esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Current Research and Future Directions
### Adaptive Digital Audio Effects
When the analysis stage is part of the effect processor itself, various limitations are introduced to its application: firstly, if the whole audio signal needs to be taken into account in order to obtain the necessary control data, these transformations cannot be applied in real time. This not only introduces limitations to the creative music production process but also renders these effects unsuitable for live performance settings. On the other hand, the approach to obtain the control data from existing, previously extracted metadata reduces the computational burden by omitting the often very complex feature analysis algorithms. Techniques to implement content-based audio transformations that are based on high-level features that are extracted and stored in a database prior to the application of the effects have been developed in the course of the Content-based Unified Interfaces and Descriptors for Audio/music Databases available Online (CUIDADO) project [148; 149] and in the development of experimental plug-in effect software [150]. A basic diagram of the signal flow of such a metadata-driven adaptive audio effect is given in Figure 9.  
[PERSON] et al. [83] proposed implementation strategies for a large number of features to be used for non-real-time adaptive audio effects, many of which have been commonly used for timbre space description based on the MPEG-7 proposals [151]. A deep review and discussion of adaptive audio effects is presented in Reference [106].
## 4 Simulation Results and Analysis
### Energy Left in the Battery:  
As shown in Figure 9, the energy left in the battery of the proposed algorithms is maximum than the CoDBR and DBR protocols. Because the CoDBR protocol forwards the data packets with the cooperation of two relay nodes, it consumes extra energy than the counterpart CoDMR scheme, which uses only a single relay. Combining it with the redundant information transmission in CoDBR, makes minimum energy left in batteries of the sensor nodes in CoDBR. While in DBR, the reason of the minimum energy left in the battery is due to the unbalance energy consumption as compared to DMR scheme. In DBR scheme, transmission of information over long paths is also reduced due to minor sink nodes availability within the network. As a result, the DMR scheme balances the energy cost, which saves the battery power. The DMR scheme also reduces the transmission path length and ensures maximum energy left in the battery of nodes. Likewise, the proposed DMR scheme has lower energy consumption than the CoDMR scheme. Because DMR is non cooperation scheme while the CoDMR scheme uses the cooperation. As a result minimum energy left in the battery of the CoDMR  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Calculated Values** \\ \hline Bandwidth & 30,000 Hz \\ Data packet size & 1600 bits \\ Depth & 500 m \\ Hello packet size & 48 bits \\ Idle mode exhaust power & 10 mW \\ Receiving power & 0.1 W \\ Total initial energy & 10 Joules \\ Total sensors nodes & 500 \\ Transmission power & 2 W \\ Transmission range & 100 m \\ Major sink nodes & 1 \\ Minor sink nodes & 4 \\ Width & 500 m \\ Wind speed & 10 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
scheme nodes, as cooperation uses extra nodes as relays in addition to transmitterreceiver pair. Initially, all nodes in the network have maximum energy left in the battery. At round 200, the value of the energy left in the battery of nodes in the DBR is near to 1000 J and that of CoDBR is lower than 1000 J. At the same time, the energy left in the battery of nodes of the CoDMR is near to 1500 J and the DMR scheme maintains the highest energy of 2000 J. This indicates that the DMR and CoDMR schemes have more energy left in the batteries of nodes than DBR and CoDBR. After round 200 to round 800, the energy left in the battery of all schemes are different due to the reason explained above.  
Figure 8: Nodes depleted of battery power.  
Figure 9: Energy left in the battery.
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 6 Discussion  
In this study, we developed and validated a pixel-based image fusion method based on brain conventional T2-weighted MRI and 18F-FDG PET-CT images for prediction of Alzheimer's disease. This method showed significantly better diagnostic performances in distinguishing patients with an Alzheimer's disease and cognitive normal patients than any single method.  
Diagnostic information in fusion image is more abundant because the fused image integrates the anatomical information and the metabolic function information. Resulting from the above, it can be analyzed that fusion images improved the performance of the two CNN models that were built by the project group, as CNN and DeU-Net have significant improvement in diagnosing accuracy. Meanwhile, DeU-Net has better accuracy or performance in processing medical images than traditional CNN. However, fusion images training in other pretrained models represents irregularity performance.  
