esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimentation and Result Analysis
### Performance Metrics  
The standard evaluation metrics such as true positive (TP), true negative (TN), false positive (FP), and false negative (FN) were utilized to validate the efficacy of the classification. In addition, false-positive rate (FPR), precision (\(\Psi\)), recall (Y), specificity (\(\theta\)), accuracy (\(\Phi\)), and F1-score were utilized to compare the efficacy of the formulated approach with other compared models. The mathematical formulation of the performance metrics is described as follows.  
\[\Psi =\frac{TP}{TP+FP} \tag{9}\] \[\mathrm{Y} =\frac{TP}{TP+FN}\] (10) \[\vartheta =\frac{TN}{FP+TN}\] (11) \[FPR =\frac{FP}{FP+TN}\] (12) \[\Phi =\frac{TP+TN}{TP+TN+FP+FN}\] (13) \[F1_{Score} =2\times\frac{\Psi\times\mathrm{Y}}{\Psi+\mathrm{Y}} \tag{14}\]  
If the outcomes of \(\Phi\), \(\Upsilon\), \(\Psi\), \(\vartheta\), and \(F1_{Score}\) are high, then the outcome of the proposed model is improved. On the other hand, the FPR value must be less to ensure enriching the classification quality.
## 5 Results and Analysis
### Probability of Coverage  
Figure 10 represents the probability of coverage in terms of small-cell BS active rate versus time. It evaluates the effectiveness of the proposed strategy against other cutting-edge techniques during the lowest (5 hers) to highest busiest (22 hers) duration as observed from the plot. It can be observed D-DOSS strategy outperforms other techniques, particularly during periods of low traffic density, thus preventing mobile users from experiencing outage regions.  
In load-aware strategies, the association between small-cell BSs and any mobile users is determined only by the static data traffic profile which may vary from the realist values, the aware strategy has the lower active rate whereas, for RSM, all small-cell BSs are switch on/off according to some probability distributions which leads towards the lowest BS active rate as shown in the plot. This phenomenon does not happen in the proposed strategy especially during low to medium traffic hours due to the dynamic on/off mechanism of femto BSs. Therefore, the small-cell BSs in the D-DOSS strategy are 60% to 80% more active than the other two strategies.
## 2 Experimental
### Cr(VI) Transport Study  
The membrane transport experiments were performed in a double-cell device with solution volume of 120 mL for each compartment. The contact area of the Aliquat 336/PolyHIPE membrane was 16 cm\({}^{2}\). The feeding phase was a Cr(VI) aqueous solution with varied initial pH adjusted with HCl. The stripping phase is a basic solution of NaOH. Separation experiments were conducted at room temperature (25 \(\pm\) 0.1 \({}^{\circ}\)C). The concentration of Cr(VI) in both phases was measured with an Atomic Absorption spectrometer (Analyst 100, Perkin-Elmer, Waltham, MA, USA) with a chromium cathode lamp (Perkin Elmer, Waltham, MA, USA) at a wavelength of 357.9 nm. All data are averages of three replicable determinations. In this study, separation experiments were performed for feeding solution with initial pH of 2, 3, 4, and 5 to simulate the pH of Cr(VI) wastewaters from electroplating plant [20]. The stripping phase was set to be pH 12, at which an optimal stripping efficiency can be obtained as determined in the pre-experiment. In this study, the prepared supported liquid membrane was conditioned for 12 h in the cell with pure water. After this induction period of time, the Cr(VI) was introduced to begin the transport study.  
The removal and recovery rate of Cr(VI) was defined as Equations (1) and (2):  
\[Removal(\%)=\frac{[C]_{fo}-[C]_{f}}{[C]_{fo}}\times 100 \tag{1}\]  
\[Recovery(\%)=\frac{[C]_{s}}{[C]_{fo}}\times 100 \tag{2}\]  
where \([C]_{fo}\) is the initial concentration of Cr(VI) in the feeding phase, \([C]_{f}\) and \([C]_{s}\) is concentration of Cr(VI) in the feeding and stripping phase at time \(t\), respectively.
