esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Four New Signal-Based Approaches to Be Compared  
### Acoustic Emission Analysis (Approach A)  
Acoustic Emission (AE) is defined as \"the class of phenomena whereby transient elastic waves are generated by the rapid release of energy from localized sources within a material\" [25]. The advantage of AE is that the application of this method is usually achieved during loading. In addition, the frequency range of AE signals is much higher than that of vibration and environmental noise [26]. Advanced modern signal processing techniques are required to analyze AE signals. In this contribution, time-frequency analysis is used to extract features related to damage. The coupling between the surface of the sensor and the material was permanent and it was assumed to be very stiff. The measured voltage was converted by a high-speed AD-converter on an Field Programmable Gate Array (FPGA) board with a suitable sampling rate, so a wide frequency range up to 1 MHz can be covered by the measurement chain.  
The aim is to detect characteristic frequencies that are strongly related to stochastic effects and to establish a unique assignment for the related wear mechanism. The Short Time Fourier Transform (STFT) is used to extract relevant frequencies that indicate transient events. The signal is multiplied by a window function in STFT algorithms. The FFT is then obtained and a sliding window is moved  
Figure 2: Material surface of wear-related specimens used for experiments.  
along the time axis to calculate the repeated chain continuously until the end of the signal. A criterion must be defined for distinguishing normal and abnormal operation. Therefore, the signals are recorded under normal operation during different operating phases. After correlating the STFT signals with the process signals, recurrent phase-specific patterns can be identified and only the frequency contents of the residual STFT pattern are used to examine the AE signals calculated over time. It is assumed that the result represents the damaging energy. It is generally conducted that a change in the AE energy indicates that there has been a significant effect on the lifetime of the system under consideration [27].  
Transient events and the characteristic frequencies in these stochastically appearing effects are analyzed to detect correlations between the transient emissions (measured as piezoelectric voltage) and physical effects (material changes). Thus, changes in the AE signals indicate related changes in the process and surface condition. The severity of the change (wear process) may be indicated by typical transient events, amplitudes, etc., in the STFT results.  
Related to a physical-oriented view evaluating the system state, the cumulative AE energy distribution obviously allows the distinction of the three main characteristic phases of system state development up to system failure [28].
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
### Related Works
#### 1.1.1 Land Use/Land Cover (LULC)  
LULC refers to how people use the land and interact with the physical land type. Various authorities propose different LULC classes. CORINE, one of the widely used LULC classes, includes 44 land cover classes in a three-level hierarchy. The main categories are artificial surfaces, agricultural areas, forest and semi-natural areas, wetlands, and water bodies. The National Oceanic and Atmospheric Administration (NOAA) also categorizes land cover such as developed land, agricultural land, grassland, forest land, scrub/scrub land, bushland, pulsating wetlands, estuarine wetlands, and water and submerged land. Additionally, Sentinel, an earth observation program initiated by the European Space Agency (ESA), offers classifications, namely residential, impervious surface, agriculture, bare land, forest, and water [8].  
The major limitation of these LULC classes is that they can be too specific or generic. Also, there are significant differences between the types proposed by different sources, making it subjective and problematic in particular cases. However, LULC classes provided by different entities can be useful because they provide a standardized way of categorizing land use and land cover types. Additionally, researchers, policymakers, and other stakeholders can use a common language to compare and contrast land use and land cover across different regions. For instance, the CORINE land cover classification system is widely used in Europe. The European Environmental Agency has adopted it to report on the state and evolution of Europe's environment. The NOAA's land cover classification system is used in the United States for environmental monitoring and reporting purposes.
# Effective Use of Flax Biomass in Biorefining Processes
## 3 Results and Discussion
### Bioethanol Production Process
#### 3.2.2 SSF Process of Flax Biomass  
The biomass of selected flax lines was subjected to the SSF process for 72 h at 37 \({}^{\circ}\)C, i.e., in conditions appropriate for the synergy of the Flashzyme Plus 200 enzyme and _Saccharomyces cerevisiae_ distillery yeast. Figure 3 shows the ethanol concentration determined by HPLC.  
The highest ethanol content was obtained for fiber flax biomass and it was 8.72 g\(\cdot\)L\({}^{-1}\), and the lowest for linseed was 7.65 g\(\cdot\)L\({}^{-1}\) (a difference of about 12% compared to the value of fiber flax). Moreover, the difference between linseed and dual purpose (8.18 g\(\cdot\)L\({}^{-1}\)) was about 0.5 g\(\cdot\)L\({}^{-1}\), the same as between dual purpose and fiber flax.  
