esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
Deterministic policy gradient (DPG) [27] applies to routing policy in continuous action space, while Q-Learning applies to finite state space in specific scenarios. Deep deterministic policy gradient (DDPG) [28] and deep Q network (DQN) [29] are the products of the combination of deep learning and reinforcement learning, which effectively improves the feature expression ability and decision-making ability of reinforcement learning. [PERSON] et al. [30] simultaneously introduced DQN and DDPG into routing policy, which greatly improves the throughput of the network and made the network load more balanced. Their experimental results show that DDPG has a better decision-making effect than DQN in continuous state space. [PERSON] et al. [31] generated a routing policy by using DDPG to predict node connection weights. The network transmission delay is therefore reduced. [PERSON] et al. [32] proposed the Orca model, which effectively solves the congestion control requirements of the TCP protocol of the transport layer. By integrating the deep policy gradient algorithm, they introduced the congestion window and the network data forwarding pacing rate. Due to the black box characteristics of deep reinforcement learning, [PERSON] et al. [33] proposed the Metis framework. By introducing two different interpretation methods based on decision trees and hypergraphs, the DNN policy is transformed into an interpretable rule-based controller. To a certain extent, it demonstrates the feasibility of using deep reinforcement learning for network traffic control. We compare the literatures using reinforcement learning mentioned above and summarize them in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Metrics** & \begin{tabular}{c} **Experimental** \\ **Platform** \\ \end{tabular} & **Drawbacks** \\ \hline \multirow{2}{*}{[PERSON] et al. [22]} & \begin{tabular}{c} Reward, file \\ transmission time, \\ utilization rate \\ \end{tabular} & Simulations:Mininet & Fixed traffic patterns \\ \hline \multirow{2}{*}{[PERSON] et al. [8]} & Lifetime & \begin{tabular}{c} Real-tested: \\ Raspberry Pi 3 \\ \end{tabular} & Limited metrics \\ \hline \multirow{2}{*}{[PERSON] et al. [24]} & \begin{tabular}{c} Energy consumption, \\ delivery rate \\ \end{tabular} & Simulations:NS2 & Limited scenarios \\ \hline \multirow{2}{*}{[PERSON] et al. [25]} & \begin{tabular}{c} Energy consumption \\ \end{tabular} & Simulations:NS3 & Lack metrics \\ \hline \multirow{2}{*}{[PERSON] et al. [26]} & \
\begin{tabular}{c} Delay, delivery ratio, \\ energy consumption, \\ overhead,lifetime \\ \end{tabular} & Simulations:NS2 & Limited scenarios \\ \hline \multirow{2}{*}{[PERSON] et al. [30]} &
\begin{tabular}{c} Flow completion \\ time,throughput, \\ link load \\ \end{tabular} & Simulations:OMNet++ & Very light traffic \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of reinforcement learning structures employed for routing control.
## 5 Conclusions and Recommendations
Even when the VIC model was recalibrated using precipitation-specific inputs, IMERG-UC and GSMaP-MVK still showed little potential for streamflow simulation over the upper Yellow River basin. This result implies that the VIC hydrologic model can tolerate relatively small precipitation errors but not unphysical bias through the hydrologic integration process. Thus, if a satellite precipitation estimate has good intrinsic data quality, then precipitation-specific calibration is recommended. Clearly, the performances of satellite precipitation estimates rely on gauge corrections, but purely satellite-derived estimates are the only available source of precipitation for areas where ground observations are nonexistent. At the current stage, the climatological calibration is proposed to make the purely satellite-derived product as consistent as possible with gauge-corrected products. In the future, more PMW/IR sources and more effective precipitation algorithms are needed for improving the retrieval ability of satellite precipitation. We hope this study here will be helpful as a reference for precipitation dataset selection in the TP, and also provide the satellite precipitation community with a better understanding of GPM-era precipitation products. Considering the finer spatiotemporal resolution and improved precision, more studies exploring the potential of IMERG and GSMaP precipitation products in sub-daily or hourly time scales will be conducted in the near future. [PERSON] and [PERSON] designed the study and conducted the experiments; [PERSON] wrote the draft of the manuscript; [PERSON] supervised the research and revised the manuscript. This work was financially supported by the National Key Research and Development Program of China (2018 YFA0605402) and the Major Research Plan of the National Natural Science Foundation of China (91547101). Also, this work was partially sponsored by the Natural Science Foundation of Jiangsu Province (BK20161502). The authors are grateful to the GPM science team at NASA and JAXA for making satellite precipitation data available, and thankful to the CMA for providing observation gauge data. The authors declare no conflicts of interest.
