esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Fundamentals System Models
#### 2.1.1 Massive MIMO  
Massive MIMO has been much researched regarding channel response. First, as illustrated in Figure 1, we investigate a typical massive MIMO system. A block diagram is assumed for delivering data sources from the X (vector of transmitted signals) to the Y (vector of received signals). In this model, the channel parameter \(\mathrm{h}\) represents the communication link between nodes X and Y, which clearly shows the behavior of Equation (1). Rayleigh channels are commonly used in massive MIMO systems, due to their ease and accuracy in simulating the internal conditions of the communication channel. They can also estimate the impact of low-risk distribution in a large number of wireless communications. However, it should be noted that using Rayleigh channels in massive MIMO often results in simulating channel states invariantly. Since the simulation is local, this approach may yield results that are not entirely accurate to natural conditions.Including the future possibilities of 6G, studies across various works have found that the current number of antennas in massive MIMO still face operational limitations in the THz frequency bands, starting with as few as 16 antennas. Meanwhile, the concept of ultra-massive MIMO, discussed in this research, explores the use of a significantly larger number of antennas, up to 256, representing a full-capacity model. Further details will be provided in the subsequent section.  
Figure 1: The structure of massive MIMO system.
## 3 Joint System Implementation  
### Filter Bank Multicarrier  
Typically, information is transmitted through pulses that overlap in time and frequency in a multicarrier system [25]. The transmitted signal \(s(t)\) of the time domain multicarrier system can be expressed as follows:  
\[s(t)=\sum_{l=0}^{L-1}\sum_{k=0}^{K-1}g_{k,l}(t)x_{k,l} \tag{1}\]  
where \(x_{k,l}\) represents the transmitted symbol at subcarrier-position \(k\) and time-position \(l\). \(g_{k,l}(t)\) in Equation (1) is the transmitted basis pulse that is defined as follows:  
\[g_{k,l}(t)=p(t-lT)e^{l2\pi kF(t-lT)}e^{i\theta_{k,l}} \tag{2}\]  
and is a prototype filter \(p(t)\) essentially, where \(F\) is the subcarrier interval, \(T\) is the time spacing, and \(\theta_{k,l}\) is the phase shift. After transmission over the channel, the received symbols are decoded by projecting the received signal \(r(t)\) onto the underlying pulse \(g_{k,l}(t)\), such that  
\[g_{k,l}=\left\langle r(t),g_{k,l}(t)\right\rangle=\int\limits_{-\infty}^{ \infty}r(t)g_{k,l}^{*}(t)dt \tag{3}\]  
which corresponds to a matched filter in an additive white Gaussian noise (AWGN) channel.  
Figure 1: Application scenario of joint communication and jamming system.  
Figure 2 shows the main differences between OFDM and FBMC, with an overlap factor \(K=4\)[26]. The former uses a rectangular window as the prototype pulse with a sinc function in the frequency domain sub-band with high partials, while the latter applies well-designed prototype filters with essentially no out-of-band leakage. PHYDYAS and Hermite prototype filters are discussed in more detail in Section 4.2. In addition, the good out-of-band suppression of the spectrum and the very high spectrum usage efficiency can make the jamming energy more concentrated in the main lobe and the jamming distance longer. These excellent properties allow FBMC to be used as an alternative signal for the joint communication and jamming system.  
We introduce time representations for FBMC-QAM and FBMC-OQAM (see Figure 3). There is no single definition for FBMC-QAM. It either compromises on frequency localization or sacrifices orthogonality [27; 28]. FBMC-OQAM, with the same symbol density as OFDM without CP, is related to FBMC-QAM. OQAM divides the complex signal of each subcarrier into two parts. A transmission delay of half a symbol period (\(T/2\)) is added so that the adjacent subcarrier interference is suppressed, ensuring that any subcarrier is orthogonal to its adjacent subcarriers while achieving full-rate transmission. Compared to OFDM transmitting complex signals, FBMC-OQAM transmits real-value signals on each subcarrier with half the coding efficiency of OFDM. However, the symbol rate of OQAM modulation is twice that of OFDM, so both signals' data rates are at the same level.
