esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods  
### Satellite Sensors  
The NDVI datasets used in this study are derived from 3 different satellite sensors. While NDVI is derived from the red and NIR bands on these sensors, each sensor has a different spectral response function (SRF) associated with each band and have varying sensitivities to vegetation. In the red band, the optimum sensitivity to vegetation is between 660 and 680 nm [43] and variations in the SRF of the red band can influence NDVI [44]. The NIR band is even more sensitive to vegetation with the optimum range from 840 to 880 nm. Two regions surrounding this range; one from 820 to 830 nm and the other from 900 to 980 nm are significant water vapor absorption regions and water vapor in the atmosphere can reduce the NIR signal reflected from vegetation thereby reducing NDVI if the sensor NIR band is sensitive to these regions [45].  
The Advanced Very High Resolution Radiometer (AVHRR) sensor's main advantage is that it has a continuous record aboard the NOAA series of satellites beginning in 1981. However, it also has the weakness of having a broad SRF in the red band (580-680 nm) and the NIR band (730-980 nm); especially in the NIR band, resulting in less accurate vegetation estimates. Also, the NOAA satellites have some difficulty in keeping their orbits stable and therefore they may drift several hours local time over a period of several years; much more than newer satellites such as SPOT, Terra, and Aqua [46].  
The VEGETATION sensor (1 km) on the Satellite Pour l'Observation de la Terre (SPOT) satellite is a newer sensor (from 1998). The red band (610-680 nm) is narrower and more sensitive to chlorophyll content than AVHRR and the NIR band (780-890 nm) is also narrower but is still influenced by the weak water vapor absorption band (820-830 nm) [47].  
The Moderate Resolution Imaging Spectrometer (MODIS) (250 m; onboard Terra (2000 to present) and Aqua (2002 to present)) has the highest spectral resolution of the three sensors explored in this study (R: 620-670 nm; NIR: 840-880 nm) and has high sensitivity to vegetation content and avoids the water vapor absorption bands of the VEGETATION and AVHRR sensors [48].
# RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities
## 1 Introduction
In this context, given the relevance of the data collected in industrial facilities, we seek to propose a forecast model of predictive maintenance activities using data mining techniques by means of this topic. This data mining will identify the dependencies between the spare parts and then predict the occurrence of each maintenance sequence. Manufacturers are still confronted with an immense downtime and have to bear additional costs for transporting spare parts [7]. Consequently, there is a need to learn the maintenance activity sequences performed on the equipment and the way to approximately use the spare parts. This process will reduce the downtime activities by determining the occurrence of the next maintenance activity and the real need for related spare parts. A Sequential pattern mining technique (i.e., one of the data mining techniques) will be a better approach for deducing the sequences and predicting the number of spare parts to be used. The sequential pattern mining approach tracks the relationships or patterns among the data objects over the time periods that are reported in the database. The records of the daily maintenance activities are captured in the maintenance database of the company with timestamp information. The usual maintenance transaction has important attributes such as the maintenance number, the date of maintenance, the equipment number, the equipment sub-section number, the maintenance type, the spare parts used, the quantity of the spare parts, the cost of the spare parts, the labor cost, etc. Using this information, one can find out the relationship between the activities conducted relating to a particular day's maintenance and the actions required to be performed for the next day's maintenance period. Sometimes this process is also useful for unearthing the lowest time gap between the same maintenance activities due to faulty maintenance work done initially. In this work, the sequential pattern mining algorithm [8] is used on a maintenance dataset which is collected from a large maintenance database of a mining company. Initially, the frequent sequential patterns of the time interval are extracted by using the support or threshold values. Next, the sequential rules are generated from the frequent patterns and a proposed rule based on the classification approach is applied to predict the occurrence of the next maintenance activities. Finally, the spare part codes are mapped on to the discovered sequential maintenance activities. The rest of the paper is organized as follows. In Section 2, we summarize the research methods presented in the literature and the procedure for mining sequential patterns and discovering sequential rules. The proposed framework for mining sequential maintenance activities and spare parts mapping are described in Section 3. In Section 4, an industrial case study is presented to illustrate the performance of the proposed method. The results and analysis of the case study are also made in the same section. Finally, the conclusions and perspectives are provided in Section 5.
# A Review of Neural Network-Based Emulation of Guitar Amplifiers
## 3 Neural Network-Based Methods
### Data Sets
The SignalTrain data set from [51] was used for training, testing, and validation of the shallower TCN architectures. This data set contains input-output recordings (at 44.1 kHz) of various instruments from a LA-2A dynamic range compressor. The training data used for the NARX network of [30] was comprised of both signals from a function generator (with frequencies in the range [100 Hz, 500 Hz]) and an electric guitar fed to a vacuum tube amplifier, a 4W Vox AC4 TV. All training data was recorded at a sampling frequency of 96 kHz and saved to 24-bit stereo wav files with one channel containing the raw input signal and the other containing the tube amplifier signal. The guitar recordings from this data set were used for the LSTM training in [31]. A summary of these data sets is presented in Table 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Set** & **Details** \\ \hline Custom & Function generator with frequencies \(\in\) [100 Hz; 500 Hz] + electric guitar fed to a vacuum tube amp; 96 kHz; stereo (1 channel input, 1 channel output) at 96 kHz used in [30] \\ \hline Custom & Guitar into tube amplifier; 96 kHz; stereo used in [31] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of the data sets used for training and their different characteristics. As the choice of training data has a decisive impact on the performance of a neural network, so does the choice of cost function used in the optimization process.
## 4 Light Intensity- and Spectrum-Associated Adjustment of Metabolism
### Light Regulation of Nitrogen Assimilation and Amino Acid Levels
#### 4.2.2 The Effect of Light Quality on Nitrogen Assimilation and Amino Acid Levels  
Similar to light intensity, the light spectrum can also affect nitrate reduction through its effect on the HY5 transcription factor or the redox system (Figure 5). Comparing the effect of WL, and monochromatic BL and RL, the nitrate reductase and nitrite reductase activities and the expression of the genes of these enzymes were the highest in BL-treated pakchoi plants (_Brassica campestris_ L.) [157]. Transcriptional regulation of nitrate reduction by supplementary BL and FRL was observed in wheat [67]. Spectrum-dependent redox control of the nitrate reduction was suggested in wheat based on simultaneous changes in the GSH/GSSG ratio in leaf tissues [67].  
The spectrum-dependent control of nitrate reduction allows the subsequent modification of the free amino acid levels, as shown for supplementary FRL, which increased the amount of Glu, Gln, and Pro in flag leaves of wheat [158]. It should be noted that, similar to light intensity, spectral changes likewise did not affect the total protein levels in flag leaves of wheat; therefore, the observed changes in their levels were not derived from protein degradation [158]. An inducing effect of BL on free amino acid accumulation was shown in barley [159]. Specific ratios of red, blue, and green spectral components seem to be important in the control of free amino acid levels, based on the results obtained in lettuce [160]. Interestingly, the low R:FR ratio in shade induced the accumulation of several amino acids (Glu, Pro, Gly, Asp, Val, Ser, Glu, Thr) and the related transcripts in leaves of tea compared to its leaves exposed to sunshine [161]. This result differs from the observations obtained in other plant species. Given that tea plants in natural habitats are adapted to shade, it should be considered that the decreased free amino acid levels in sunshine indicate a disturbed metabolism.  
Similar to light intensity, light spectrum was also involved in the adjustment of amino acid levels to adverse environmental conditions (Figure 5). Namely, BL and FRL participated in the transcriptional control of salt-induced Pro accumulation by inducing its synthesis and repressing its degradation in _Arabidopsis_[162]. This regulation is based on the interaction of HY5 with the promoter region of the genes of Pro metabolism. In addition, supplementary BL or FRL strongly increased the level of several free amino acids during drought, only in a tolerant wheat variety, which indicates the relationship between their spectrum-dependent changes and adaptation processes [91].
## 2 Concept of the Photothermal Approach
\begin{table}
\begin{tabular}{l l} \hline \hline
**Challenge** & **Solution** \\ \hline Heating the surface with a & Photothermal heating by a short pulse from a flash lamp or a laser with beam shaping while the pulse length should be much shorter than the observation period \\ \hline  & Knowing the reproducible energy density released from the used external energy source (\(Q_{ext}\)), the absorbed energy density (\(Q\)) can be calculated by subtracting the actual reflected energy on the inspected surface using the reflection coefficient (\(R\)): \\ Measuring the actual & \(Q=Q_{ext}(1-R)\) \\ absorbed energy (\(Q\)) & where \(R\) is an effective parameter and results from the integral of the spectral intensity distribution of the source weighted with the reflection spectrum of the surface \\ \hline Measuring the fast & Using an infrared camera with a sufficiently high frame rate (\(>\approx\)10 Hz) or a fast pyrometer with emissivity correction \\ \hline Selection of a suitable zero & Taking the half of the pulse length is mathematically correct \\ point for the time scale in & (Dirac pulse is the limit of a series development of square pulses \\ relation to the pulse length & with area \(Q\) of decreasing pulse width) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of challenges and possible solutions for a technical implementation of the photothermal approach as an on-site inspection method.
## 3 Results and Discussion
As shown in Figure 6, the bending properties of the electrodes also showed a strong correlation to the type and dimension of the second phase fillers. After 3000 bending cycles, the composite electrodes with AC, CNT and G fillers had a high K value, above 100%. However, the K value of pure Ag NPs and Ag NP/Ag NW electrodes were less than 20%. It suggests that the Ag electrodes can maintain their conductivity during deformation unlike the Ag/C electrodes (with AC, CNTs, or G). This is due to more interconnections and better interfacial strength for silver-silver than silver-carbon after sintering. In Ag NP/Ag NW electrodes, fillers were bonded through metallic bonds. However, the connections between carbon and silver were mainly by van der Waals bonds, which results in a lower interface bonding strength [42]. As shown in Figure 7a, in printed pure Ag NP electrode after bending test, Ag was still connected due to its good ductility. As for Ag NP/AC composite electrodes (Figure 7b), Ag debonded with AC to form isolated particles and reduced their interconnections. However, as 1D material, CNTs were pulled out from silver due to weak bonding between silver and carbon (Figure 7c), while some Ag NWs deformed plastically and some were still in bridging (Figure 7d). For Ag NP/carbon electrodes, Ag NP/G shows  
Figure 4: The resistivity as a function of laser scanning times. Figure 5: Scanning electron micrograph of sintered electrodes with: (**a**) AgNP; (**b**) AgNP/AC; (**c**) AgNP/CNT; (**d**) AgNP/AgNW; and (**e**) AgNP/G inks. highest K value after 3000 bending cycles, meaning bad flexibility. Figure 7e shows the microstructure of Ag NP/G electrodes after bending. During bending, graphene sheets could detach with silver and generate large microcracks due to the weak interfacial bonding and large stiffness mismatch between silver and graphene [43]. This study shows that electrical and mechanical properties can be tuned by adding different dimensional fillers in binder-free inks, which could benefit the ink formulation and ink-printing technique for electrode fabrication.
# Performance Analysis of Multihop Underground Magnetic Induction Communication
## 3 Performance Analysis
### Multihop MI Communication with Statistically Independent Non-Identical Hops (Unbalanced Hops)  
In the practical realisation of MI-WUSNs, when static nodes are buried underground, the fading amplitude can vary despite the well-controlled conditions, owing to unforeseen circumstances such as sudden seismic changes, ground water penetration, and interference. Even the assumption of equidistant nodes does not hold over time because of the possibility of the position and orientation of the deployed static antenna changing because of, say, pressure caused by factors such as growth of plant roots or interaction with subsurface animals such as rabbits. Therefore, one of the most critical aspects of underground communication is establishing long-range communication in the heterogeneous underground environment. For such non-identical hops, the error probability of each hop \(E_{h}\) for \(h\in\{1,2,\ldots,K-1\}\) depends on the state of the underground channel. The underground channel can be considered to be slowly fading [35], and hence, the instantaneous CSI varies across hops, with the variation depending on the fading amplitude (7); this variation causes the SNR \(\gamma_{i}\) to vary. In this case, consecutive links cannot be characterised by the same BEP (\(E_{i}\)). This error probability is a function of the SNR and is bounded by the link with the weakest SNR [39], that is, \(\gamma_{i=2}=min\{\gamma_{1},\gamma_{2}\}\). The end-to-end SNR \(\gamma_{c2e}\) can then be written as  
\[\gamma_{c2e}\approx\underset{i=1,\ldots,H}{min}\gamma_{i} \tag{24}\]  
For a multihop communication model, (24) is a tight approximation for determining the end-to-end system SNR. The end-to-end channel capacity of a relay channel is also bounded by (24), so applying the max-flow min-cut theorem [40], the upper bound for channel capacity of a multihop relay based MI-WUSN can be given as:  
\[C_{c2e}=\underset{i=1,\ldots,H}{min}C_{i}(\gamma_{i}). \tag{25}\]If there are INID states between hops, the error probability of each hop is different and (21) becomes an inequality. Using the multinomial theorem [41], we can express the term \(\left(1-2E(H)\right)^{H}\) in (20) as the recurrence relation  
\[\left(1-2E(H)\right)^{H}=\prod_{j=1}^{H}\bigl{(}1-2E_{j}\bigr{)}. \tag{26}\]  
Therefore, substituting (26) in (20), we can obtain the end-to-end BER for the INID of multihop in an MI-WUSN as  
\[E_{H}=\sum_{i=1}^{H}\left(E_{i}\prod_{j=i+1}^{H}\bigl{(}1-2E_{j}\bigr{)}\right) \tag{27}\]  
A closed-form upper-bounded expression for (27) is presented in [38] as  
\[E_{H}=1-\prod_{i=1}^{H}(1-2E_{i}) \tag{28}\]  
We can also see that (27) converges to (22), provided the initial condition \(E_{i}\) remains constant for all hops under condition (21):  
\[E_{H}=\sum_{i=1}^{H}\left(E_{i}\prod_{j=i+1}^{H}\bigl{(}1-2E_{j}\bigr{)}\right) =\sum_{i=0}^{H-1}E_{i}(1-2E_{i})^{i}. \tag{29}\]  
This expression proves the validity of both (22) and (27) for the IID and INID of hop states in an MI-WUSN, respectively.
## 3 Methodology
### Agent Architecture  
Figure 3 shows an example of the selection process for the five hyperparameters of a CNN with an agent. The initial state is randomly initialized, and then the action distribution output at each time step is used as the input for the next time step. As depicted in Figure 4, the agent consists of an input fully connected (FC) layer, three LSTM layers, and an output FC layer.  
LSTM is a variant of the recurrent neural network (RNN) that solved the long-term dependency problem of vanilla RNN by using a gating system. There is a cell state \(C_{t}\) and three gates in the LSTM cell: input gate \(i_{t}\), output gate \(o_{t}\), and forget gate \(f_{t}\). The input gate \(i_{t}\) determines how much new information is allowed to be added to the cell state; the forget gate \(f_{t}\) determines what information is discarded from the cell state \(C_{t}\); and the output gate \(o_{t}\) determines what values are output. The following formulas denote the forward process of the LSTM cell:  
Figure 4: The structure of the agent. The agent consists of an input fully connected layer, three LSTM layers, and two output fully connected layers which use tanh and softplus activation functions, respectively.  
Figure 3: An example of our agent selecting hyperparameters sequentially for CNN model. Agent selects the required hyperparameters for the CNN at each time step and then feeds them to the next time step as input.  
\[f_{t} =\sigma_{g}(W_{f}\cdot[h_{t-1};x_{t}]+b_{f}) \tag{3}\] \[i_{t} =\sigma_{g}(W_{i}\cdot[h_{t-1};x_{t}]+b_{i})\] (4) \[o_{t} =\sigma_{g}(W_{o}\cdot[h_{t-1};x_{t}]+b_{0})\] (5) \[c_{t} =f_{t}\odot c_{t-1}+i_{t}\odot(tanh(W_{c}[h_{t-1},x_{t}]+b_{c}))\] (6) \[h_{t} =o_{t}\odot tanh(c_{t}) \tag{7}\]  
where \(W_{f}\), \(W_{i}\), \(W_{c}\), \(W_{o}\) are weight matrices and \(b_{f}\), \(b_{i}\), \(b_{c}\), \(b_{o}\) are bias vectors need to be learned. \(\sigma_{g}\) and \(\odot\) are the sigmoid function and Hadamard product, respectively. Furthermore, \(h_{t}\) is the hidden state and \(x_{t}\) is the input at step \(t\). This structure can also be extended into a multi-layer structure by leveraging the output of the last layer as the input to the next layer.  
