esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The NC-RRT Method
### The Node Control Mechanism
The random tree will keep this state, which can be expanded in local areas only until it reaches the set maximum number of failures. Therefore, we need to change the value of _control_ at the appropriate time to increase the number of optional extension nodes to prevent this problem. This phenomenon is likely to occur when the tree encounters obstacles. Thus, we consider the collision occurrence in the expansion process approximately as the judgment condition for the occurrence of the \"local trap\" phenomenon, that is the condition for changing the value of the node control factor, similar to the change condition of the sampling radius in the CSA-RRT method. When collision occurs, the _control_ value is increased, and the tree can effectively escape from this area. After the new node is successfully added, the _control_ value is restored to one to continue the exploration. At this point, the NC-RRT algorithm proposed in this paper is obtained, and its pseudocode is shown in Algorithm 4. Figure 3: Under a _control_ value of one and the combination of the exploration with the CSA method, the random tree exhibits different behaviors when it does and does not encounter obstacles. (**a**) Efficient exploration in an environment without obstacles. (**b**) The “local trap” phenomenon during exploration in an environment full of obstacles. Figure 2: The change strategy of node state value \(\delta\) in the node control mechanism. \(q_{start}\) is the initial node, and \(q_{n}\) is any node in the tree. \(q_{n+1}\) and \(q_{n+2}\) are child nodes of \(q_{n}\). The values in parentheses are the node state values \(\delta\) we recorded. ```
1:\(T\leftarrow\text{InitTree($q_{start}$)}\);
2:\(\Delta_{T}\leftarrow\{\delta(q_{start})=0\}\);
3:\(R\gets D_{farthest}\);
4:\(control\gets 1\);
5:for\(i=1\) to n do
6:\(q_{rand}\leftarrow\text{RandomSample($i$)}\);
7:if\(\text{Distance}(q_{rand},q_{goal})>R\)then
8:continue;
9:endif
10:\(T_{ctrl}\leftarrow\text{LessThanControl}(\Delta_{T},control,T)\);
11:\(q_{near}\leftarrow\text{NearestNeighbor}(q_{rand},T_{ctrl})\);
12:\(q_{new}\leftarrow\text{Extend($q_{rand},q_{near},\epsilon$)}\);
13:if\(\text{CollisionFree($q_{near},q_{new}$)}\)then
14:\(\text{AddNewNode}(T,q_{new})\);
15:\(\Delta_{T}\leftarrow\{\delta(q_{new})=0\}\);
16:\(\Delta_{T}\leftarrow\text{BranchNodeUpdate($\Delta_{T},q_{new},T$)}\);
17:\(R=\text{Distance}(q_{new},q_{goal})\);
18:\(control=1\);
19:else
20:\(R=R+k\times\epsilon\);
21:\(control=c\); \((c=2,3,...)\)
22:continue;
23:endif
24:if\(\text{Distance}(q_{new},q_{goal})<\rho_{min}\)then
25:return\(T\);
26:endif
27:endfor
28:return\(Failed\);
```  
**Algorithm 4** NC-RRT algorithm.
# Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets
## 5 Conclusions  
In this paper, we present a way to factorise the task of full transcription into multiple intermediate tasks in order to improve performance for low-resource datasets. We propose two intermediate tasks of audio event detection on a single class and audio tagging, referred to as WHEN and WHO tasks, respectively. Additionally, we introduce a balanced input training and a new loss function in the multi instance learning (MIL) setting for the WHEN task. We train these tasks with three different approaches. Firstly, an independent training for each task and then two multi-task learning (MTL) approaches that use hard parameter sharing. One of them is the most commonly used joint training and the other one is our proposed tied weights training. In order to evaluate our approaches, we trained each network using a low-resource dataset for birdsong transcription. Our results show that, even though our proposed tied weights training outperforms joint training for these tasks, separate training still performs better than both.  