## 4 Experiment
### Experiment on the Data Preprocessing Method  
In this paper, we use the sliding window segmentation method to process the Ruijin dataset in the NER task, and experimentally verify that this method can actually improve the entity recognition results compared to the segmentation method using punctuation.  
Figure 5: Input example of the BIBC model.  
In particular, different _window_sizes_ are applied for the text segmentation for comparison between different settings.  
As shown in Figure 6, the validation on the BERT-BiLSTM-CRF model shows that a _window_size_ value around 180 is relatively good in F1 score. If the sliding window value is too small, the sentence contains too little contextual information, which will lead to the degradation of the effect; and, if the value is large, the sentence length is too large, which is not conducive to the training of the model, so, in this paper, we select a _window_size_ of 180. Our analysis of the data characteristics shows that the maximum length of the entity in the dataset is 14. In order to avoid the entity being divided and retain some contextual information, the _pad_size_ is set to 30, so the size of the divided sentences is _window_size_\(+2*pad\_size=240\).  
In order to demonstrate the effect of different division methods on the results, comparison experiments were conducted on the models BiLSTM_CRF and BERT_BiLSTM_CRF, respectively, and the specific experimental results are shown in Figure 7.  
As the experimental comparison using different data processing methods on different models can be seen from Figure 7, the method using sliding windows is better than the one using punctuation symbols to divide the data, and the worst results are one level of punctuation symbols to divide the data. It is because the part of sentences obtained by using a single level of punctuation symbols division is too long, which affects the training performance of the model. In addition, the direct interception of sentences with too large length will lead to a decrease in information capture. The data set is not very standardized because of the format conversion, and using multi-level punctuation may divide two entities in two sentences, resulting in the entity segmentation not being recognized correctly.  
Figure 6: The experimental results of different _window_size_.  
Figure 7: NER results based on different partition methods.  
All the data used in the following models are experimentally validated using the processing that works best.
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 4 Optimization Results for Single-Beam and Dual-Beam Cases
These sidelobes are also present in the project pattern \(F(u,v)\) and are due to the rather small aperture size and small number of elements in a \(10\times 3\) PMS. Figure 9: Regenerated optimized electric field patterns in the \((u,v)\) space and in spherical coordinates, as produced by a \(10\times 3\) PMS. (**a**) Single-beam scenario in \((u,v)\)-space. (**b**) Single-beam scenario in spherical coordinates. (**c**) Dual-beam scenario in \((u,v)\)-space. (**d**) Dual-beam scenario in spherical coordinates.
## 3 Results and Discussion
### Sugar Content, pH, Surface Tension Measurement  
The pH (acidity) measurement is considered to be a factor in quality control and the evaluation of differences in the fermentation process. In addition, surface tension and sugar content measurements can be used to help evaluate texture. The sugar content of the wines was measured as a convenient sweetness index. The sugar contents of Samples A, B, and C were 10.3%, 10.0%, and 9.8%, respectively (Table 4). Regarding the carbohydrate content, Sample C had a higher glucose content than Samples A and B. However, the results of the sugar content measurement were considered to indicate the overall sugar content of each sample.  
The pH measurements were performed to determine the properties of the sample solutions. The pH values of Samples A, B, and C were 4.05, 4.24, and 4.32, respectively (Table 4). Generally, beverages with a pH value less than 4 can cause acid erosion [34].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Amino Acid (μmol/mL)** & **A** & **B** & **C** & **Amino Acid (μmol/mL)** & **A** & **B** & **C** \\ \hline PEA & 0 & 0.01 & 0 & Ile & 0.09 & 0.10 & 0.12 \\ Asp & 0.12 & 0.11 & 0.15 & Leu & 0.23 & 0.25 & 0.32 \\ Thr & 0.07 & 0.05 & 0.08 & Tyr & 0.13 & 0.17 & 0.19 \\ Ser & 0.15 & 0.11 & 0.17 & Phe & 0.08 & 0.10 & 0.13 \\ Asn & 0.12 & 0.08 & 0.12 & GABA & 0.04 & 0.04 & 0.05 \\ Glu & 0.25 & 0.28 & 0.35 & MEA & 0.44 & 0.36 & 0.38 \\ Gly & 0.5 & 0.53 & 0.51 & Orn & 0.02 & 0.01 & 0.04 \\ Ala & 0.58 & 0.78 & 0.9 & His & 0.01 & 0.04 & 0.07 \\ Val & 0.14 & 0.21 & 0.23 & Lys & 0.09 & 0.08 & 0.14 \\ Cys & 0.02 & 0.03 & 0.02 & Arg & 0.03 & 0.17 & 0.47 \\ \hline  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contents of amino acid in Samples A, B, and C.  
