esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Adaptive Droop Control of the ILC
### Add Startup Threshold  
When the ILC is operating near standby mode, fluctuations in DGs and loads cause the ILC to frequently switch between rectifier and inverter states. In order to prevent the frequent switching of the ILC state, it is necessary to add a startup threshold so as to ensure  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operating Condition** & **Adaptive Control Characteristics** \\ \hline \(f_{\text{pu0}}\gg U_{\text{pu0}}\) & \(P_{\text{ILC}}^{*}=-kf_{\text{pu0}}\) \\ \(f_{\text{pu0}}\ll U_{\text{pu0}}\) & \(P_{\text{ILC}}^{*}=kU_{\text{pu0}}\) \\ \(f_{\text{pu0}}\to 1\), \(U_{\text{pu0}}\to 1\) & \(P_{\text{ILC}}^{*}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adaptive bidirectional droop control characteristics.  
Figure 8: Two types of droop control for ILC transmission power surfaces.  
that the ILC is in standby mode. The decomposition and simplification of Equation (11) yields Equation (12).  
\[P_{\text{ILC}}^{*}=k_{d}\big{(}U_{\text{pu0}}-f_{\text{pu0}}\big{)}, \tag{12}\]  
where \(k_{d}\) is the adaptive bidirectional droop coefficient, which can be expressed as  
\[k_{d}=\left\{\begin{array}{ll}\dfrac{U_{\text{pu0}}^{2}+f_{\text{pu0}}^{2}}{ \big{(}u_{\text{pu0}}^{2}+f_{\text{pu0}}^{2}-f_{\text{pu0}}U_{\text{pu0}}\big{)} k},&|f_{\text{pu0}}-1|>\varepsilon\,\cup|U_{\text{pu0}}-1|>\varepsilon\\ \dfrac{1}{10}k,&|f_{\text{pu0}}-1|<\varepsilon\,\cap|U_{\text{pu0}}-1|< \varepsilon\end{array}\right. \tag{13}\]  
Taking \(U_{\text{pu0}}-f_{\text{pu0}}\) in Equation (12) as the new control variable of adaptive bidirectional droop control, its value range is \([-2,2]\), and the magnitude of \(U_{\text{pu0}}-f_{\text{pu0}}\) determines the active power regulation direction of the ILC. The ILC should remain in standby mode under the following two conditions: (1) AC frequency and DC voltage are both near their rated values. (2) The power of the AC and DC sub-microgrids is both in surplus or deficit and near equal. These two cases can be represented by \(U_{\text{pu0}}-f_{\text{pu0}}\approx 0\). Based on the above analysis, the bidirectional droop characteristic curve shown in Figure 9 can be obtained, and the ILC dynamic performance is better achieved with a slope change in (b) compared to (a).  
According to the bidirectional droop control curve with a threshold in Figure 9, the improved droop control equation can be obtained as shown in Equation (14).  
\[P_{\text{ILC}}^{*}=\left\{\begin{array}{ll}\dfrac{2k_{d}}{2-\eta}(U_{\text{pu0 }}-f_{\text{pu0}}-\eta),&U_{\text{pu0}}-f_{\text{pu0}}>\eta\\ 0,&-\eta\leq U_{\text{pu0}}-f_{\text{pu0}}\leq\eta\\ \dfrac{2k_{d}}{2-\eta}(U_{\text{pu0}}-f_{\text{pu0}}+\eta),&U_{\text{pu0}}-f_{ \text{pu0}}<-\eta\end{array}\right. \tag{14}\]
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 4 Experimental Results
### Throughput  
We measured throughput for YCSB workloads while varying the number of clients. We used YCSB workloads with both Zipfian distribution and uniform distribution.  
**Zipfian distribution.** Figure 6 shows the throughput of the range index structures for disaggregated memory. In Write-intensive workloads, such as load A and workload A, Sherman shows 16% higher throughput than RF-tree. This is because Sherman optimizes RDMA one-sided verbs, such as RDMA Write batching and on-chip lock management, which require lots of engineering efforts. Note that RF-tree also can apply Sherman's optimization. When the Read ratio is increased, as in workloads A-D, RF-tree shows better throughput than Sherman. This is because RF-tree has a sorted node layout, and RF-tree's internal nodes are sequentially stored. In workload E, RF-tree and Sherman show similar performance. Both experience performance degradation, due, however, to different reasons. Sherman can reduce RDMA Read operations by batching RDMA Read operations, but it suffers huge overhead of in-memory operations, such as cache management and unsorted node layout. Meanwhile, RF-tree suffers higher RDMA Read operation overhead than Sherman, as it does not provide the batching RDMA Read operation. Blink-tree suffers high overhead from the RDMA Read operation because it does not use the local cache of the compute server.  
