esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Gene Expression of APP, MAPT, and Caspase-3 under Mixed Chemical Minerals Stress in Rat Brain
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Group** & **Tailed \%** & \begin{tabular}{c} **Untailed** \\ **\%** \\ \end{tabular} & \begin{tabular}{c} **Tails Length** \\ **\(\mu\)m** \\ \end{tabular} & \
\begin{tabular}{c} **Tail** \\ **DNA\%** \\ \end{tabular} &
\begin{tabular}{c} **Tail** \\ **Moment** \\ \end{tabular} \\ \hline  & & & & **Liver** & & & \\
**A** & **Control \(-\)** & 1.75 & 98.25 & 1.25 \(\pm\) 0.26 \({}^{\text{\,a}}\) & 1.41 & 1.76 \\
**B** & **Control \(+\)** & 2.25 & 97.75 & 1.36 \(\pm\) 0.24 \({}^{\text{\,a}}\) & 1.45 & 1.97 \\
**C** & **G3-1** & 14.00 & 86.00 & 4.05 \(\pm\) 0.56 \({}^{\text{\,a}}\) & 4.94 & 20.00 \\
**D** & **G3-2** & 9.00 & 91.00 & 2.94 \(\pm\) 0.42 \({}^{\text{\,b}}\) & 3.43 & 10.08 \\
**E** & **G3-3** & 6.00 & 94.00 & 2.18 \(\pm\) 0.29 \({}^{\text{\,cd}}\) & 2.62 & 5.71 \\
**F** & **G3-4** & 7.00 & 93.00 & 2.33 \(\pm\) 0.34 \({}^{\text{\,c}}\) & 2.90 & 6.76 \\
**G** & **G3-5** & 5.00 & 95.00 & 2.04 \(\pm\) 0.24 \({}^{\text{\,d}}\) & 2.46 & 5.02 \\
**A** & **Control \(-\)** & 2 & 98 & 1.31 \(\pm\) 0.24 \({}^{\text{\,d}}\) & 1.39 & 1.82 \\
**B** & **Control \(+\)** & 2.5 & 97.5 & 1.35 \(\pm\) 0.26 \({}^{\text{\,d}}\) & 1.44 & 1.94 \\
**C** & **G3-1** & 16 & 84 & 4.93 \(\pm\) 0.58 \({}^{\text{\,a}}\) & 5.37 & 26.47 \\
**D** & **G3-2** & 10 & 90 & 3.85 \(\pm\) 0.48 \({}^{\text{\,b}}\) & 3.94 & 15.17 \\
**E** & **G3-3** & 7 & 93 & 2.35 \(\pm\) 0.32 \({}^{\text{\,c}}\) & 2.89 & 6.79 \\
**F** & **G3-4** & 9 & 91 & 3.59 \(\pm\) 0.40 \({}^{\text{\,b}}\) & 3.58 & 12.85 \\
**G** & **G3-5** & 6 & 94 & 2.11 \(\pm\) 0.29 \({}^{\text{\,c}}\) & 2.60 & 5.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comet assay parameters obtained by the image analysis in rat livers and brains for all of the groups. sessed using comet assay and caspase 3 gene expression. Apoptosis was relatively more induced by all of the chemicals, which may have been as a result of excess DNA damage. Figure 5: Effect of heavy metal treatment on the expression of APP APP, MAPT, and Caspase3 genes in rat brains.Values with different letters in each column showed a significant difference.
# Context-Based Dynamic Meshed Backhaul Construction for 5G Heterogeneous Networks
## 3 SDN-Based Implementation As A Proof-Of-Concept
### MmWave Mesh Backhaul Route Multiplexing Against Intensive Traffic  
The dedicated C-plane design for the testbed in this section is provided in Figure 13. In this experiment, a virtual user is created by VUM (Virtual User Manager) and requests data from the server stochastically. The distribution of the instantaneous traffic demand follows a Gamma distribution as in [16], with the average traffic value of 25 Mbps. The data transfer is performed through the data plane e.g., WiGig link. Since macro LTE is not available, the C-plane for SDN orchestration is performed via Ethernet instead. The orchestrator calculates the optimum route of the mesh backhaul network and transfers it to the OVS of each small cell base station via the SDN controller.  
