esa-sceva/satcom-chunk-collection huggingface dataset:
# A DDPG-Based USV Path-Planning Algorithm
## 2 Methodology  
### Ddpg  
DDPG (The Deep Deterministic Policy Gradient algorithm) is an enhancement of the DPG (Deterministic Policy Gradient algorithm), inheriting its deterministic policy gradient. In this framework, an intelligent body determines specific actions based on state decisions, utilizing a deep neural network to bolster the decision function fitting. Unlike stochastic policies, DDPG significantly diminishes the required amount of sampled data, thereby improving algorithm efficiency and promoting intelligent learning within continuous action spaces. Adhering to an Actor-Critic architecture, the DDPG algorithm primarily consists of two components: the Actor network and the Critic network. The Actor network focuses on generating actions and mediating environmental interactions for the USV, while the Critic network evaluates state and action performance. The insights from this evaluation guide the policy function to formulate the subsequent phase of actions. Both the Actor and Critic components operate through a dual network structure, employing target and eval networks. The overall structure of the DDPG algorithm is visually depicted in Figure 1. The symbols and their meanings of DDPG are detailed in Table 1, the DDPG Symbol Definition Table. Figure 1: Network Structure of DDPG. In Figure 1, the network known as Actor-eval primarily deals with the recurrent modification of the policy network parameter denoted by \(\theta^{u}\). It chooses action a based on the prevailing state \(s\), generating the ensuing state \(s^{\prime}\) and reward \(r\) as a consequence of the current action's execution during environmental interaction. Concurrently, the Actor-target network has the duty of identifying the best subsequent action \(a^{\prime}\), relying on the following state \(s^{\prime}\), which is sampled from the stored experiences. Periodic copying from \(\theta^{u}\) in the Actor-eval network to \(\theta^{u^{\prime}}\) in the Actor-target network is undertaken. Additionally, the Critic-eval network is fundamentally engaged in the repeated refinement of the network parameters \(\theta^{Q}\) through calculation of the present \(Q\) value:  
\[y_{i}=r_{i}+\gamma Q^{\prime}\Big{(}s^{\prime},a^{\prime},\theta^{Q^{\prime}} \Big{)} \tag{1}\]  
where the variable \(\gamma\) signifies a discount factor impacting the priority given to future over present rewards in the learning process. In the Critic-target network, the principal
## 3 Results  
### Visualization of Sorbent  
The images of the synthesized polymeric sorbent and poly(DVB) obtained using an optical microscope are presented in Figure 4. As can be seen, the CyP(Ph)4-DVB sorbent is characterized by an irregular shape. The particle size ranges from 60 to 200 \(\upmu\)m. The average particle size of the polymeric adsorbents based on DVB-co-GMA (GMA--glycidyl methacrylate) and DVB-co-GMA-TETA (TETA--triethylenetetramine) was found to be 117-120 \(\upmu\)m [20]. For comparison, the bead size of the commercially available polystyrene-based adsorbent Amberlite XAD4 resin is 490-690 \(\upmu\)m [45].
## 2 Materials and Methods
### The Architecture of MA1 and MA2 Matrices  
[PERSON] et al. [32] proposed two matrix architectures for analyzing the relationships between two time series. Given two time series \((x_{n}:n=0,1,2,3\ldots)\) and \((y_{n}:n=0,1,2,3\ldots)\) the second-order MA1 reads:  
\[A_{n}=\begin{bmatrix}2\ x_{n}&x_{n+1}-y_{n+1}\\ x_{n-1}-y_{n-1}&2y_{n}\end{bmatrix} \tag{3}\]  
From Equation (3), it is evident that all six requirements raised for PMLD are also satisfied for MA1. However, the only difference between Equation (3) and Equation (1) is that the elements on the main diagonal are multiplied by a scalar factor. As detailed in reference [13], the presence of cross derivatives inherently amplifies the subtle variations in the investigated signals. One of the hypotheses which will be investigated in this paper is based on the assumption that the multiplication of the zero-order elements by a scalar factor greater than one does decrease the ability to detect those subtle variations in the investigated signals.  
