esa-sceva/satcom-chunk-collection huggingface dataset:
## Data and Methodology
### Correlation between Land Surface Temperature Data and Land Use Data  
A spatial analysis was performed to find a possible correlation between the observed LST trends and the corresponding changes in the LC data. In other words, the specific aim was to verify whether the ROI pixels that experienced the same LC changes during the twenty years showed similar LST growth rates or not. Operatively, on the basis of the adopted linear temperature growth model (Equation (1)), a (\(3\times 3\)) matrix (_Rate Matrix_) was computed for each MA investigated for the rate (\(r\)) parameter. In particular, each cell (\(i,j\)) of this matrix (Figure 5):  
* aggregates all the ROI pixels characterized by the LC class \(i\) in the initial year (1992) and the LC class \(j\) in the final year (2011);
* stores in three different layers the hereafter described statistical parameters (mean, median, numerosity, Permanence/Variation Index), computed considering the values of \(r\) of all the ROI pixels that underwent the LC change from class \(i\) to class \(j\).  
A schematic representation of the obtained _Rate Matrix_ is shown in Figure 5.  
Figure 5: Representation of the LC changes within the _Rate Matrix_: the row index (\(i\)) represents the initial LC class and the column index (\(j\)) the final LC class. As an example, the highlighted cell represents the transition from the _Forest_ class (\(i=3\)) to the _Urbanized_ class (\(j=1\)), with an estimated LST increasing rate of 0.4 \({}^{\circ}\)C/year.
## 5 Conclusions  
This work reported new type of designed and manufactured MMFs with extremely enlarged core diameter up to 100 \(\upmu\)m and typical \"telecommunication\" cladding diameter 125 \(\upmu\)m with specially optimized graded index refractive index profile providing ability of laser-based Gigabit data transmission. Some results of tests, performed for pilot 520 m length of proposed LOMF 100/125 and concerned with key optical fiber parameters--attenuation and bandwidth--are represented. Here, optical fiber loss is not more 4.20 dB/km over \"O\"-band, while analysis of measured DMD map, performed according to ratified standards TIA-455-220-A/FOTP-220 and IEC 60793-2-10 showed only pulse spreading without DMD distortions under total dispersion 150...300 ps over whole researched offset launching conditions range 0...25 \(\upmu\)m. By comparing with estimated acceptable dispersion values for 1000 Base-LX and 10 GBase-LX specifications for 520 m MMF link 874.5 ps and 87.1 ps, computed according to \"the worst case\" link model, developed by IEEE 802.3z Task Force, a good bandwidth potentiality of presented LOMF 100/125 should be noticed. Moreover, for the first time we demonstrated guaranteed successful laser-based 1 Gbps bit rate data transmission over 520 m link with proposed LOMF 100/125 under 1000 Base-LX interface without any special launching conditions under 70 h testing by certified channel analyzer and total BER = 0.  
Conceptualization, A.V.B.; methodology, A.V.B., V.A.B., O.G.M.; optical fiber fabricating, V.V.D., K.V.D., A.S.M., E.V.T.-N.; measurements of geometry parameters and attenuation, V.V.D., A.S.M., E.V.T.-N., A.A.V.; measurements of bandwidth parameters, M.V.D., A.S.E., A.A.K., E.S.Z., A.E.Z.; writing--review and editing, A.V.B.; visualization, A.V.B., M.V.D., V.V.D., V.A.B.; supervision, A.V.B.; project administration, A.V.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by RFBR, DST, NSFC and NRF according to the research project 19-57-80006 BRICS_t.  
The authors would like to specially thank to JSC \"Leningrad Laser Systems\" (LLS) team for help, support and assistance in optical fiber preform manufacturing process, especially to the following engineers: [PERSON], [PERSON] and [PERSON].  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Vision of 6 th Generation of Fixed Networks (F6G): Challenges and Proposed Directions
## 2 Services and Requirements for F6G
### Upcoming Categories of Services and Their Requirements
In the fifth category, the AR-assisted remote education requires an increased bit rate in order to provide a high level of interaction between students and teachers. In addition, low latency is required in order to allow interactions in real-time. In the final category, various safety-related services can be found, such as health hazard monitoring and AI-assisted incident detection, which, depending on the target-monitored metric, may require low latency (e.g., detecting an imminent threat) or high connectivity (e.g., monitoring and sensing several places concurrently for health hazards).
