esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Synchronous Machine Control of RES Plants in Isolated Power Systems
## 4 Results and Discussion
### Strong Grid: SCR = 10  
The trends of the active power obtained in the simulation for this case are reported in Appendix A Figure A1. The main results of the simulations are summarized below:  
Figure 4: Flow chart of the proposed control strategy.  
* Throughout all of the simulations, a typical trend in grid stability was observed after the fault, with a loss of load, depending on the unbalance between SG and RES. However, because of the action of the speed regulators on the SG and of the internal controllers of the VSC, the active power returned to the previous steady-state values as soon as the fault was removed.
* When the contribution of RES was greater than 60% of the load (Scenarios B1 and B2), and when \(R_{sc}\) assumed its lowest value, the active power showed more significant oscillations. On the other hand, the grid stability improved when angle compensation was used and \(T_{psm}\) was raised.
* In Scenario A1, the system had a positive behavior; this feature was due to the contribution of both SG and the control option chosen for the VSC (RC-1) during the fault. These choices ensured that scenario A1 did not present unstable situations even in the case of a low \(T_{psm}\) and \(R_{sc}\).
* In Scenario A2 with a low value of \(R_{sc}\), the system became unstable when RC-2 was chosen, regardless the value of \(T_{psm}\).
* In the steady-state operation, RC-2 showed a better performance than RC-1, and when using this control option, the system stabilized quickly, as shown in the results for scenarios A2 and B2. However, in the presence of faults, this control option was not able to maintain the desired power.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 4 Proposed Architecture
### Classification  
In this work, the classifier as shown in Figure 1 consists of a 1D GAP layer and two FC layers whose ReLU activation functions are used in the first FC layer while the Softmax activation function is used in the second FC layer. The numbers of neurons in the first and second FC layers are 1024 and \(K\), respectively, where \(K\) is the number of classes (depending on the datasets). As specified in Section 3, the number of classes \(K\) is 12, 13, and 19 for the PAMAP2, DaLiAc, and DSAD datasets, respectively. Note that other classifiers' structures are applicable.
## 4 Conclusions  
In the study presented herein, there has been a systematic approach to identify the most effective DES for the extraction of _S. fruticosa_ polyphenols, by screening several citrate salts combined with two common HBDs, lactic acid and glycerol. The highest performing system was a DES composed of lactic acid and sodium citrate dibasic, at a molar ratio of 15:1, and for the first time, there has been evidence that the extraction performance of DES might depend on their pH. Optimization of the extraction and examination of the effect of temperature showed that blending ultrasonication pretreatment with optimized stirred-tank extraction may be a highly efficient green method to produce polyphenol-enriched extracts from _S. fruticosa_. This was also demonstrated by comparison with other pre-existing green extraction methodologies. The major polyphenolic phytochemicals identified in the extracts produced under optimized conditions were chlorogenic acid, luteolin 7-O-glucuronide and rosmarinic acid. The method developed is proposed as a green and efficacious methodology to recover bioactive polyphenols from the medicinal plant _S. fruticosa_. Testing of this solvent on several other matrices and comparison with other natural DES may reveal its full potential. Such a work is currently under progress.  
Conceptualization, S.G. and D.P.M.; formal analysis, S.G. and A.H.; investigation, S.G., A.H. and D.P.M.; writing--original draft preparation, S.G. and D.P.M.; writing--review and editing, S.G. and D.P.M.; supervision, D.P.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 8: Chromatographic analysis of polyphenols in a _S. fruticosa_ extract, produced under optimized conditions (S\({}_{\text{S}}\) = 900 rpm, \(C_{\text{DES}}\) = 77% (_w/o_), and \(t_{\text{US}}\) = 15 min). The chromatogram was obtained at 330 nm. Peak assignment: 1, chlorogenic acid; 2, luteolin 7-O-glucuronide; 3, rosmarinic acid.
