esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Limitation and Future Work  
The control strategy in this paper depends on the TF estimation. According to the system under test, it may be necessary to adjust the filter parameters in detail according to the actual situation.  
The purpose of simplification is to reduce the amount of computation, and from the results, the simplified strategy is also available. Due to the limitation of causality, the calculation process of the current iteration coefficient can only be performed with the last drive and response, so it may have some influence on the convergence speed and stability.  
In this paper, the iterative convergence optimization of nonlinear systems is discussed, and the influence of external disturbances on the iterative process is not discussed.  
If the control method needs to be extended to multichannel system in the future, a better multichannel TF estimation method is needed. For further improvement of the iteration coefficient, it is possible to improve the effect of the iteration coefficient on the convergence speed and stability of the iterative process by predicting the response of the system.  
**Author Contributions:** conceptualization, M.L. and Y.Z.; methodology, M.L.; software, M.L.; validation, M.L.; formal analysis, M.L.; investigation, M.L.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, M.L.; writing--review and editing, M.L.; visualization, M.L.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z.  
**Funding:** This work was supported by the key project of the ministry of science and technology of China (project number: 2017 YFB0103700).  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this article.
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
## 3 Simulation Results and Discussion
The relationship between the IM torque error and the sampling frequency is shown in Figure 9. The figure indicates that the torque error can be reduced by increasing the sampling frequency. Conversely, the switching frequency will increase if the sampling is increased. Figure 10 presents the torque steady-state error for proposed Method (1) and the traditional DTC. The torque error of the traditional DTC is a constant compared to proposed Method (1), which is relatively greater than that of the traditional. Figure 6: The speed responses of the conventional and proposed Method (1); at rated load. Figure 7: The stator flux trace of (**a**) Method (1) (**b**) traditional DTC. Figure 8: The relationship between the inverter switching frequency and the sampling frequency at different speeds and full load conditions. Figure 10: The torque steady-state error of (**a**) Method (1) and (**b**) the traditional DTC. Figure 9: The relation between the IM torque error and the inverter sampling frequency. Figure 11 shows the comparison of the speed response of proposed Method (2) and the traditional DTC at different set speed values and torque loads. Changing the set value with the PI controller gives an unacceptable system response [32]. Hence, the PI controller is replaced by an adaptive PI controller in this situation. Seen at all set points, there were no overshoots and no steady-state errors. The settling time differs according to the set value. The step changes of the load torque barely affect the IM speed due to the high performance of the adaptive controller. Figure 11 shows the response of the system using proposed Method (2) and how the response is faster than the traditional DTC response. It is noted that the responses may be typical at some set points. The cause of this phenomenon is that the optimization of the voltage vector is always performed between two vectors inside the hysteresis band. Sometimes the result of the optimization is similar to that given by the traditional DTC. This produces the same speed response in random situations. However, the proposed methods give a response that is equal to or faster than that of the traditional DTC.
# Deep-Learning-Based Identification of Broad-Absorption Line Quasars
## 3 Method  
### Data Dimensionality Reduction  
Spectral data from BAL and non-BAL quasars were used in this study. While these datasets contain valuable information from astronomical observations, they also face major challenges. Dimension reduction is a way to convert larger dimension data into a smaller dimension representation [25; 26]. This process preserves the essential features of the data while information which makes little contribution to the analysis or construction of the model is eliminated, making the data more suitable for subsequent classification tasks.  