In order to verify the operation of our proposed SDN-based meshed network construction over the testbed, the following experiment is conducted. We have set up 100 virtual users distributed in the implemented network, of which \(\rho\) is distributed to AP2 (N2) and the remaining \(1-\rho\) is equally distributed to AP3 (N3) and AP4 (N4). By controlling the ratio \(\rho\), we can simulate the situation of dense hotspot at AP2 (N2), where route multiplexing is required. To show the benefit of the proposed method, the experiments were conducted in two scenarios with and without backhaul route multiplexing.  
The results of verification in the range of \(0.33\leq\rho\leq 1\) are shown in Figure 14. Blue line and orange line show the results of with and without backhaul route multiplexing respectively. In the case of none, the system satisfaction degree defined as the ratio of the total delivered traffic over the total demanded traffic decreases as more users are concentrated at AP2 (N2). On the other hand, we are able  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Failover** & **Mean (ms)** & **Stdev (ms)** \\ \hline BFD 10 ms & 31.18 & \(\pm\)3.04 \\ BFD 15 ms & 45.68 & \(\pm\)6.23 \\ BFD 20 ms & 58.86 & \(\pm\)6.31 \\ SDN Controller & 6.14 & \(\pm\)0.87 \\ No Failover & 1226.99 & \(\pm\)18.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interruption time against different resilience mechanisms.  
to maintain a satisfaction level of 97% to 98% when our proposed method is applied. The reason why it cannot reach perfect delivery is due to the UDP protocol employed, where a part of failed packets is discarded without being retransmitted.  
Figure 14: Validation of the proposed method via experimental results.  
Figure 13: System configuration of each AP.
## 4 Experiments
### Experiment Results and Discussion  
To fairly compare the performance of the proposed framework, we implement the end-to-end method (E2 ESA without multi-task learning), resolution maintained method (DRN), and multi-scale feature representation method (GLAM without data augmentation) for evaluation. The experimental results are shown in Table 1. In the MSCNN-based method, GLAM's multiple feature representations are conducted on the high-level semantic features, which are limited by the fine-grained temporal dynamics and local representation learning. Compared to the end-to-end method, E2 ESA uses single-scale correlation modeling and limits global-local representation learning for context-aware emotion classification. The DRN method appropriately preserves the resolution of deep features, which avoids the loss of temporal structure of speech in hierarchical CNN. However, directly applying the MSCNN module in the DRN leads to a gridding effect when learning high-level temporal dynamics upon the mediate hidden features. In this paper, the MSFPN mainly benefits from the high semantic and full resolution feature map for global-local representation learning and achieves the highest results with 3.69% UA (GLAM), 3.39% UA (Xie [24]), 2.53%UA (E2 ESA), and 1.8%UA (DRN) improvements, respectively. Additionally, the model inference time on the IEMOCAP dataset is 0.5 s, ensuring efficient real-time processing.  