## 2 Methodology
### Multi-Fidelity Kriging  
In order to create a multi-fidelity surrogate model one has to map the trend of the more intensively sampled low-fidelity (LF) data to the less intensively sampled high-fidelity (HF) data. Here, a hybrid bridge function approach adopted from [PERSON] et al. (2018) is used that has been presented in previous work ([PERSON] et al., 2019). The relationship between low- and high-fidelity surrogate model values (indicated by a hat) in any location \(x\) is given by  
\[\hat{f}_{HF}(x)=\hat{\phi}(x)\hat{f}_{LF}(x)+\hat{\gamma}(x) \tag{7}\]  
where \(\hat{\phi}(x)=g^{T}(x)\hat{\rho}\) is a low-order polynomial with \(q+1\) basis functions \(g^{T}(x)=[1,g_{1}(x),\)\(\ldots,g_{q}(x)]\) and corresponding coefficients \(\hat{\rho}=[\hat{\rho}_{0},\hat{\rho}_{1},\ldots,\hat{\rho}_{q}]^{T}\) and \(\hat{\gamma}(x)\) is an additive bridge function. A multi-fidelity kriging surrogate model is constructed via the following three steps (visualized in Figure 1):  
Figure 1: MF kriging algorithm flowchart.  
Using \(N_{LF_{1}}\) lowest fidelity training points construct a kriging model, \(\hat{f}_{LF_{1}}\).
2. Construct another kriging model for the additive bridge function, \(\hat{\gamma}_{2}\), where \(\gamma_{2}(x_{i})=f_{LF_{2}}(x_{i})-\hat{\phi}_{2}(x_{i})\hat{f}_{LF_{1}}^{ \prime}(x_{i})\) in \(N_{LF_{2}}\) training points. Compute an optimal \(\hat{\rho}_{2}\) (and \(\hat{\theta}_{2}\)) during the maximum likelihood estimation updates for \(\hat{\gamma}_{2}\), which means training point values \(\gamma_{2}(x_{i})\) will change constantly, but once converged one can evaluate \(\hat{f}_{LF_{2}}(x)=\hat{\phi}_{2}(x)\hat{f}_{LF_{1}}(x)+\hat{\gamma}_{2}(x)\).
3. Repeat step 2 until reaching the highest fidelity level (this is analogous to a multi-grid strategy).  
Numerical experimentation showed what intuition suggests namely that the HF training locations should be a subset of the LF locations to make sure that no error is added from \(\hat{f}_{LF}(x)\) when computing \(\gamma(x)=f_{HF}(x)-\hat{\phi}(x)\hat{f}_{LF}(x)\) in all \(N_{HF}\) training points. The other \((N_{LF}-N_{HF})\) LF training locations are selected via latin hypercube sampling subject to a distance constraint to the existing HF points. Furthermore, whenever the dynamic training point algorithm [9] adds a HF point to the set, the corresponding LF point is also added.
# Feasibility Study of Passive Bistatic Radar Based on Phased Array Radar Signals
## 4 Coherent Integration Method Based on Radon-NUFFT  
Since the radar signal we want to perform coherent integration on has random PRI and staggerd PW, after analyzing the signal model of the echo of moving target, a coherent integration method based on Radon-NUFFT is proposed to solve the problems caused by the random PRI and staggered PW.
## 5 Simulation Results and Analyses  
For the convenience of comparison, the proposed algorithm in this paper is called ant colony optimization clustering routing (ACOCR). Five existing popular algorithms: LEACH [18], DUCS [19], LEACH-ANT [47], CUWSN [23], and EOCA [11] were chosen as the references to validate the proposed algorithm according to the number of surviving nodes, the energy consumption of the network, and the packet loss ratio. MATLAB was used to carry out the simulation where sensor nodes were randomly placed in a 3D area of 5000 m \(\times\) 5000 m \(\times\) 1000 m and the coordinate of the SN was (2500, 2500, 0). The network was divided into 64 cubes. The number of sensor nodes ranged from 300 to 500 for different scenarios. The data packet was 1024 bits in length and the data transmission rate was 2048 bps, by which the time duration for a node to transmit and receive data packets could be calculated. The broadcast and other message packets were 64 bits in length. The sound speed was 1500 m/s. As for the energy consumption parameters, the receiving power \(P_{0}\) was set to 50 \(\mu\)W and the initial energy for every node was 120 J. The frequency \(f\) was 10 kHz.