**Uniform distribution.** The YCSB workload evaluation with uniform distribution shows similar performance trends to the YCSB workload evaluation with Zipfian distribution, as shown in Figure 7. In Write-intensive workloads, RF-tree shows comparable performance to Sherman because a uniform distribution reduces the possibility of contention.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 3 Research Methodology  
The algorithm proposed in this paper is divided into four parts, namely the initial stage, the tracking and positioning stage, the 3D reconstruction stage, and the pose calculation stage. The algorithm framework is shown in Figure 3. In the initialization phase, the candidate region can use the pre-trained model to perform initial frame processing on the image and then apply the epipolar geometric constraints in stereo vision to detect the consistency of the candidate image. In the tracking and positioning stage, the pre-training model is used to detect the initial position of the target. The 3D positioning result obtained by the detection result, the online tracking result, the target motion estimation, and the stereo vision constitute a confidence determination formula. The confidence of the target and final position are then located. In the 3D reconstruction stage, the tracking and positioning areas are matched in the binocular camera, and, finally, the 3D data of the target area are formed. In the pose solution stage, 3D data and 2D information are combined. In the disordered 3D data, the key points on the wings and fuselage are screened and marked separately to form a key point set. The key point set is used to solve the intersection of the wing edge line and wing plane, and the space line is used to calculate the attitude angle of the target aircraft in three directions. Motion estimation and tracker module in the tracking stage can use the target position information obtained in the solving stage to update.
# A Three-Port Power Electronic Transformer Based on Magnetic Integration
## 1 Introduction
Moreover, this topology is only about two ports (MVAC/LVDC). Therefore, an MMC-based three-port PET based on magnetic integration is proposed in this paper. As shown in Figure 1, by using the proposed three-port PET, the MVDC and LVDC grids can be interconnected with the traditional medium-voltage AC (MVAC) grid. Then with the flexible power control ability of the proposed three-port PET, the MVDC, MVAC and LVDC grids are able to support each other, particularly for the case of grids with high penetration of renewable energy, and DC loads. The proposed PET enables a compact design that reduces the number of magnetic components and power cells. It is especially suitable for the applications which require 100s of kW of power tapped from an LVDC port.
# Extraction of City Roads Using Luojia 1-01 Nighttime Light Data
## 3 Method  
The proposed approach consisted of four parts, which are described in Figure 3. The three steps were as follows: (1) Image enhancement: Image sharpening was conducted to increase contrast of original Luojia 1-01 image, thus improving the characteristics of city roads. (2) Distinguishing of urban regions: Different from high-resolution images, the light intensity in nighttime light data is highly correlated with human activities and regions' development degree. The light intensity in rural areas was too weak to extract the roads. Therefore, the extraction process could just be conducted in urban regions. (3) Road extraction: An unsupervised neural network-PCNN was established to extract the road networks in urban regions. The aim of the PCNN was to identify the objects edges in an image. (4) Shape Optimizing: A series of optimizing operations were proposed to eliminate the residential regions along the roads and extrude the shape or extracted roads. The final extraction results after shape optimizing were then compared with OSM (OpenStreetMap) data.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 3 System Description  
The experimental analysis of electromechanical systems is not feasible for both healthy and faulty operation because the equipment could be damaged through experiments. Moreover, the damage caused in laboratory and/or industrial environments cannot be fully controlled, causing fault parameters to exhibit large deviations that do not contribute to proper system fault diagnostic verification and fine tuning. Hence, a 3-phase low-voltage squirrel-cage motor was simulated in MATLAB Simulink. Specifically, a 3-phase squirrel-cage motor was chosen because it is used in a wide variety of electromechanical systems, including of ships, and it was simulated as a low-voltage motor since a similar setup was available for future experimental analysis.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture
### Temporal Convolutional Network  
The topic of sequence modeling has been commonly associated with the Recurrent Neural Network (RNN), such as LSTM and GRU. However, there are bottlenecks in these models. Up until recently, researchers began to consider CNN and found that certain CNN architectures can achieve better performance in many tasks that are underestimated. In 2018, [PERSON] et al. [8] proposed the Temporal Convolutional Network (TCN), which is an improvement of the CNN network.  