## 5 MATLAB-Simulink System Model
### Control Strategies and Power Converter Modulation  
The DC/DC converter adapts the fuel cell voltage to the DC bus voltage, which is set by the battery, while controlling the current extracted from the fuel cell.  
The DC/DC control is dedicated to keeping the fuel cell current close to its reference value, which is set by the Energy Management Strategy of the hybrid FC-battery vehicle. In Figure 8, the DC/DC converter control is presented in detail. The output is the pulse pattern, which will be used to trigger the bottom IGBT of the converter.  
With respect to the inverter, the selected modulation strategy is conventional space vector modulation (SVM), which implies a constant switching frequency [32]. In Figure 9, the complete Simulink model is presented.  
The machine control system operates on two levels. The upper level is the motion controller, which generates a current reference, [I\({}_{\text{d}}\)*, I\({}_{\text{q}}\)*], in dq coordinates (rotor reference frame for PMSM) based on the speed reference value (from the WHTC driving cycle) using a PI controller. The lower level, known as the machine controller, handles the modulation  
Figure 8: Control scheme for the fuel cell DC/DC converter.  
Figure 9: MATLAB-Simulink model of the fuel cell–battery EV powertrain.  
technique. In this block, [I\({}_{\text{d}}\)*, I\({}_{\text{q}}\)*] are compared to the actual motor currents, [I\({}_{\text{d}}\), I\({}_{\text{q}}\)], and two PI regulators generate the voltage references, [V\({}_{\text{d}}\)*, V\({}_{\text{q}}\)*], that should be converted to a stationary reference for the SVM modulation. The firing signals coming from the SVM block control the three branches of the inverter feeding the motor. In this model, the current reference does not adhere to the MTPA trajectory shown in [33], so I\({}_{\text{d}}\)* = 0 is used instead [36], without applying flux weakening above rated speed.  
Table 3 presents the main vehicle parameters used within the two simulation environments.
## 2 Circuit Design and Analysis  
The proposed LNA was designed based on the operating frequencies of TD-LTE, mid-band 5G, LTE-U, and IEEE 802.11 a/b/g/n/ac standards. To achieve high compatibility, Band 40 and Band 41 range from 2.3 to 2.4 GHz and 2.5 to 2.7 GHz, respectively. Band 42 and Band 43 range from 3.4 to 3.6 GHz and 3.6 to 3.8 GHz, respectively [16]. Moreover, 3.4-3.8 GHz is also the range for mid-band 5G communications for the EU licensed band [17]. The shared 5-GHz LTE-U band ranges from 5.150 to 5.925 GHz [2]. Furthermore, WLAN (IEEE 802.11a/b/g/n/ac) covers a frequency range of 2.4-2.5 and 5.1-5.9 GHz. Therefore, the target bands are the operating frequencies covering 2.3-2.7, 3.4-3.8, and 5.1-5.9 GHz for TD-LTE/LTE-U and WLAN applications.
## 5 Further Optimization Based on Direct Optimization Module
### Analysis of the Further Optimization Results  
Through the analysis results of the Direct Optimization module in Workbench, the optimization objectives corresponding to 30 groups of different design variables were obtained [19]. Thirty groups of test points and the corresponding maximum stress results were drawn into a line chart of maximum stress change, as shown in Figure 16.  
According to the set optimization objectives and candidate points, the candidate point of \"minimum\" maximum equivalent stress was selected as the design point from 30 groups of test data. The results are shown in Figure 17. When the transition arc length \(L\) was 92.83 mm and the transition arc radius \(R\) was 187.76 mm, the maximum equivalent stress was the smallest, and its value was 465.69 MPa.  
Figure 16: Maximum stress curve of 30 groups.  
Figure 15: Direct optimization flow chart.  