This result demonstrated that multimodal fusion can improve the diagnostic rate of Alzheimer's disease and confirms that multimodal fusion technology is greatly significant in the diagnosis of Alzheimer's disease. The result also proved that the multimodal image  
Figure 18: Confusion matrix of DeU-Net with fusion images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset Modality** & **ACC (\%)** & **AUC (\%)** \\ \hline CNN & Single & 84.5 & 89.9 \\  & Fusion & 90.9 & 91.6 \\ \hline DeU-Net & Single & 89.5 & 90.6 \\  & Fusion & 90.5 & 94.1 \\ \hline GoogLeNet & Single & 80.0 & 83.9 \\  & Fusion & 86.0 & 89.7 \\ \hline InceptionV3 & Single & 77.3 & 80.9 \\  & Fusion & 81.2 & 84.9 \\ \hline MobileNetV2 & Single & 81.0 & 92.0 \\  & Fusion & 88.1 & 89.9 \\ \hline VGG16 & Single & 81.1 & 90.5 \\  & Fusion & 84.2 & 85.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison between single modality images and fusion images implemented in training different deep learning models.  
fusion based on convolutional neural networks has important research value. At the same time, one of the problems in this paper is that the accuracy of the diagnostic models still needs to be improved. The number of samples currently diagnosed by Alzheimer's disease is 182 (102 patients and 80 people without disease) much less than the MNIST set, while the number of sample sets for digital identification is 60,000. One of the reasons for the low accuracy is that the number of training samples is too small, so the extracted features are not obvious.
## 2 Dynamic Model of the Feed System
### Calculation of Restoring Force of Bearings  
To calculate the restoring force of bearings, the position relationship of the raceway centers should be determined. The coordinate systems \(\text{CS}_{\text{Ab}}(x_{\text{A}},y_{\text{A}},z_{\text{A}})\) and \(\text{CS}_{\text{Db}}(x_{\text{D}},y_{\text{D}},z_{\text{D}})\) are established at the geometric centers of the two angular contact ball bearings and a deep groove ball bearing, respectively, and their axes are parallel to those of \(\text{CS}_{0}\), as shown in Figure 4.
# Research on Transmission Task Static Allocation Based on Intelligence Algorithm
## 2 Preliminaries  
### Model of TTSA  
The problem of TTSA exists in the automatic control system of radio and television transmission stations. The data acquisition of the transmission equipment is completed by the computer. According to the analysis of the transmission effect data, the appropriate transmission tasks are assigned to the transmission equipment with the best effect, and then the automatic transmission is completed by the computer control [29; 30; 31; 32]. The TTSA problem is to solve the optimal allocation scheme between the task and the equipment performing the task. The problem framework is shown in Figure 1.  
In Figure 1, the task queue is composed of \(N\) tasks, each with different frequencies. The matrix of evaluation value is the basis for the evaluation of the transmission effect. It stores the evaluation value of the transmission effect obtained when each piece of equipment works in different transmission frequency ranges. The initial data are set according to the data collected by polling the transmission effect during the test run of the transmission station. Combined with the idea of supervised learning, the matrix of evaluation value modifies the matrix according to the collected data after the daily transmission task to ensure the practicability of the algorithm. Each transmission equipment item can only complete one transmission task at a time. In order to ensure the integrity of the algorithm, this paper sets the number of transmission tasks to be consistent with the number of transmission equipment items.
## 5 Conclusions  
This paper quantitatively evaluates the flexibility potential of retrofitted TPUs for accommodating RES fluctuations through coupling integration. Through a case study from a real-world system in Northeast China, the following conclusions can be summarized as follows:  
1. The proposed approach can fully exploit the flexibility potential of retrofitted TPUs to improve the accommodation capabilities of RESs following coupling integration by time-step-based optimization.
2. The enhancement of RES accommodation capabilities following the retrofitting of TPUs and coupling integration can be quantitatively validated through the optimal scheduling of power sources and index calculations.
3. The CGS with retrofitted TPUs can increase WT utilization by 2.59% and PV utilization by 4.66% annually compared to the independent operation condition. Moreover, compared to independent operations, the annual total revenue of the CGS increased by 15.1 million CNY.
4. After the flexibility retrofitting of TPUs, the CGS eliminates WT and PV curtailments during the summer and significantly reduces them in the winter while also possessing the potential for further renewable energy integration.