## Materials and Methods
### Evaluation of the EO and FW Antioxidant Activity  
An improved depiction of the antioxidant activity of marigold EO and FW can be obtained by utilizing three different assessment techniques (DPPH, ABTS, FRAP). The basic ideas behind each technique and their synergy allow for a more precise evaluation of the compounds [24].  
1. The scavenger activity of the DPPH radical (2,2-diphenyl-1-picrylhydrazyl) is based on the ability of antioxidants to reduce the DPPH radical. The rate of DPPH remaining in the solution is determined using Formula (2): \[\text{\%DPPH}=\frac{A_{\text{control sample}}-A_{\text{sample}}}{A_{\text{ control sample}}}\times 100\] (2) where \(A_{\text{control sample}}\) is the absorbance of the control sample and \(A_{\text{sample}}\) is the absorbance of the sample.  
The abbreviation \"IC\({}_{50}\)\" refers to the quantity of samples needed to decrease DPPH absorbance by 50%. Each sample was tested at five different concentrations in triplicate in order to determine its associated IC\({}_{50}\).  
1. The scavenger activity of the ABTS radical (2,2\({}^{\prime}\)-azinobis (3-ethylbenzothazoline-6-sulfonic acid)): Because the expression of the extract antioxidant capacity can be related to Trolox equivalents, the technique is known as TEAC (Trolox equivalent antioxidant capacity). Three Trolox standard calibration curves were used to express the antioxidant capacity in milligrams (mg) of Trolox.
* Ferric ion reducing antioxidant power (FRAP): This relies on the capacity of antioxidants to reduce the yellow-colored tripyridyltriazine Fe\({}^{3+}\) (Fe (III)-TPTZ) complex to the blue-colored tripyridyltriazine Fe\({}^{2+}\) (Fe (II)-TPTZ) complex by the action of electron release by antioxidants. The evaluations were tested in triplicate, and the FRAP values of each sample were expressed in mM Trolox g-1 for EOs and L-1h for FWs.
## 1 Introduction
However, there are no systematic studies concerning how the analytes affect the optical parameters of the SERS substrates, only the initial parameters and their effect on enhancement factors are studied. Many different analyte molecules are used for SERS developments and measurements. For example, heterocyclic aromatic compounds such as melamine [19; 20], or explosives such as TNT [21; 22], or drugs and pharmaceuticals such as methamphetamine [23; 24]. For our studies, we selected two frequently used compounds, riboflavin and rhodamine 6G, which are both widely used in SERS research, to study and understand the SERS process, and as an analyte molecule to optimize the production of different SERS substrates and to determine the enhancement factor [2; 25; 26; 27; 28; 29; 30]. However, no systematic studies have been performed before concerning how the properties of the analyte influence the enhancement factor at different excitation wavelengths. As the electronic structure of the selected analytes is different, the obtained results could add important knowledge about the influence of the analyte on the SERS enhancement. In this study, SERS enhancement was investigated on gold nanoislands made by heat treatment of a gold thin film formed on a glass substrate by thermal vacuum evaporation. The enhancement factor was studied for two different analytes and two excitation wavelengths, and the role of the adsorption and electronic structure of the analytes in the SERS enhancement was examined.
## 5 Slowly Varying Fading Parameter in 2 WRC  
Above, we briefly addressed how an ANN might be simply utilized for the classification of hierarchical symbols in 2 WRC. Therein, we considered the training dataset \(\mathcal{D}\) to be obtained for a single fixed value of \(\measuredangle h\). In this section, we address a more practical situation, when the value of \(\measuredangle h\) is slowly varying. Practically, effects such as slow user mobility, changes in the propagation environment, or drift of the internal oscillators might be considered as the causes of it.  