Compared with conventional one-dimensional convolution, TCN employs two different operations7: casual convolution and dilated convolution. It is more adaptive for dealing with sequential data due to its temporality and large receptive fields. In addition, the residual connection is used, which is a simple yet very effective technique to make training deep neural networks easier. The structure of the TCN is shown in Figure 7.
## 2 Materials and Methods
### Design of Variable Formula Fertilizer Control System
#### 2.2.4 Fertilizer Drive System  
The fertilizer discharge drive system is composed of a servo motor drive system, a fertilizer discharge actuator, a PLC controller, etc. Among them, the servo motor drive system includes servo motor, servo motor driver and reducer; the discharging actuator includes a trough wheel type fertilizer discharging device and a discharging shaft, as shown in Figure 5. In the process of fertilization, the PLC controller performs calculations based on the soil prescription map, the forward speed information of the equipment detected by the speed sensor, and the actual fertilizer output data detected by the pressure sensor. It calculates the formula fertilization data information that the servo motor should reach and sends instructions to the drive. It adjusts the speed of the servo motor, and then controls the proportion of the formula and the amount of fertilizer.  
Figure 4: Pressure sensor installation diagram.  
Figure 5: Structural block diagram of fertilizer driving system.  
In order to achieve precise control of variable formula fertilization, it is necessary to accurately calculate the fertilizer output of the grooved wheel fertilizer [22]. According to the working principle of the grooved wheel fertilizer discharging device and the analysis based on the basic knowledge of agricultural machinery design: the amount of fertilizer discharged per revolution of the grooved wheel is composed of the weight of the fertilizer carried between the outer side of the grooved wheel and the shell and the weight of the fertilizer forcedly discharged by the grooved wheel. Then, its calculation formula [23; 24] is:  
\[\left\{\begin{array}{l}Q=Q_{1}+Q_{2}\\ Q_{1}=\frac{2\pi\rho\pi l\lambda}{1000}\quad(g/r)\\ Q_{2}=\frac{\rho\tau zsl}{1000}\end{array}\right. \tag{4}\]  
That is, the amount of fertilizer discharged per revolution of the grooved wheel is:  
\[Q=\frac{\rho l(2\pi r\lambda+\tau zs)}{1000}(g/r) \tag{5}\]  
where \(Q\) is the weight of fertilizer discharged per revolution of the grooved wheel, g/r; \(Q_{1}\) is the weight of fertilizer brought out between the outer side of the sheave and the shell, g/r; \(Q_{2}\) is the weight of fertilizer forced to be discharged by the groove wheel, g/r; \(\tau\) is the fullness factor of the fertilizer in the groove of the grooved wheel; \(z\) is the number of grooves in the sheave; \(\rho\) is the density of fertilizer particles, g/cm\({}^{3}\); \(s\) is the cross-sectional area of a single groove, mm\({}^{2}\); \(l\) is the effective working length of the grooved wheel, mm; \(r\) is the radius of the groove wheel, mm; \(\lambda\) is the fullness coefficient of the driving layer fertilizer.
## 3 Proposed Approaches
### Spectrum Attention Mechanism  
The introduction of the attention mechanism aims to replicate the human capacity for selectively focusing on specific key information while disregarding extraneous data [25]. When processing ENF data containing spectral information using deep learning models, we also endeavor to selectively process crucial spectral information while filtering out irrelevant details. Therefore, we devised a spectral attention module tailored for ENF data based on its unique characteristics and the underlying principles of the attention mechanism. The UniTS model incorporates a CLS tower (depicted in Figure 1) for classification tasks, with the inclusion of the spectral attention module preceding the category embeddings (as illustrated in Figure 4) within the CLS tower.  
In Europe and most of Asia, the nominal value of the ENF is 50 Hz [2]. The allowable fluctuation range typically falls within \(\pm\)0.1 Hz, indicating that the range of 49.9 Hz to  
Figure 4: Spectrum attention mechanism improvement diagram.  
Figure 3: Comparison of activation function effects.  
50.1 Hz is considered normal under standard conditions [26]. Therefore, this range becomes the focal point in our analysis of ENF classification in the frequency domain.  
