
5. No onboard propulsion;
6. All satellites have three-axis attitude control capabilities.  
The first three assumptions ensure scalability across different constellation sizes while maintaining predictable dispersal behavior. The fourth assumption standardizes disengagement parameters across all deployment stages. The fifth assumption is not intended to limit applicability but rather to establish a baseline case from which the efficacy of passive control methods can be evaluated. The final is a necessity, as it is central to the concept of differential drag control, with active attitude control required throughout the mission. This is also required for detumbling and pointing during disengagement.  
Additionally, this study focuses on satellite constellations deployed at initial orbital altitudes of up to 600 km, beyond which the decreasing atmospheric density significantly limits the effectiveness of differential drag-based dispersal. For the analysis presented here, only eccentricities up to 0.01 are considered. Higher eccentricities introduce greater modeling challenges for differential drag control for two main reasons. First, the substantial variation in atmospheric density between periapsis and apoapsis complicates the modeling of available control authority. Second, maintaining relative angular configurations becomes more difficult due to orbit perturbations associated with higher eccentricities, particularly the precession of the argument of perigee.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions  
The SDN-based solutions fulfill parameters DL4 (permitting smooth handoff), DL2 (by offering the decentralized mechanism), DL3 (the capability to re-write the code of paths in CN by the organization of guidelines), and DL1 (the ability to use network information for traffic routing with the CN) for satisfying the criteria of dependability. In meeting the adaptability condition, the SDN-based approach fulfills the parameters AL5, AL2 and AL1. Next, in respect of meeting extensibility conditions, SDN-based solutions fulfill parameters EL1 and EL2 and EL5. Furthermore, in respect of meeting the condition of power-saving, SDN-based solutions fulfill parameters PL1 and PL5.
## 3 Results and Discussion
### Comparison between FFU and Concrete Sleepers  
In previous sections, the positive effect of unsupported FFU bearers is observed, particularly at crossing nose. This behavior was unexpected since high impact forces emerge at this section (Figure 2). Thus, it is believed that a comparison between FFU and concrete bearers could address the positive effect. In Figures 30 and 31, the maximum bending moments of FFU and concrete bearers are plotted when one bearer under crossing nose is unsupported. As can be seen from both figures, FFU sleepers enhance the performance of the turnout in terms of positive and negative bending moments. It provides smooth transitions between turnout panels such that the maximum positive bending moment at closure panel is approximately 28% higher than maximum positive bending moment at the normal track section where as it is 80% for concrete bearers. Moreover, FFU bearers seem to be insensitive to variations in the velocity. Bending moment fluctuations, which are the result of track surface irregularities, are limited in comparison to concrete bearers, as clearly seen in the figures. Lastly, figures show that impact forces produce significant positive and negative bending moments when concrete bearers are used, despite the existing unsupported sleeper. This behavior is not observed for FFU bearers due to the positive effect of unsupported sleeper.  
Figure 29: The flexural responses of adjacent bearers when the selected point have maximum and minimum bending moments: (**a**) Bearer at normal track section; (**b**) Bearer under switch blades; (**c**) Bearer at closure panel; (**d**) Bearer under crossing nose; (**e**) The longest bearer; (**f**) The shortest bearer.
## 5 Data Products, Processing and Distribution
### Data Processing System
#### 5.2.5 Profiling LiDAR Data  
Level 0 profiling LiDAR data is captured in ASCII format through a 10/100 Mbit TCP/IP port, and can be processed in the same manner as PALS data [16].
## 4 Results and Discussion
### Automatic Hand Gesture Recognition
Considering the entire latency time is significantly less than the human reaction time, the algorithm's latency can be deemed negligible, as it does not interfere with the application's correct functioning. When comparing RGB and RGB-D solutions, it is noteworthy that their performance levels are strikingly similar, with only subtle distinctions between the two approaches. However, the RGB-D solution exhibits a slight advantage, particularly in scenarios characterized by light variations. This added robustness allows the RGB-D approach to effectively address challenges that may cause performance drops in purely RGB systems. Furthermore, in previous works utilizing RGB-D, we observed that this approach reduces, though does not completely eliminate, the need to generalize the dataset [50; 51]. This characteristic is crucial for applications requiring adaptability to diverse environmental conditions, reinforcing the value of incorporating depth information in enhancing classification accuracy and reliability. The RGB-only solution offers the significant advantage of utilizing common devices like smartphone and tablet cameras, making it accessible and convenient for a wide range of users. In contrast, the RGB-D approach necessitates a more specialized 3D acquisition system, which can limit its applicability in everyday scenarios, and has been evidenced by the present study also by the need of creating an ad hoc database. However, advancements in technology, particularly with time-of-flight cameras, are rapidly changing this landscape. These innovations have led to improvements in both camera performance and compactness, making it increasingly feasible to integrate RGB-D capabilities into personal devices. As a result, the distinction in accessibility between RGB and RGB-D solutions is becoming less pronounced, potentially leveling the playing field for both approaches in these applications, and allowing it to be available to a wider range of users having devices with different hardware capabilities. This is in line also with previous findings related to the perceived user experience by learners using 2D and 3D gamified Virtual Reality on American Sign Language [22].
## 2 Methodology
### Light Dense Block  
The first modification is related to the conventional dense network, which uses regular convolutional layers and has the advantage of parameter simplicity, vanishing-gradient  
Figure 5: Schematic representation of (**a**) conventional vs. (**b**) depthwise separable convolution.  
minimization, and feature reuse [56]. In this paper, a proposed light dense block was designed as the main building block of Light-Convolution Dense Selection U-Net (LDS U-Net). The basic structure of the light dense block is shown in Figure 6 and here, unlike a conventional dense network, all the convolutional layers are depthwise separable convolution layers.  
The first layer of the light dense block is a depthwise convolution unit that consists of a depthwise convolution block followed by a pointwise convolution block. The next layers are the batch normalization, rectified linear unit (ReLU) activation function, another depthwise convolution unit, and a dropout layer. The first depthwise convolution unit is also connected densely to the dropout layer, as shown by the green dotted arrow in Figure 6. Through this new design, the light dense block delivers the same advantages as a conventional dense block, but with a smaller number of parameters.