## 3 Results  
The data were divided by randomly selecting 80% of the data to train and 20% of the data to test each methodology used. In the training process, k-fold cross-validation with \begin{table}
\begin{tabular}{c c} \hline
**Algorithm** & **Description** \\ \hline Quadratic SVM & Type: Support vector machine \\  & Kernel type: Quadratic polynomial \\ \hline Cubic SVM & Type: Support vector machine \\  & Kernel type: Cubic polynomial \\ \hline Fine Gaussian SVM & Type: Support vector machine \\  & Kernel type: Gaussian \\  & Kernel scale: 5.3 \\ \hline Medium Gaussian SVM & Type: Support vector machine \\  & Kernel type: Gaussian \\  & Kernel scale: 21 \\ \hline Fine k-NN & Type: \(K\)-nearest neighbor \\  & Number of neighbors: 1 \\  & Distance: Euclidean \\ \hline Medium Gaussian k-NN & Type: \(K\)-nearest neighbor \\  & Number of neighbors: 10 \\  & Distance: Euclidean \\ \hline Cosine k-NN & Number of neighbors: 10 \\  & Distance: Cosine \\ \hline Cubic k-NN & Type: \(K\)-nearest neighbor \\  & Number of neighbors: 10 \\  & Distance: Minkowski distance with exponent p = 3 \\ \hline Weighted k-NN & Type: \(K\)-nearest neighbor \\  & Number of neighbors: 10 \\  & Distance: Euclidean \\  & Distance weight: Squared inverse \\ \hline Bagged Trees & Type: Ensemble classifier \\  & Method: Bootstrap aggregating \\ \hline Subspace k-NN & Type: Ensemble classifier \\  & Method: Subspace-based KNN \\ \hline \end{tabular}
\end{table} 
Table 1: This table shows a description of the 11 algorithms implemented. k = 5 was used. For the training dataset and the testing dataset, the accuracy, sensitivity, specificity, and precision [33] were calculated for each class. These parameters are given by  
\[accuracy(C_{i})=\frac{TP_{i}+TN_{i}}{TP_{i}+TN_{i}+FP_{i}+FN_{i}} \tag{7}\]  
\[sensitivity(C_{i})=\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{8}\]  
\[specificity(C_{i})=\frac{TN_{i}}{TN_{i}+FP_{i}} \tag{9}\]  
\[precision(C_{i})=\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{10}\]  
where:  
\(TP_{i}\): true positives for class \(i\). \(TN_{i}\): true negatives for class \(i\). \(FP_{i}\): false positives for class \(i\). \(FN_{i}\): false negatives for class \(i\). Tables 2-12 show the results for each algorithm.
## 5 Cleaning of the PV System  
### Losses and Dust Effects  
Once the prediction models are developed, one can utilize them in investigating the behavior of the PV system based on a single variable, while all other variables are excluded. In this regard, the objective is to investigate the effect of the dust accumulation on the behavior of the PV system conversion efficiency. To this end, the built prediction models can then be fed with the average daily ambient temperature only, while excluding the dust accumulation (the exposure days); i.e., the exposure days variable is set to zero, which indicates that there is no dust accumulation. The output of the prediction models will be the PV daily system conversion efficiency due to all factors without the effect of dust accumulation, as depicted in Figure 7. It can be seen that the daily conversion efficiency curves no longer have decreasing trends due to dust accumulation as time passes without cleaning (refer to Figure 6).  
Specifically, when comparing the actual (measured) daily conversion efficiency to the predicted one while excluding the dust effect using the optimized single hidden layer ELM model, it is found that the average daily efficiency drop is 0.615%/ day. In addition, based on Equation (1), the energy loss due to the dust effect over the whole study period is found to be 8.329 kWh/m\({}^{2}\) (408.652 kWh).  
Considering that the electricity tariff in Jordan (universities sector) during the study period is 0.366 US$/kWh [54], then the loss for the whole PV system will be equal to 3.76 US$/m\({}^{2}\) (US$ 184.627) with an average loss due to dust accumulation of 0.836 US$/day. For completeness, the same analysis is carried out for the other prediction models, and the obtained results are reported in Table 2.