Therefore, this study applied various dimension reduction techniques including PCA, t-SNE (t-distributed random neighborhood embedding), and variety learning algorithms to extract features. The simplified data were used as inputs into the deep learning algorithm for the quasar classification. This approach allowed for a more efficient processing of the high-dimensional data, improved model formation efficiency, and prediction accuracy, leading to better research results.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 2 System Model and Problem Formulation
### System Model
Therefore, the UAV's mobility constraints can be formulated as follows:  
\[\left\|\mathbf{U}_{q}[n]-\mathbf{U}_{q}[n-1]\right\|^{2}\leq L_{\max}^{2}, \quad n=2,\ldots,N, \tag{5}\]  
\[\left\|\mathbf{U}_{q}[1]-\mathbf{U}_{q}[I]\right\|^{2}\leq L_{\max}^{2}, \tag{6}\]  
\[\left\|\mathbf{U}_{q}[F]-\mathbf{U}_{q}[N]\right\|^{2}\leq L_{\max}^{2}, \tag{7}\]  
\[\left\|\mathbf{U}_{q}(n+1)-2\mathbf{U}_{q}(n)+\mathbf{U}_{q}(n-1)\right\|_{2} \leq a_{\max}\delta_{t}^{2},\quad n=2,\ldots,N-1. \tag{8}\]  
Accordingly, the instantaneous data transmission rate between the UAV and the communication destination at time slot \(n\) is expressed as  
\[R_{g}(n)=\log_{2}\biggl{(}1+\frac{h_{ug}(n)\,p}{\sigma^{2}}\biggr{)}, \tag{9}\]  
where \(\sigma^{2}\) denotes the noise power of the additive white Gaussian noise (AWGN) at the ground communication destination, and \(p\) denotes the UAV's instantaneous transmit power. Likewise, the instantaneous information leakage rate to the \(k^{th}\) eavesdropper at time slot \(n\) is given by  
\[d_{uk}(n)=\sqrt{\left\|\mathbf{U}_{q}[n]-\mathbf{W}_{k}\right\|^{2}+H^{2}}, \quad\forall k\in\mathcal{K},\,\,\forall n\in\mathcal{N}. \tag{2}\]0  
Consequently, the achievable secrecy rate at the communication destination during the \(n^{th}\) time slot is defined as  
\[d_{uk}(n)=\sqrt{\left\|\mathbf{U}_{q}[n]-\mathbf{W}_{k}\right\|^{2}+H^{2}}, \quad\forall k\in\mathcal{K},\,\,\forall n\in\mathcal{N}. \tag{2}\]1  
where \([x]^{+}\triangleq\max\{0,\;x\}\).
## 6 Conclusions  
This paper has systematically investigated HVAC and HVDC voltage breakdown characteristics of HFO-1336 mzz (E) mixed with CO\({}_{2}\)/N\({}_{2}\) as an environmentally friendly alternative for gas-insulated applications. The mixture ratio and gas pressure influence on the insulation characteristics of the proposed gas mixture are investigated under uniform (plane-plane) and quasi-uniform (sphere-plane) electric fields and compared with the hazardous SF\({}_{6}\) gas. Finite element analysis is performed on HFO-1336 mzz (E)/CO\({}_{2}\) to study the electric field distribution of electric potential. The field utilization factor and maximum field intensity point are determined for uniform and weakly non-uniform electric fields. The FEM results reveal a uniform electric field in plane-plane configuration comparable to experimental results. Experimental results show that under quasi-uniform electric fields, the 30% HFO-1336 mzz (E) at a pressure below 0.2 MPa has relative HVAC and HVDC dielectric strength equivalent to SF\({}_{6}\). Under uniform field configuration at 0.3 MPa, the gas mixture's AC and DC dielectric strength is comparable to SF\({}_{6}\). Furthermore, a synergistic effect of the proposed gas mixture shows favorable results, as synergistic coefficient S values between HFO-1336 mzz (E) and CO\({}_{2}\) are in the range of 0.05-0.75. The GWP potential of the test mixture for all the mixing ratios is calculated, and it is found that the maximum GWP value is 18, which proves the claim of an eco-friendly alternative to SF\({}_{6}\). Additionally, the self-recoverability testing of the 30% HFO-1336 mzz (E) + 70% CO\({}_{2}\) mixture reveals that after 40 consecutive breakdowns shots, only a 5% minimum difference is noted between the first and last breakdown. The 30% HFO-1336 mzz (E)/CO\({}_{2}\) gas mixture at 0.15-0.20 MPa pressure and constant 0.6 mm air gap in relevance to practical MV-GIE reveals superior insulation tolerance and less sensitivity to the electric field brings about its potential engineering applications.  