Rounding the data in Figure 17, \(L\) was 93 mm and \(R\) was 188 mm. Taking this result as the current point of the ANSYS simulation verification program, the results are shown in Figures 18-20. The optimization results are as follows: The contact stress was reduced from 345.61 MPa to 288.3 MPa, which is a reduction of 17%. The contact sliding distance decreased from 1.315 mm to 0.23 mm, which is a decrease of 83%. The maximum equivalent stress still occurred in the transition arc area of the inner ring of the roller sleeve, and the stress value decreased from 651.03 MPa to 464.49 MPa, a decrease of 29%, meeting the strength requirements of the extrusion roller. The maximum deformation occurred at the end of the inner ring of the roller sleeve without the transition arc. The deformation was 0.997 mm, which is 0.382 mm lower than that without optimization, with a decrease of 28%, meeting the deformation requirements of the extrusion roller. The maximum equivalent stress decreased with the increase of the area of the stress concentration area, and the stress distribution on the roller surface was more uniform. It can be seen that this scheme is expected to prevent the cracking of the extrusion roller and improve the service life of the extrusion roller.  
Figure 17: The stress value P\({}_{3}\) corresponding to P\({}_{1}\) and P\({}_{2}\).  
Figure 18: Interference contact nephograms after optimization: (**a**) contact stress nephogram; (**b**) sliding distance nephogram.
## 1 Introduction
Solving the trend of reliability over the whole life cycle of a product requires discretizing the life cycle and calculating the reliability in different time intervals, which is inefficient [18]. Among the TVR methods based on the crossing rate, Rice [25] pioneered the first crossing rate theory to study the problem of dynamic response exceeding a certain fixed threshold at a given time, laying the foundation for the development of the first crossing rate method. Andrieu-Renaud et al. [3] proposed the PHI2 method to convert the time-variant problem into a static parallel system reliability problem, improving the efficiency greatly. To address the lack of stability of the PHI2 method, Sudret et al. [26] proposed the PHI2+ method with a more concise process for the calculation of the crossing rate. Hu et al. [27] proposed the Rice/FORM to solve the TVR of the structure by converting the time-variant performance function to the Gaussian process to calculate the crossing rate. Jiang et al. [28] converted the calculation of the crossing rate in the time-variant system into a solution of reliability in the time-invariant system, improving the efficiency of the solution. Although the crossing rate approach is effective in solving TVR problems, there are some inherent drawbacks, such as the assumption of Poisson distribution of crossing events and the limitations imposed by the application of FORM. In this paper, a first crossing approach for analyzing TVR based on the error distance function (EDF) and Kriging surrogate model is presented, referred to as EDFK-FCTP. The EDFK-FCTP method establishes the functional relationship between the first crossing time point (FCTP) and input random variables by constructing the adaptive Kriging model. Moreover, kernel density estimation (KDE) is used to estimate the probability density function (PDF) of the FCTP, eliminating the probabilistic assumptions of traditional crossing rate methods. Hence, in this paper, (1) a Kriging-based adaptive surrogate model of FCTP is developed; (2) the EDF is constructed by combining model error and sample distance; (3) the geometric mean of probability density of training samples and the maximum error of the first four order origin moments of two adjacent iterations are used as the convergence condition to establish the optimal surrogate model for FCTP; (4) on the basis of the optimal surrogate model, KDE is used to solve PDF of the FCTP to obtain trend of failure probability during the product life cycle.
# Using Artificial Intelligence for Space Challenges: A Survey
## 2 Background on AI
These networks are designed to process data in multiple arrays, including language, images or audio spectrograms, video, or volumetric images. Recurrent neural networks (RNNs) are used to interpret sequential and temporal information. These networks can analyze time series, audio, text, and speech with the more profound understanding of a sequence they allow. RNNs process an input sequence one element at a time, maintaining information about the history of all the past elements of the sequence [13]. Furthermore, recurrent networks can compress the whole history into low-dimensional space and form short-term memory [14]. Deep learning is a branch of AI implementing computational models composed of multiple processing layers able to learn representations of data with multiple levels of abstraction [13]. Generally, neural networks and deep learning are improving the state of the art in image recognition, speech recognition, and natural language processing. Natural language processing (NPL) [15] focuses on the analysis of language structure in order to develop systems for speech synthesis and recognition. These systems are applied in several applications where a machine can understand fundamental human interactions, such as translation engines, robot assistants, and sentiment analysis. Furthermore, fuzzy logic reflects human reasoning in making decisions and includes all the intermediate possibilities between digital values of yes or no [16]. This technique represents uncertain information by analyzing how much the hypothesis is true. Key application areas of fuzzy logic are automotive systems and electronic and environmental devices. We conclude this background on AI branches by introducing expert systems (ESs). Originally defined as computer programs able to solve complex problems, ESs can replace human experts in decision-making. These systems extract knowledge stored in their knowledge base and are called experts because they contain the expert knowledge of a specific domain and can solve any complex problem. The performances improve by increasing the amount of knowledge available, as well as the accuracy and efficiency [17]. An ES responds to any complex query in a brief period and in a human-understandable way. Indeed, it takes inputs in human language and provides outputs in the same form. The techniques presented above will be referred to in the following sections as they are used to solve space application problems.
