esa-sceva/satcom-chunk-collection huggingface dataset:
# New Methods of Series Expansions between Three Anomalies
## 2 Materials and Methods  
### The Geometric Relationship among True Anomaly, Elliptic Anomaly, and Mean Anomaly  
As shown in Figure 1, the point \(O\) is a focus of the satellite orbit ellipse. \(O^{\prime}\) is the center of the ellipse. Point \(O^{\prime}\) is the center of the circle. \(r\) is the radius vector of the satellite orbit. And the major half-axis of the ellipse is the radius of the auxiliary circle. \(S\) is the position of the satellite; perpendicular lines through \(S\) intersect \(H\) and extend the \(HS\) intersection auxiliary circle at \(S^{\prime}\). \(P\) is the perigee, \(f\) is the true anomaly, \(E\) is the elliptic anomaly, and \(M\) is the mean anomaly. It can be seen from Figure 1 that the coordinate expression of the satellite orbital radius vector \(r\) is as follows [22; 23]:  
\[\left\{ \begin{array}{l}x=r\cos f=a\cos E-ae\\ y=r\sin f=a\sqrt{1-e^{2}}\sin E\end{array} \right. \tag{1}\]  
In the above formula, parameter \(e(e\ll 1)\) is the eccentricity of the satellite's elliptical orbit. The relation between the radius vector \(r\) and the true anomaly \(f\) can be obtained from the polar equation of the ellipse [24; 25]:  
\[r=\frac{a\big{(}1-e^{2}\big{)}}{1+e\cos f} \tag{2}\]  
The above formula can be transformed by shifting terms to obtain the following formula:  
\[r\cos f=\frac{a\big{(}1-e^{2}\big{)}-r}{e} \tag{3}\]  
The following formula can be obtained by combining the first equation of Formula (1) with Formula (3) [26; 27]:  
\[r=a-ae\cos E=a(1-e\cos E) \tag{4}\]  
By substituting Formula (4) into Formula (1) and replacing the radius vector \(r\), we can obtain the formula for calculating the true anomaly \(f\) from the elliptic anomaly \(E\)[28; 29]:  
Figure 1: Schematic of the three types of anomalies. \[\left\{ \begin{array}{l}\cos f=\frac{\cos E-e}{1-e\cos E}\\ \sin f=\frac{\sqrt{1-e^{2}}\sin E}{1-e\cos E}\end{array} \right. \tag{5}\]  
From Formula (5), we can also obtain the formula for calculating the elliptic anomaly \(E\) from the true anomaly \(f\):  
\[\left\{ \begin{array}{l}\cos E=\frac{\cos f+e}{1+e\cos f}\\ \sin E=\frac{\sqrt{1-e^{2}}\sin f}{1+e\cos f}\end{array} \right. \tag{6}\]  
From Formula (5), we can also derive the formula for the relationship between the true anomaly and the elliptic anomaly [30;31]:  
\[\tan\frac{f}{2} =\sqrt{\frac{1-\cos f}{1+\cos f}}=\sqrt{\frac{(1+e)(1-\cos E)}{(1- e)(1+\cos E)}} \tag{7}\] \[=\sqrt{\frac{1+e}{1-e}}\tan\frac{E}{2}\]  
The relationship between the mean anomaly \(M\) and the elliptic anomaly \(E\) is determined by [PERSON]'s equation [32;33]:  
\[M=E-e\sin E \tag{8}\]  
The Derivation of the Expansion Formula for the Relationship between True Anomaly and Elliptic Anomaly  
The essence of the Lagrange conjugate series is the series expansion of the tangent relationship between two variables [34]. The definition of the Lagrange conjugate series is as follows.
# Energy Efficient Power Allocation for the Uplink of Distributed Massive MIMO Systems
## 1 Introduction  
With the world-wide popularization of fourth-generation networks, mobile data traffic and the number of user equipments (UEs) are increasing exponentially. To satisfy the growing requirement, key technologies have been proposed and studied recently for fifth-generation (5G) networks [1; 2].