## 4 Field Test
### Data Gathering Time Test  
The test evaluates the influence of data size on data gathering time. The cluster head node was placed at the target position of the UAV and then the UAV moved to the cluster head along its initial heading over a straight distance of 1300 m. According to Table 3, we defined the time when the UAV enters an area 1300 m away from the cluster head as the beginning of the data gathering task, and the data gathering time is from the beginning of the data gathering task to the completion of the data transmission. We tested the data gathering time using DCMDR, DR0, DR3, and DR6 data rate modes, with the stored data of cluster head from 0.1 to 100 Mbits, respectively. The results are shown in Figure 9. The data gathering time of DCMDR is always the smallest regardless of the data size. The data gathering time of DR0 and DR3 modes increases rapidly as the amount of data increases. When the data size of DR0 exceeds 1 Mbits, and the data size of DR3 exceeds 25 Mbits, their data gathering time exceeded 2000 s. The reason is that the data rate of DR0 and DR3 is lower, when the amount of data is large, the UAV cannot guarantee that the data is gathered within their \(Dist\). It needs to reduce flight speed to ensure complete data gathering according to (14), and the data gathering task cannot be completed due to the limited flight time. Although the DR6 has the fastest data rate, when the data size is less than 1 Mbits, the data gathering time is much longer than DCMDR. It is because the \(Dist\) of DR6 is the smallest, and the UAV has to fly into the coverage area of DR6 for a long time, while the DCMDR can start gathering data from a long distance with the lower DRs. Therefore, DCMDR has good adaptability to the size change of data gathered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DR** & **SNR** & **RSSI** & **Dist (m)** \\ \hline
6 & 0 & \(-85\) & 250 \\
5 & 0 & \(-95\) & 380 \\
4 & 0 & \(-112\) & 600 \\
3 & \(-5\) & \(-114\) & 750 \\
2 & \(-10\) & \(-119\) & 950 \\
1 & \(-15\) & \(-120\) & 1100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The thresholds of SNR, RSSI, and Dist for each DR.  
Figure 9: Data gathering time varying with data size.
# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 4 Experiment
### Data Preprocessing  
#### 4.3.1 Sliding Window  
A total of 135 ST and 755 non-ST companies were selected, with financial indicator data extracted for each company for 12 quarters over 3 years. This resulted in 1630 time series points for ST companies and 9060 for non-ST companies. The sliding window technique can generate feature windows and corresponding labels for subsequent analysis and model training. As shown in Figure 4, as an example of a sliding feature window of 6, windows are slid over the time series data to create overlapping windows. Each window contains six consecutive data points, and, for each feature window, a label was assigned from the subsequent time step, T, i.e., whether it was an ST company, which can effectively utilize the time series data to generate more training samples and improve the generalization ability of the model. Specifically, a six-step feature window was slid along each company's financial time series with a stride of 1, creating overlapping input sequences. Each feature window consists of six consecutive quarters of financial data, and the corresponding label is determined by the ST status of the quarter immediately following the window (denoted as time T). The distribution of sliding window samples is shown in Table 3.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline
**Indicator Name** & **Description** \\ \hline \multirow{2}{*}{Debt Service Capacity} & Current ratio, quick ratio, cash ratio, working capital, debt-to-asset ratio, equity ratio, multiplier equity ratio \\ \hline \multirow{2}{*}{Ratio Structure} & Current assets ratio, cash assets ratio, working capital ratio, non-current assets ratio, current liabilities ratio, fixed assets ratio, operating profit margin \\ \hline \multirow{2}{*}{Operating Capacity} & Accounts receivable turnover, inventory turnover, accounts payable turnover, current asset turnover, fixed asset turnover, total asset turnover \\ \hline \multirow{2}{*}{Earnings Capacity} & Return on assets, net profit margin on total assets, net profit margin on current assets, net profit margin on fixed assets, return on equity, operating profit rate \\ \hline \multirow{2}{*}{Cash Flow Capacity} & Operating index \\ \hline \multirow{2}{*}{Development Capability} & Capital preservation and appreciation rate, fixed asset growth rate, revenue growth rate, sustainable growth rate, owners’ equity growth rate \\ \hline \multicolumn{2}{p{113.8 pt}}{Per Share Metrics} & Earnings per share, earnings before interest, taxes per share \\ \hline \end{tabular}
\end{table}
Table 1: Corporate financial data indicators.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt} p{284.5 pt}} \hline
**Company Type** & **Number of Company Samples** & **Number of Time Series Data Points** \\ \hline ST & 135 & 1620 \\ Non-ST & 755 & 9060 \\ \hline Total & 890 & 10,680 \\ \hline \end{tabular} Notes: Data sourced from the quarterly financial analysis module of the CSMAR database.  