The fundamental concept behind our designed spectral attention module is to transform the time domain signal into the frequency domain, focusing on the frequency range of 49.9 Hz to 50.1 Hz. This approach enhances the model's noise robustness by allowing it to effectively capture information within the critical frequency domain of the ENF. Following the attention weight calculation, the resulting attention weight matrix is multiplied by weighting and then reconverted into a time domain signal for category embedding and classification. By concentrating on this specific frequency range, the model can better distinguish the essential features from noise, thereby improving the classification accuracy and reliability.  
\[A=softmax(W_{2}\cdot tanh(W_{1}\cdot X+b_{1})+b_{2}) \tag{4}\]  
The equation for calculating the attention weight is presented in (4). Let \(X\) represent the input spectral data, with dimensions (\(N\), \(T\), \(F\)), where \(N\) denotes the batch size, \(T\) represents the time step size, and \(F\) indicates the frequency dimension. \(W_{1}\) and \(W_{2}\) denote weight matrices, while \(b_{1}\) and \(b_{2}\) are bias vectors. The activation function, \(tanh\), is utilized along with \(softmax\) for normalizing the weights. Subsequently, the weighted signal is derived by multiplying the attention weight matrix with the input signal; here, \(A\) signifies the calculated attention weight matrix and \(X^{\prime}\) denotes the weighted input signal. The module processing flow proceeds as follows.
## 5 Delay and Duty Cycle Analysis of CD-ICC
### Delay Analysis of CD-ICC
\tag{28}\]  
As is clear from (28), the intermediate sensor that overheard the transmitted packet calculates the proposed back-off time as shown in (27). Then, \(T_{CW}^{CD-ICC}>T_{CW}\) implies an average back-off time of the relay node (intermediate) is less than the proposed average back-off time (27), and the node cannot participate in cooperation. On the other hand, if \(T_{CW}^{CD-ICC}\leq T_{CW}\), this implies the sensors whose overheard the transmitted packet are near to the source, such sensor utilised proposed back-off time shown in (27). Finally, the average failure time due to fading but no collision is expressed as:  
\[\begin{array}{l}T_{fail}=(1-P_{sd}^{s})\ (1-P_{sr}^{s})P_{sd}^{s}\ T_{ act}^{sd}+\left(T_{act}^{sd}+T_{act}^{sd}\right)\ (1-P  
The packet error rate (PER) of each link is defined as the probability which at least one bit in a packet is erroneous and can be expressed as:  
\[ER=1-\left(1-P_{b}\right)^{S} \tag{32}\]  
where, \(P_{b}\) is the bit error rate (BER) and \(S\) is the payload size, and it set to '1'. In this paper, the DPSK modulation is utilized and \(P_{b}\) expressed as:  
\[P_{b}=Q\left(2\gamma_{i,j}\left|a_{i,j}\right|^{2}10^{\sigma_{ij}}\right) \cong\frac{1}{2}\exp\left(\gamma_{i,j}d_{ij}^{-\rho}10^{\sigma_{ij}}\right) \tag{33}\]  
where, \(\gamma_{i,j}\) is the signal to interference and noise ratio between two nodes. The \(P_{b}^{CD-ICC}\) of the proposed protocol can be expressed as:  
\[P_{b}^{CD-ICC}=P_{\xi}(\xi_{thd})\ P_{b}^{ICC}+\left(1-P_{\xi}(\xi_{thd})\right) P_{b}^{sd} \tag{34}\]  
where, \(P_{b}^{ICC}\) is the probability of the ICC and can be expressed as:  
\[P_{b}^{ICC}=P_{b}^{sd}(1-P_{b}^{sr})+\left(1-P_{b}^{sd}\right)P_{b}^{sr}\ P_{b}^{rd} \tag{35}\]  
where, \(P_{b}^{sd}\), \(P_{b}^{sr}\) and \(P_{b}^{rd}\) are bit error probability of \(S-D\), \(S-R\) and \(R-D\) links. Insert (20), (25), (29) and (34) in (31), we obtain the duty cycle of the proposed protocol. The complete average power transmission, \(P_{av}\), can be obtained by multiplying DC, \(V_{dd}\), and \(I_{act}\) where \(V_{dd}\) is the radio frequency (RF) of the module supply voltage, and \(I_{act}\) is the average RF active average current in a one-time frame [39]:  
\[P_{av}=DC_{CD-ICC}\ V_{dd}\ I_{act} \tag{36}\]
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 3 Materials and Methods
### Step 3. Perform the Criticality Analysis
#### 3.3.1 Defining the Occurrence Index
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Score** & **CONDITIONED PROBABILITY: Relative Frequency or Likelihood of Occurrence of a Failure Mode at Sub-System or Part Level** \\ \hline \multirow{2}{*}{1} & There is no direct connection between Failure Mode and Effect (or between effect and Specific Fail) \\ \hline \multirow{2}{*}{2} & There are direct and non-direct connections between Failure Mode and Effect (or between Effect and Specific Fail), but they are not probable. \\ \hline \multirow{2}{*}{3} & The Effect is one of the probable consequences of the Failure Mode under exam, even as secondary effect (the same correlation is implied for Effect and Specific Fail). \\ \hline \multirow{2}{*}{4} & The Effect is the most probable consequence of Failure Mode under exam (the same correlation is implied for Effect and Specific Fail). \\ \hline \multirow{2}{*}{5} & The Effect is a direct (proven) consequence of the Failure Mode under exam (the same correlation is implied for Effect and Specific Fail). \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Conditioned probability coefficients (both at subsystem and part level). Table 1. Therefore, the qualitative parameters in Table 1 remain always valid, and Table 1 even allows associating specific failure rate (if any) with the same scores.