Conceptualization, R.A.R. and R.A.; methodology, R.A.; software, R.A. and A.J.; validation, R.A.; formal analysis, A.S.A., B.A.-R. and A.J.; investigation, R.A. and R.U.; resources, R.U.; data curation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, R.A., A.J. and R.U.; visualization, B.A.-R. and A.S.A.; supervision, R.A.R.; project administration, A.S.A., A.J. and R.U.; funding acquisition, R.A.R., A.S.A. and B.A.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 15: Self-recoverability of HFO-1336 mzz (E)/CO\({}_{2}\) mixture.
# A Sampling-Based Unfixed Orientation Search Method for Dual Manipulator Cooperative Manufacturing
## 1 Introduction  
### Subsection  
At present, six-degree-of-freedom (6-DOF) industrial manipulators play an increasingly important role in automated manufacturing due to numerous advantages, including the provision of tireless repetitive labor, faster-moving speeds, and a higher accuracy performance [1]. Thus, tasks requiring numerous workers can undoubtedly be accomplished preoperatively by multiple manipulators, meaning that multi-manipulators will not only work side-by-side but as dyads and teams. Analogous to the definition of Human-Robot Interaction (HRI) [2, 3], the case of a dual manipulator manufacturing system can be divided into cooperation and collaboration according to the arrangement of the tasks. For the case of a collaborative manufacturing system, all manipulators together with the executed mechanism constitute a complete multi-degree-of-freedom closed-loop or parallel manufacturing system. Thus, the non-collision manufacturing control strategy under such circumstances can be transformed into the currently mature internal obstacle avoidance strategy [4]. However, for the case of the multiple manipulator cooperative manufacturing system, problems and limitations arise in the small overlapping workspaces that accommodate numerous cooperative manipulators, resulting in collisions if no related countermeasures are put in place. Under this circumstance, a path-planning algorithm that can circumvent theconstraints of the mechanical structure, simplify the planning space, and determine the paths of all manipulators involved in the cooperative system in real-time will be the kernel.
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 2 Materials and Methodology
### Test Procedure  
The experimental program included compressive and splitting tensile tests for the mechanical characterization of the prepared mixtures. A four-point bending test was also used to evaluate the post-cracking behavior of the bamboo fiber mixtures. These were assessed on 50 mm cubes and beams with dimensions of \(50\times 50\times 300\) mm. Compression tests were performed at a loading rate of 55 kN/min, following the ASTM C109 standard [50]. Splitting tests were performed in accordance to ASTM C496 [51], at a loading rate of 1.0 mm/min. Four-point bending tests were performed according to ASTM C1609 [52] on prisms with a span, \(L\), of 150 mm. The load was applied in the 1/3 points at a rate of 0.2 mm/min. The deflection of the beam was measured with two Linear Variable Differential Transducers (LVDTs) at the mid-span (Figure 3).  
With the recorded load-deflection data at hand, the load at the limit of proportionality, F\({}_{\text{max}}\), and the corresponding modulus of rupture (MOR) could be assessed according to the RILEM TC 162-TDF [53] recommendation. According to the RILEM recommendation, the limit of proportionality is equal to the maximum load recorded up to 0.05 mm. The modulus of rupture, corresponding to the maximum force in a four-point bending test, can be calculated by the following expression:  
\[\text{MOR}=\text{F}_{\text{max}}\;l/(b\;h^{2})\;(\text{MPa}) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixture** & **Unit** & **Control** & **Bamboo 1** ** & **Bamboo 1**  \\
**Fiber content** & [Vol \%] & 0 & 4/6/8 & 4/6/8 \\
**Cement: Cem I** & [kg/m\({}^{3}\)] & 547 & 547 & 547 \\
**Fly ash** & [kg/m\({}^{3}\)] & 656 & 656 & 656 \\
**Fine aggregates** & [kg/m\({}^{3}\)] & 541 & 435/382/329 & 435/382/329 \\
**Bamboo fibers** & [kg/m\({}^{3}\)] & - & 32.6/48.9/65.2 & 32.6/48.9/65.2 \\
**Water/binder—ratio** & [-] & 0.30 & 0.30 & 0.30 \\
**Water** & [kg/m\({}^{3}\)] & 361 & 361 & 361 \\
**Superplasticizer** & [w\% of binder] & 0.42 & 0.53/0.61/0.70 & 0.53/0.61/0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Compositions of mortar mixtures.  