Considering the ANN, its functionality is fully determined by its structure (number of layers and number of neurons). The training dataset is obtained as a pilot signal known to both Tx and Rx, as described previously, and we shall focus on the minimization of resources required for training. The reason is two-fold. Firstly, the number of training samples used for training directly affects signaling overhead. Thus, it is desirable to minimize the number of training samples \(D\). Secondly, the training process of ANN using backpropagation itself is time consuming. Besides the number of training samples \(D\), its time complexity is typically dependent on the number of training epochs \(E\) and learning rate \(\eta\). From a matrix implementation point of view, it can be easily seen that the time complexity of backpropagation scales quadratically with the number of nodes. The time complexity dependence is linear w.r.t. the size of the training set. Therefore, we relaxed the employment of a relatively small ANN with 4 layers. The core of our contribution in this paper is exploring the ideas of how to minimize the resources required for training the ANN in the case of hierarchical symbol classification in the 2 WRC.  
Note that in the simulations, we consider a 2 WRC with BPSK modulation, and the employed ANN consists of 2 hidden layers, each having 40 neurons. The network has 2 inputs, corresponding to the coordinates in the constellation space. Its two outputs are designed to indicate the binary value of the hierarchical symbols, which might be easily scaled using the soft-max approach to interpret the outputs as classification likelihoods.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
###### Abstract
The main contributions are as follows:  
* We construct a multi-satellite SECN scenario, in which a single task is assumed to be divided into sub-tasks and offloaded to adjacent satellites. These sub-tasks will be processed on board. This offloading strategy makes full use of heterogeneous resources in the satellite network, reduces the load burden on individual satellites, and enables a more flexible resource scheduling scheme. * We formulate an optimization problem mathematically, to minimize the maximum delay through resource allocation, in which transmission power and task assignment ratios are optimized. * We propose a distributed balance increasing penalty dual decomposition (DB-IPDD) algorithm. Our experimental validation across various orbital configurations demonstrates consistent 14.3% latency reduction compared with baseline approaches. * We derive the optimal solution theoretically for the LEO satellite case, in which the resources are abundantly available. We further extend the analysis to the general case under specific conditions. * Simulation results clearly show that the proposed solution greatly outperforms the baseline methods, and the transmission delay is significantly reduced. The rest of the paper is organized as follows. Section 2 introduces the current research and development status related to LEO satellite edge computing. Section 3 provides a depiction of the distributed satellite edge computing network system model and the resource allocation problem. In Section 4, we introduce the algorithm for slave satellite selection, data offloading, and transmission power allocation. Simulation results of the proposed algorithm are presented in Section 5. Finally, the paper is concluded in Section 6.
## 5 Results and Discussion  
### Model Comparison  
First, we show the difference between our method and other baseline models in Table 3, then we compare the performance of our model with five baseline models and list the experimental results in Table 4.  