5. The CGS can effectively leverage the flexibility potential of TPUs to balance RES fluctuations, thereby alleviating the main power grid's burden of accommodating RESs with high penetration.  
Conceptualization, Y.Z. and Q.W.; methodology, Y.Z. and Y.C.; software, Y.Z. and Q.X.; validation, Q.X.; formal analysis, Y.Z.; investigation, Y.Z. and Q.X.; resources, Q.X.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.C. and Q.W.; visualization, Y.Z.; supervision, Q.W.; project administration Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by International Postdoctoral Exchange Fellowship Program (No. YJ20210337) and Chongqing Technical Innovation and Application Development Special Program (No. CSTB2022 TIAD-KPX0053).  
The data presented in this study are available on request from the corresponding author.  
Figure 6: Comparison of total output for thermal power units. (**a**) After coupling integration with retrofitted TPUs; (**b**) Before coupling integration and without retrofitted TPUs.  
**Acknowledgments:** We would like to extend our sincere gratitude to [PERSON] from China. Metallurgical CCID Electric Technology Co., Ltd., Chongqing, China. His support has been crucial to the success of our study, and we are thankful for his contributions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 System Architecture
### Receiver and Graphical User Interface  
The receiver (see Figure 2) consists of a quadrature zero-IF down-converter (MAX3580) using an LNA with a noise figure of \(4.7\) dB at \(400\) MHz, RF and baseband tunable filters and automatic gain control circuits. The quadrature signals are converted by a dual channel, \(20\) MSps, \(10\) bit ADC (AD9201) and fed to a Xilinx FPGA module (OpalKelly, XEM3005), which performs frequency demodulation and Manchester decoding and sends the received data to a PC via a USB link. In addition, a  
Figure 1: Block diagram of the wireless neural recording IC.  
Graphical User Interface (GUI) based on Labview/C software was implemented to allow data saving and on-line elaboration.
## 2 Structure of the Grid-Connected PV System
### H-Bridge VSI Design and Control  
To implement the traditional automatic synchronization technique, the VSI inverter should have a similar line voltage and current with a matched phase and frequency similar to those of the grid system [32]. Robust control methods are applied in synchronization to quickly identify the frequent changes in voltage, phase, and frequency magnitudes, and maintain the status quo of the grid [33]. The grid synchronization method is advantageous in two major ways. Firstly, a grid synchronizer not only reproduces a filtered signal that is in phase with grid voltage but also emulates an orthogonal component of the grid voltage, which can be used to generate reactive power reference to the inverter. Therefore, the inverter gains the ability to control the reactive power flow compared to the conventional inverters that only transfer active power due to their inability to produce an orthogonal component of the current reference. Secondly, to lock on the phase of the grid voltage, SRE-PLL would need a zero-voltage crossing detection to reset the integrator and the d-q transformations used, which would need sine and cosine calculations. A 1-\(\phi\) feedback current loop is used to regulate the grid current. A proportional resonant (PR) compensator is used to track a sinusoidal current reference signal. To remove harmonic components, obtain balanced conditions, and maintain the same phase of the input and output signals of the grid, a synchronous reference frame (SRF)-PLL is deployed in the VSI control technique. A PR controller in the SRF-PLL method is applied for a faster and more robust process than that of others because it does not need decoupling and intermittent operations shown in Figure 6.  
Figure 6: A Complete closed-loop control system for the voltage-sourced inverter (VSI).  
Table 2 gives the VSI and LCL filter specifications A simple bipolar SPWM modulation technique is chosen to generate the complementary pulses at the same switching frequency of the VSI [34]. In the bipolar SPWM control, the switch pairs, S1-S4 and S2-S3 of the VSI, are turned ON and OFF, respectively. The amplitudes at the positive and negative sides of the generated square pulses of the SPWM method are equal in magnitude with opposite polarity. The output voltage shifts between \(+\)VDC and \(-\)VDC voltage levels. The frequency of the carrier wave is chosen as 10 kHz and the frequency of the sine wave is 50 Hz (fundamental frequency). In the bipolar control technique, the two switching signals are shown in Figure 7.