For our future plans, we first intend to explore whether soft parameter sharing in MTL can further improve the performance of our intermediate tasks. Then, we plan to use the intermediate transcription to boost the performance of a full transcription network. To our current knowledge and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Training** & **Input Type** & **WHEN** & **WHO** \\ \hline
**Method** & **WHEN \(\mid\) WHO** & **AUC** & **AUC** \\ \hline Separate & HnH \(\mid\) nonHnH & **0.90** & **0.94** \\ Joint [WHEN: 0.5; WHO: 0.5] & HnH & 0.89 & 0.52 \\ Joint [WHEN: 0.5; WHO: 0.5] & nonHnH & 0.47 & 0.57 \\ Joint [WHEN: 0.5; WHO: 5.0] & HnH & **0.90** & 0.50 \\ Joint [WHEN: 0.5; WHO: 5.0] & nonHnH & 0.82 & 0.75 \\ Tied Weights & HnH \(\mid\) nonHnH & 0.87 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Area under the ROC curve (AUC) for the predictions of all training approaches. [WHEN: xx; WHO: yy] indicate the weights xx for WHEN task loss and yy for WHO task loss that were used during joint training. Best values are marked in bold.  
based on our latest experiments, attempting to train a network to perform full transcription without any intermediate tasks for this low-resource dataset is not feasible. Hence, we will attempt to achieve a satisfactory performance when using the intermediate transcription as a guide for the attention of the full transcription network.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analysed the data; [PERSON] wrote the paper.  
This research was funded by Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/L020505/1.  
We would like to acknowledge the contributions of [PERSON] (AGH University of Science and Technology in Krakow, Poland) for acquiring the transcriptions of the NIPS4B 2013 training set by manual annotations.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:
# A Method for Estimating Source Depth Based on the Adjacent Mode Group Acoustic Pressure Field
## 3 Simulation
### The Influence of the Temperature Coefficient on the Simulated Annealing Algorithm  
In order to reduce the error rate of source depth estimation, all parameters related to the simulated annealing algorithm, except for the temperature coefficient, were controlled, and the error rate of the method was reduced by increasing the temperature coefficient in advance. This was necessary due to the influence of the temperature coefficient on the cooling rate and the number of simulated annealing algorithm iterations. Figure 10 shows the estimated results with a temperature coefficient of 90%. The temperature coefficients were modified to 92%, 94%, 96% and 98%, respectively, and simulated annealing was performed to obtain the results. The comparison of source depth estimation under different temperature coefficients is shown in Figure 11.  
As shown in Figure 11, as the temperature coefficient increased, the estimated depth of the source gradually matched the true depth of the source. We calculated the average error rate of the experiment under four different temperature coefficients: \(\Delta_{92\%}=4.05\%\), \(\Delta_{94\%}=2.77\%\), \(\Delta_{96\%}=1.78\%\) and \(\Delta_{98\%}=0.24\%\); the higher the temperature coefficient, the better the depth estimation result. However, when starting from the temperature coefficient, the overall algorithm time complexity was O(\(n\)), so as the temperature coefficient increased, the execution efficiency of the method decreased.
## 3 Materials and Methods
### Experimental Design
#### 3.3.3 Experiments Performed on Real-World Data  
Three distinct analyses were carried out on the case study data to assess the model's performance and robustness: a Decision-Making Analysis, an Ablation Analysis, and a Sensitivity Analysis. Each analysis was designed to provide valuable insights into the model's behavior when applied to real-world quality inspection scenarios.  
The Decision-Making Analysis aimed to understand the decisions suggested by the model when applied to the real-world case study. This analysis focused on the model's ability to optimize the allocation of inspection resources, considering the various constraints and objectives defined by the case study. The results of this analysis provided an overview of the model's decision-making insight.  
Figure 4: Dependence of defect probability on product and characteristics.  
Figure 5: Evolution of demand over time for the products examined in the case study.  