Figure 3: Four-point bending test performed according to ASTM C1609 [52].  
where \(b\), \(h\) and \(l\) are the width, height and span of the tested specimens and equal to 50 mm, 50 mm and 150 mm respectively.  
The density as well as the compressive and tensile splitting tests were evaluated on three specimens after 28 days of curing. The notation throughout this study was chosen as XXX-YP, where XXX referred to the bamboo fiber diameter in microns and Y referred to the volumetric fiber content. Therefore, notation 300-6P referred to the mixture containing 6 V% (volume %) of bamboo fibers with a nominal diameter of 300 \(\upmu\)m.
## 2 Related Work
### Optimal Placement of UAVs Using RL Techniques  
In recent decades, the application of machine learning, especially reinforcement learning, has gained significant attention, for solving UAV deployment problems by enabling adaptive and optimized operations in dynamic environments. The proposed approach in [26] used BS-equipped UAVs to enhance cellular network capacity in high-traffic scenarios under 5G networks and to optimize 3D UAV-BS locations, based on user requirements, to maximize spectral efficiency. In [27], a novel framework addressing UAV deployment and movement for QoE optimization was introduced, incorporating genetic algorithm-based cell division, Q-learning-based deployment, and movement strategies to enhance cumulative user satisfaction. These papers concentrated on improving the positions of UAVs in a multi-UAV scenario, with a focus on communications-related issues. Additionally, the UAV-assisted MEC system was explored by investigating the flexible movement of UAVs in [28; 29]. A cooperative multi-agent DRL framework was used to investigate collaborative task offloading in a multi-UAV-multi-EC MEC system, optimizing trajectories, communications, and computation, to reduce delays and energy consumption during execution [28]. A flying MEC architecture that offloaded tasks to UAVs was introduced, aiming to achieve energy efficiency with DRL-based and convex-optimization-based trajectory control techniques [29].
## 4 Experiments and Results
### Results and Analysis
#### 4.4.2 Results of the Comparison Experiment  
To further validate the effectiveness of our method, we conducted comparison experiments using Faster RCNN with resnet50 as backbone, SSD with vgg16 as backbone, YOLOv2 with darknet19 as backbone, YOLOv3 with darknet53 as backbone, YOLOv3-SPP with darknet53 and spatial pyramid pooling (SPP) module as backbone, YOLOv4 with CSPdarknet53 as backbone, and DFPN-YOLO with SGEdarknet53 as backbone to compare the accuracy of the algorithms based on the mAP, and the results on the DIOR datasets are shown in Table 4.  
Classes 1-20 represent the following categories: airplane, airport, baseball field, basketball court, bridge, chimney, dam, expressway service area, expressway toll station, golf field, ground track field, harbor, overpass, ship, stadium, storage tank, tennis court, train station, vehicle, and windmill. Similarly, we performed comparison experiments on the RSOD datasets, with Classes 1-4 representing the oil tank, playground, aircraft, and overpass, respectively. The results are shown in Table 5.  