A similar ethanol concentration was obtained for hemp biomass, which was pretreated with sodium hydroxide of the same concentration, i.e., 2%, and then the SSF process with the addition of the Flashzyme Plus 200 enzyme. The ethanol content for hemp biomass was in the range of 6-7 g\(\cdot\)L\({}^{-1}\)[29]. In other studies the SSF was carried out on the pretreated Miscanthus species and the ethanol concentration from this biomass was 7.42 g\(\cdot\)L\({}^{-1}\)[44].  
Summing up, the obtained ethanol concentrations for flax biomass are at a satisfactory level, and the biomass of each selected line of flax can be a potential raw material for obtaining bioethanol.
## 2 Simplified Vulnerability Assessment Methods for Masonry and RC Building Structures
### Application of the Method of Group A and B to a Reduced Sample of Buildings
#### 3.4.1 Simplified Vulnerability Assessment via Quantitative Safety Factors  
Methods of Group B give a quantitative estimation of the seismic vulnerability, based in most cases on a simplified assessment of the seismic capacity-to-demand ratio (expressed in terms of ground acceleration or base shear).  
In Table 14, the values of such \"safety factor\" are listed for the 14 selected masonry buildings and in Figure 12 the comparisons among the examined are graphically shown. Note that, in the assessment of the capacity-to-demand ratios, for the DPCM method both shear and flexural failure modes were considered, since more detailed information about the buildings were not available and the lowest safety factor was finally considered (it resulted in being the one associated to flexural failure, in all the cases). For the L&R method, the lowest value between those related to x and y directions is reported for each building. In Table 14, the 'envelope' column summarizes the most severe results obtained by applying the quantitative methods of Group B and corresponding to the lowest seismic capacity-to-demand ratio.  
Table 14 and Figure 12 show that the GNDT method was the less safe approach since for all examined buildings the safety factor was largely \(>1\). This is probably due to an overestimation of the PGA capacity that depends both on the correlation given by Equation (3) and on the judgments assigned in the relief form, which were strongly affected by the subjectivity of the compiler or by a lack of information.  
Conversely, the DPCM method was the safest one, since it provided safety factors lower than 1 for more than the 85% of the buildings, with 43% of buildings (7) with safety factors lower than 0.5.  
The RE.SIS.TO method provided values of safety factors similar to those provided by the DPCM method, with the exception of buildings 3, 8, and 10 characterized by quite different predictions.  
Index \(\gamma_{3}\) given by the L&R method seemed to be the most balanced one, since no building had a factor \(\gamma_{3}\) lower than 0.5, but it is worth noting that for buildings 2, 4, 7, and 11 the index was significantly higher than the values provided by the DPCM and RE.SIS.TO methods.  
Finally, the values of the capacity-to-demand ratio given by the S&C method of Group A are listed in Table 14 too; they were always lower than 1 and in most cases lower than the values provided by the DPCM method, resulting, thus, in the safest approach. However, as previously underlined, it is worth noting that the S&C method gave a very approximate prediction of PGA\({}_{\mathrm{c}}\). Note that the only parameter variable within the 14 buildings and influencing the judgment was the construction age, and thus it had sense that the method was very safe.
## 1 Introduction
Using probabilistic models, it is also possible to estimate the capacity, the probability of system overflow, and the loss probability (refuse to service an incoming request), i.e., the most significant system operation risks [15]. For completeness, it should be noted that QS are not the only models that can consider the influence of random factors on railway transport operation. Other types of probabilistic models can be found in the scientific literature [36; 37], in particular, the job shop models and the delay propagation models [24; 38; 39]. Through this review, we want to emphasize that, firstly, at present, the apparatus of the queuing theory is often used in world practice to study railway transport. Secondly, our study presented below combines and develops ideas above as follows:  
* queuing networks are used to describe the multi-level structure of railway stations in detail and take into account the influence of random factors on the servicing of cars [25; 26; 27; 29];
* the limited capacity of the station, i.e., the presence of finite queues in the QS, is assumed [29; 31; 34];
* special models are used to describe incoming group car traffic [18; 19; 40];
* group service of requests in channels is allowed [40; 41]. Based on the mathematical apparatus of queuing theory, we developed a method for modeling the operation of passenger transport systems [40; 41]. In the proposed method, multiphase QS are applied to describe the system structure [12; 13], and the _BMAP_ model (Batch Markovian Arrival Process) to specify the incoming transport flow [42]. It is suitable for describing systems with a linear structure, i.e., in which service devices (subsystems) are arranged sequentially, and requests (passengers) are served once in each one. Unfortunately, as will be shown later, RS do not always have a similar (simple) structure. This paper develops the method by applying more complex models based on queuing networks (QN) [12; 13]. QN allow us to define in detail the route of requests within an object with a non-linear hierarchical structure [29; 30], as well as to describe a new class of systems--freight railway stations and marshalling yards. Here the functioning process of RS of a general form is considered, and its generalized mathematical model, which has the form of a queuing network, is constructed. Mathematical models of two RS are presented. The first one is a freight railway station located in Russia. The second one is a marshalling yard located in Nebraska (USA). They are implemented as simulation software, and a numerical experiment is carried out. We determine the probability of failure of an incoming train and \"bottlenecks\" in the structure of the studied stations based on the numerical results and make recommendations for their elimination.