## 4 Problem Definition
### Formulation of UAV Placement Optimization as an MOP
#### 4.2.1 Minimize the Number of UAVs  
We start by identifying a set of potential UAV placement points \(Q\), by finding a sub-area \(a^{\prime}\subset\mathcal{A}\), which corresponds to the area inside the convex hull (convex envelope) [25] formed by the GNs in \(\mathcal{A}\), as shown in Figure 2. We compute the convex hull to reduce the search space of the UAVs' placement points in the target area. We intend to cover all GNs in \(a^{\prime}\). Therefore, we discretize \(a^{\prime}\) in a grid layout according to Equation (4).  
\[\Delta=\mu\mathcal{D};\,\mu\in[0,1] \tag{4}\]  
where \(\Delta\) is the distance between two neighboring UAVs, which is adjusted using \(\mu\). Let \(q_{j}\in Q\) be the \(j\)th potential UAV placement point. We define \(\delta^{u}_{q_{j}}\) as a binary variable to indicate which points are currently being used by a UAV as presented bellow.  
We also define \(\xi^{u}_{v}\) as a binary variable to indicate which GNs are being serviced by each deployed UAV. It is assumed that a GN will be connected to the closest deployed UAV.  
\[\xi^{u}_{v}=\begin{cases}1&\quad\text{if $v$ is connected to UAV $u$}\\ 0&\quad\text{Otherwise}\end{cases}\]  
Our objective is to select points in \(Q\) such that:  
\[min\,\sum_{q_{j}\in Q}\sum_{u\in U}\delta^{u}_{q_{j}} \tag{5}\]  
subject to:  
\[\sum_{q_{j}\in Q}\delta^{u}_{q_{j}}\leq 1,\forall u\in\mathbf{U} \tag{6}\]  
\[\sum_{v\in\mathbf{V}}\xi^{u}_{v}\geq 1,\forall v\in\mathbf{V} \tag{7}\]  
Constraint (6) indicates that each UAV \(u\) cannot be placed in more than one point at the same time. Constraint (7) ensures that a GN is in the communication range of at least one UAV. The cardinality of the set \(Q\) defines the maximum number of UAVs that can be used for each formed convex hull. In order to ensure connectivity among UAVs, we have considered using Algorithm 1, which verifies if each UAV has a path to the selected destination, which may be used as the control station. UAVs are  
Figure 2: Convex hull formed by the GNs.  
assumed to have two main attributes: serving, when the UAV is used to serve GNs and to connect the network, and bridging, when it is solely being used to connect the serving UAVs.  
```
1:Input:\(u_{dest}\), adjacency matrix
2:Result: Connected UAV network
3:For each\(u\in\mathbf{U}\)
4:IF\(u\) is serving, and \(u\) is not bridging
5:\(q^{curr}=q^{u}\); /*\(q^{curr}\in Q\) is the current point towards the destination*/
6:Until not reachable (\(u\),\(u\),\(u\)\({}_{dest}\))
```  
**Algorithm 1** Construction of the connected UAV network.
# A Keyword Detection and Context Filtering Method for Document Level Relation Extraction
## 4 Experiment and Results
### Results on the Biomedical Datasets  
In experiments on two biomedical datasets, we compared SAM-LSTM with many models, including EoG [6], LSR [7], DHG [17], GLRE [9], and ATLOP [14]. We applied the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**DocRED**} & \multicolumn{2}{c}{**CDR**} & **GDA** \\  & **BERT** & **RoBERTa** & **SciBERT** & **SciBERT** \\ \hline Batch size & 4 & 4 & 4 & 16 \\ Epoch & 30 & 30 & 30 & 10 \\ lr for encoder & \(5\times 10^{-5}\) & \(5\times 10^{-5}\) & \(2\times 10^{-5}\) & \(2\times 10^{-5}\) \\ lr for LSTM & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters Setting.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} \\  & & **lgn F1** & **F1** & **lgn F1** & **F1** \\ \hline GEDA-BERT\({}_{base}\)[16] & 2020 & 54.52 & 56.16 & 53.71 & 55.74 \\ LSR-BERT\({}_{base}\)[7] & 2020 & 52.43 & 59.00 & 56.97 & 59.05 \\ GLRE-BERT\({}_{base}\)[9] & 2020 & - & - & 55.40 & 57.40 \\ GAIN-BERT\({}_{base}\)[8] & 2020 & 59.14 & 61.22 & 59.00 & 61.24 \\ HeterGSAN-BERT\({}_{base}\)[18] & 2021 & 58.13 & 60.18 & 57.12 & 59.45 \\ \hline BERT\({}_{base}\)[19] & 2019 & - & 54.16 & - & 53.20 \\ BERT-TS\({}_{base}\)[19] & 2019 & - & 54.42 & - & 53.92 \\ HIN-BERT\({}_{base}\)[20] & 2020 & 54.29 & 56.31 & 53.70 & 55.60 \\ CorefBERT\({}_{base}\)[21] & 2020 & 55.32 & 57.51 & 54.54 & 56.96 \\ ATLOP-BERT\({}_{base}\)[14] & 2021 & 59.22 & 61.09 & 59.31 & 61.30 \\