## 4 Control Algorithms
### Control System for the Position Dynamics  
As deeply discussed in [24] and described for spacecraft maneuvers in [25], internal and external disturbances affecting the system due to the real implementation and to the external environment must be taken into account. Sliding mode methods provide controllers that are robust under large uncertainties. SMC can counteract uncertainties and disturbances, if the perturbations affecting the system are matched and bounded (first order SMC) or smooth matched disturbances with bounded gradient (second order SMC) [26; 27].  
For the position tracking, as already described in the introduction, a first order sliding mode is designed, motivated by the intrinsic nature of the thrusters, which cannot provide continuously modulated thrusts but can only be switched on and off.  
The input vector \(u_{x}=F_{\text{thr}}\in\mathbb{R}^{3}\) (as in Equation (5)) is designed according to the following first order sliding mode control law  
\[u_{x}=-B_{x}^{-1}K\text{sgn}(\sigma_{x}), \tag{18}\]  
where \(B_{x}^{-1}=m_{c}I_{3}\), \(K=nT_{max}\), being \(n=2\) to reflect that two thrusters are switched on simultaneously, and \(\sigma_{x}\) represents the designed sliding output. In general, the control gain \(K\) in (18) must guarantee that the sliding motion on the desired sliding manifold is reached and maintained. The sliding output \(\sigma_{x}\), which is the switching function in the controller (18), is  
\[\sigma_{x}=(\dot{x}-\dot{x}_{d})+c_{x}(x-x_{d}), \tag{19}\]where \(\dot{x}_{d}\) and \(x_{d}\) are the vectors of the desired speed and the desired positions, respectively. The vector of positions \(x=[x,\,y,\,z]^{T}\) and speeds \(\dot{x}=[\dot{x},\,\dot{y},\,\dot{z}]^{T}\) are measured at each time step from the Hill's equations. The constant \(c_{x}\) is chosen positive. The desired sliding surface is \(\sigma_{x}=0\). The desired conditions are defined with the artificial potential fields, as described in the previous section.
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
## 3 DIC and SiDIC Experiments on a Rubber Balloon
### DIC and SiDIC Measurements of Net-Covered Balloon Displacements  
Next, DIC was applied to a balloon covered just with a net, as shown in Figure 3c. The x-direction deformation map shown in Figure 8a was obtained. Also, SiDIC was applied, and Figure 8b shows the corresponding results. Figure 8a shows that most parts of the net were well read, though some were not measured properly such as the areas limited by the red circles in the figure. In addition, similar results were obtained in the y-direction deformation. Since DIC reads light intensity values, errors arise when this quantity cannot be read properly.  
Figure 6: Horizontal displacement determined by DIC superimposed onto the photograph of the balloon.  
Figure 7: Comparison of horizontal displacement distributions obtained by DIC and SiDIC for the control path highlighted in Figure 6.  
We measured the deformation field along the middle and upper dotted control lines and compared the results of DIC and SiDIC. Figure 9a presents results for the center dotted line. Figure 9a shows that DIC recovered fairly well on the deformation field although with some localized errors. SiDIC results were more stable yet overall consistent with those obtained by DIC. The same conclusion can be drawn from Figure 9b for the upper control path where stronger oscillations in displacement values are present. The observed behavior occurred because SiDIC works on a smaller amount of data than DIC. Furthermore, DIC may misrecognize displacements of net intersections and it is sensitive to light reflection on the balloon surface. Figure 10 shows an example of the misrecognition algorithm. From Figure 10, the intersection actually moved to point (2) after deformation. DIC errors occurred when it misrecognizes the intersections after the deformation as (1) or (3). On the other hand, the method read the coordinates of the intersections to prevent errors, thus measuring the deformation correctly.  