## 4 A Case Study of the LW7434
### Relationship between Main Impact Factors and the Seismic Events
Therefore, in the quantification of subsequent weights, the inclination of the coal bed should be considered with moderate importance. Figure 7 illustrates the relationship between the variation of key parameters and the seismic event distribution associated with faults during the mining of LW7434. The correlation between fault density and seismic event distribution is more pronounced than that with fault strength. Notably, when a high fault density zone extends throughout the entire longwall, a significant accumulation of seismic events is observed, indicating that the number of fault structures exerts a considerable influence on the distribution of seismic events. Figure 5: Relationship between coal seam thickness variation and seismic distribution in LW7434 mining. The red circles indicate areas of abnormal increase in coal seam thickness. Figure 6: Relationship between the change of coal dip and seismic distribution in LW7434 mining. (**a**) Inclination along the direction of dip. (**b**) Inclination in the direction of the strike. Figure 8 displays the relationship between the monthly advancement of LW7434 and the frequency of seismic events. It reveals that from October 2018 to March 2019 and from July 2019 to December 2019, there is a notable correlation between the longwall mining speed and the frequency of seismic events. This observation suggests that longwall mining speed significantly impacts localized areas, while its influence varies considerably across the entire longwall range. Consequently, the significance of mining speed in identifying the main control factors of seismic hazard differs. It follows from the above statement that the coal depth, coal seam thickness, coal dip, and mining speed are the overall factors, which means that these factors pose different  
Figure 8: Relationship between monthly advancement of LW7434 and seismicity frequency. Figure 7: Relationship between variation of key parameters and seismic distribution of faults in LW7434 mining. The red lines are the horizontal projection of the faults. (**a**) Fault density versus seismic distribution. (**b**) Fault intensity versus seismic distribution. Please refer to Appendix A for definitions of fault density and fault strength. levels of dynamic hazards during mining. In addition, the influence of mining speed covers the whole mining stage of the longwall, while the influence of fault structure is only prominent in the local area.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 3 Results
### Experiment 1: Algorithm Comparison  
In this experiment, we compared three Abel integral algorithms and two interpolation methods. The BA of FY3D was the true value, while the forward-modeled EC4 Dvar BA was the experimental value. The FY3D observation data were filtered by the strict collocation  
Figure 2: Flow chart of experiment two, evaluation of forward modeled analysis.  
and 4 QC strategies. Subsequently, 368 FY3D profiles in September 2019 remained, which were regarded as the true value (T). EC4 Dvar refractivity data collocated with the true values were the experimental values (E). We used two statistics in this experiment: the difference (A \(-\) B) and relative difference (A \(-\) B)/B \(\times\) 100%, where A and B can be either E and T or E and E, respectively. The obtained statistics were all mean values. Note that we compared the exp algorithm with the direct and exp_T algorithms independently because the exp_T algorithm is too close to the exp algorithm. If the exp_T algorithm was drawn in the same figure with the direct algorithm, it would coincide with the graph of the exp algorithm.
## 5 Discussion
### Transplanting Phase Determination  
MODIS data have been used in several case studies to retrieve rice phenology on a large scale [11; 19; 47], which has explicitly spatial information and finer resolution than the agricultural phenological calendar released by the agricultural administration, which is usually at the state-level or province-level. In the HJH plain, the 500 m resolution has been able to reflect the general trend of phenology: the difference between distant pixels may be more than 40 days while the difference between adjacent pixels is small. To accommodate potential field variation within the same pixel, a wide time window is adopted at the expense of introducing false signals. Finer resolution of phenological information can narrow the time window, which requires the use of medium resolution satellites, such as Landsat or Sentinel-1/2. Although revisit cycles can be shortened by combining multiple satellites [27]--for example, 5-7 d for Sentinel-2 twins. However, such observation frequency still cannot guarantee a smooth vegetation index time series to be generated, as in the case of exemplar field in Figure 7, where only one valid Sentinel-2 observation is obtained from 153 d to 203 d due to cloud coverage. Cloud coverage is more frequent in a low-latitude area, such as Vietnam, so daily observation frequency is desirable, which could be achieved by image fusion techniques, such as Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM, [48]). [PERSON] [47] used STARFM to fuse MODIS and Landsat data to obtain daily Landsat-like observations, and successfully retrieve plot-level phenology in cloudy areas. However, it is currently not easy to implement this in GEE, so this promising technique is not integrated in our PODS algorithm.  