Furthermore, we also evaluate the performance of MSFPN on the RAVDESS dataset. Since most existing studies report results on the RAVDESS dataset using unweighted accuracy (UA) as the evaluation metric, we adopt UA as the evaluation metric for MSFPN on this dataset as well. The experimental results are shown in Table 2. Compared to the methods proposed by [PERSON] [40] and [PERSON] [41], MSFPN better extracts multi-granularity emotional features across different scales. As shown in Table 2, the ability to capture multi-granularity features significantly improves emotion recognition accuracy on the RAVDESS dataset as well. The comparison results demonstrate that the proposed method effectively captures multi-scale contextual information in speech, achieving superior performance on the RAVDESS dataset as well. The model inference time on the RAVDESS dataset is 0.2 s, further emphasizing the efficiency of the proposed method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **UA** & **WA** \\ \hline GLAM [13] & 69.70\% & 68.75\% \\ E2 ESA [39] & 70.86\% & 69.25\% \\ Xie [24] & 70.0\% & 68.8\% \\ DRN [10] & 71.59\% & 70.23\% \\
**MSFPN** & **73.39\%** & **71.79\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of UA and WA with state-of-the-art methods on IEMOCAP. The best results are highlighted in bold.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods
### Data Preprocessing  
The datasets mentioned in Sections 2.2.1-2.2.5 have different spatial resolutions. All of these datasets were aggregated at the county level since the yield data provided by USDA are at the county level [40]. The datasets were filtered by including only those cells that are classified as corn in CDL, and the rest of the cells were excluded. The time-series predictors for remote sensing and climate data were aggregated to derive 16-day composites using GEE, which resulted in 12 time-series datasets for each predictor in the growing season (April to September). Eleven soil-related predictors mentioned in Section 2.2.4 were processed using ArcGIS Pro 2.8.0 [68] because soil data are not available in GEE. The data collected from GEE were further processed to remove missing and invalid values. The counties with no yield data were excluded from the analysis. The final dataset includes the data for 976 counties with a total of 12,372 records.
## 4 Simulation Results  
Our analysis conducted simulations for each pricing scheme, evaluating metrics such as market prices, power flow, power purchase costs for the retailer, and generation costs, revenue, and profits for power producers throughout 2023.  
The subsequent subsections are structured as follows. First, to establish the reliability of our model, we compare the actual 2023 Korean electricity market prices with the Uniform (SMP) market prices simulated using our model in Section 4.1. The Uniform (SMP) scheme in our model was designed to simulate the pricing scheme currently adopted in the Korean electricity market. Subsequently, we analyze the results of the seven proposed pricing schemes and divide our examination into the following sections: market price in Section 4.2, financial impacts on power producers and the retailer in Section 4.3, and power flow in Section 4.4. Finally, through a sensitivity analysis of Table 4 in Section 4.5, we examine how transitioning to zonal and nodal pricing schemes affects the potential conflicts of interest for a Transco in a market structure wherein a single Transco also acts as a monopoly retailer.
## 5 Performance Evaluation
### Performance Analysis
However, compared with D-ACB and dynamic \(L_{DS}\) with fixed \(p_{ACB}\), our scheme shows its superiority in terms of average access delay as depicted in Figure 9, and this is the most important parameter for optimization of DS devices. In conclusion, our proposed scheme can not only satisfy the delay requirements of DS devices, but also save as much preambles as possible for DT devices. Above analysis shows that our scheme is best suited for the communication requirements of delay-sensitive devices. Figure 10: Average Preamble Utilization Rate for varying arrival rates. Figure 9: Average Access Delay for varying arrival rates.
# Development of Operator Theory in the Capacity Adjustment of Job Shop Manufacturing Systems
## 1 Introduction  
As customer demand is changing quickly, production and logistic systems become more complex and dynamic. Moreover, various delays, stochastic disturbances, and bottlenecks in the production system induce additional challenges for manufacturers. To deal with these problems, numerous advanced technologies, such as reconfigurable machine tools (RMT), Internet of Things (IoT), radio-frequency identification (RFID), and cyber-physical systems (CPS), have been proposed for a more automatic, accurate, and reliable monitoring and controllable manufacturing system [1; 2; 3]. This displays that the manufacturing industry is on its way toward a new generation, named Industry 4.0 [4]. However, to use these technologies efficiently, effective integration plays a vital role, which has attracted researchers' increasing attention. Capacity adjustment is one such approach for manufacturing systems, especially for job shop systems, to deal with volatile customer demands. This paper focuses on developing an effective capacity control strategy for the job shop system by means of RMTs.  