It is clear that the structure of MA1 also allows a straightforward expansion of the dimension of the matrix. For example, three time series \((x_{n}:n=0,1,2,3\ldots)\), \((y_{n}:n=0,1,2,3\ldots)\), and \((z_{n}:n=0,1,2,3\ldots)\) would be mapped into the MA1 architecture in the following way:  
\[MA1_{n}=\begin{bmatrix}2x_{n}&x_{n+1}-y_{n+1}&x_{n+1}-z_{n+1}\\ y_{n-1}-x_{n-1}&2y_{n}&y_{n+1}-z_{n+1}\\ z_{n-1}-x_{n-1}&z_{n-1}-y_{n-1}&2z_{n}\end{bmatrix} \tag{4}\]  
Note that all six requirements are enforced for Equation (4) too. However, the expansion of the second-order MA1 to a third-order matrix can be executed in a different way (compared to Equation (4)). Such an expansion is represented by MA2 [32]:  
\[MA2_{n}=\begin{bmatrix}x_{n}+z_{n}&x_{n+1}-y_{n+1}&z_{n+1}-x_{n+1}\\ x_{n-1}-y_{n-1}&2y_{n}&y_{n+1}-z_{n+1}\\ z_{n-1}-x_{n-1}&y_{n-1}-z_{n-1}&z_{n}+x_{n}\end{bmatrix} \tag{5}\]  
The major difference between MA2 (Equation (5)) and MA1 (Equation (4)) is based on the elements on the main diagonal. Unlike MA1, the lexicographical balance is not preserved in Equation (5).
# Review of Advanced Medical Telerobots
## 3 Autonomy Levels
### Bilateral Teleoperation
We cite this work here for readability concern, but it corresponds to shared control solutions detailed in the next section. Bilateral teleoperation is also applied for remote ultrasonography [320; 321]. Nowadays, over a quarter of emergency admissions requires an ultrasound examination for preliminary diagnosis purposes. This is a low-cost radiation-free examination technique, which implies that the physicians remain very close to their patients to position the ultrasound probe on the targeted anatomic area. Since the late 1990s, several solutions of teleobotized ultrasonography have been developed to compensate for the lack of ultrasound experts in medically isolated settings (see, for instance [18]). Two concepts have been proposed to teleoperate the ultrasound probe: either with a robotic arm or with a light-weight specific robot maintained on the patient by a paramedic, while the physician remotely actuates the probe to collect and then later analyze the ultrasound images. Ultrasound probe guiding robots can also be used for enhanced ultrasound examination as an assistance [16; 17], such as described in the following shared control section. Nowadays, bilateral-control based solutions are mainly studied in terms of stability [64] (or passivity [182; 322]) and transparency [182; 323] taking into account communication delays [182; 274; 320], packet drops, disconnections, data quantization, actuator saturation, operator dynamics [168] to provide safer low-level architectures that can then be used with embedded operator assistance intelligence, to generate the following categories.
## 3 Study Area and Dataset
### Data Acquisition  
We used street view images obtained from Tencent Maps, which provides an application programming interface (API) for querying and downloading street view images with parameters such as size, location/pano, heading, pitch, and key [12]. The TSV panoramas capture photos from horizontal and vertical cameras and stitch them together to produce panoramas of the original street view. The TSV image's visual fields are approximately equal to those of the normal human visual field when the pitch angle of the camera is 0\({}^{\circ}\)[12]; considering that the four images were sufficient to form panoramic images that simulated the 360\({}^{\circ}\) visual landscape of a pedestrian at a fixed point, we downloaded a total of four images (each with a pitch angle of 0\({}^{\circ}\) and headings of 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\)) at a location to represent its panoramic view.  
The procedure for obtaining street view images included three steps: (1) obtaining the location coordinates at an interval of 100 m along all the streets in the vector road network within the Fifth Ring Road, (2) searching the nearest TSV ID within 50 m based on the Tencent web service API and using the TSV API to download the four TSV images at each location, and (3) cleaning the data and eliminating the location coordinates without obtaining all four TSV images.  