Figure 11: The detection results of DFPN-YOLO on the DIOR datasets. (**a**) Golf field; (**b**) windmill; (**c**) ship; (**d**) airplane; (**e**) ground track field; (**f**) airport; (**g**) basketball court; and (**h**) dam. Figure 12: The analysis of detection results on the DIOR datasets. (**a**) Detection results of TP and FP; (**b**) detection results of vehicles without labeled. The experimental results show that, based on the YOLOv3, when only the SGE attention module was added, the overall detection performance of the four categories improved due to the enhanced feature extraction ability of the backbone. After the fourth detection feature layer was added for small object detection, the mAP of category three, i.e., small objects in the aircraft category, significantly increased from 88.4% to 91.4%. After the Dense-FPN structure was added, the overall detection accuracy of the four object categories of objects improved, which shows that the Dense-FPN structure has a strong feature fusion capability for objects of different scales. In addition, compared with the original YOLOv3 without improvement, the mAP improved from 83.9% to 92% after adding the SGE module, the fourth detection feature layer, and the Dense-FPN structure, demonstrating the effectiveness of our method. On the DIOR datasets, our method has the highest mAP from the original 62.41% of YOLOv3 to 69.33% while outperforming other advanced methods, even higher than the 66.73% of YOLOV4, and we have the best detection performance in most categories. Of the RSDO datasets, our method is also the most accurate. Compared with 83.9% of YOLOv3, DFPN-YOLO reaches 92%, which is even 0.7% higher than YOLOv4. Furthermore, in the two categories of oil tank and playground, our detection performance is much higher than other methods, with AP reaching nearly 98%.
## 2 Materials and Methods
### Methods  
To assess the impact of time of day, all participants were scanned three times throughout the course of their normal working day. Firstly, between 08:00-08:30 h, secondly between 12:30-13:00 h and lastly between 17:00-17:30 h. To assess the impact of leg dominance, SWE measures were taken on both the dominant and non-dominant legs. Participants were asked to maintain any daily walking activity they deemed normal but refrain from any other exercise for 48 h before testing and during the testing period.  
To assess the impact of an acute bout of running, participants were randomly placed into the intervention group of running (RUN) or the control (CONT) group. If assigned to the CONT group, participants were asked to remain seated in the examination room for a period of at least 30 min, and no more than 40 min during their testing session. The participants were asked to remain seated keeping their legs still during this period and not move around, the experimenter was always present to ensure this occurred. Following assignment to each intervention group, the measures outlined below were taken from each participant immediately before and immediately after their sitting or running intervention. Subsequent follow up measures were also taken at 6 h, 24 h, 48 h and 72 h post intervention. In the RUN group, participants were given the possibility to warm up on the treadmill for no more than 5 min. Following the warm up, they were asked to keep the incline on the treadmill at 1% and increase the speed of the treadmill to a pace they felt comfortable with, and to run for a period of 30 min. A Rating of Perceived Exertion (RPE) scale [43] was placed in the direct eye line of the participants and they were asked to maintain an RPE of at least 13 (representing an intensity of somewhat hard), but below 15 (hard). The participants were asked every 5 min to rate their current RPE value and were encouraged by the experimenter to maintain an RPE of between 13 and 15 if it was outside of these values. The participants were able to alter their speed during their 30-min run to maintain RPE between these boundaries. Following 30 min of running, participants could complete a cool down at a walking speed of their choice for a period of no more than 5 min.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.6 Server Impersonation Attacks  
In these attacks, for \(\mathcal{A}\) to convincingly mimic the aggregation server \(S_{j}\), it is imperative to accurately process \(\{K_{3},M_{3}\}\). The derivation of \(M_{3}\) depends on \(\left\{R_{i},V_{i}^{*},ID_{i}^{*}\right\}\), facilitated by a secure hashing algorithm, necessitating \(\mathcal{A}\)'s comprehensive understanding or viable approximation of these pivotal parameters within a realistic timeframe. Furthermore, \(\mathcal{A}\) is required to access key data \(\{x,r_{i},ID_{i}^{*}\}\). The endeavor to decipher these confidential parameters presents a substantial computational impediment to \(\mathcal{A}\). Thus, due to the infeasibility of generating precise messages by \(\mathcal{A}\), the proposed protocol effectively thwarts impersonation attempts targeting the aggregated server.