A job shop system is one production mode with flexible producing paths for a variety of products. Compared with traditional flow shop manufacturing systems with fixed producing paths, job shop systems have the advantage of being able to satisfy changing demand. However, they also have several drawbacks, such as high work-in-process (WIP) levels, high cost, and lowproductivity [5]. In the literature, some researchers applied scheduling methods to deal with these problems. For example, [PERSON] and [PERSON] [6] discussed the scheduling for combination of made-to-stock and made-to-order jobs in a job shop environment considering mean flowtime. In [7], simulation-based multiobjective optimization as a hyper-heuristic was utilized in the automatic design of scheduling rules for complex job shop systems. Further, other algorithms, such as tabu search [8], genetic algorithm (GA) [9], genetic programming based hyper-heuristic approach (GA-HH) [10], and particle swarm optimization (PSO) [11], were applied to the flexible job shop scheduling problem. The aim of these works was to improve productivity as well as to minimize cost and completion time. Another possibility to solve these problems is capacity adjustment in short- or mid-term horizons [12]. Related work is introduced in the next section.
## 4 WT/PV ES Optimization Decision
### Stage 2 and 3: Cluster Partition and ES Siting and Sizing
#### 4.3.1 Objective Function  
The objective function of ES planning is determined by  
\[f=\min(f_{1},f_{2},f_{3}). \tag{32}\]  
1. Voltage fluctuation\(f_{1}\)  
After the storage is connected, the voltage of each node is expected to be more stable and the fluctuation with time is small. The node voltage fluctuation is selected as the objective function:  
\[f_{1}=\sum\limits_{i=2}^{N}\sum\limits_{l=1}^{T}|U_{i,l}-U_{1}|, \tag{33}\]where \(n\) is the number of nodes and \(U_{1}\) and \(U_{i,t}\) are the amplitudes of node 1 (slack bus) and node \(i\), respectively, at time \(t\).
2. Line loss\(f_{2}\)  
The line loss of the system can be effectively adjusted by adjusting the storage output, and the total line loss is defined as the objective function; that is:  
\[f_{2}=\sum_{t=1}^{T}\text{Re}[\sum_{i=2}^{N}\Delta S_{i}]=\sum_{t=1}^{T}\sum_{ i=2}^{N}\frac{P_{i,t}^{2}R_{i-1,i}+Q_{i,t}^{2}R_{i-1,i}}{U_{i,t}^{2}}, \tag{34}\]  
where \(P_{i,t}\) and \(Q_{i,t}\) are the active and reactive power injection of node \(i\), respectively, at time \(t\).
3. Capacity of storage system\(f_{3}\)  
While considering the voltage fluctuation and loss of ES regulation, the cost of ES unit capacity should also be considered. Therefore, the total ES capacity is selected as an important indicator to measure the economy of ES, and the corresponding objective function can be obtained by examining the maximum charge/discharge capacity and the upper and lower limits of the SOC of ES in a day:  
\[f_{3}=\frac{\sum_{i}^{N_{e}}\text{max}(\left|\int_{t_{j,s}}^{t_{j,s}}P_{\text{ ch}/\text{dis},i}(t)dt\right|)}{SOC_{\text{max}}-SOC_{\text{min}}}, \tag{35}\]  
where \(t_{j,s}\) is the starting time of continuous charge/discharge of the \(j\)-stage ES; \(t_{j,e}\) is the end time of continuous charging/discharging of the \(j\)-stage ES; \(P_{\text{ch}/\text{dis},i}\) is the charge/discharge power of the \(i\)-th ES period; \(SOC_{\text{max}}\) and \(SOC_{\text{min}}\) are the upper and lower limits of the SOC of ES, respectively; and \(N_{e}\) is the amount of ES.