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 4 Experiments and Results
### Analysis of Forecast Results  
#### 4.4.1 Comparative Experimental Analyses  
In this experiment, we used different models to make predictions in latitude and longitude; the actual AUV trajectory is denoted as \"real\", and the predicted trajectory is denoted as \"prediction\". The prediction results of the GAT-ABiGRU model proposed in this paper are shown in Figure 6. Figure 6a shows the prediction results of the GAT-ABiGRU model in latitude, while Figure 6b shows the prediction results of the GAT-ABiGRU model in longitude. To make a valid comparison, in addition to the GAT-ABiGRU model proposed in this paper, we also tested several other neural network models that have performed well in the field of time-series prediction, including RNN, GRU, and CNN-LSTM.To ensure the fairness of the comparison, all models were evaluated using the same training and test sets. Figure 7a shows the prediction results of RNN, GRU, CNN-LSTM and GAT-ABiGRU models in latitude. Figure 7b shows the prediction results of these models in longitude.  
After comparative analysis, the GAT-ABiGRU model proposed in this paper shows significant advantages in fitting real trajectories, and its predicted trajectories almost exactly match the actual trajectories with excellent performance. This is closely followed by the CNN-LSTM model, which also performs similarly excellently in predicting latitude  
Figure 6: (a) Latitude prediction results from the GAT-ABiGRU model; (b) Longitude prediction results from the GAT-ABiGRU model.  
Figure 7: (a) Latitude prediction results from different models; (b) Longitude prediction results from different models.  
and longitude. In contrast, the GRU and RNN models, although capable of predicting trajectories, are relatively less accurate in matching the actual trajectory.  
In Table 1, the evaluation metrics for latitude and longitude are denoted by \({}^{\circ}\)N and \({}^{\circ}\)E, respectively. The experimental results reveal that the RNN model has the lowest predictive accuracy for the AUV test dataset. It is outperformed by the GRU model and the CNN-LSTM model, with our proposed GAT-ABiGRU model demonstrating the highest performance. Compared with the GRU model, which is assessed using the RMSE metric, the GAT-ABiGRU model improves the accuracy of latitude predictions by 0.04-0.05 \({}^{\circ}\)N and longitude predictions by 0.03-0.04 \({}^{\circ}\)E. These results underscore the significant advantage of our model in accurately predicting AUV navigation trajectories.
# Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach
## 3 Machine Learning HRTF Restoration
### Data Pre-Processing  
The training and testing data were extracted from different HRTF databases, including ARI [42], ITA [43], RIEC [44], SADIE I [45], SADIE II [9], IRCAM Listen [46] and the Bernschutz KU100 [47].  
The SH interpolation process takes place in the time domain, as HRIRs which are then converted to frequency domain HRTFs after the interpolation process for input to the restoration model. The phase is discarded after the conversion because using complex numbers in conventional NN and CNN can be problematic for certain functions. Whilst alternative methods for using complex numbers have been proposed, it is questionable whether these nontrivial methods are necessary for this project [48; 49; 50; 51]. Note that due to the randomness of machine learning models, there is a chance that a model could incorrectly give negative amplitude spectra in the output. To avoid this, the input and output data were scaled to decibels. The output data were then rescaled before converting back to the time domain.
## 4 Measurement Setup
### Satellite Setup  
In this article, we present the first measurement results using the algorithm described on-board the LUME-1 satellite. The measurement areas were decided based on where interference has previously been reported in the UHF amateur radio band (America, Europe, north of Africa, and the Middle East) [11; 12; 13]; over areas where there are known radar sources and can be of interest to Norway (the Arctic) [21]; and areas in the ocean (South Pacific and South Atlantic). All measurements are referred to as interference over populated areas, except for the last category. The latter are named noise measurements over non-populated areas because the spectra measured had lower power and because there are lower population concentrations.  
The Concept of Operations (CONOPS) for the measurement campaigns is as follows:  
1. **Plan schedule**. The date and time of passes where the satellite is over the areas of interest are estimated using the Python library _pyorbital_ and the corresponding Two-Line Element (TLE) from Celestrak [30].
2. **Upload schedule**. When there is availability over the ground station, the schedule for the measurements is uploaded to the satellite. The schedule includes the date and time to execute the command to run the measurement software, the command, and its parameters.
3. **Run measurement**. TOTEM is turned on and the measurement software runs for five seconds every minute for 10 min. Afterwards, TOTEM is turned off until the next measurement set.