For detailed instructions on the TSV API, see reference [34]. An example of the acquisition of TSV images through the TSV API is shown in Figures 2 and 3 with a uniform resource locator (URL) example of the Tencent static image API: [[https://apis.map.qq.com/ws/streetview/v1/image?size=960x640&pano=10011512120530135909300&](https://apis.map.qq.com/ws/streetview/v1/image?size=960x640&pano=10011512120530135909300&)]([https://apis.map.qq.com/ws/streetview/v1/image?size=960x640&pano=10011512120530135909300&](https://apis.map.qq.com/ws/streetview/v1/image?size=960x640&pano=10011512120530135909300&)) pitch=0&heading=0&key=5 MJBZ-NEIE6-QMLSW-MX4 KF-50 EDE-C7B4Z.  
Response:  
Figure 3: Tencent Street View image download parameter configuration.  
Figure 2: Tencent Street View static image application program interface.  
In this study, the resolution of the TSV images was 960 x 640, and the format was jpg. After data cleaning, a total of 272,244 TSV images were obtained for 68,061 street points with streetscape images in four directions. The TSV images in the study area were downloaded during November 2018.
## 3 Wireless Localization Technology
### Range-Free Techniques
#### 3.2.1 Proximity  
A representative proximity-based technology is Cell-ID technology. Cell-ID is a technology that uses the unique number assigned to each base station in wireless communication. The localization technology based on Cell-ID determines the position of the device based on the location of the base station it is connected to as shown in Figure 6. It is simple to determine the location through information processing, while the disadvantage is that  
Figure 4: An illustration of the concept of TDoA.  
Figure 5: An illustration of the concept of AoA.  
the entire communication range of the base station is the error range, making it difficult to achieve precise localization.  
Another proximity-based technology is device-to-device (D2D) communication. D2D communication means direct communication between devices without passing through the anchor. This communication method can expand the active area by continuously connecting devices through a relay method. This method can also be applied to localization, where the location of the next device can be measured based on the device that has determined its location, without direct communication with the anchor. This reduces the load on the anchor that communicates with multiple devices; however, the error can increase if the connection between devices becomes overlapping.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 4 Experiments  
### Datasets  
In the experiment, the clean corpus used comes from the TIMIT corpus [41], which includes 630 speakers of 8 major dialects of American English with each reading 10 utterances. All sentences in the TIMIT corpus are sampled at 16 KHZ, with 4620 utterances in the training set and 1680 utterances in the test set, resulting in a total of 6300 utterances. Then, 1000, 200, and 100 clean utterances are randomly selected for training, validation, and testing, respectively. Training and validation dataset are mixed under different SNR levels ranging from \(-5\) dB to 10 dB with the interval \(1\) dB while the testing datasets are mixed under \(-5\) dB and \(-2\) dB conditions.  
For training and validation, we used two noisy datasets. One dataset is a noise library recorded in the laboratory of Prof. [PERSON] at Ohio State University, which has 100 sounds with different durations and a sampling rate of 16 kHz. A total of 100 kinds of noise, which includes machine, water, wind, crying noise, etc, were used. The other is NOISEX92 [42], with 15 noises, a duration of 235 s, and a sampling frequency of 19.98 kHz. A total of 15 kinds of noise, which includes truck, machine gun, factory, etc, were used. Another five  
Figure 4: (**a**) RRSENet: recursive recurrent speech enhancement network, (**b**) RRBlock: recursive recurrent block.  
types of noises from NOISEX92, including babble, f16, factory2, m109, and white, were chosen to test the network generalization capacity.  
The dataset is constructed using the following steps. First, the noise is downsampled to 16 kHz and stitched into a long noise. Second, a clean speech is randomly selected, and a noise of the same length is selected. Finally, during each mixed process, the cutting point is randomly generated, which is subsequently mixed with a clean utterance under one SNR condition. As a result, totally 10,000, 2000, and 400 noisy-clean utterance pairs are created for training, validation, and testing, respectively.