## 2 Materials and Methods
### Energy Management Strategy  
Energy-efficient operation of a DICPS depends on two crucial elements: a good choice of operating mode (dual-motor or single-motor operation) and, in dual-motor operation, a well-selected power distribution. Therefore, a real-time energy management strategy was introduced to improve the overall efficiency of the DMET, which consisted of power distribution control and mode stability control.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **PDR = 0.2** & **PDR = 0.35** & **PDR = 0.5** & **PDR = 0.65** & **PDR = 0.8** \\ \hline \multirow{4}{*}{\(i_{s}\)} & \(k\) = 1.6 & 5.49 & 3.14 & 2.198 & 1.69 & 1.37 \\  & \(k\) = 3.6 & 3.1 & 1.77 & 1.24 & 0.96 & 0.78 \\  & \(k\) = 5.6 & 2.16 & 1.237 & 0.866 & 0.666 & 0.541 \\  & \(k\) = 7.6 & 1.661 & 0.949 & 0.665 & 0.511 & 0.415 \\ \hline \multirow{4}{*}{\(i_{r}\)} & \(k\) = 1.6 & 2.198 & 2.70 & 3.516 & 5.023 & 8.791 \\  & \(k\) = 3.6 & 2.795 & 3.44 & 4.472 & 6.388 & 11.18 \\ \cline{1-1}  & \(k\) = 5.6 & 3.03 & 3.729 & 4.848 & 6.926 & 12.121 \\ \cline{1-1}  & \(k\) = 7.6 & 3.156 & 3.885 & 5.049 & 7.214 & 12.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 4 Experimental Setup and Evaluation
### Goals, Metrics and Methodology  
The goal of the experiments is to show the performance of the proposed joint learner and conduct a thorough comparison to show the advantage of the proposed model, which enhances the performance of activity recognition and particularly the activities with less frequency. To perform the experiments efficiently, free online Google Colab is used in order to train the networks in this study that continuously provides a single 12 GB NVIDIA Tesla K80 GPU for 12 h.  
The leave-one-day-out cross-validation is used in the evaluation for all of the models, specifically the human activities on an individual day are used for testing of the models and the models are trained on the human activities of the rest of the days. This procedure is circulated until the human activities data from all the recorded days are involved in the testing set [52]. The average F-score is calculated from the results of the cross-validation for all the models that has successfully been performed in [18; 44; 53]. Because the classes of the human activity datasets collected from smart homes are imbalanced, the proposed joint learning of temporal models handles the imbalanced human activity classes and avoids having classifiers biased toward the majority classes.  
The evaluation plays an important role in this study. Accuracy is often employed to measure the performance of classifiers. However, accuracy in the presence of imbalanced classes cannot be appropriate measured for classification because less presented classes have a very little impact on accuracy as compared to the prevalent classes [54]. Hence, the F1-score is employed to measure and evaluate all of the temporal models since the F1-score is the weighted average of recall and precision that can provide more insight into the functionality of the temporal models than the accuracy metric [55]. The F1-score is calculated in Equations (8)-(10), respectively.  
\[F1=\frac{2\cdot precision\cdot recall}{precision+recall} \tag{8}\]  
\[recall=\frac{TP}{TP+FN} \tag{9}\]  
\[precision=\frac{TP}{TP+FP} \tag{10}\]  
where TP, FP, and FN are the number of true positives, false positives, and false negatives, respectively. Moreover, the F1-score is widely used in activity recognition [18; 45; 56].
## 5 Results and Discussion
### Comparison with the DCASE 2017 Task 4 Results  
For comparison, the results of our models and the top results from the DCASE 2017 task 4 are presented in Table 8. In the DCASE 2017 task 4, [PERSON] et al. [33] and [PERSON] et al. [31] showed the best performance in audio tagging (clip-level) and sound event detection (segment-level), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{\lambda}\) & **F1** & **ER** \\ \hline
0 & 0.5168 & 0.6564 \\
0.005 & 0.5184 & 0.7048 \\
0.01 & 0.5354 & 0.6213 \\
0.02 & 0.5281 & 0.6867 \\
0.05 & 0.5039 & 0.8109 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Segment-level results for the DSNet-RNN at different \(\lambda\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Before CRF**} & \multicolumn{2}{c}{**After CRF**} \\ \cline{2-5}  & **F1** & **ER** & **F1** & **ER** \\ \hline CNN & 0.4987 & 0.7568 & 0.5195 & 0.6680 \\ DSNet & 0.5135 & 0.7039 & 0.5265 & 0.6849 \\ DSNet-RNN & 0.5354 & 0.6213 & 0.5432 & 0.6131 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of CRF on segment-level performance.  