4. **Downlink data**. After all measurements are carried out, the UHF transmitter is turned on to downlink the measurement files.  
The size of each data file depends on the number of frequency bins (\(M\)), the number of bits per sample (\(n_{b}\)), and the number of outputs of the software (\(n_{u}\)): \(M\cdot n_{b}\cdot n_{u}\).  
In Table 2, the data budget for the measurement algorithm described in this paper, with the configuration of Section 4, is compared to raw spectrum monitoring with the same parameters for five seconds and for one minute continuously. By raw spectrum monitoring, we refer to a spectrogram (commonly known as waterfall plot), which is a representation of the power spectrum of a signal as it changes in time. The on-board processing software  
Figure 2: Flatsat setup. The USRP is used as the transmitter for test signals, and the TOTEM SDR (Software-Defined Radio) is the receiver.  
provides a data reduction of 203,400 times compared to the 10 min waterfall and 1920 times compared to the five-second waterfall.  
Assuming an available downlink rate of 1 kbps, the time estimated to downlink one set of 10 measurements is shown in Table 3. The number of days to downlink is estimated assuming an average of one pass per day with average five minutes for the 4.8 kbps configuration.
###### Abstract  
A soil temperature estimation model for increasing depth in a permafrost area in Alaska near the Bering Sea is proposed based on a thermal response concept. Thermal response is a measure of the internal physical heat transfer of soil due to transferred heat into the soil. Soil temperature data at different depths from late spring to the early autumn period at multiple permafrost sites were collected using automatic sensor measurements. From the analysis results, a model was established based on the relationship between the normalized cumulative soil temperatures (\(CRCST^{*}\),\({}_{im}\) and \(CST^{*}\),\({}_{ul,m}\)) of two different depths. \(CST^{*}\),\({}_{ul,m}\) is the parameter of the soil temperature measurement at a depth of 5 cm, and \(CRCST^{*}\),\({}_{im}\) is the parameter of the soil temperature measured at deeper depths of \(i\) cm (\(i\) = 10, 15, 20, and 30). Additionally, the fitting parameters of the mathematical models of the \(CRCST^{*}\),\({}_{im}\)-\(CST^{*}\),\({}_{ul,m}\) relationship were determined. The measured soil temperature depth profiles at a different site were compared with their predicted soil temperatures using the developed model for the model validation purpose. Consequently, the predicted soil temperatures at different soil depths using the soil temperature measurement of the uppermost depth (5 cm) were in good agreement with the measured results.  
soil temperature; thermal response; cumulative soil temperature; soil temperature modelling +
Footnote †: journal: Applied Sciences
# A Novel Finite Element Method Approach in the Modelling of Edge Trimming of CFRP Laminates
## 5 Results and Discussion
### Numerical FE Model Validation  
For the validation of this model, the average force data provided in the research of [PERSON] et al. [44] were used. All the machining configurations tested in the machining trials developed by [PERSON] et al. [44] with a cutting tool edge radius of 10 \(\upmu\)m were simulated. In total, eight simulations with feed rates of 800 mm/min and 1200 mm/min, spindle speeds of 6000 rpm and 8000 rpm, and fibre orientations of 45\({}^{\circ}\) and 90\({}^{\circ}\) were performed. The inclusion of the spring back phenomenon, together with the limitation in the damage of shear and matrix applied, can simulate trends in machining forces very similar to those obtained experimentally. Finally, a definite improvement was found when compared to the predictions made by [PERSON] et al. [44], as shown in Figures 11-14.  
Figure 10: Representation of relevant machining parameters analysed: (**a**) spring back phenomenon. (**b**) machining relative angle, (**c**) machining relative angle distribution for 45\({}^{\circ}\) laminates and (**d**) machining relative angle distribution for 90\({}^{\circ}\) laminates.  
Figure 11: Validation of FE predictions with \(f_{r}\) = 800 mm/min, \(w\) = 6000 rpm, and fibre orientation of (**a**) 45\({}^{\circ}\) and (**b**) 90\({}^{\circ}\).  
Figure 12: Validation of FE predictions with \(f_{r}\) = 800 mm/min, \(w\) = 8000 rpm, and fibre orientation of (**a**) 45\({}^{\circ}\) and (**b**) 90\({}^{\circ}\).  