\begin{tabular}{p{113.8 pt} p{284.5 pt} p{284.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 4 MIPS Architecture  
The Microprocessor without Interlocked Pipeline Stages (MIPS) architecture has had a major share in the embedded processor market since 1990. The architecture has evolved and assimilated new technologies, including virtualization technologies. The MIPS architecture includes a virtualization module, which allows the hypervisor to run the fully virtualized guest OS. The provision of a feature to run a guest OS without modification is known as full virtualization. First, we describe the virtualization module of the MIPS architecture that forms the background on which ASMI is built for memory isolation.  
To execute guest operating systems in full virtualization mode, additional registers, instructions, operating modes, and interrupts are required. The additionally required components in the MIPS architecture to enable full virtualization to guest operating systems are collectively called the virtualization module (MIPS VZ) [31]. The virtualization module helps to create separate operating environments for the guest OS and the hypervisor.
## 4 Discussion
While both experimental groups showed improved isometric strength, the COMB group (d = 1.14) improved by a greater magnitude than the CST group (d = 0.40). Since shifting the force portion of the power equation (mechanical power = force \(\times\) velocity) is known to improve dynamic performance, the greater improvement in strength is a plausible explanation for the COMB group experiencing greater magnitudes of improvement in the single effort sprint (d = 0.95-1.93 vs. d = 0.26-0.44), jump (d = 0.74-0.85 vs. d = 0.39-0.41), and change of direction T-test (d = 1.75 vs. d = 0.81) tasks compared to the CST group. However, the reason behind the greater strength increase in the COMB group is hard to determine as the isometric and 'heavy load' exercises were prescribed at the same relative loads for the COMB and CST groups, respectively. Nevertheless, it is possible that the total time was greater for the COMB group. Another potential explanation is that plumetrics in isolation can effectively improve maximal strength due to high mechanical and neural stress levels while unloaded. Indeed, a meta-analysis determined that plyometrics can substantially improve dynamic and static strength assessments (Hedges' g = 0.57-1.11) [40]. While the CST training program also incorporated higher velocity movements, the velocity achieved during low-load resistance training was still far slower than during the sprinting and plyometric exercises [41]. Therefore, the general superiority of COMB over CST could be attributed to the aforementioned factors. This work had limitations that should be considered. Indeed, the study did not incorporate electromyography measurements, which could have provided further insight into muscle activation patterns and the neuromuscular response during the training interventions. Additionally, we compared the combined isometric and plyometric training program with a contrast training program, not with more traditional training methods. Therefore, we cannot say the COMB program is superior to other training approaches. Likewise, the isometric contractions in the present study were performed at submaximal intensities, begging the question of whether similar results would have occurred with intensities over the proxy movements' 1 RM. In addition, to fortify the study's impact, future research should consider a longer follow-up period and expand the sample size to enhance the study's generalizability. Furthermore, incorporating female participants in subsequent studies could provide valuable insight into gender-specific responses to training interventions. Finally, our findings may not have been similar in other age groups, sporting backgrounds, or resistance training-naive athletes.
## 2 Related Work  
### Multimodal Relation Extraction  
In recent years, an annotated MRE dataset MNRE and baseline models have been constructed and released, demonstrating that utilizing visual information can improve Relation Extraction (RE) capabilities for social media text. The authors of MEGA [17] further corrected the MNRE dataset and proposed a dual image alignment method to capture aligned information between visual object relations and textual object relations, making better use of visual object relation information. The authors of FL-MSRE [18] proposed using few-shot learning methods to extract social relations from text and facial images.  