Next, an Ablation Analysis was conducted using the same case study data to evaluate the impact of excluding specific features from the proposed model. Four ablated models were created for comparison. The first ablated model was generated by fixing the number of controls (\(N_{s,p,h,k,l}^{control}\)) to its maximum value, thereby removing its variability. The remaining three ablated models were generated by restricting the model from selecting, in turn, each of the three predefined inspection technologies. A summary of all models included in the Ablation Analysis is presented in Table 4.  
These experiments allowed us to understand how much the inclusion of these features affects both the objective function and the computational time required to solve the problem.  
Lastly, a Sensitivity Analysis was conducted to examine how variations in the values of key parameters impact the results obtained from the Ablation Analysis. This analysis focused on understanding the impact of demand (\(D_{p,t}\)) and defect rate of product (\(def_{p,k}^{prob}\)) variations on the results of the Ablation Analysis. Table 5 reports the value range of the multipliers adopted to vary demand and defect rate.
## 5 Results and Discussion
### Qualitative Analysis  
Some representative examples are shown in Figure 8. Baseline without CTC outputs were more noisy and over-segmented actions. CTC outputted a degenerated path, however the order was correct. Reduced-CTC did not suffer from over-segmentation, and had better localization and ordering.  
Specifically, we found that Transformer + RCTC captured longer-range temporal dependencies than the state of the art, especially in cases when distinct actions were visually very similar. For example, Baseline wrongly predicted the ground-truth class 'add coffee' as 'pour coffee', while neglecting the temporal dependencies between certain action pairs. Our hybrid encoder also made more reliable predictions of extremely short action instances that fell in between two long actions ('butter pan', 'withdraw stove', etc.). This suggests that using modified temporal classification is critical for improving the accuracy of prediction of action boundaries.  
Figure 7: Loss curves in log-scale w.r.t. the number of epochs and elapsed clock time. Vertical dotted lines of different colors indicate that the loss function on the corresponding validation no longer dropped between two consecutive epochs, which is the position where the virtual learning process actually terminated. (Note: Due to time and hardware limitations, only a subset of \(\sim\)1k samples were selected for cross-validation training on the YouCook2 dataset).
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 3 Results
### Multiple Drones for Aerial Surveillance  
Following this initial single-drone mission, the study was expanded to include the deployment of five drones. The positioning of these drones was strategically orchestrated using the DT Drones Application, implementing Delaunay triangulation for an optimal spatial arrangement. Each of the five drones then captured images of the same target location, and these images were subsequently processed using the \"Recognize\" application. The results from both experiments, comparing the outcomes of the single-drone and multiple-drone approaches, are scrutinized and discussed, providing insights into the effectiveness of employing multiple drones and advanced image processing techniques for comprehensive location surveys.
## 3 Material Testing of Window-Type NBSDs and the Results
### NBSD Material Test Loading and Measurement Methods  
Hysteretic steel dampers serve to dissipate energy through their plastic behavior following yielding, and their energy dissipation capacity is proportional to the displacement applied, i.e., displacement dependent. Thus, both maximum and repetitive deformation capacities are dominant factors that determine the overall performance of hysteretic steel dampers. In the present study, material testing was conducted according to KDS 17 10 00 (Seismic Building Design Code) [5]. Detailed descriptions of the applied NBSD specimen and measuring device setup are presented in Figure 4.  
The lateral force was applied to the specimens using an actuator, and the actuator's loading points were aligned with the center of the upper steel structure for loading on top of the specimens. According to \"17.6 Device Prototype Test of KDS 41 17 10 00 (Seismic Building Design Code) [5],\" the specimens were subjected to static cyclic loading with the respective displacements 0.33 times the expected device displacement for ten times, 0.67 times the expected device displacement for five times, and 1.0 times the expected device displacement for three times (See Figure 5). The maximum device displacement when the maximum-magnitude earthquake was considered was set to 1% (33 mm), which corresponded to the allowable inter-story displacement angle of school buildings (special grade) as the target subjects [37].  