In Table 4, our model performs better than the five baseline models. Specifically, our model achieves a 6.5% (from 72.5% to 79.0%) improvement in F1-score compared to the SVM classification based method proposed by [PERSON] et al. [9]. This shows that although SVM is a good classification model, since sarcasm detection needs to mine inconsistent information between multiple modalities, the SVM method cannot better solve the automatic classification of sarcasm. Compared with the Two-attention-based encoder method proposed by [PERSON] et al. [28], our model offers an improvement of 8.5% (from 70.5% to 79.0%). This shows that fusing multimodal features for prediction is more effective than using an attention mechanism to simulate dynamic information between modalities. Compared with the Sequential Context Encoder proposed by [PERSON] et al. [30], our model achieves an improvement of 6.7% (from 72.3% to 79.0%). This shows that using the encoder-decoder architecture is insufficient to reflect the inconsistent information between modalities, and the deep learning network using the feature fusion strategy performs better. Compared with the ViViT-VAT model using ViViT [33] as the backbone network, our model improves by 1.3% (from 77.7% to 79.0%), which shows that although  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline Random & 49.5 & 49.5 & 49.5 \\ [PERSON] et al. [9] & 72.5 & 72.5 & 72.5 \\ Two-attention-based encoder [28] & 71.5 & 71.4 & 70.5 \\ Sequential Context Encoder [30] & 72.3 & 72.3 & 72.3 \\ ViViT-VAT & 69.3 & **88.4** & _77.7_ \\ Ours & **79.0** & 79.0 & **79.0** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sarcasm detection results of different models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Fusion Method** & **Classification Method** \\ \hline [PERSON] el al. [9] & Concat & SVM \\ Two-attention-based encoder [28] & Two attention mechanisms & Linear decoder \\ Sequential Context Encoder [30] & Sequential context encoder & Linear decoder \\ ViViT-VAT & Add & MLP \\ Ours & Adaptive feature fusion & MLP \\ \hline \hline \end{tabular}
\end{table}
Table 3: The differences between the proposed model and the baseline model are compared. Two attention mechanisms refer to inter-segment inter-modal attention and intra-segment inter-modal attention, SVM refers to support vector machine, and MLP refers to multi-layer perceptron.  
the ViViT [33] network achieves state-of-the-art results in single-modal video classification, for the multimodal video classification task, the ViViT network is still not able to fuse multimodal features well. However, the network achieves the best results in the baseline model. Furthermore, the best performance obtained by our model is 79.6%, with a standard deviation of 0.54.
## 4 Analytical Formula for Grouting Pressure
This indicates that the theoretical formula is more suitable for predicting grouting behavior at lower injection velocities, whereas its accuracy diminishes as the injection speed increases. To investigate the influence of grout viscosity on grouting performance, two additional models were developed with grout viscosities set to 0.007 Pa-s and 0.02 Pa-s, respectively. In both models, the grouting port was positioned at \(\alpha=150^{\circ}\). Figure 20 presents the time-dependent grouting pressure curves for the two viscosity conditions. The results show that an increase in grout viscosity leads to a corresponding increase in grouting pressure. Furthermore, both the simulated and predicted pressure values for the 0.02 Pa-s model are approximately twice those observed in the 0.007 Pa-s model, indicating that grout viscosity has a significant impact on pressure development during the grouting process. Figure 18: Effect of porosity variation on grouting pressure. To validate the reliability of the simulation results, two sets of laboratory grouting experiments conducted by [PERSON] [28] and [PERSON] [29] were introduced for comparative analysis. In both experiments, a constant grout injection rate was applied, and the variation in grouting pressure over time was recorded, as shown in Figure 21. The red line represents the results from [PERSON]'s experiment, in which the water-cement ratio of the grout was 1.5, the sand particle size ranged from 0.05 to 1.2 mm, and the injection rate was 2.85 L/min. The blue line corresponds to [PERSON]'s experiment, where the grout had a water-cement ratio of 1.1, the sand particle size ranged from 1 to 2 mm, and the injection rate was 4 L/min. Under conditions of constant grout injection velocity, the grouting pressure exhibits an approximately linear increase over time, which aligns well with the trend observed in the present study. Additionally, the particle size range of the granular medium appears to have a significant impact on the grouting pressure response, indicating a need for further investigation into its influence. Figure 19: Effect of injection velocity on grouting pressure. Figure 20: Effect of grout viscosity on grouting pressure.
## 3 Energy-Saving Optimal Control Strategy of Refrigeration Station
### Control Systems Using Edge Computing Architecture
#### 3.4.2 Edge Computing Architecture Technology  
As shown in Figure 6, an optimized architecture (edge computing architecture technology) for distributed computing is implemented. A large number of computations that were originally handled entirely by the centralized control cabinet (network center node) are divided into smaller, more targeted, and easier-to-handle parts, and scattered to the  
Figure 4: One pump or fewer pumps for multiple chillers technology.  