Figure 2: The results of the DSNet-RNN before and after CRF: (**a**) log mel spectrograms of audio events; (**b**) segment-level ground truth labels; (**c**) predicted segment-level labels before CRF; and (**d**) predicted segment-level labels after CRF.  
[PERSON] et al. [33] used the learnable gated activation function and [PERSON] et al. [31] used a multi-scale input framework. Both also used the fusion or ensemble of models for the best performance. For a fair comparison, we compared the segment-level results of our proposed model in 1 s time resolution. Our models showed better performance in both clip-level and segment-level results, even without the fusion or ensemble of models. The proposed models outperformed [PERSON] et al. [33] in clip-level F1 score. In the segment-level metrics, the DSNet-RNN achieved a similar performance as [PERSON] et al. [31] in F1 score and showed a better performance in ER.
# Design and Switching Characteristics of Flip-Chip GaN Half-Bridge Modules Integrated with Drivers
## 3 Switching Characteristics  
The switching characteristics of the modules were evaluated using a double-pulse test circuit with a load inductor of 600 uH. In the test, the high-side input logic signal was kept at zero to ensure the high-side HEMT was used as a freewheeling channel, while the double-pulse signal was applied on the low-side HEMT. The \(V_{ds}\) and \(V_{gs}\) of the low-side HEMT were measured using a Tektronix MSO64B oscilloscope.  
The \(V_{gs}\) waveforms of these two modules are shown in Figure 4. During the turn-on period, as the input of \(V_{gs}\) was 6 V, the overshoot of \(V_{gs}\) in the wire-bonding module was 1.24 V, while there was almost no overshoot in the module in the flip-chip package. In addition, there was strong oscillation in the wire-bonding module in the turn-off period, whose maximum amplitude was about 1.24 V. This is higher than the \(V_{th}\) of the HEMT (1 V), meaning that there might be false turn-on operations in the wire-bonding module. The rise time of \(V_{gs}\) was 13.5 and 12.5 ns for the flip-chip and wire bonding modules, respectively. The fall time of \(V_{gs}\) was 10.0 and 9.1 ns for the flip-chip and wire bonding  
Figure 3: Microscope photograph of the power loop and the golden ball distribution of the HEMT.  
Figure 2: Microscope photograph of the driver loop (**a**) packaged by wire bonding technology; (**b**) packaged by flip-chip technology.  
modules, respectively. These findings indicate that the parasitic inductance of the driver loop was effectively reduced by the application of flip-chip technology.  
The \(V_{ds}\) waveforms of the flip-chip module under a \(V_{dc}\) of 100 V are shown in Figure 5. The test was carried out under 100 V because the module had no additional insulation protection and heat sink. The fall time of \(V_{ds}\) was 5.8 ns, which corresponds to a dv/dt of 17.2 V/ns during the turn-on period. The rise time of \(V_{ds}\) was 12.9 ns for the module, with a dv/dt of 7.8 V/ns during the turn-off period. The \(V_{ds}\) waveform of the integrated module was stable after a small overshoot of 4.8 V during the turn-off period.  
To illustrate the advantages of the flip-chip technology, the test results of the integrated module were compared with that of [8], whose circuit structure is similar to this work. In reference [8], a GaN half-bridge module was fabricated with integrated drivers by the wire bonding process. Under the same test conditions of 100V/2.7A, the overshoot of \(V_{ds}\) of the module in [8] was approximately 25V, which is four times larger than the result of this work. During the turn-off period, the current decreased gradually. Due to the existence of parasitic inductance in the power loop, a voltage in phase with \(V_{dc}\) was induced on it, which manifested as overshoot of \(V_{ds}\). This means that the parasitic inductance of power loop in reference [8] is larger than this work, which indicates advantages of the flip-chip technology.