esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### OFDM-Based Communication Systems
#### 2.1.1 OFDM Systems for Image Transmission and Signal Processing  
In the OFDM system for image transmission, initially, the pixel values of the image data are converted into a binary sequence, and then the binary stream is mapped into a complex-valued in-phase and quadrature (IQ) constellation plan according to modulation schemes such as \(M_{1}\)-PSK (\(M_{1}=2,\ 4,\ 8\)) and \(M_{2}\)-QAM (\(M_{2}=16,\ 32,\ 64\)). The frequency domain OFDM symbol \(S(k)\) is transformed into the time domain OFDM symbol \(\mathrm{s}(n)\) using an \(N_{\text{DFT}}\)-point inverse discrete Fourier transform (IDFT) through parallel-to-serial (P/S) transmission. The IDFT equation is as follows:  
\[s(n)=\frac{1}{N_{\text{DFT}}}\sum_{k=0}^{N_{\text{DFT}}-1}S(k)\mathrm{exp} \left(j\frac{2\pi kn}{N_{DFT}}\right), \tag{1}\]  
where \(j\) is an imaginary unit, and \(N_{\text{DFT}}\)  
tortion, and interference. If not adequately addressed, these factors can lead to significant performance degradation, especially in OFDM-based image communication systems.  
Common sources of distortion include environmental factors such as changes in the physical landscape, the presence of obstacles that obstruct the signal paths, and variations in the channel conditions over time. Multipath propagation, where signals arrive at the receiver through multiple paths with different delays, can cause inter-symbol interference (ISI), further complicating the recovery of the transmitted data. As a result, effective strategies to mitigate these distortions are crucial for maintaining the integrity and reliability of OFDM signals.  
A comparative analysis of processed images across various modulation schemes and SNR levels for the CIFAR-10 image dataset is presented in Figure 2. The rows correspond to different modulation schemes, i.e., \(M_{1}\)-PSK and \(M_{2}\)-QAM. The columns display the original image alongside images with distorted received signals at SNR levels of 0 dB, 5 dB, 10 dB, and 15 dB. Notably, it was determined that in the same SNR condition, the distortion of the images increases as the complexity of the modulation scheme increases. Higher-order modulation techniques, such as 32 QAM and 64 QAM, respond more sensitively to noise, leading to a more pronounced degradation in image quality compared to lower modulations such as BPSK and QPSK. These results indicate the trade-off between modulation complexity and the ensuing distortion.
## 4 Optimization Design Process and Results
### Optimization Results  
After adding sample points for three times, the optimization process is finished according to the progressive optimization strategy. And the evolutionary process of optimization objectives is shown in Figure 17. Overall sound pressure level (OASPL) values are dispersed, and the average value is larger in the initial population. With the optimization, the population of the OASPL improves, and the average value tends to converge. Therefore, the optimization design is carried out smoothly. Comparing further the optimization in the second and third rounds, there is little change in the optimal population of the two rounds.  
Figure 18 shows the variation of the constraints in the optimization process. The lift-drag ratio values are dispersed and the mean value is small in the initial population. With the second round of optimization, most of the lift-drag ratio values are larger than the constraints and the mean value tends to converge.  
The variation of overall sound pressure level (OASPL) values and lift-drag ratio (L/D) values with the optimization iteration are shown in Figure 19. With the optimization proceeding, the evolutionary tendency of the population becomes smaller and converges.  
After the three rounds of optimization design, the configuration parameters of the optimized shape are compared with those of the initial shape, as shown in Table 7. With the constraint of L/D ratio, there is little change on the flap deflection angle. After the change of the position of the flap, the width of the channel between the flap and the wing changes, so the effect of reducing noise is realized.  
The optimization results are validated by CFD/CAA, as shown in Table 8. The errors of OASPL value and lift-drag ratio value between the two models are small, which verifies the accuracy of the surrogate model and the correctness of the optimization method.  
Figure 16: Flow chart of modified particle swarm optimization (PSO) algorithm.  
Figure 17: Variation of objectives in optimization process.  
Figure 19: Variation of far-field lift-drag (L/D) and OASPL.  
Figure 18: The variation of the constraints in the optimization process.
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy  
[PERSON] 1,2,*  
1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (Z.S.); [EMAIL_ADDRESS] (Z.L.)