Figure 8: Horizontal displacement distribution by (**a**) DIC; (**b**) SiDIC, on the photograph of net covered balloon.  
Figure 9: Comparison of horizontal displacement distributions obtained by DIC and SiDIC for the control paths highlighted in Figure 8: (**a**) Central dotted line; (**b**) Upper dotted line.
## 4 Discussion
This analysis also demonstrated, for example, wider LoAs for the maximal diameter within the proximal part of the aorta (i.e., aortic root, proximal and distal ascending aorta) compared to thoracic aorta distal to the ascending part (intraobserver LoA \(\leq\)2.7 and \(\leq\)1.8 mm; interobserver LoA \(\leq\)7.6 and \(\leq\)3.3 mm, respectively). In contrast, a recent study assessing the interobserver reproducibility of manually measured diameters imaged with computed tomography demonstrated in the aortic root, ascending and descending aorta an LoA of 2.5, 2.1, and 1.9 mm, respectively [37]. For MRI, it is recommended to measure the maximal diameter on an MRI sequence with an isotropic voxel size \(\leq\)1.5 mm to achieve a clinically acceptable accuracy [38]. Since the 4D flow MRI sequence applied in the current study had an isotropic spatial resolution of 2.5-3.0 mm, the morphologic parameters derived from this sequence should not be used to define the maximal aortic diameter of TAA patients. However, for the quantification of hemodynamic parameters, an isotropic resolution of \(\leq\)3.0 mm is indicated as sufficient for the 4D flow MRI [23]. Of note, the 4D flow MRI was acquired to assess the patient's aorta hemodynamics and therefore was not meant to describe the patient's aorta morphology (i.e., TAA diameter). The decreased LoA within the validation cohort compared to the training cohort indicate that the reproducibility likely is affected by the observer's experience, especially for the aortic root, proximal, and distal ascending aorta. Moreover, the observer's experience likely plays a larger role in the assessment of morphologic parameters compared to the hemodynamic parameters, considering the decrease in LoA between the training and validation cohort. This result demonstrates the robustness of the image analysis for the quantification of hemodynamic parameters, despite the observer-dependent variability and experience. No major limitations were observed for the hemodynamics quantification in the segments of the thoracic aorta. Although no clear reproducibility limitations were observed for TAA patients with a TAV, still some variation is present in the hemodynamic parameters. This variation must be taken into account when these hemodynamic parameters are used for the prediction of progressive dilation and adverse aortic events in TAA patients with a TAV. A limitation of the present study was the small population size. However, the robustness of the study was improved by including multiple observers, analyzing five systolic phases, and dividing the thoracic aorta into six anatomical segments. This resulted in a total of 2400 lumen segments for which the hemodynamic and morphologic parameters were quantified. Moreover, future research should include patients with other aortic pathologies. Finally, for patients with TAA, no data were available on repeated 4D flow MRI acquisition; thus, interexamination reproducibility could not be assessed.
## 5 Experimental Evaluation  
### Implementation Details  
In this work, the experiments were carried out on NVIDIA Quadro P600 and Intel Xeon Silver 4114 CPU Processor with GP107 GPU 384 NIVIDIA CUDA Cores. The segmentation models are trained on the resized image of the original image size, 256 \(\times\) 256 for U-net, LinkNet, and FPN, while a 384 \(\times\) 384 image size is utilized for PSPNet. The possible choices of training parameters used were optimization algorithm, epoch, batch size, weight initialization, and learning rate. Based on the Keras framework, segmentation Model libraries were used to perform the experiments in this work [26]. Each model was trained for 20 epochs, with Learning Rate (LR) set to 0.001. The batch size was optimized to four, and the Adam optimization algorithm was preferred. 20 epoch size is chosen as the default number of iterations for training all the models in this work, so that the overall performance of all the models can be compared easily. It was also observed from the experiments conducted that increasing the number of iterations for training the model leads to the overfitting criteria for the dataset provided.