## 5 Vibration Analysis of the Cable under Double-End Excitation  
The partial differential equations derived for the cable vibration under the double-end excitation were numerically solved using MATLAB software. The displacement and acceleration time history of the cable endpoint extracted from the moving load analysis by the finite element were input into the MATLAB solution program as the cable end excitation, and all orders vibration of the cable could be obtained. The vibration of any cable point could be derived by adding the cable's first six-order vibration, which was accurate enough. It is important to note that higher-order vibrations only contributed to an insignificant extent, within 1%. Therefore, their impact can be considered negligible when evaluating the overall performance.  
The comparison of the displacement time history between the input end excitation and the vibration of the midpoint of the No. 38 cable, considering the first order, the first three order, and first six order vibrations of the cable, is shown in Figure 10. It can be seen that the input displacement excitation at the end of the cable was amplified by a factor of nearly two and would cause irregular vibrations of the cable simultaneously. The frequency of the forced vibration of the cable was close to the excitation frequency, so it is necessary to keep the frequency of the dynamic excitation generated by the passing train away from the fundamental frequency of the cable to avoid resonance.  
Figure 9: The spatial distribution of the extreme value: (**a**) displacements; (**b**) accelerations.
## 4 Experiments
### Tool Learning  
#### 4.1.1 Settings for Tool Learning  
Datasets  
* ToolBench [36] is a dataset with multiple types of data that covers multiple domain application scenarios and contains over 16,000 real-time APIs collected from the RapidAPI Hub ([[https://rapidapi.com/hub](https://rapidapi.com/hub)]([https://rapidapi.com/hub](https://rapidapi.com/hub)) accessed on 30 January 2025) that cover 49 categories. We evaluate our approach using the most complex subset of ToolBench, including intra-category multi-tool instructions (I2) and intra-collection multi-tool instructions (I3). The final evaluation is divided into three subsets for testing, I2-Inst (200 test data), I2-Cat (200 test data) and I3-Inst (100 test data).
* API-Bank [34] is a multi-task benchmark consisting of various tools used to evaluate the performance of tool-augmented LLMs. API-Bank involves the offline usage of multiple API tools. We conduct an evaluation on the test data ranging from level-1 to level-2 of this dataset, which comprises a total of 534 test data.  
Figure 5: The process of the Reflect Agent for subtask reflection and plan reflection. The Reflect Agent first conducts subtask reflection to assess the appropriateness of the selected tool, the execution result and the alignment of the result with the target (noted as the yellow part). Finally, the Reflect Agent conducts plan reflection to perform the next single-step planning process and compare it with the corresponding subtask in the Plan Agent’s plan (noted as the green part).  
Baselines  
We compared the CoReaAgents framework with the following recent and classic baselines:  
* ReAct [15] guides LLMs to take appropriate actions at each step to solve a particular problem and carry out reasoning for the actions, iterating the process to finally solve the problem.
* Reflexion [17] enables the LLMs to learn from error information and retry when an error occurs, continuing until the task is successfully completed or the maximum number of reflection attempts is reached.
* DFSDT [36] simulates the Depth-First Search process by constructing a decision tree to explore different solution paths.
* EASYTOOL [42] distills the necessary information from tool documentation and converts it into concise tool descriptions, enhancing LLMs' ability to select tools more accurately.
* ToolNet [28] organizes the tools into a directed graph, allowing the LLM to start from an initial tool node and iteratively select the next node in the graph until the task is resolved.
* Tool-Planner [66] performs dynamic solution tree planning based on toolkits, which group APIs with the same functionality into a single toolkit. When tool errors occur, LLMs can reselect tools based on toolkits. Evaluation metrics For the ToolBench, we use the Pass Rate from ToolEval [36] to evaluate our framework, which is calculated from the proportion of tasks successfully completed with a limited budget. For API-Bank, we use Exact Match (EM) to compare the execution output of LLMs with the real answers.