In the development process, it is very important to predict the limit of rotation angle. The location and fit clearances of the positioning mechanisms are the factors affecting the limit of the re-orientation angle. In establishing the layout position, the design of the fit clearances directly determines the limit of the re-orientation angle. Researchers have done a great deal of research on fit clearance under different application conditions. [PERSON] et al. studied the influence of fit clearance between the bearing outer rings and the housing on the machine tool spindles error motion, and optimized the fit clearance [15]. [PERSON] et al. studied the effect of joint fit tolerance on joint failure [16]. [PERSON] et al. studied the influence of different fit clearances on the load-carrying performance of the wind turbine shrink disk [17]. In this paper, we propose a simple fit clearance design method using an angular displacement vector. By studying the positioning method of the optical unit, the rotation action of the unit was decomposed, and the algebraic relationships between the fit clearances and the rotation angle limits were obtained using the angular displacement vector. The fit clearances were designed according to the re-orientation angle index requirements. A microgravity experimental environment was established, and the gravity of the optical unit was unloaded by suspension method. The accuracy of the re-orientation angle of the unit was tested and verified by theodolites.  
The rest of the paper is organized as follows: In Section 2, the kinematic positioning principle of the on-orbit replaceable optical unit is introduced, the rotation action is decomposed by the positioning principle, the mathematical models between fit clearances and rotation angle limits are deduced, and the fit clearances are designed. In Section 3, the method of measuring the rotation angle with theodolites is introduced and the error analysis is given. In Section 4, the re-orientation angles of the optical unit are tested and validated, and the results are discussed. Finally, conclusions are drawn in Section 5.  
The Kinematic Positioning Principle of the Optical Unit and the Mathematical Model of Rotation Angles
## 4 Results
### Temporal Comparative Matrix of Interface Zones  
To deepen the analysis of our model and generate guidance for decision-makers, professionals in charge of forest fire prevention and preparedness, and communities, we analyzed three interface zones severely damaged by the 2017 megafire (Figure 8). The matrix shows the results of the ignition risk modeling (a), the ignition points of the 2016-2017 season (b), the scar of the 2017 megafire (c), and the ignition points of the 2019-2020 season (d).  
The results show that fire ignition in the two seasons under analysis occurred in areas the model classifies with a high probability (see columns a, b, and d). In these areas, crop areas of diverse composition can be observed--especially forestry--near the ignition points by roads and cities. These characteristics correspond to the variables with the most significant weight in the probability of ignition of our model.  
Comparative analysis of satellite images indicates that the number of ignitions in each WUI is lower in the 2019-2020 season. Changes in the territory's characteristics can also be observed, including a notable decrease in the density of forest crops in each of the analyzed areas (column d). This change in the territory's composition can be attributed to the 2017 megafire scar (column c). A large part of the forest plantations, grassland, and scrubs in these areas was destroyed by the fires of 2016-2017. Our model classifies these variables with a medium influence on the probability of ignition.  
It should be noted that the town of Santa Olga was destroyed by the fires of 2017. As a reparative measure, it was quickly rebuilt in the following years. The reconstruction of the town meant an improvement in the conditions and quality of life of its population, which received better quality housing and urban infrastructure that did not exist before the megafire [67]. These improvements, however, do not mean a reduction in the ignition risk in the area adjacent to this town, since it continues to be surrounded by extensive and dense forest plantations.  
Figure 8: Matrix comparative of interface zones.
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 5 Conclusions  
In term of the research goal to gain the ability to learn a Maximus weather model that can predict the air temperature for a given micro-location, we have implemented it successfully. Simultaneously, we have trained several models and compared their results, and found that the proposed Maximus optimal model predicts the air temperature for the next 12 h much better in certain situations.The objectives about the research question have, thus, been accomplished successfully.  
In summary, the following conclusions can be drawn from the results of our study:  
* The primary advantage of the proposed prediction model Maximus against the reference ICON-D2 is reflected in the fact that the former employs data obtained from the mobile weather station, which are updated each hour.
* The proposed solution Maximus considers a longer history of the regional weather situation due to using the CERRA dataset.
* On the other hand, the weakness of the Maximus model is that it considers a limited number of LSTM input parameters. In line with this, the possibility of causing Type II errors is increased. However, this type of error can be reduced by extending the time sequences and increasing the number of input parameters. Unfortunately, both mentioned issues demand powerful hardware for processing.  
Comparing different prediction models with different combinations of input data, the LSTM architecture and their corresponding hyper-parameter settings could help to improve the results even further. Improved training data could also help significantly in addressing this problem. It is also worth mentioning the possibility of implementing another DL model, or building on it with specific systems that would improve local air temperature changes in a very short time. Forecasting could also benefit from air temperature forecasts with an interval more frequent than one hour.  
Let us mention only few promising directions for future work: The new DL methods should replace LSTM NN, like LSTM with CNN or BiLSTM. The new sensors for monitoring the weather condition could be included as inputs to LSTM NN. Also the number of packets into the sliding window could be increased. Maximus would be moved to the computer grid in order to increase the learning speed from 3 h to 10 min. Some preliminary tests were already conducted in this direction.  