## 2 Modeling of Vehicles Interactions Due to Simultaneity  
As already remarked, the probability that several vehicles are traveling the bridge at the same time can be determined examining two subcases in succession: Firstly subcase 1, vehicles on one lane, and subsequently subcase 2, vehicles or vehicle convoys on two or more lanes.  
Referring to a traffic flow consisting of a set of \(q\) types of (standardized) vehicles, these two subcases can be solved independently, suitably modifying the classical [PERSON]'s approach to the queuing theory.  
Hereafter, let \(L\) be the reference length of the relevant influence surface and \(N_{s}\) the total flow of vehicles on the \(s\)-th lane in a given time interval, for example one year.  
\(L\) is the portion of the bridge length which, if loaded, causes significant effects, or, in other words, where the ordinates of the influence surface are not negligible. Evidently, if \(L_{t}\) is the total length of the bridge, it is \(L\ \leq\ L_{t}\).  
Said \(N_{js}\) the annual flow of the \(j\)-th vehicle on the \(s\)-th lane, the total annual flow \(N_{s}\) results  
\[N_{s}\ =\ \sum_{j\ =\ 1}^{q}N_{js} \tag{1}\]  
Fundamental hypothesis of the theory is that the vehicle arrival is a Poisson's process. This assumption, suggested by [PERSON] [1] considering that the number of vehicles is very large and that their arrivals are independent of each other, is commonly adopted in modeling flowing traffic and it has been confirmed on experimental basis by several studies, already cited [2; 3; 4; 5; 6; 7].  
Under the additional hypothesis that the time needed to travel a given length is distributed according an exponential law, the process is memoryless; moreover, as known, the exponential distributions are the sole memoryless continuous distribution. In fact, starting from the state of the system at a given time \(t_{0}\), \(\Xi_{0}\), the evolution of the system for \(t\ >\ t_{0}\) depends only on \(\Xi_{0}\) and not on the previous history of the states of the system itself, i.e., on which way the system reached the state \(\Xi_{0}\). The stochastic process of vehicle arrivals is then a Markov process.  
Said \(v\) the average speed of the vehicles, the average time \(\Theta\) to travel the reference length of the influence line is given by \(\Theta\ =\ L/v\), and the rate \(\alpha\) of the distribution, which may be regarded as the rate of availability of the lane, by \(\alpha\ =\ \Theta^{-1}\). The exponential probability density function \(f(t)\) of the time \(T\) that a vehicle takes to travel \(L\) is:  
\[f(t)\ =\ \Theta^{-1}\exp\Bigl{(}-\Theta^{-1}t\Bigr{)}\ =\ \alpha\,\exp(\alpha\ t). \tag{2}\]
# Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios
## 3 Results
### Spacecraft Case
#### 3.1.3 Spacecraft Case Conclusions  
The goal of the method was to create a dataset of thermal infrared images of a spacecraft in order to be able to test navigation algorithms. The reported results show that all the details of the geometry of the target, the effects of the multiple materials emissivities, and the effects of the different view factors between the camera and the object geometries are retained in the final thermal image. In this way, the method can produce a realistic thermal images dataset to start testing the performance of navigation algorithms in the infrared spectrum. The interested reader can refer to [10; 17] to see the usage of such images for navigation purposes. In particular, the ASTRA Polimi research team is now focused on testing the use of such an image in combination with VIS images in order to cover the phase of proximity operations in which there are low or adverse illumination conditions as reported in [10] and also reported in Section 4.1.  
It must be highlighted that the method as it is presented is still not validated; therefore, the accuracy of the thermal images is not guaranteed. Nonetheless, given the details of the output and the qualitative correctness, it can be used right away as a starting point.  
Figure 9: Tango temperature field (**left**) and radiance field intercepted by the camera \(q_{fc}\) (**right**).  
Figure 11: In the last row, the received radiative heat flux reduces due to the view factors when surface normals are almost parallel with the camera normal. Temperature field (**left**) and radiance field intercepted by the camera \(q_{fc}\) (**right**).  
Figure 10: Temperature field (**left**) radiance field \(q_{fc}\) (**right**) for orbit at 5 m around TANGO.  
The validation of the method requires the calibration of the thermal camera and the correlation of real thermal images taken in a controlled environment with the ones produced by the method. The whole method is built to be fully tuned; thus, the camera calibration curve, i.e., the function that provides the DN, can be easily modified inside the pipeline. The ASTRA Polimi team is now working on such topics; the interested reader can refer to the ESA e.Inspector project for future developments (more on this in Section 4).  