Figure 4: Specim and measuring device setup: (**a**) schematic test setup and (**b**) actual setup image.
## 2 Data and Methods
### Image Registration  
Although the images collected by the Solar Dynamics Observatory (SDO/AIA) have a comprehensive field of view, their resolution is relatively low; in contrast, the High-Resolution Coronal Imager (Hi-C) captures images that cover only a partial area but have a higher resolution. In light of this, we need to register images from different sources that are captured at the same moment and of the same astronomical targets. We adopted the Scale-Invariant Feature Transform (SIFT) registration algorithm [18] to accomplish the registration work between high-resolution and low-resolution image pairs. This algorithm can extract key points (feature points) and their descriptors from the images, and these key points exhibit invariance to changes in image scale, rotation, and even brightness. For example, Figure 1a shows a high-definition coronal image from Hi-C; Figure 1b shows a lower-resolution image from SDO/AIA. After registration, the green box in Figure 1b indicates the low-resolution area corresponding to Figure 1a Subsequently, we cropped the registered images to serve as the low-resolution (LR) images.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM  
Filter bank multi-carrier is proposed to be an alternative MCM scheme that improves on the shortcomings of OFDM based systems while emphasis is made on spectral efficiency improvement [10; 128]. FBMC systems employ the use of finite impulse response prototype filters that possess longer impulse response than the symbol period (\(T\)) [129; 130]. In other words, the length of the prototype filter (\(L_{p}\)) is greater than the total number of sub-carriers (\(M\)). A higher spectral efficiency is achieved by FBMC based schemes in comparison to CP-OFDM systems because of the absence of CP. In this section, the system model of the FBMC waveforms is analysed assuming NPR and Non-PR prototype filters design. In NPR, the distortion of signals is minimised from an accurate design of the FIR low-pass prototype filter which subsequently reduces the amplitude distortions. For Non-PR, aliasing and amplitude distortions are more prominent which may reduce system efficacy.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### NPs of Other Metals  
#### 4.6.1 MgO NPs and Mn\({}_{2}\)O\({}_{3}\) NPs  
Elicitor Effect  
MgO NPs immobilized on the surface of nanoperlite using _Melissa officinalis_ extract as a capping agent applied at doses 25, 50, and 150 mg/L of MgO/perlite NCs, showing the size 30 nm elicited in vitro biosynthesis of RA in _M. officinalis_ plant organ cultures [270]. Mn\({}_{2}\)O\({}_{3}\) NPs (ca. 30 nm) supplemented to MS culture medium with tissue culture of _Atropa belladonna_ at doses 25-200 mg/L affected growth characteristics, leaf relative water content, Chl levels, H\({}_{2}\)O\({}_{2}\), and MDA contents, and electrolyte leakage by altering the protein content and activation of antioxidant and defense enzymes. Mn\({}_{2}\)O\({}_{3}\) NPs also affected biosynthesis of secondary metabolites, including TPC, TFC, and alkaloids in a dose-dependent manner. Application of 25 mg/L Mn\({}_{2}\)O\({}_{3}\) NPs was found to promote not only plant growth, but also the production of alkaloids in shoot tip cultures of _Atropa belladonna_, which reached 1.84- and 2.92-times greater levels as that of positive (22.3 mg/L MnSO\({}_{4}\cdot 4\)H\({}_{2}\)O) and negative (medium without Mn) controls, respectively. Extracts prepared from plantlets showed antioxidant properties [151].  
Attenuation of Abiotic Stress  
Mn compounds are able to stimulate crop growth and mitigate abiotic stress, whereby at application of nanosized Mn particles, higher efficiency in attenuation of abiotic stresses and less toxic effects can be achieved compared to bulk Mn particles. In addition, plant Mn deficiency can be overcome by additional supply of Mn, resulting in induction of manganese superoxide dismutase at the transcriptional level, to suppress ROS production and promote Mn-dependent proteins for preservation of cell integrity [271].  