Alcoholic beverages such as plum wine and wine have a pH less than 4 and may dissolve teeth [35]. All three samples of rice wine, A, B, and C, had a pH of 4 or higher, suggesting that daily consumption of rice wine is unlikely to cause dental caries due to its acid content. Since the pH of each rice wine was approximately 4.0, it was inferred that bacterial growth was suppressed and the acidity level due to fermentation was comparable.  
Surface tension measurements were performed to study the variation of viscosity across the sample solutions (Table 4). The surface tensions of the samples were A: 43.20 mN/m; B: 44.08 mN/m; C: 43.90 mN/m; 15% ethanol: 43.35 mN/m; and distilled water: 71.42 mN/m. The surface tension of each sample was lower than that of distilled water due to the weakening of the hydrogen bonds between the water molecules in the aqueous solution. Samples A, B, and C exhibited lower surface tension values than 15% ethanol, and Sample A showed lower values than Samples B and C. This suggests that the composition of the rice wines lowered the surface tension and that the differences among the samples were due to differences in their compositions. Although it was thought that sugar mass affected surface tension, no relationship was observed between sugar mass and surface tension.
## 4 Results and Discussion
### Simulation Results in the Rotating Reference Frame
The IPC schemes achieve this reduction in the DEL at the cost of greater control effort on the pitch actuators compared with the CPC. Figure 7 shows this greater activity in the pitch signal of blade 1. This is also indicated by the average normalized actuator travel indices (\(\overline{\text{NAT}}(\beta)\)) shown in Table 4, which increased from values of around 5% to between 40% and 50%. If we compare the IPC schemes with each other, the schemes with integral controllers plus the azimuth offset are the ones that, in addition to the greatest reduction in DEL, had the lowest NAT values, which were between 2% and 4% lower than those of the IPC schemes without the azimuth offset. The IPC schemes with proportional controllers showed the highest NAT values, which were as much as 10% higher than those of the other IPC schemes.
## 5 Analysis and Discussion  
### Determination of the Fracture Toughness  
The indentation hardness and indentation modulus of 4H-SiC single crystals on the (0001) face without cracks were 35.81 GPa and 461.3 GPa, respectively [28]. The results in Section 4.1 were used in Equations (5) and (6) and the fracture toughness results of 4H-SiC single crystals on the (0001) face were obtained, as shown in Figure 9. With the increase of the _c/a_ ratio, the fracture toughness values, whether from Equation (5) or Equation (6), showed a decreasing trend. When the value of the _c/a_ ratio was small (i.e., less than 1.6), the nanogrooves on the sample surface after the CMP treatment and the cracks were hard to identify, as shown in Figure 10, leading to a large crack length measuring error and causing the calculated fracture toughness to be far away from the real value. The effect of the nanogrooves decreases as normal force increases, so this kind of situation with a small value of _c/a_ ratio should be avoided. When the value of the _c/a_ ratio was large (i.e., greater than 2.9), peeling and crack bifurcation were observed, as shown in Figure 7, leading to effective crack extension termination. From the above, an appropriate value of _c/a_ ratio is significant to calculate the fracture toughness of 4H-SiC single crystals. Table 4 shows the results of fracture toughness of 4H-SiC single crystals using a _c/a_ range of 2 to 2.5. Table 4 shows that a _c/a_ ratio of 2, where the results of two calculations are the closest, used to determine the crack system of a 4H-SiC single crystal was reasonable. The fracture toughness of a 4H-SiC single crystal on the (0001) face was shown to be 3.42 MPa\(\cdot\)m\({}^{1/2}\) via employment of Equation (5) (where the c/a ratio is less than 2) and 3.47 MPa\(\cdot\)m\({}^{1/2}\) via employment of Equation (6) (where the c/a ratio is more than 2). These results demonstrated good agreement with other references, as shown in Table 5.  