On the other side, SAR backscatter coefficients time series [28] and interferometric coherence time series [49] can be used to retrieve rice phenology, but there are some caveats: SAR data are sensitive to soil moisture (dielectric constant), surface roughness as well as terrain, so non-vegetation factors (e.g., tillage, precipitation) can introduce substantial uncertainty in the time series. Moreover, imaging defects in mountainous regions, such as fore-shortening, shadowing, and overlaying phenomenon, limit its use.  
When the region of interest is large, it is necessary to take into consideration all kinds of interfering factors, including terrain, tillage, and precipitation. The vegetation index from optical data is less influenced by these factors and thus is a better proxy of crop progress [50]. Built upon vegetation index time series, the PhenoRice algorithm is reported to be robust and reliable in several case studies [4; 51].
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model
### EPT-VMD Decomposition Layer  
#### 3.2.1. EPT Decomposition  
From the previous analysis of the dataset, it is clear that the load data are not only non-stationary but also have a certain periodicity. If the VMD is used directly to decompose it, the trend of the load series will not be extracted. In this paper, we use EPT to process the load data f and extract the trend of the original load series, so as to obtain the trend component t and the fluctuation component R. The results of decomposing the load data series of Area 1 by using EPT are shown in Figure 10. The results of decomposing the load data series of Area 2 by using EPT are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Samples** & **Range** & **Numbers** & \begin{tabular}{c} **Mean** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **Max** \\ **(MW)** \\ \end{tabular} & \
\begin{tabular}{c} **Min** \\ **(MW)** \\ \end{tabular} &
\begin{tabular}{c} **Std.** \\ **(MW)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Area 1} & All samples & 1 January 2012–10 January 2015 & 106,176 & 6915.33 & 12,296.85 & 1306.08 & 2094.08 \\  & Training & 1 January 2012–2 June 2014 & 84,864 & 6657.81 & 11,446.85 & 1306.08 & 2034.47 \\  & Testing & 3 June 2014–10 January 2015 & 21,312 & 7945.53 & 12,296.85 & 2267.67 & 2010.75 \\ \hline \multirow{3}{*}{Area 2} & All samples & 1 January 2012–10 January 2015 & 106,176 & 7357.43 & 13,536.74 & 1986.32 & 2180.43 \\  & Training & 1 January 2012–2 June 2014 & 84,864 & 7038.44 & 12,466.10 & 1986.32 & 2054.60 \\ \cline{1-1}  & Testing & 3 June 2014–10 January 2015 & 21,312 & 8627.67 & 13,536.74 & 3259.12 & 2204.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset load details.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Max. Temperature (\({}^{\circ}\)C)** & **Min. Temperature (\({}^{\circ}\)C)** & **Avg. Temperature (\({}^{\circ}\)C)** & **Relative Humidity (avg.)** & **Rainfall (mm)** \\ \hline
1 August 2012 & 36.0 & 23.1 & 30.3 & 71.0 & 26.5 \\
2 August 2012 & 35.6 & 27.8 & 31.9 & 61.0 & 0.0 \\
3 August 2012 & 33.9 & 28.0 & 30.9 & 63.0 & 0.0 \\
4 August 2012 & 31.3 & 27.8 & 29.5 & 75.0 & 0.0 \\
5 August 2012 & 31.0 & 26.2 & 28.5 & 82.0 & 1.8 \\
6 August 2012 & 30.8 & 24.7 & 26.9 & 87.0 & 12.3 \\
7 August 2012 & 34.5 & 25.8 & 29.4 & 79.0 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of meteorological factors from 1 August to 7 August 2012 (Area 1).  
Figure 9: Load trend chart for four consecutive weeks for Area 1.  
In Figures 10 and 11, it can be seen that the Tr(t) component obtained using EPT extraction has a strong periodicity and regularity for both Area 1 and Area 2. The Re(t) component has non-smoothness and needs further processing to obtain a relatively smooth subsequence.
