esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This could be a result of the type of signals being analysed, which, in this study, are 3.8 ms-long impulsive signals as opposed to longer speech samples. Compared to more recent NN-based algorithms [15], the proposed algorithm under performs compared to reported findings of 83.8% to 100% accuracy across different test scenarios. However, their analyses only considered signals in the frontal hemisphere around the head and considered longer audio samples for the localisation problem. Comparing the proposed method to that presented in [12] shows that the proposed method achieves lower relative errors for the direct sound and reflections measured with both binaural dummy head microphones, compared to the 24.0% reported for real test sources using a multi-layered perceptron in [12]. The average errors reported in this paper are lower than that presented in [4], which reported average errors in the range of 28.7\({}^{\circ}\) and 54.4\({}^{\circ}\) when analysing the components of measured BRIRs. However, the results presented in [4] considered reflections with reflection orders greater than first, and therefore, further analyses of the proposed NNs' performance with full BRIRs is required for more direct comparisons to be made. Future work will focus on improving the accuracy of the model for azimuth DoA estimation, using measured binaural room impulse responses to assess the accuracy of the neural network as reflection order and propagation path distance increases. The proposed model will then be extended on to consider estimation of elevation DoA, providing complete directional analysis of the binaural room impulse responses; the aim being for the final method to be integrated within a geometry inference and reflection backpropagation algorithm, allowing for in-depth analysis of the acoustics of a room. However, this will require higher accuracy in the DoA predictions for the reflections. Further avenues of research to improve the robustness of the algorithm could include: the use of noise reduction techniques to ideally reduce the ambiguity in the binaural cues, increasing the size of the training database used to train the neural network, investigation into using different representations of interaural cues and how they are extracted from the signals, using reflections to train the NN in addition to the HRIRs or the use of a different machine learning classifier.
###### Abstract
From the above studies, it concludes that the traditional feedforward neural networks use the gradient descent method, which mainly has the following shortcomings and deficiencies: (1) the training speed is slow because the gradient descent method requires multiple iterations to achieve the purpose of correcting weights and thresholds; (2) it is easy to fall into a local minimum point and cannot promise reaching a global minimum; and (3) the choice of learning rate is sensitive, which has a great impact on the performance of the neural network, and a suitable learning rate must be chosen to obtain an ideal network [27; 28; 29]. To circumvent the above-mentioned problems, novel training algorithms are required, which aim at improving the performance of artificial neural networks [30; 31; 32]. In this paper, an improved extreme learning machine (ELM) is proposed. Compared with the common machine-learning methods, the ELM has the advantages of fast learning speed and good generalization performance [33; 34; 35]. In addition, the ELM has fewer tuning parameters and completes the training task without repeated optimization steps as it bypasses the iterative process [36; 37; 38]. Moreover, the ELM is a single-layer feedforward neural network and is popular for accurate classification or regression [39; 40]. In the original ELM method, the weights between the input and hidden layers are randomly generated, which degrades its performance [41; 40; 42]. To solve this problem, the weights between the input and hidden layers are determined using a genetic algorithm (GA). The genetic algorithm, an optimization strategy based on natural selection and evolution, is well-suited to solve a variety of optimization problems [43]. Based on selection, mutation, and crossover operations, the GA can better explore the solution space. Additionally, techniques such as simulated annealing or tabu search can be also used to prevent the search from getting stuck in local optima [44; 45]. Moreover, flight parameter recorders normally record tens or even hundreds of flight parameter data, but not all flight parameter data are suitable for building parameter-based flight load prediction models, which increases the complexity and time of neural networks [46]. Therefore, it is important to perform a sensitivity analysis of the flight parameter, which can help to determine the crucial flight parameters for flight load prediction. Moreover, for the proposed improved ELM, only the neuron number in the hidden layer needs to be determined, and cross-validation is used to evaluate the neuron number of the hidden layer. With the aim of solving the above problems, the structure of this paper is as follows. First, an improved extreme learning machine method based on a genetic algorithm is presented in Section 2. In Section 3, a novel flight parameter sensitivity analysis method and a method for determining the neuron number of the hidden layer are proposed. In Section 4, the above-proposed methods are demonstrated to be effective and efficient. Finally, some important conclusions are drawn in Section 5.
## 2 Materials and Methods
### Instruments  
#### 2.3.1 Sample Selection and Characterization  
A questionnaire, developed in accordance with the consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable [55], was applied to assess the inclusion and exclusion criteria, as well as the sociodemographic and clinical profiles of the participants. Body mass (kg) and height (m) were measured using a seca(r) 760 scale (seca--Medical Scales and Measuring Systems(r), Birmingham, UK), with a precision of 0.1 kg, and a seca(r) 222 stadiometer (seca--Medical Scales and Measuring Systems(r), Birmingham, UK), with a 1 mm accuracy.  
The MMSE scale, adapted for the Portuguese population [48], was used to assess the cognitive level of the participants in the study [48; 49]. A cognitive deficit is identified when the score is \(\leq\)15 for individuals who are illiterate, \(\leq\)22 for those with 1 to 11 years of education, and \(\leq\)27 for individuals with more than 11 years of education [48; 49].  
The physical activity level was assessed using the short form of the International Physical Activity Questionnaire (IPAQ-SF) for the Portuguese population [56], a widely employed self-report instrument designed to evaluate physical activity over the previous seven days [57]. To interpret the obtained results, the Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)--Short Form were followed (IPAQ, 2005) [52].  