Figure 8: Experimental results of a ramp-load scratch test on 4H-SiC single crystals with a Berkovich indenter: (**a**) SEM image of the scratch track and (**b**) normal depth and lateral force as a function of lateral displacement.
## 2 Channel Propagation Characteristics
### Characteristics of Mid-Band and High-Band Frequency Spectra  
The mid-band frequency spectrum provides a balanced combination of coverage and capacity, making it suitable for widespread wireless communication systems. On the other hand, the high-band frequency spectrum offers greater data transfer rates and capacity but requires denser infrastructure due to limitations in signal propagation. Both frequency bands have their own unique advantages and considerations, depending on the specific application and requirements [14].
## 5 Evaluation
### Results and Discussion
#### 5.2.2 Energy Consumption  
For energy consumption of HMIMO, we consider 100 nodes randomly deployed within a square region. Figure 4 shows a comparison between hierarchical MIMO, CMIMO and traditional SISO. For this set of experiments, we tested our algorithm for a grid size from \(100\times 100\) to \(1,000\times 1,000\) and intercluster range taken as 33% of the grid size. In hierarchical MIMO, data is forwarded via hierarchical routing. We use cost based link state routing for the other two algorithms. We verify our results for a path loss exponent between 2.7 (semi-furnished rooms) and 3 (densely furnished buildings) [17]. For smaller grid sizes, performance of all the three algorithms is similar. This is because the intracluster distance between the clusterhead and sensory nodes and the intercluster distances between the communicating clusterheads is less. For shorter distances, the proposed algorithm and CMIMO use SISO mode of communication. As we increase the grid size, traditional multihop SISO continues to provide communication via SISO approach. However, other two algorithms start switching to cooperative modes. In such scenarios, HMIMO outperforms CMIMO since number of hops for the data to reach CCS are less in HMIMO. The reason for decrease in number of hops is the hierarchical nature of the proposed algorithm. As we increase the network area, there is an upto 15%-20% energy saving for HMIMO compared to CMIMO. In another setting, we set the intercluster range to be 25% of the grid size. This is well depicted in the graph in Figure 5. Proposed HMIMO outperforms the traditional approaches even for large intercluster distance. This is because at large inter-cluster distance, clusters use MIMO communication in most cases which is energy conservant communication mode.  
In state-of-the-art CMIMO (described in Section 2), as the intra-cluster range (range among individual clusters) increases, fewer clusters are formed. Thus, transmission distances between CHs become larger and circuit energy becomes less significant than transmission energy, which results in making modes other than SISO more favorable. A decrease in number of clusters increases number of nodes in each cluster which over-burdens the clusterheads which are the communication centers. In our scheme, clustering parameters are not changed for the purpose of saving energy in routing. Distance dependent routing takes place by selecting appropriate nodes as routing clusterheads with the purpose of energy conservation. The advantage of our scheme is that routing and clustering jointly conserve power.  
Figure 3: Number of clusters.
# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 3 Measurement Method
### Odometry  
The feature points of image frames are calculated as the wheeled robot passes underneath the vehicle, allowing for the estimation of visual odometry [32]. This visual odometry serves as a supportive component to the dominant LIDAR odometry, enhancing the alignment process by incorporating relevant vehicle data and reducing alignment errors.  
The wheeled robots are programmed to move in a fixed direction at a constant speed. With this knowledge, the rotation and translation components can be processed separately. The rotation increment can be considered constant, while the translation increment can be approximated as uniform. Therefore, the knowledge of the motion constraint can be applied to the odometry to reduce the error in Algorithm 1.  
Figure 5: Unrobust corner feature points.  
The averaged odometry data from the previous ten instances of approximately uniform motion are used as a reference value as follows:  
\[\overline{d}=\frac{1}{10}\sum_{p=1}^{10}\frac{1}{2}(d_{l}^{p}+d_{v}^{p}) \tag{6}\]  
where \(d_{l}^{p}\) and \(d_{v}^{p}\) denote the laser odometry and visual odometry of the \(p\) frame, respectively.  