Genotoxicity  
Chromosomal aberrations and a decrease in MI compared to the control, with a pronounced raise in H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\)\({}^{-}\) production and lipid peroxidation, was observed in _Allium cepa_ roots treated with 12.5-100 \(\upmu\)g/mL MgO MPs and MgO NPs, the toxicity of NPs being higher [272].
# A Comparative Study for Control of Quadrotor UAVs
###### Abstract  
Modeling and controlling highly nonlinear, multivariable, unstable, coupled and underactuated systems are challenging problems to which a unique solution does not exist. Modeling and control of Unmanned Aerial Vehicles (UAVs) with four rotors fall into that category of problems. In this paper, a nonlinear quadrotor UAV dynamical model is developed with the Newton-Euler method, and a control architecture is proposed for 3D trajectory tracking. The controller design is decoupled into two parts: an inner loop for attitude stabilization and an outer loop for trajectory tracking. A few attitude stabilization methods are discussed, implemented and compared, considering the following control approaches: Proportional-Integral-Derivative (PID), Linear-Quadratic Regulator (LQR), Model Predictive Control (MPC), Feedback Linearization (FL) and Sliding Mode Control (SMC). This paper is intended to serve as a guideline work for selecting quadcopters' control strategies, both in terms of quantitative and qualitative considerations. PID and LQR controllers are designed, exploiting the model linearized about the hovering condition, while MPC, FL and SMC directly exploit the nonlinear model, with minor simplifications. The fast dynamics ensured by the SMC-based controller together with its robustness and the limited estimated command effort of the controller make it the most promising controller for quadrotor attitude stabilization. The outer loop consists of three independent PID controllers: one for altitude control and the other two, together with a dynamics' inversion, are entitled to the computation of the reference attitude for the inner loop. The capability of the controlled closed-loop system of executing complex trajectories is demonstrated by means of simulations in MATLAB/Simulink(r).  
unmanned aerial vehicles; feedback linearization; sliding mode control; MPC; PID; LQR; UAV modeling; UAV control; attitude stabilization; trajectory tracking; performance evaluation
## 5 Case Study  
### Input Data and Parameters  
The case study is presented based on the local network of the Beijing-Shanghai High-speed Railway Line, illustrated in Figure 6. The local network contains 26 stations and 54 unidirectional sections. For readability convenience, the stations are presented by their abbreviations, shown in Table 3. The stations are classified into three levels by their administration capabilities, the stop time is set as 2 min, and the acceleration/deceleration time is set as 3 min. The network is decomposed into eight unidirectional segments by JNW, XZD, and NJS.  
The travel demand data are obtained from ticket sale data on 10 September 2023. There were 423,417 passengers in total traveling along this line. The required service frequencies for stations and O-D pairs are obtained by statistical data. The train set contains 585 trains (including in-line trains and cross-line train segments) running in this HSR network on 10 September 2023. Trains are classified into two classes: the high-class trains are benchmark trains with low stop ratios and high travel speeds, while the low-class trains would stop at more stations to provide services for local travel demand. The other operation parameters are obtained by practical data.  
The model and algorithm parameters are set as follows: the objective weights \(w_{1}=100,w_{2}=1,w_{3}=100\), the population size \(P=15\), the elitist individual number \(m=1\), the tournament size \(n=5\), maximum and minimum crossover probabilities \(p_{c-\max}=0.9,p_{c-\min}=0.7\), maximum and minimum mutation probabilities \(p_{m-\max}=0.12,p_{m-\min}=0.08\), the neighborhood searching probability \(p_{skip}=0.2\), the initial temperature \(T_{0}=0.01\), the temperature drop ratio \(\theta=0.8\), and the maximum generation number \(g_{\max}=350\).