## 4 Simulation and Evaluation
### Complexity Analysis  
The computational complexity of the generation of the \(K\)-Polygon diagram can be considered negligible compared with the computational complexity of the convolutional computation. In the \(c\)-th depthwise separable convolution layer, \(K_{c}^{2}\), \(I_{c}\), \(O_{c}\), and \(D_{c}^{2}\) represent the depthwise convolution kernel size, input channel number, output channel number, and output image size, respectively. In the fully convolutional layer, \(K_{C}^{2}\), \(I_{C}\), \(O_{C}\), and \(D_{C}^{2}\), respectively, denote the convolution kernel size, input channel number, output channel number, and output image size. \(N_{F}\) and \(N_{O}\) represent the number of neurons in the fully connected layer and the output layer, respectively. Taking into account the impact of stride, the number of Multiply-Accumulate (MAC) operations of our KDCD is as follows:  
\[\text{MAC}=\sum_{c=1}^{4}\left(K_{c}^{2}I_{c}D_{c}^{2}+I_{c}O_{c}D_{c}^{2} \right)+K_{C}^{2}I_{C}O_{C}D_{C}^{2}+N_{F}N_{O}. \tag{9}\]  
The comparison between the KDCD, learning-aided SCMA (D-SCMA) [12], and DLD-R [15] on computational complexity is shown in Table 3. Compared with D-SCMA and DLD-R, our KDCD achieves reductions of 29.4% and 24.9%, respectively, in terms of the number of MAC operations. In addition, to demonstrate the effective reduction in communication latency achieved by our KDCD, Figure 7 compares the decoding time of different decoders in a more intuitive manner. As shown in Figure 7, our KDCD possesses the lowest communication latency, aligning with the low latency requirement of 5G wireless communication. This can be attributed to the fact that, compared with other decoders, our KDCD does not require iterations for message updates. Furthermore, our KDCD employs the modified MobileNet model with a lightweight structure as the classification network, which is highly efficient and
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
## 5 Discussion
### Current Challenges
#### 5.1.3 Lightweight Real-Time Object Detection Algorithms Based on UAVs  
Real-time object detection algorithms run on resource-limited edge computing platforms, and those models need to be relatively lightweight, thereby reducing the reliance on computational resources as well as reducing inference time to meet real-time requirements. In [6], the authors highlighted that it has been challenging to improve the detection time of detectors, and they provided an overview of the speed-up techniques, including feature map shared computation, cascaded detection, network pruning and quantification, lightweight network design, and numerical acceleration. In our review study on UAVreal-time object detection, the most commonly used methods to improve the speed of the algorithm are network pruning [54; 65; 65; 112], quantification [67; 82; 92], and the use of a lightweight network design.  
Network pruning is an essential technique for both memory size and bandwidth reduction, and it can remove the redundancy of a network and parameters, which can reduce computations without a significant impact on accuracy. In [133], the authors demonstrated the possibility of pruning 90% of all weights in a ResNet-50 network trained on ImageNet, with a loss of less than 3% accuracy. However, in [134], the authors noted that although network pruning sometimes progressively improves accuracy by escaping the local minimum, the accuracy gains are better realized by switching to a better architecture [135]. In the meantime, the performance could also be bottlenecked by the structure itself, and a network architecture search and knowledge distillation can be options for further compression [136]. From the practical point of view, due to the limitation of embedded device resources, large-capacity optimization networks are not allowed, so in [137], the authors pointed out that when pruning a network, it is best to consider the deployment environment, target device, and speed/accuracy trade-offs to meet the needs of the constrained environment.  
To perform network quantization is to compress a neural network by reducing its value precision, i.e., by converting FP32 numbers to lower-bit representations, which can accelerate inference speed [136]. However, the literature also mentioned that quantization usually leads to accuracy loss due to information loss during the quantization process. In UAV real-time object detection tasks, it is a common idea to adopt a compact network as the initial model. Unfortunately, the accuracy drop during quantization is especially obvious in compact networks. Some advanced quantization techniques [136], including asymmetric quantization [40] and calibration-based quantization [64], can improve the accuracy. In practice, 8-bit quantization is widely used, and in our review studies [67; 74], good trade-offs between accuracy and compression were achieved in real-time object detection.
## 5 Results and Discussion
### VSWT Hybrid WDPS Performance Under Abnormal Conditions  
A sudden increase in load demand or loss of wind generation is considered an abnormal condition for hybrid systems. However, this case was already discussed in Section 4.3 by considering a step response of 0.5 pu as an increase in load demand. In addition, different strategies were discussed in the literature to validate the performance of VSWTs in load fluctuations. A step random signal in [24] and a random signal in [102] from the MATLAB library are used as load variation. In fact, random signals generated by MATLAB are also a random step response. Therefore, a step random signal considered as load variation, shown in Figure 25, is assumed to reflect the real-world scenario. Load variation dramatically affects the system frequency. During such contingencies, the VSWT also provides synthetic inertia using an inertial and proportional control loop (Figure 14) in addition to the inertial support provided by the DG itself. Figure 25 justifies this statement.  