Recently, the authors of RDS [19] separated the image-text dataset of Internet media into unimodal and multimodal subsets based on reinforcement learning, so that unimodal models perform better on unimodal subsets while multimodal models perform better on  
Figure 1: An example of image-text multimodal data from MEGA. For an entity pair appearing in the text, the accompanying image that supplements the textual information is used to determine the relation between the entity pair.  
multimodal subsets. The authors of HVPNeT [20] first introduced pyramidal features in the multimodal field, using visual representations as insertable visual prefixes to guide error-insensitive predictive decisions of textual representations. On this basis, they proposed a dynamic gated aggregation strategy to realize multi-level multi-scale visual features as fused visual prefixes. The authors of EEGA [21] were the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE). In addition to aligning visual objects and textual entities, they also aligned relations between visual objects and relations between textual entities. The authors of MKGformer [22] proposed a Transformer structure that mixes text and image modalities at multi-levels, where the prefix-guided interaction module (PGI) pre-reduces heterogeneity across modalities, and the correlation-aware fusion module (CAF) further reduces the model's sensitivity to irrelevant information between text and images.  
The visual encoders of these models have evolved from early CNNs (e.g., Fast-RCNN [23], Resnet [24]) to later Vision Transformers (e.g., clip [25]), while most textual encoders are Transformer [26] models (e.g., bert [27], roberta [28]). These works have achieved great success. However, they fine-tune pre-trained unimodal models, while the multimodal parts are trained directly on MRE data. That is, modality alignment is secondary to MRE. Moreover, our analysis of image-text media shows that visual information is more abundant yet noisier than text. This is also evidenced in ALBEF [29], which indicates that larger visual models are needed to fully utilize visual information. Therefore, we argue plain small models are insufficient for proper MRE due to dataset scale limitations and model capacity constraints. Previous models faied to achieve good modality alignment. To address this, we propose the MRDA and FTL methods, resolving data volume and relation category distribution problems.
## 6 Fault Detection and Classification Case Studies
### Fault Incidence Angle Variation  
The impact of the changing fault incidence angle over the sinusoidal waveform of the current signal on the performance of the algorithm is investigated by recognizing all categories of faults incident at angles of \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), \(90^{\circ}\), \(120^{\circ}\), and \(150^{\circ}\). These faults are created at a location 30 km away from the sending terminal. The impedance of all kinds of faults is maintained at zero at the moment of the varying angles of the fault incidence. Figure 10 presents the FI for the faults of all natures at different angles of the fault incidence.  
From Figure 10a, it is clearly seen that the peaks of the FI corresponding to Phase A are greater compared to the pre-set threshold. The FI related to Phases B and C is lower than the threshold quantity (\(5\times 10^{9}\)) at the time of the LG fault on Phase A for different values of the angle of the fault incidence. From Figure 10b, it is clear that the peak quantities of the FI associated with Phases A and B are greater relative to the threshold value, whereas the FI related to Phase C is lower relative to the pre-set threshold at the moment of the LL fault with different quantities of the angle of the fault incidence. From Figure 10c, it is clear that the peak quantities of the FI associated with Phases A and B are greater relative to the threshold value, whereas the FI related to Phase C is low relative to the pre-set threshold at the moment of the LL-G fault for all quantities of the angle of the fault incidence. Figure 10d indicates that the peak quantities of the FI pertaining to all phases are high relative to  
Figure 9: Effect of the varying impedance involved during the fault for detecting and classifying (**a**) L-G, (**b**) LL, (**c**) LL-G, and (**d**) LLL-G faults.  
the pre-set threshold quantity at the moment of the LLL-G fault with different values of the fault incidence angle.
## 3 Methodology  
The simulations performed in this paper are divided into two main subsections of steady-state and transient analysis. Each of the subsections is further divided into smaller segments to represent different types of studies. A detailed flowchart of all of the simulations performed in this paper is shown in Figure 7.  
Finally, it needs to be highlighted that all of the studies presented in this paper show the potential available energy in the system. Any electrical losses such as alternator, converter and battery losses have not been considered at this point.
## 2 Materials and Methods
### Evaluating Differences between VPP Estimates and MODIS-LSP Products  
In order to evaluate differences between VPPest and LSP products, the most commonly used LSP products (MODIS-LSP) were compared with VPPest products. In addition, two global GPP data sets were used to analyze whether the VPPest products have closer relationships to GPP than the LSP products.