Figure 5: Control strategy chart of traditional refrigeration station.  
edge nodes (The edge nodes refer to the control cabinets or control boxes distributed at various locations in the refrigeration station, such as the chillers, cooling towers, cooling water pumps, and chilled water pumps.) to deal with. Because each edge node can work independently, the network center node only takes up less than 10% of the data calculation work, and the edge node takes up at least 90% of the data calculation work. The control terminal (network center node) is only used as a platform to display and record data, perform manual operation, and issue integrated instructions. After each control cabinet or control box (edge node) collects the related equipment signal independently, the data and logic strategy is processed by the mathematical models (The mathematical model refers to the control models established by the control modules in each edge node control cabinet based on thermodynamic theories and empirical methods related to chillers, cooling towers, cooling water pumps, and chilled water pumps. The control modules in each edge node control cabinet will automatically select appropriate control algorithms and logic based on the results calculated from the mathematical model.) and control strategies internally, and the related equipment is directly driven by the set target parameter (range) step by step.  
Its advantages include the following: (1) It requires no special programming and offers strong versatility, allowing for quick setup as a control system. (2) The network center node only undertakes up to less than 10% of the calculation work, and the processing capacity and processing speed are guaranteed. (3) The data transmitted by a single control box (edge node) are small in amount and simple in form and do not easily malfunction. (4) Each control cabinet or control box (edge node) operates independently, so a partial failure will not result in a complete control system failure.
## 3 Results and Analysis
### Settlement Changes with Time  
As shown in Figure 11, the most significant settlement deformation occurs at depths of 40 cm and 20 cm, with a more pronounced settlement observed at points closer to the ditch at the foot of the slope. The maximum settlement, 0.81 cm, occurs at point I-I. The settlement behavior of each layer exhibited slow deformation from December 13 to December 16, followed by an increase in deformation from December 16 to December 23. This trend can be attributed to the fact that during the initial stage of water seepage, the external temperature was at its lowest for the entire test period, causing the water infiltrating into the soil to freeze. Additionally, the infiltration process took time, resulting in small settlement deformations in the early stages of the test. A settlement of more than 0.1 cm is considered the threshold for the effective affected area. The settlement lag time at a depth of 40 cm is approximately 4 days, while at 20 cm, the lag time at points I and II is about 5 days, and at points III and IV, it is approximately 7 days. The lag times for the interface points I and II are around 4 days, while for points III and IV, they are approximately 8 days.  
Figure 11: Settlement with time at different locations. (**a**) At a depth of 40 cm in the foundation; (**b**) at a depth of 20 cm in the foundation; (**c**) interface between the subgrade and foundation.  
By comparing Figure 11a-c, it is evident that the settlement at points I and II of each layer is most pronounced, with settlement exceeding 0.5 cm by the end of the test. In contrast, the settlement deformation at points III and IV, located at the interface between the subgrade and foundation, is minimal. This is attributed to the distance of the monitoring area from the water injection point, which limits the spread of water flow. The results indicate that the wetting and softening damage caused by infiltration from the side ditch of the fill subgrade primarily affects the foundation. Overall, the model exhibits a \"bending dump\" failure due to water seepage.
## 2 System Model
RVs, i.e., \(\lambda_{\mathrm{B}_{\mathrm{B}}\mathrm{S}}=\lambda_{\mathrm{BS}}\) and \(\lambda_{\mathrm{B}_{\mathrm{B}}\mathrm{R}_{m}}=\lambda_{\mathrm{BR}}\) for all \(k\) and \(m\). Next, denote \(\mathrm{T}\) as the duration of each data transmission from the source to the destination. By using the TSR protocol [11], a duration of \(\alpha\mathrm{T}\) is used for the EH process, while the time spent for both the S-R and R-D transmission is \(\left(1-\alpha\right)\mathrm{T}/2\), where \(0\leq\alpha\leq 1\).