Then, the variation in the odometry at \(k\) time is obtained as follows:  
\[\begin{cases}\Delta d_{l}^{k}=d_{l}^{k}-\overline{d}\\ \Delta d_{v}^{k}=d_{v}^{k}-\overline{d}\end{cases} \tag{7}\]  
where \(d_{l}^{k}=[Q_{l}^{k},T_{l}^{k}]\), \(\Delta d_{l}^{k}=[\Delta Q_{l}^{k},\Delta T_{l}^{k}]\), \(d_{v}^{k}=[Q_{v}^{k},T_{v}^{k}]\) and \(\Delta d_{v}^{k}=[\Delta Q_{v}^{k},\Delta T_{v}^{k}]\). Here, \(Q=[q_{x},q_{y},q_{z},q_{w}]\) denotes the component of the rotation and \(q_{x},q_{y},q_{z},q_{w}\) are the quaternions. \(T=[x_{T},y_{T},z_{T}]\) denotes the component of the translation.  
Therefore, the odometry \(d_{o}^{k}\) at \(k\) time is obtained by combing laser odometry and visual odometry according to the constraints.
# Infrared Multi-Scale Small-Target Detection Algorithm Based on Feature Pyramid Network
###### Abstract
[24] proposed the TBC-Net in 2021, which consists of a target extraction module (TEM) and a semantic constraint module (SCM). It can effectively reduce false alarms caused by complex backgrounds. [PERSON] et al. [25] proposed ALC-Net, which contains a bottom-up attention modulation module to integrate detailed information from low-level features into deeper high-level features. It also utilizes multi-scale local contrast measures to address the issue of target scale variations. [PERSON] et al. [26] proposed DNA-Net, which implements progressive interaction between high-level and low-level features and can adaptively enhance multi-level features. In addition, some other methods based on deep learning and feature pyramid networks [27; 28; 29] have also yielded promising results. However, further improvements in detection accuracy are still required for applications where infrared multi-scale small targets need to be accurately recognized and analyzed. Therefore, this paper proposes an infrared multi-scale small-target detection method based on a dual attention fusion mechanism of a feature pyramid network to improve the detection accuracy of infrared multi-scale small target images. The main contributions of the paper are as follows. 1. Three pyramid segmentation-connection (PSC) modules are incorporated into the feature pyramid network to obtain both local and global context information from various shallow and deep features. 2. A dual attention fusion (DAF) module is proposed to fuse the feature maps containing context information and the deep features that have been upsampled twice through the attention mechanism of the DAF module. The module is used to highlight important semantic information.
# A Comprehensive Experimental Emulation For OTFS Waveform RF-Impairments
## 2 Waveform Modulation and Demodulation
### Channel Estimation and MP Detection  
At the receiver side, the doubly-dispersive channel response has to be estimated for OTFS detection. Many methods have been proposed to estimate channel parameters, such as the flag method [36], iterative algorithm [37], and embedded pilot-aided channel estimation scheme [38]. In this paper, we adopted embedded pilot-aided channel estimation  
Figure 1: OTFS transceiver block diagram.  
scheme, where the receiver simultaneously proceeds with a threshold approach channel estimation followed by MP data detection in the same OTFS frame. Encouraged by the embedded pilot-aided estimation, in this paper, the OTFS frame is arranged as shown in Figure 2a, where the data is distributed in the delay-Doppler domain. A guard is adopted to prevent interference between the modulated data and the embedded pilot at the receiver detection, as illustrated in Figure 2b. Finally, the delay and Doppler taps (given in Figure 2c) are estimated and passed to the MP algorithm to detect the transmitted symbols. The MP detection approach is used in this research because the representation of the communication channel in the delay-Doppler domain has a 3D (delay, Doppler, and angle) structured sparsity [39; 40]. As a result, the MP algorithm provides good performance with minimal complexity for the indoor environment with a small number of channel taps [41]. After estimating the channel parameters, MP can be used to extract \(\hat{x}[k,l]\) from the \(\hat{y}[k,l]\)[37; 42; 43; 44; 45]. The detected received symbols are founded by evaluating the joint maximum a posteriori probability (MAP), which is represented as follows [46]:  
\[\hat{x}=\operatorname*{arg\,max}_{\mathbf{x}\in\mathbb{A}^{\text{N}M}}\Pr(x \mid y,H). \tag{11}\]  
Figure 2: The transmitted and received signal structure in the delay-Doppler plane. (**a**) Transmitted symbols; (**b**) received signal; (**c**) received embedded pilot.