Conceptualization, A.K. and I.F.J.; methodology, A.K., I.F. and I.F.J.; software, A.K.; validation, A.K., I.F. and I.F.J.; formal analysis, A.K.; data curation, A.K.; writing--original draft preparation, A.K., I.F. and I.F.J.; writing--review and editing, A.K., I.F. and I.F.J.; visualization, A.K.; supervision, I.F.J.; project administration, I.F.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# A New Approach to Quantify Soccer Players' Readiness through Machine Learning Techniques
###### Abstract  
Previous studies have shown that variation in PlayerLoad (PL) could be used to detect fatigue in soccer players. Machine learning techniques (ML) were used to develop a new locomotor efficiency index (LEI) based on the prediction of PL Sixty-four elite soccer players were monitored during an entire season. GPS systems were employed to collect external load data, which in turn were used to predict PL during training/matches. Random Forest Regression (RF) produced the best performance (mean absolute percentage error = 0.10 \(\pm\) 0.01) and was included in further analyses. The difference between the PL value predicted by the ML model and the real one was calculated, individualized for each player using a z-score transformation (LEI), and interpreted as a sign of fatigue (negative LEI) or neuromuscular readiness (positive LEI). A linear mixed model was used to analyze how LEI changed according to the period of the season, day of the week, and weekly load. Regarding seasonal variation, the lowest and highest LEI values were recorded at the beginning of the season and in the middle of the season, respectively. On a weekly basis, our results showed lower values on match day \(-\) 2, while high weekly training loads were associated with a reduction in LEI.  
soccer; fatigue; machine learning; training load; PlayerLoad
## 2 Materials and Methods
### Statistical Analysis  
Of the 31 participants who underwent assessments, 1 participant has been excluded because of a difference in the event that she is an expert in (race walking), resulting in the inclusion of 30 participants. Normality of data was examined using the Shapiro-Wilk test and normal Q-Q plots. Variables with confirmed normality are expressed as mean \(\pm\) standard deviation, while those that did not show normality are expressed as median and quartiles. Measured stature was compared with reported \"ideal\" height to calculate difference between current and ideal values (\(\text{Difference}_{\text{IdealFit}}\)). Similarly, difference between measured body mass and reported ideal weight (\(\text{Difference}_{\text{IdealFit}}\)) was calculated as well as difference between current and ideal weight for the current height (\(\text{Difference}_{\text{CurrentHt}}\)) and difference between current and perceived ideal weight for the competition (\(\text{Difference}_{\text{Competition}}\)). Furthermore, BMI was calculated on the basis of (1) measured stature and body mass (BMI), (2) perceived ideal height and weight (BMI\({}_{\text{Ideal}}\)), (3) measured stature and perceived ideal weight for the current height (\(\text{BMI}_{\text{Ideal}\text{Wt}}\)) and (4) measured stature and perceived ideal weight for the competition (\(\text{BMI}_{\text{Competition}}\)). The above-mentioned differences between measured and perceived values and calculated BMIs were compared using paired t-tests. Chi-squared tests were conducted to assess associations between current BMI and perceived physique as well as body fatness. In order to examine associations between anthropometric and body composition variables on body-image scales (i.e., BSS, BAQ, EAT-26 and their subscales), stepwise regression analyses were conducted. All statistical analyses were conducted using the IBM SPSS Statistics package (version 27.0, IBM, Tokyo, Japan). All statistical tests used a significance level of 0.05 unless otherwise stated.
# Transitioning to Society 5.0 in Africa: Tools to Support ICT Infrastructure Sharing
## 1 Introduction  
Society 5.0 represents an opportunity to transform the economy and create a digital society with the goal of long-term sustainable development and economic growth [1]. In order to effectively and efficiently achieve this transformation, there is--among many factors--a growing importance of boosting ICT, and there is a close link between UN Sustainability Goals and visions of Society 5.0 [2]. In this respect, target 9c of the UN Sustainable Development Goals is to 'Significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries'. Nigeria is the fastest growing telecommunication market in Africa. The executive vice president of the Nigeria Communication Commission stated that the number of 3G and 4G base transceiver masts has increased from 30,000 to 53,460 over the past 5 years in a report published in January 2021. This statement implies that 1G, 2G, radio and television station masts, which are largely concentrated in urban areas, are excluded [3]. As a result of increasing subscribers, all mobile network service providers in Nigeria are building new BTSs, often without considering existing infrastructure. This has led to aproliferation of masts, defacing the environment and causing unnecessary environmental pollution as BTSs are largely powered by diesel generators. In addition, this proliferation of masts is not only unsustainable but presents unnecessary economic and service barriers to end users. It is therefore becoming paramount for the telecommunication regulatory body in Nigeria to address these issues and support transition to a sustainable digital society, with inclusive and reliable access to ICT without compromising business goals and profitability [4]. In this article it is suggested that principles of infrastructure sharing and the colocation of sites for all mobile network service provider BTSs should be enforced to improve network availability, reliability, scalability, customer satisfaction and sustainability. This work argues, through the development of ICT tools and their application to a case study, that infrastructure sharing and colocation of sites is not only feasible if supported correctly but also offers the potential to reduce operational and capital expenditure, reduce the number of BTSs required for the rapidly growing mobile telecoms industry in Nigeria and in doing so reduce environmental pollution and support the transition to a digital society with the goal of long-term sustainable development and economic growth.