## 3 Results and Discussion
### Study of Coffee Silverskin Oil  
#### 3.2.1 TG/DTG Analysis of Coffee Silverskin Oil  
One of the components of silverskin that is worth being isolated and reused in other industrial fields due to its potential functional and nutritional properties is fat. There is no information in the literature dealing with silverskin oil characteristics. Taking the above into consideration, one of the goals of the study was fat extraction from the silverskin sample in order to define its properties by means of TG, PDSC and GC techniques. The profiles of the TG and DTG curves of fat samples  
Figure 2: (**a**) Thermogravimetry/derivative thermogravimetry (TG/DTG) curves of coffee silverskin in oxygen atmosphere. (**b**) TG/DTG curves of coffee silverskin in nitrogen atmosphere. (**c**) TG/DTG curves of coffee silverskin fat samples in oxygen atmosphere. (**d**) TG/DTG curves of coffee silverskin fat samples in nitrogen atmosphere.  
extracted from coffee silverskin, analyzed in oxygen and nitrogen atmospheres, are presented in Figure 2c,d, respectively.  
The thermal decomposition of fat samples in oxygen occurs in three stages. The most significant changes in weight, associated with 60% of mass loss, are observed in the region of 150 to 350 \({}^{\circ}\)C and correspond to the decomposition of polyunsaturated fatty acids (PUFAs). It is thought that the initial step associated with unsaturated fatty acid decomposition is of great importance in defining the thermal stability of studied oils. The second step in the thermal decomposition of silverskin oil with 16% of mass loss is present in the temperature range from 350 to 450 \({}^{\circ}\)C and corresponds to the decomposition of monounsaturated fatty acids, such as oleic acid. The third event in the thermal decomposition, which occurs in a region of 450-560 \({}^{\circ}\)C, followed by 24% of mass loss corresponds to the thermal decomposition of saturated fatty acids, such as palmitic and stearic acids. It is worth mentioning that at the temperature of 560 \({}^{\circ}\)C, the decomposition of material is complete. These results are in agreement with study of [PERSON] et al. [40], who determined thermal properties of sunflower oils with artificial antioxidants and without addition of artificial antioxidants. The authors observed three thermal steps in the range of 230 to 550 \({}^{\circ}\)C on TG/DTG curves, related to the decomposition of polyunsaturated, monounsaturated and saturated fatty acids, with no residue remaining at 800 \({}^{\circ}\)C. The thermal decomposition of fat samples in nitrogen atmosphere occurs in two stages ranged from 150 to 300 \({}^{\circ}\)C in the case of first event with 37% of loss mass to 300-520 \({}^{\circ}\)C for the second step, associated with 63% of loss mass.
# Field Emission from Carbon Nanostructures
## 1 Introduction  
Several technological applications need controlled electron sources, examples are X-ray sources [1], flat panel displays [2; 3], electron microscopy/lithography [4], and microwave vacuum electronic devices [5]. One of the most common techniques for generating an electron beam is to emit electrons from metals by thermionic effect, in which a metallic emitter is heated at temperatures typically above 1000 \({}^{\circ}\)C so that electrons in the conduction band gain enough energy to overcome the work function of the emitter. However, the main drawbacks of thermionic emission are the high energy consumption, the wide energy spectrum of the emitted electrons and the material degradation caused by the high operating temperature. A different approach is that of the \"cold cathodes\", which operate at room temperature. Electrons are extracted from the surface of a metal or a semiconductor by an electric field (field emission, FE) through quantum mechanical tunneling. This process, which typically requires a high electric field (10\({}^{7}\)-10\({}^{8}\) V/cm) [6], creates high current density with thinner energy distribution of the emitted electrons [7; 8]. Moreover, cold cathodes provide very stable emission over time, and can be easily and advantageously miniaturized.  
In this review, we focus on the field emission properties of various carbon nanostructures. In particular, after a brief introduction to the field emission phenomenon, we examine the different experimental setups used for FE characterization as well as the technology in which FE has been traditionally exploited. We turn then to the different carbon allotropes, and we review the main results concerning field emission from carbon nanotube (CNT) films, individual CNTs, diamond and related structures, and finally graphene.
## 3 Results
### Comparison of the Model with Other Models  
To validate the superiority of the SpectraViT model used in this study, comparisons were first made with traditional classification algorithms, including SVM and PLS_DA. Then, comparisons were conducted with CNN-based models such as AlexNet and DeepSpectra, as well as with the Transformer-based SpectraTr model for time-series sequences.