For what concerns the computational time for the generation of thermal images, it must be highlighted that it strictly depends on the number of faces of the mesh, as all the view factors for each camera pose must be computed in order to calculate the actual infrared heat flux received by the camera. Once the heat flux is computed, the rendering of the image through Blender depends on the performance of the GPU mounted on the PC. As an order of magnitude, for the TANGO spacecraft case, the mesh has six million faces and it takes six hours in total to compute the heat flux for two hundred camera poses and then render such images.
# Model-Based Design and Simulation of Paraxial Ray Optics Systems
## 5 Conclusions  
The model-based design was validated as a useful tool to build and simulate paraxial optics systems from pure-optical to multi-domain optomechatronics projects. Ray tracing can be combined with dynamic simulations to move lenses and mirrors and perform precise focus control. Moreover, multiple rays as in resonators can be split into branches and computed simultaneously. Therefore, starting from a very basic model, students and professionals can integrate several subsystems to upgrade their simulation and achieve practical and realistic analyses, whereas the modular approach allows for exchanging parts of the system to update its functionalities without modifying the overall project.  
Despite the promising results, this methodology is currently restricted to paraxial geometrical optics models, i.e., extreme bending of light rays will probably yield inaccurate tracing, whereas important phenomena, such as diffraction, interference, and attenuation, cannot be represented with this proposal so that eventual deviations may occur regarding the experimental realization of the optical systems. Besides, most commercial, dedicated solutions integrate wave and Gaussian optics simulations with the ray tracing analyses, which is crucial for the optimization of optical systems. Nevertheless, one can develop blocks for non-paraxial optical elements, as well as additional functions for establishing an interface between ray and wave optics. Further studies will focus on such improvements to create a more versatile, simple, and feasible multi-domain environment for simulating optical systems.  
Conceptualization, E.F.; methodology, E.F.; validation, E.F. and C.M.B.C.; formal analysis, E.F. and C.M.B.C.; writing--original draft preparation, E.F.; writing--review and editing, E.F. and C.M.B.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sao Paulo Research Foundation (FAPESP), grant number 2017/25666-2, by Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), and by Coordenacao de Pessoal e Nivel Superior (CAPES), finance code 001.  
The authors declare no conflict of interest.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.2 Deep Learning  
Deep learning (DL) is a subfield of ML that uses neural networks with multiple layers to extract high-level features from raw data. It can be used to develop models that automatically detect and diagnose failures in complex systems and then take appropriate actions to recover from them. One application of DL in self-healing systems is in predictive maintenance, where ML models are trained to detect anomalies in the system data that may indicate potential failure. The models can be trained on historical data to learn the system's expected behaviour and then use that knowledge to detect deviations from normal behaviour that may indicate a failure. Once a failure is detected, the self-healing system can take appropriate actions to prevent or mitigate the effects of the failure. A study by [PERSON] in [1] demonstrated that DL is an efficient feature extraction method in machine learning. The feature of deep learning aims to establish a deep structure model by integrating the more non-representational feature of data and achieving more detailed characteristics of the data. The three main aspects of DL are unsupervised training, data sample alignment, and data sample testing. The authors of [1] noted that the essence of DL is to find out more valuable features of the dataset by constructing an ML model within many hidden layers and an extensive training dataset to improve the accuracy of classification and prediction. The predictive nature of DL can be deployed to perform systems fault diagnosis, where ML models can be used to identify the root cause of a failure. These models can be trained to analyse sensor data or other system inputs to identify patterns associated with specific types of failures. Once the root cause is identified, the self-healing system can take appropriate actions to address the underlying issue and prevent future failures.
## 5 Connectivity Limitations of Smart Farming in Remote Areas
* **Low and remote maintenance requirement:** Inaccessibility is one of the major limitations of remote areas. Hence the IoT devices set in remote locations should have higher durability, improved reliability and low maintenance requirement. They should be equipped with proactive and remote management and maintenance for remote areas. * **Minimal power usages using optimised techniques:** Power is a scarce resource in remote area. Therefore, low power consuming techniques are desirable for battery operated nodes in remote areas. * **Privacy assurance:** Many of the existing IoT systems ignored users and objects privacy requirement. For sustainable growth and trust, the IoT system must assure privacy of users and objects at minimum. The recent development of LoRaWAN technologies is working to address some limitations mentioned in the list above, but others requires more attention from researchers in this area. In this subsection, we present two case studies addressing this issue. In the first case study, we propose meshed LoRaWAN gateways to address connectivity limitations of Smart Farming in remote areas, and, in the second case study, we present satellite communication systems to provide connectivity to smart farms in remote areas of Australia. The following subsections describe the details of these case studies: