esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 5 Testing and Validation
### Evaluate the Execution Time for Selecting the Services from the Large-Scale Repository
The service selection is lesser than other methods because the search space for service selection is reduced using the K-Means clustering algorithm. The Improved Eagle Strategy (IES) demonstrates superior performance compared to the Whale Optimization Algorithm (WOA) and Eagle Strategy (ES), as evidenced in Figure 19[41]. The IES model excels in selecting services more rapidly, especially in larger search spaces. Unlike the ES [26], which primarily concentrates on non-functional WS attributes, the IES comprehensively addresses both functional and non-functional aspects. This holistic approach, coupled with the use of K-Means clustering, reduces the search space, thereby enhancing service selection efficiency. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Improved Eagle Strategy (ES)**} \\ \hline
**NOS 1** & \multicolumn{3}{c}{**Time Taken (ms)**} & \multicolumn{1}{c}{**Mean (ms)**} \\ \hline
300 & 0.002 & 0.002 & 0.003 & 0.0023 \\
400 & 0.003 & 0.002 & 0.003 & 0.0027 \\
500 & 0.005 & 0.003 & 0.006 & 0.0047 \\
600 & 0.006 & 0.005 & 0.004 & 0.0050 \\
700 & 0.007 & 0.006 & 0.007 & 0.0067 \\
800 & 0.007 & 0.007 & 0.007 & 0.0070 \\
900 & 0.007 & 0.006 & 0.009 & 0.0073 \\
1000 & 0.007 & 0.014 & 0.008 & 0.0097 \\
2000 & 0.017 & 0.0018 & 0.02 & 0.0129 \\
3000 & 0.017 & 0.015 & 0.02 & 0.0173 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time taken to select the service by IES. Figure 19: Graphical illustration for IES service selection. When compared to the Skyline + Genetic Algorithm (GA) approach by [22], the IES stands out due to its superior compatibility capabilities, addressing the key limitations of the GA method's scalability and computation time. Furthermore, the IES's adaptable framework allows it to function effectively in distributed cloud environments, overcoming the limitations of the GA + FOA combination, which is mainly efficient in single cloud setups. Additionally, methods like PSO + GA [24] and ABC + DE [6], although known for their high-speed convergence, grapple with challenges such as high time complexity and constraints in multi-objective service selection. The IES addresses these challenges by offering a balanced solution that optimizes both speed and multi-objective service selection capabilities.
## 4 Discussion
The effect of Cd was also observed on the distribution of carbohydrates in the seeds and pods of peas. As mentioned earlier only control plants and plants treated with 10 uM CdSO4 produced matured seeds [14]. [PERSON] et al. [90] also showed a reduced number of pods per plant, fewer seeds per pod, and lower seed weight after Cd treatment of pea plants. The present analysis of soluble carbohydrates in the seeds showed that Cd affected the accumulation of sucrose, _myo_-inositol, raffinose, and verbascose. It is worth noting, that the level of RFOs in pea seeds is comparable to that observed by [PERSON] et al. [91]. Interesting results were noticed in the soluble carbohydrate distribution in pods after Cd treatment. The accumulation of sucrose and glucose in pods of plants treated with 50, 100, and 200 uM CdSO4 was noticeable. Additionally, the highest level of maltose, galactinol, and raffinose but the lowest level of _myo_-inositol was observed in pods of plants treated with 50 uM CdSO4. In contrast to that, the highest level of _myo_-inositol and the lowest level of galactinol and raffinose were observed in pods of the plants treated with 200 uM CdSO4. The pods play an important role as a sink in the allocation of plant resources for the development of seeds [92; 93]. In the early stages of seed development, the pod walls act as a temporary reservoir for assimilates, e.g., carbohydrates transported from the leaves/shoots before they are translocated to the developing seeds. In the pod wall, the level of soluble sugars falls during the later stages of development. These are possibly transported into the developing seeds and utilised there for starch synthesis, and thus a pod wall acts as a temporary reservoir of carbohydrates to be later transferred to seeds [94]. The studies carried out by [PERSON] [95] showed that the proportion of the total \({}^{14}\)C fixed by a \({}^{14}\)CO\({}_{2}\)-fed leaf which was exported, was related to the \({}^{14}\)C-sink capacity of the pods. Out of the total \({}^{14}\)C fixed by a leaf, the proportion that was exported within 24 h was related to the \({}^{14}\)C-sink capacity of the pods. Four days from anthesis, pea plants retained a substantial amount (51-67%) of the \({}^{14}\)C in the fed leaf and exported only a small portion (6-17%) to the pod. However, at 22 days from anthesis, the plants exported a higher average percentage (78%) to the pods and retained less (14-26%) in the fed leaf, indicating a shift towards increased carbon allocation to the pods as they mature [95]. This indicates that the proportion of \({}^{14}\)C exported to a pod increased with the plant age. Presented studies show that Cd-induced accumulation of the soluble carbohydrates in the shoots, in comparison to the control plants, where the level of sugars decreased during the experiment. This confirms the inhibitory effect of Cd on the translocation of assimilates to pods, and thus the inhibition of pod and seed development.
# A Phase Correction Model for Fourier Transform Spectroscopy
## 2 Phase Correction by the Mertz Method  
Figure 1 depicts a common imaging FTS system based on a Michelson interferometer. In FTS, the incident beam undergoes modulation within the interferometer, generating two outgoing beams with varying optical path differences. These two beams interfere on the image plane, creating interferograms. The collected interferogram and the spectra of the incident beam are related through Fourier transform:  
\[B(\
u)=\int_{-\infty}^{+\infty}I(\Delta)e^{-j2\
u\Delta}d\Delta \tag{1}\]  
\(B(\
u)\) is the recovered spectrum from interferogram \(I(\Delta)\), \(\
u\) is the wavenumber, and \(\Delta\) is the optical path difference.  
In the Mertz method, small double-sided interferograms near the zero-path difference (ZPD) point, with high interference signal intensity and SNR (signal-to-noise ratio), are usually used to calculate the error phase:  
Figure 1: An imaging FTS system based on a Michelson interferometer.  
\[\varphi=\arctan\frac{Im(\
u)}{Re(\
u)} \tag{2}\]  
Im (\(\
u\)) and Re (\(\
u\)) are the imaginary and real parts of the spectra recovered by a small double-sided interferogram.  
In this method, we assume that the error phase is a slowly varying function with respect to the wavenumber. Therefore, the small double-sided interferograms can be interpolated to the desired number of data points, which is used to guide the phase correction. The correction formula is as follows:  
\[S(\
u)=R(\
u)\cos(\varphi(\
u))+I(\
u)\sin(\varphi(\
u)) \tag{3}\]  
\(R(\
u)\) and \(I(\
u)\) represent the real and imaginary parts of the original recovered spectra, respectively. \(\varphi(\
u)\) represents the error phase obtained from the small double-sided interferograms and \(S(\
u)\) represents the spectra corrected by the Mertz method [9].  
In sampling of interferograms, it is challenging to precisely sample the ZPD position. If the sampled ZPD point is sampled after the real ZPD position with an offset of \(\varepsilon\), according to the displacement property of the Fourier transform, the corresponding phase error is \(\varphi(\
u)=\varepsilon\
u\). However, from the study of [PERSON] [17] we know that the collected interferograms are always finite, i.e., they cannot be infinitely long, and so, do not meet the ideal time-shift property of the Fourier transform, so the phase error caused by the zero-point drift will also have nonlinear components. At the same time, the general Fourier transform spectrometer system can also produce spectra with a nonlinear phase error due to the instrument line shape (ILS) of the spectrometer, caused by optical aberrations [18].
# Viability of Cupola Slag as an Alternative Eco-Binder and Filler in Concrete and Mortars
## 3 Results
### Consistence of Fresh Mortars
#### 3.2.2. Mortars with Different Replacement Proportions  
Figure 9 shows the results of the mechanical characterization of mortars incorporating various levels of Portland cement replacement. The compressive strength (Figure 9a) increases with curing age for all replacements following a logarithmic trend (R\({}^{2}\approx\) 0.85), while intermediate replacements show intermediate behaviors between maximum replacement and reference mortars. After 7 days of curing, mortars with 30% replacement show a 27% loss in compressive strength, with respect to the reference mortar, demonstrating slow hydration reactions. This loss is reduced to 12% after 28 days and 5% after 60 days of curing. At 90 days of curing, the strengths shown for all replacements tend to converge because wing hydration onset and hydration peak occur much later when cupola slag is used than when using Portland cement [17]. ASTM C311 [35] defines the pozzolanicity index (strength activity index) of supplementary cementitious materials as the relationship between the compressive strength of mortars, incorporating 20% replacement, and mortars without replacement, for ages 7 and 28 days. The studied mortars present a pozzolanicity index of 73% and 92% at 7 and 28 days, respectively, active admixtures being considered those that reach 75%.  
Figure 8: Mechanical properties of mortars with different admixtures: compressive strength (**a**); flexural strength (**b**).  
Figure 9: Compressive strength (**a**) and flexural strength (**b**) of mortars with various cement replacements by cupola slag.  
Flexural strength (Figure 9b) also increases with the curing age for all replacements and in a manner analogous to compressive strength. After 7 days of curing, the strength shown by replacements of 30% is 25% lower than that obtained with the reference mortars. For 28 and 60 days of age, the losses reach 10% and 6%, respectively. Again, there is a more pronounced age strength development when cupola slag is incorporated into the mortar. For ages over 90 days, the values tend to converge once more, showing once again the slowness of the reactions and demonstrating the strong pozzolanic character of the ground cupola slag.
## 3 Materials and Methods
### Datasets
The audio data were collected by two instructors (one male and one female) and carefully segmented and labeled. The development of the SHEIE dataset involved a four-step process. First, it was determined that real interactions, rather than acted emotions, would be recorded during live lectures via the Blackboard e-learning system. Second, the emotions represented in the dataset were selected based on their relevance to the instructor's experience during the lecture. Third, volunteer instructors from the two universities were selected to participate in the study, and they provided speech data via their lecture recordings. Finally, the emotions in the dataset were labeled based on self-reporting by instructors, which meant selecting an emotion from a prompt every ten minutes during their lectures. The time interval was chosen in response to research indicating that student attention begins to wane around the ten-minute mark. The SHEIE dataset underwent extensive preprocessing, which included splitting the data into ten-minute segments, manually labeling the emotions, cleaning the data to exclude noise and unwanted sounds, and normalizing the data into equal three-second intervals. This produced 7515 audio files, each labeled with a specific emotion and formatted for use in SER research: 490 files for anger, 1888 files for happiness, 1439 files for sadness, 516 files for boredom, 1654 files for excitement, and 1528 files for neutrality. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & \multicolumn{2}{c}{**Number and Types of**} & **Sample Size** & **Type of Dataset** & **Status** \\ \hline Ryerson Audio–Visual Database of Emotional (RAVDESS) & Eight: disgust, fear, sadness, angriness, happiness, surprise, calmness, neutrality & 7356 & Multimodal: speech and song & Acted \\ \hline Berlin Database of Emotional (EMO-DB) & Seven: anger, disgust, fear, happiness, neutrality, sadness, boredom & 535 & Speech & Acted \\ \hline Surrey Audio–Visual Expressed Emotion (SAVE) & Seven: anger, disgust, fear, happiness, neutrality, sadness, surprise & 480 & Speech & Acted \\ \hline Interactive Emotional Dyadic Motion Capture (IEMOCAP) & Eight: happiness, anger, sadness, frustration, surprise, fear, excitement, neutrality & 7513 & Multimodal: speech, video, and motion capture & Acted \\ \hline Saudi Higher–Education (SHEIE) & Six: anger, happiness, sadness, excitement, boredom, neutrality & 7515 & Speech & Natural \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the datasets. Figure 2 shows the distribution of the different emotional classes in the five datasets. Some emotional classes occur more often in most datasets, whereas others demonstrate the inverse. Thus, there are imbalances in the emotional class distributions across the datasets, indicating the need for DA before model training. Table 1 presents a description of all five datasets as well as the distribution of the classes in these datasets.
## 2 Proposed EPP-Based Beam Scanning Approaches
### Proposed EPP-BSA
#### 2.1.1 Simulation Results of the Proposed EPP-BSA
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Figure** & **Cosine Wave Amplitude \(A_{c}\)** & **Main Beam Direction \(\theta_{r}\), in \({}^{\circ}\)** & **SLL (dB)** & **HPBW in \({}^{\circ}\)** & **Maximum Scanning Range** \\ \hline Figure 7a & 0 & 90\({}^{\circ}\) & \(-\)12.8 dB & 12.8\({}^{\circ}\) & \\ \hline Figure 7b & 0.19 \(\lambda\) & 82.99\({}^{\circ}\) & \(-\)10.09 dB & 12.67\({}^{\circ}\) & \\ \hline Figure 7c & 0.25 \(\lambda\) & 80.92\({}^{\circ}\) & \(-\)9.346 dB & 12.65\({}^{\circ}\) & \\ \hline Figure 7d & 0.5 \(\lambda\) & 72.16\({}^{\circ}\) & \(-\)6.82 dB & 12.33\({}^{\circ}\) & \\ \hline Figure 7e & 0.75 \(\lambda\) & 63.91\({}^{\circ}\) & \(-\)4.963 dB & 11.85\({}^{\circ}\) & \\ \hline Figure 7f & \(\lambda\) & 56.69\({}^{\circ}\) & \(-\)3.618 dB & 11.1\({}^{\circ}\) & \\ \hline Figure 7g & 1.25 \(\lambda\) & 50.5\({}^{\circ}\) & \(-\)2.671 dB & 10.45\({}^{\circ}\) & \\ \hline Figure 7h & 1.5 \(\lambda\) & 45.34\({}^{\circ}\) & \(-\)2.002 dB & 9.8\({}^{\circ}\) & \\ \hline Figure 7i & 1.75 \(\lambda\) & 40.7\({}^{\circ}\) & \(-\)1.454 dB & 9.3\({}^{\circ}\) & \\ \hline Figure 7j & 2 \(\lambda\) & 37.09\({}^{\circ}\) & \(-\)1.233 dB & 8.75\({}^{\circ}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The relation between the amplitude changes of the cosine waveform and the scanning angle, HPBW, SLL, and maximum scanning range \(\theta_{r}\) for 8-element uniform LAA at compression ratio \(R=1\). On the other hand, when the cosine wave amplitude is changing from negative to positive and its magnitude is changing from \(A_{c}=0\) to \(A_{c}=0.19\lambda\), the array main beam is scanned from \(0^{\circ}\) to \(-7.01^{\circ}\) with a constant HPBW and the SLL is less than \(-10\) dB. However, as the cosine wave amplitude increases from \(0.19\ \lambda\) to \(1.5\ \lambda\), the scan angle is increased from \(-7.01^{\circ}\) to \(-44.6^{\circ}\), the SLL is increased by more than \(-10\) dB, and the HPBW is decreased by \(3^{\circ}\). But, as the cosine wave amplitude increases from \(1.5\ \lambda\) to \(2\ \lambda\), the scan angle is increased from \(-44.6^{\circ}\) to \(-52.9^{\circ}\) and the grating lobe appears with increased SLL as shown in Figure 8. The achieved maximum scanning range is \(-52.91^{\circ}\)\(\leq\theta_{r}\leq 52.91^{\circ}\) around the broadside direction. Unlike traditional progressive phase shift scanning, as the scanning angle increases away from the broadside direction, the SLL increases, and the HPBW is significantly increased many folds of its value in the broadside case as shown in Figures 7 and 8. However, in the proposed EPP-BSA approach, as the amplitude of the cosine wave \(A_{c}\) increases, the HPBW of the main beam decreases as the length or size of the array projection on the plane normal to the main beam direction \(\theta_{o}\) is increased.
# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 3 Task Scheduling Problem
### Application Model
During the course of execution of the tasks, the processors can be idle, ready to execute, or running. \(Avail(i)\) is the earliest time at which the processor \(P_{i}\) is ready to execute. The actual time to finish the exit task is denoted by \(AFT(t_{exit})\). The edge transfer cost is denoted by associating a weight with each edge that specifies the required time of data transfer from task \(t_{i}\) to \(t_{k}\). The edge \(e(i,k)\) denotes the cost to transfer the data on an \(e(i,k)\) and is defined as  
\[TC_{i,k}=L_{m}+\frac{Data_{i,k}}{Bw_{i,k}} \tag{4}\]  
where, \(Data_{i,k}\), is the cost to transfer data from task \(t_{i}\) to \(t_{k}\) and \(Bw_{ijk}\) (bits/s) is known to be the transfer rate of the link that connects the processors. \(L_{m}\) is the latency time (communication start up time) of the processor and is represented in seconds. The average transfer cost is given by  
\[\overline{TC_{i,k}}=\overline{L_{m}}+\frac{Data_{i,k}}{Bw_{ijk}} \tag{5}\]  
The average transfer cost of an edge is zero if the resources are the same, because the intra-resource data transfer cost is very low when contradicted to the inter- resource data transfer cost  
\[\overline{TC_{i,k}}=0\ if\ r_{i}=r_{k} \tag{6}\]  
The earliest start time of the task \(t_{i}\) is denoted by EST. The EST for the task \(t_{i}\) on processor \(Pk\) is defined by  
\[\text{EST}(t_{i},P_{k})=max\Bigg{\{}Avail(k),\ \max_{t_{j}\in pred(t_{i})}\{ AFT\big{(}t_{j}\big{)}+TC_{j,i}\}\Bigg{\}} \tag{7}\]  
The earliest finish time of the task \(t_{i}\) on Processor \(Pk\) is given by  
\[EFT(t_{i},P_{k})=\text{EST}\big{(}t_{i},P_{j}\big{)}+CT_{i,j} \tag{8}\]  
The level of task \(t_{i}\) is given by,  
\[ \begin{array}{c}level\big{(}t_{entry}\big{)}=1\\ level\big{(}t_{i}\big{)}=\max_{j\in pred(t_{i})}\{level\big{(}t_{j}\big{)}\} \end{array}  \tag{9}\]  
Makespan is also known as the schedule length which is defined as the total implementation time  
\[\text{Makespan}=\max\{AFT(t_{exit})\} \tag{10}\]  
The performance of every processor is measured in 'FLOPS' and bandwidth, and latency is the performance measure of every communication link. The objective function of this task-scheduling approach is to define the allocation of tasks to the resources, such that the cost and makespan are minimized.
## 2 Islanding Detection Methods
### Remote Methods
#### 2.4.6 Phasor Measurement Unit  
The phase angle and magnitude of an electrical phasor, such as voltage or current, are monitored in this method, which is referred to as a Synchrophasor and ROCOF, utilizing a time synchronization source that can be provided directly from a local clock using standard coding or time broadcast, such as GPS. Two units are utilized, one of them is inserted at the utility grid, and the other one at the DGs side. Moreover, time is stamped before sending it to the receiver. That way, it can be easy to determine whether DG is synchronized with the grid or not [35; 36].
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
###### Abstract  
Classification is needed in disaster investigation, traffic control, and land-use resource management. How to quickly and accurately classify such remote sensing imagery has become a popular research topic. However, the application of large, deep neural network models for the training of classifiers in the hope of obtaining good classification results is often very time-consuming. In this study, a new CNN (convolutional neutral networks) architecture, i.e., RSSCNet (remote sensing scene classification network), with high generalization capability was designed. Moreover, a two-stage cyclical learning rate policy and the no-freezing transfer learning method were developed to speed up model training and enhance accuracy. In addition, the manifold learning t-SNE (t-distributed stochastic neighbor embedding) algorithm was used to verify the effectiveness of the proposed model, and the LIME (local interpretable model, agnostic explanation) algorithm was applied to improve the results in cases where the model made wrong predictions. Comparing the results of three publicly available datasets in this study with those obtained in previous studies, the experimental results show that the model and method proposed in this paper can achieve better scene classification more quickly and more efficiently.  
Keywords:neural network; deep learning; cyclical learning rate; remote sensing; scene classification +
Footnote †: journal: Applied
## 5 Experiments
### Baseline Methods  
We compared DSTGFCN with eight baselines, including a traditional time series analysis method (ARIMA), deep learning-based methods (FC-LSTM, STGCN, and DCRNN), and excellent existing GNN-based methods (GW-net, AGCRN, DSTAGCN, and STG-NCDE).  
* ARIMA [14]. A statistical model commonly used for analyzing and predicting time series data.
* FC-LSTM [14]. LSTM network with the fully connected network to generate traffic series predictions.
* STGCN [11]. This model employs graph convolution and 1D convolution to capture spatial and temporal features, respectively.
* DCRNN [14]. DCRNN combines dual directional diffusion convolution and GRUs for traffic prediction.
* GW-Net [12]. Graph WaveNet combines diffusion causal convolution with GCNs based on an adaptive adjacency matrix to capture potential spatial correlations.
* AGCRN [13]. The model captures spatial correlations between roads through the two proposed adaptive learning modules.
* DSTAGCN [33]. The model connects multiple time frames to construct a dynamic spatio-temporal graph, capturing global spatio-temporal correlations.
* STG-NCDE [39]. The model employs two neural control differential equations to forecast traffic states.