**SAM-LSTM-BERT\({}_{base}\)** & & **60.18 \(\pm\) 0.14** & **61.96 \(\pm\) 0.15** & **60.68** & **62.02** \\ \hline BERT\({}_{large}\)[19] & 2019 & 56.67 & 58.83 & 56.47 & 58.69 \\ CorefBERT\({}_{large}\)[19] & 2020 & 56.82 & 59.01 & 56.40 & 58.83 \\ RoBERTa\({}_{large}\)[20] & 2020 & 57.14 & 59.22 & 57.51 & 59.62 \\ CorefRoBERTa\({}_{large}\)[21] & 2020 & 57.35 & 59.43 & 57.90 & 60.25 \\ ATLOP-BERT\({}_{large}\)[14] & 2021 & 61.32 & 63.18 & 61.39 & 63.40 \\
**SAM-LSTM-RoBERTa\({}_{large}\)** & & **62.13 \(\pm\) 0.13** & **63.89 \(\pm\) 0.16** & **62.25** & **64.03** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results (%) on the development and test set of DocRED.  
SciBERTbase [31] model pre-trained in the scientific publications corpus. The results are shown in Table 4, where SAM-LSTM improved the F1 scores of GDA 0.8%. We also note that [22] achieved better results than we did on the CDR dataset. We believe this is due to their use of a pre-trained biomedical language representation model and the injection of domain expertise in the RE process.
## 4 The Virtual Astronaut for Santa Maria
### DEM Integration  
Several DEMs generated from orbital and rover-based data are used in the Santa Maria version of the VA. The HiRISE orbital DEM has a spatial resolution of a few decimeters covering the study area around 300 m \(\times\) 300 m. The HiRISE DEM provides good regional control of the crater, but it lacks the detailed terrain information at the rover scale. Rover-based DEMs providing additional details of a rock or surface target are of interest. One source of the higher resolution DEM was generated by The Ohio State University (OSU) using data from a wide-baseline imaging campaign at Santa Maria. Wide-baseline stereo images are acquired by imaging the same region with Pancam with the rover sitting at two spots separated by about 5 m. These data are more accurate at distances further from the rover than those can be derived from normal stereo images with the fixed baseline between the cameras on the rover mast. The other type of rover-based DEM is derived from normal baseline stereo images. These data are standard products archived by the PDS [39]. Transforming these three sources of DEMs into the same reference frame and overlaying them together showed there was a tilt variation between the DEMs, especially in the east and north direction. This tilt was due to the error accumulated over time in the rover's position and orientation.  
To remove the tilt variations among the DEMs, a set of operations were carried out to geo-reference the rover-based DEMs to the orbital derived data. These DEMs were first transformed to the same coordinate system. Then, a number of features found on both HiRISE DEM and Pancam wide baseline stereo DEM were manually selected. These features were served as control points for the data transformation. A rigid 3D transformation could not completely remove the tilt variation between orbital and ground DEMs. Rubber sheeting had to be applied to minimize the inconsistency between the data sets and to make smooth transition between the overlapping areas of both data sets. The two transformed data sets were integrated into a single DEM. After that, DEM obtained from the normal baseline stereo images was transformed in a similar manner to match the HiRISE and wide baseline data. In order to minimize the size of the terrain model, only subsets of normal baseline data covering the two rock targets were registered. Because the ground and orbital DEMs had different resolutions, a grid format for the merged DEM was large in size and limited the performance of the 3D visualization. A TIN (Triangulated Irregular Network) model was generated for the final fused DEM as shown in Figure 4.