Following [15], we represent the distribution of each hyperparameter as a Gaussian distribution. Then, both the input and output of our agent are vectors containing two values: the mean \(\mu\) and standard deviation \(\sigma\) of the Gaussian distribution, respectively. The output at step \(t\) will be sent to the agent to obtain the result at step \(t+1\). The initial input we set to the standard normal distribution. The input FC layer is used to expand the input into the same dimensions as the LSTM layer, with the aim of learning more patterns. The LSTM layer is applied to learn the temporal dependency between the steps. The output layer consists of two FC layers. After receiving the output of the LSTM layer, one of the FC layers uses the tanh function to output a value as \(\mu\), while the other uses the softplus function to output \(\sigma\).
# Guaranteeing QoS for NOMA-Enabled URLLC Based on \(\kappa\)-\(\mu\) Shadowed Fading Model
## 1 Introduction
### Background and Motivation
Furthermore, it is still an urgent issue to exploit the effect of small-scale fading characteristics on the performance of the CF mMIMO system under the \(\kappa\)-\(\mu\) shadowed fading model to achieve NOMA-enabled URLLC with high resource efficiency.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Data Quality  
One participant did not complete the NASA-TLX questionnaire, resulting in a sample size of 21 for the workload analysis (9 experts and 12 novices). The temporal variables analysis excluded two experts and two novices who had a low gaze sample percentage (<60%) and one novice due to incomplete eye data recording (8 experts and 9 novices). The gaze sample percentages for both groups were as follows: between 60% and 70% for two experts, between 70% and 80% for three experts and one novice, and more than 80%  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Units** & **Definition** \\ \hline Absolute duration & min & \begin{tabular}{c} Difference between the timestamps of the \\ milestones bordering the time \\ period studied. \\ \end{tabular} \\ \hline Document consultation duration & min & \begin{tabular}{c} Sum of gaze fixation duration performed \\ within the AMM during the time \\ period studied \\ \end{tabular} \\ \hline Execution duration & min & \begin{tabular}{c} Difference between document consultation \\ duration and absolute duration \\ \end{tabular} \\ \hline Percentage of time spent consulting the \(\text{documentation}\) & \% & \begin{tabular}{c} Ratio between the documentation \\ consultation duration and the \\ absolute duration \\ \end{tabular} \\ \hline Number of consultations & \(\emptyset\) & \begin{tabular}{c} Number of consultations in the time \\ period studied. \\ \end{tabular} \\ \hline Average consultation duration & s & \
\begin{tabular}{c} Average duration of consultations over the \\ time period studied. \\ \end{tabular} \\ \hline NASA-TLX scores & \(\emptyset\) &
\begin{tabular}{c} The weighted scores on the six dimensions \\ of the NASA-TLX. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dependent variables and their definitions.  
Figure 3: Decomposition of the maintenance operation and evolution of document consultation for one participant.  
for three experts and nine novices. The decision to exclude participants demonstrating a gaze sample percentage below 60% was rooted in our commitment to precision and to avoid potential biases in the analysis.
## Abstract:  
The correct estimation of the mineral content of cassava (_Manihot esculenta_) genotypes is vital from a nutritional point of view. This study evaluated the effects of the storage root section, maturity, and sampling method on the macro- and microelements in yellow-fleshed cassava root genotypes. In total, 44 genotypes were grown in replicated field trials of 2 sets (set 25 and set 19) and were harvested at 9 and 12 months after planting. Two sampling methods, sampling with a cork borer (A = proximal, B = middle, C = distal or method 1) and sampling without a cork borer (L = Longitudinal or method 2), were used. The minerals of the samples from the two methods were determined using inductively coupled optical emission spectrometry (ICP-OES). K and Mn were the most abundant minerals, and Na, Mo, and Co were the least abundant. Genotype, method, and maturity had a strong influence on mineral concentrations. Harvesting time affected the concentration level of some macro- and microelements in cassava roots. Additionally, Ca, Mg, K, P, Mn, Cu, Ni, and Zn contents were significantly (\(p<0.05\)) higher in the proximal and middle portions for method 1. K and P and Mn and B were positively correlated, but K and Na and Fe and Ni were negatively associated.  
macroelements; microelements; yellow-fleshed cassava; genotypes; storage portions; sampling method; maturity