The small error percentages simulated show the reliability of this FE model in predicting accurate machining forces in any cutting configuration. These errors do not exceed 10%  
Figure 14: Validation of FE predictions with \(f_{r}\) = 1200 mm/min, \(w\) = 8000 rpm, and fibre orientation of (**a**) 45\({}^{\circ}\) and (**b**) 90\({}^{\circ}\).  
Figure 13: Validation of FE predictions with \(f_{r}\) = 1200 mm/min, \(w\) = 6000 rpm, and fibre orientation of (**a**) 45\({}^{\circ}\) and (**b**) 90\({}^{\circ}\).  
in most of the average forces simulated, as shown in Table 4. Only the configuration with \(f_{r}=1200\) mm/min, \(w=8000\) rpm, and \(90^{\circ}\) obtained an error higher than \(10\%\) in \(F_{Y}\) forces. However, a glance to Figure 14 reveals that the standard deviation calculated for these trials is considerably bigger than the standard deviation obtained in the rest of the trials. Furthermore, the model predicts a similar trend observed in the experimental findings for \(F_{X}\) and \(F_{Y}\) forces. From these factors it is concluded that a small error in the measurements of these forces could be the cause of this discrepancy.
## 4 Model Design  
Traditional traffic prediction methods such as ARIMA, SVM, ELM, RNN, etc., usually separate the sampling and prediction processes, treating them as two distinct, independent procedures. Unfortunately, the use of constant time stepping of sampling in these methods can often lead to excessive sampling and decreased efficiency. Although the literature does not explicitly state it, using a fixed sampling rate in traffic prediction methods that do not take into account the changes in traffic speed may lead to computational complexity and inaccuracies in traffic predictions. To optimize resources and reduce costs, our innovative solutions must account for computational efficiency and prediction performance while also being tied to cost and public good estimations.  
Figure 2: Structure of traffic prediction system model.  
This article showcases the implementation of adaptive time steps in a GRU prediction model and introduces a simple and easy method for determining time step divisors for highly dynamic traffic load events. Additionally, this paper proposes a new algorithm, referred to as ATS-GRU, that merges adaptive time stepping and GRU. Our method employs a feedback loop where the prediction algorithm error is used to control the time stepping, considerably decreasing overhead while still ensuring traffic prediction accuracy.
## 2 Results and Discussion
### Microbial Analysis
So, during refrigerated storage, microbial cells wall repaired, and microorganisms reverted to their vegetative and culturable state. This is why we decided to end the refrigerated storage at T20 as, although physicochemical, enzymatic, and antioxidant properties were still well preserved and maintained, the microbial safety of the PEFJ was not ensured after 20 days. Previous studies have reported longer microbial stability during shelf-life studies. [PERSON] et al. [44] reported that PEF-processed orange juices (40 kV/cm for 97 \(\upmu\)s) maintained the microbial counts at less than 1 Log at 4 \({}^{\circ}\)C for 112 days. [PERSON] et al. [25] showed that PEF-processed orange juice (35 kV/cm for 1000 \(\upmu\)s) was stable for 56 days at 4 \({}^{\circ}\)C. Other authors [20] reported that PEF-treated (35 KV/cm; pulse width 3 \(\upmu\)s; measured outlet temperature 67 \({}^{\circ}\)C) Opuntia cactus juice maintained good microbial stability after 15 days of storage at 8 \({}^{\circ}\)C for total colony, yeast/mold, and acid tolerant microorganism counts. In these studies, stronger electric fields were applied and, as a consequence, higher temperatures were probably reached during the process, therefore allowing a longer microbial stability during the juice shelf-life to be achieved. The PEF process herein proposed was operated in continuous mode using a patented treatment chamber, which guarantees a constant flow and homogeneous treatment, avoiding the onset of \"hot spots\". Moreover, a minimum increase in temperature was recorded during the process, never exceeding 45 \({}^{\circ}\)C. It is plausible, therefore, to state that all microorganisms were inactivated due to electroporation and no additional thermal damage had occurred. It must be also pointed out that untreated juice (UJ) was very rapidly spoiled by microorganisms and reached almost 6 Log CFU/mL for all the investigated microbial consortia after 5 days at 4 \({}^{\circ}\)C. Our results suggest that PEF-stabilized freshly squeezed blood orange juice might not be considered an alternative to the thermally pasteurized one, but as a freshly squeezed blood orange juice to be allocated within a new retail segment, i.e., that of freshly squeezed juices with better health quality and an extended refrigerated shelf-life. Figure 6: Mesophilic viable bacterium (**A**), yeast and mold (**B**), and orange juice spoilage microorganism (**C**) growths in PEFJ (PEF-treated juice) during refrigerated storage (4 \({}^{\circ}\)C). Red dots in each graph indicate the initial microbial load in UJ (untreated juice). Data are expressed as means of three analytical replicates \(\pm\) standard errors.