Figure 23: Blade pitch angle and VSWT inertial and proportional response against wind variations.  
Figure 24: A detailed graphical representation of a sudden loss of wind generation at a time of 800 s.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 6 Conclusions  
The study described in this report examined a proposed time domain room acoustic solver using an explicit TD-FEM and demonstrated its practicality as a room acoustic design tool. The present TD-FEM achieves fourth-order accuracy in both space and time using a dispersion-reduced low-order FEs and a specially designed linear multi-step time integration method. It completely overcomes the shortcomings of earlier presented methods fourth-order accurate explicit formulation [25] by which the staircase approximation is introduced into boundary modeling to maintain higher order accuracy in time discretization. In addition, as a noteworthy feature of the proposed method, it does not necessitate any additional computational cost in the matrix-vector product operations per time step. Therefore, the main operation complexity of the present method is the same as that of an earlier method. We conducted both dispersion and dissipation error analyses of the present method. The dispersion error analysis revealed that the maximum and minimum dispersion errors occur respectively at sound propagation in the axial direction and in an oblique direction \(\theta=\pi/4\) in a polar coordinate system. In addition, the dispersion error decreases with a smaller time interval. The dispersion analysis also revealed that the present method includes a dissipation error that appears as a numerical complex sound speed in the resulting expression. However, we demonstrated that the dissipation error magnitude is dependent only on time interval values. It can simply reduce the control of time interval values using a proposed control method. Performance of the proposed explicit TD-FEM was examined against the standard implicit TD-FEM using sound propagation problems in a concert hall and in a rectangular room including acoustic diffusers. The results clearly demonstrated that the proposed method can predict complex sound fields at kilohertz frequencies accurately with a much lower requirement for computational resources, suggesting its promising potential for use as a time domain room acoustic solver. Although this paper only describes 2D analysis and its results, extension of the proposed method to 3D analysis is a trivial task. It will be a subject of future research.  
Figure 17: Comparison of time-integrated sound pressure level until 0.1 s for Cond. 1 (upper) and Cond. 2 (lower).  
**Author Contributions:** Conceptualization, T.Y. and T.O.; Investigation, T.Y.; Methodology, T.Y.; supervision, T.O.; Project administration, K.S.; Validation, T.Y.; Visualization, T.Y.; Writing--Original draft, T.Y and T.O.; Writing--Review & editing, T.Y., T.O. and K.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest related to this report or the study it describes.
## 7 Discussion
### Optimization Case Studies  
The use of the DMs for the problems investigated aided in the following ways: For both optimization problems, the initial choice was to use either a fast algorithm with low expected solution quality (heuristic, SucPP) or a slow one with a good convergence behavior (ES, SimPP).  
In the case of the boom configuration, initially the good convergence behavior in combination with the other considered criteria, the ES should have been chosen. Once the results were available,  
Figure 12: Decision Matrix for Boom Configuration before optimization ((**left**), with Demanded Quality Criteria) and after optimization (**right**). Note that, for a better representation, only the scoring for comparing the algorithms are shown.  
the heuristic showed that it can generate good solutions and ultimately proved to be the better fit for this problem.  
In the case of the plate partitioning, SimPP was to be expected as the better fit before experiments were performed. After having the results available, the differences in the algorithms were not as pronounced as expected (SimPP was faster, while SucPP provided high-quality solutions), providing more evidence in selecting SimPP as the more suitable algorithm for the problem.  
Both cases show that end-user requirements can have a significant impact on the algorithm selection. If solution quality would have been the only decision criterion, the ES instead of the heuristic would have been selected.  
Finally, for both optimization problems, all algorithms provided feasible solutions within an acceptable time. For the boom configuration problem, the obtained results are better than existing configuration and the run time for the plate partitioning problem is less than 5 s for booms up to 70 m. This provides clear evidence that using an optimization approach can improve the manual design process.