# On the Spectral Efficiency for Distributed Massive MIMO Systems
## 1 Introduction
This study deals with the above-mentioned issues and proposes the following contributions:  
Figure 1: Evolution roadmap of the communication standards and major technological challenges for future mobile communications. A field study on the spectral efficiency of distributed MIMO systems for the sub-6 GHz band that quantifies the losses of spectral efficiency due to power unbalance in several distributed MIMO systems working together. In this paper, channel propagation measurements are acquired in reverberation chambers to emulate different environments;
* A theoretical study is carried out in order to simulate a distributed massive MIMO system in the mmWave band. A deep analysis on the spectral efficiency is presented using the NYUSIM simulator;
* A field study is performed to emulate a distributed MIMO system in the mmWave band. A measurement campaign is performed to emulate several distributed MIMO scenarios above 30 GHz. A comparison with the power unbalance in the sub-6 GHz band is made in order to see the implementation viability of distributed MIMO scenarios for future mobile generations. The study is organized as follows. Section 2 introduces the distributed MIMO model and the channel matrix that provides the spectral efficiency of the measured scenario. Sections 3-5 show the analysis of the D-MIMO scenarios in real deployments and simulations, and sub-6 GHz and mmWave bands. Finally, the main conclusions of the article are drawn in Section 6.
## 5 Performance Analysis  
In this section, we analyze the proposed precoding-based CF implementation under light of the probability of error. Note that we will make use of the geometrical properties of lattices in obtaining closed form expressions of the probability of error [14, 15].  
The end-to-end probability of error of the precoded CF is culminated by two error probabilities: (i) the probability of error from the source to relay node and (ii) the probability of error from the relay to destination node. In this subsection, we obtain two probabilities separately and, then, formulate the end-to-end probability of the error expression.
## 1 Introduction
Savanna is a relatively productive ecosystem with an average of \(720\pm 200\) g\(\cdot\)C\(\cdot\)m\({}^{-2}\)\(\cdot\)yr\({}^{-1}\)[18], and the entire African savanna biome gives an NPP of 8.9 Pg\(\cdot\)C\(\cdot\)yr\({}^{-1}\), accounting for 13.6% of the global NPP. There are still large uncertainties about the contribution of the African continent to the global carbon budget, since few long-term measurements have been carried out [19]. The degradation of savannas, which is defined as the reduction in vegetation production, will influence the long-term carbon balance and other ecosystem services [20]. There is a pressing need for quantitative assessments of land degradation as savanna ecosystems are essential for local populations [17] and have been changing largely as a result of climate change and overgrazing. Research on long-term NPP change can provide an early warning of land degradation and be used to support policy development for food security and economic development [21]. The emergence and development of remote sensing has provided a powerful instrument to observe, monitor and characterize landscapes, since it is able to offer repeat data of large areas of the terrestrial earth surface at longer temporal scales [22]. Satellite data can provide realistic information on vegetation dynamics, which is helpful to reduce uncertainties in the estimation of the carbon-budget [14]. Based on remote sensing data, partially or entirely, a variety of models have been developed to estimate NPP, e.g., the Carnegie-Ames-Stanford approach (CASA) [1], GLObal Production Efficiency Model (GLO-PEM) [2], the Biosphere model integrating Eco-physiological And Mechanistic approaches (BEAMS) [23], and MODIS daily photosynthesis and annual NPP product (MOD17) [24]. However, a long-term NPP time series is still unavailable, although the introduction of the newly released AVHRR GIMMS3g dataset will invariably help to address this gap. In this study two globally available data sets of the earth's surface will be analyzed and we will investigate the variations in NPP derived from (1) GIMMS3g NDVI data and (2) the monthly MODIS NPP product (MOD17A2). These datasets will be used initially to compare the products and then to apply developed methods form the comparison to the entire AVHRR GIMMS3g thirty year time-series. We will then link this longer time series of vegetation changes to precipitation variability from 1982 to 2010. We address three main research questions: (1) Can we use the GIMMS3g products to estimate NPP from 1982 to 2010 based only on its relationship to the MODIS MOD17 NPP product? (2) How has NPP changed from 1982 to 2010 across the study area? and (3) What are the relationships between mean annual precipitation (MAP) and annual NPP, as they vary across the main three savanna types and MAP intervals?