# Current Trends on Green Wireless Sensor Networks
## 2 Energy Conservation
### Radio Optimization  
Since the radio module is the main component that causes the node battery to be exhausted, many researchers have addressed several mechanisms in wireless communications such as optimizing modulation or coding, cooperative communications, power transmission control, directional antennas or cognitive radio.  
Figure 1: Taxonomy of energy-efficient mechanisms proposed in [10].
## 2 Geological Setting  
### Regional Geological Setting  
The Huangling Anticline is located at the intersection of the northern margin of the Yangtze Plate and the multi-directional tectonic lines within the plate [30]. The latitude and longitude of the Huangling Anticline are 30\({}^{\circ}\)45\({}^{\prime}\) N to 31\({}^{\circ}\)30\({}^{\prime}\) N and 110\({}^{\circ}\)45\({}^{\prime}\) E to 111\({}^{\circ}\)30\({}^{\prime}\) E, respectively. Since the discovery of primary quartz vein-type gold deposits in 1966, nearly three hundred gold-bearing quartz veins have been identified in this area, accompanied by over seventy occurrences of gold mineralization. Notable deposits include Mahuagou, Baizhuping, Shuiyuesi, and Bancanghe [31]. The region predominantly showcases the Middle to Lower Ordovician metamorphic rock series. In the northern part, these rocks are classified as the \"Shuiyuesi Group\".  
Meanwhile, in the southwestern part, they belong to the \"Kongling Group\", as per the West Hubei Geological Brigade reports from 1987 and 1991. Both rock units form the foundational rocks of the Huangling Anticline, and the Huangling Anticline has been intruded by subsequent granitic formations [32]. They have later been collectively termed the Kongling Group, also known as the Kongling Complex or Huangling Complex, by the Hubei Provincial Bureau of Geology and Mineral Resources. The ancient exposed basement metamorphic rocks in this region have yielded an amphibolite Sm-Nd isochron dating age of 3290 \(\pm\) 170 Ma from the Kongling Complex [33]. Hence, this region is crucial for gold mineralization in Hubei Province and offers valuable insights into the ancient basement of the Yangtze Plate [34; 35].  
As depicted in Figure 2a, the Huangling Anticline is a nearly north-south trending dome-shaped short-axis fold, spanning roughly 73 km in the north-south direction and about 36 km in the east-west orientation. The core of the Anticline is mainly composed of the Huangling Complex and Huangling Granite. In contrast, its limbs are predominantly made up of marine carbonate and clastic rocks from the South China Series to the Triassic, all dipping away from the core [36].  
Figure 1: Geographical location of the study area (data sources: the new (2022) version of the national provinces, cities, districts, and counties administrative boundaries of the nine-dash line vector map data).  
The Huangling Granite primarily characterizes the magmatic rocks exposed in the Huangling Anticline area. This granite is one of the few intracontinental Neoproterozoic granitic bodies within the Yangtze Plate [38]. The Huangling Granite mainly consists of granite porphyry, tromhjemite, biotite monzogranite, tonalite, and quartz diorite. In the study \"Sequence, Evolution, and Gold-bearing Properties of Precambrian Metamorphic Complexes in the Huangling Anticline\", the West Hubei Geological Brigade (1996) categorized the acidic rocks in the core of the Huangling Anticline into four super units: Duanfangxi, Sandouping, Huanglingmiao, and Dalaoling, as well as the Xiaofeng composite pluton, with ages ranging from early Neoproterozoic to the middle Neoproterozoic.