The Fugl-Meyer assessment of sensorimotor recovery after stroke--lower extremity (FMA-LE), adapted and validated for the Portuguese population [58], was used to evaluate sensorimotor impairment in the stroke group [55]. Individuals scoring below 34 are generally considered to have sensorimotor impairment of the lower limb [59], while a score below 21 indicates more severe motor impairment, accompanied by significant limitations in mobility and functional abilities [60].  
Postural control was clinically assessed using the European Portuguese Mini-BETest [61], a reliable and valid tool proposed to measure dynamic postural stability consisting of fourteen items, divided into four sections: anticipatory postural adjustments, reactive postural control, sensory orientation, and dynamic gait, with a total score of 28 points, representing optimal balance performance [62].
## 6 Conclusions  
Owing to the exceptional achievements of U-Net in medical image processing, it has gained widespread adoption in liver and liver tumor segmentation tasks. Nonetheless, its straightforward network architecture hinders the comprehensive utilization of valuable features, leading to reduced feature mobility within the network. Moreover, the presence of a semantic gap impedes the effective fusion of shallow and deep features, consequently impacting the segmentation performance.  
To address these issues, we proposed MDAU-Net, a novel segmentation network. MDAU-Net introduces a double-flow linear pooling enhancement unit within the jump connection segment, effectively narrowing the semantic divide and facilitating the fusion of shallow and deep features at each layer. Additionally, it incorporates a cascaded adaptive feature extraction unit as a bottleneck layer, which combines attention mechanisms with dense connectivity to enhance the network's capacity for exploring deep semantic information and improving feature mobility. Furthermore, a cross-level information interaction mechanism, based on bidirectional residuals, was introduced in the jump connection to mitigate the problem of a priori knowledge loss during training. Finally, we redesigned the encoder to incorporate the residual structure, not only enhancing the network's ability to retain and extract original features but also mitigating the gradient vanishing problem. Through experiments conducted on the LiTS and Sliver07 datasets, we confirmed that MDAU-Net consistently delivered outstanding performance across various datasets. It excelled not only in accurately segmenting the target region but also in handling intricate details such as edges with remarkable precision, demonstrating strong generalization capabilities.  
[PERSON], [PERSON], [PERSON] and [PERSON] designed the experiments. [PERSON] conducted the experiments, interpreted the data, and drafted the manuscript. [PERSON] and [PERSON] provided professional suggestions. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Confusion matrix from liver tumor segmentation in Section 5.2 on LiTS.  
**Funding:** This research was funded by the Natural Science Foundation of Ningxia (Nos. 2023 AAC03264, 2022 AAC03268); Basic Scientific Research in the Central Universities of North Minzu University (Nos. 2021 KJCX09, FWNX21); Graduate Innovation Project of North Minzu University (YCX23148), and Image and Intelligent Information Processing Innovation Team of the National Ethnic Affairs Commission.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets analyzed during the current study are available in the LiTS and Sliver07 repositories: [[https://competitions.codalab.org](https://competitions.codalab.org)]([https://competitions.codalab.org](https://competitions.codalab.org)); www.sliver07.org (accessed on 13 June 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 5 Why Nonlinear Control Strategies
### History of Nonlinear Control in UAV Development
#### 5.2.6 Key Concepts and Techniques  
1. **Backstepping Control:** A recursive method that transforms the control problem into a series of simpler subsystems, allowing for systematic control design while addressing nonlinear dynamics [100].
2. **Sliding-Mode Control:** A technique that aims to drive the system state onto a predefined sliding surface, ensuring robustness against uncertainties and disturbances [101].
3. **Lyapunov-Based Control:** Lyapunov stability theory is widely used to design control laws that ensure stability and convergence to a desired state while considering system dynamics and nonlinearities [102].
4. **Adaptive Control:** Adaptive control techniques adjust control parameters in real-time to account for uncertainties and variations in UAV dynamics, making the control system more adaptive and responsive [103].
5. **Model Predictive Control (MPC):** MPC involves optimizing a control trajectory over a finite time horizon while considering constraints and system dynamics. It is particularly useful for UAVs operating in constrained and dynamic environments [104].
6. **Hybrid Control:** Hybrid control systems combine different control approaches to leverage the strengths of each technique. For instance, combining nonlinear control with machine-learning-based controllers can enhance UAV performance [105].
7. **Nonlinear Observers:** These are used to estimate unmeasured states of the UAV, which is crucial for feedback control. Observers are designed to handle nonlinearities and disturbances [46].
8. **Neural Networks and Machine Learning:** In recent years, machine learning techniques like neural networks have been integrated into UAV control systems for tasks like state estimation, control policy optimization, and adaptive control in nonlinear systems [106].  
The history of nonlinear control in UAV development reflects a continuous effort to improve the capabilities, autonomy, and robustness of UAVs, enabling them to perform complex missions in diverse environments. Ongoing research continues to push the boundaries of control theory and its application in UAVs.
## 5 Discussion
### Strategies of the PLS-MARS Method  
The performance of the PLS-MARS method was analyzed using the simulated data set and real application. In addition, the effectiveness and accuracy of PLS-MARS were compared with those of several commonly used prediction methods (i.e., MLR, PLS, SVM and PLS-GWR methods) in literature. The results in the simulated application implied that the PLS, SVM, and PLS-MARS methods could efficiently estimate the relationship between numerous explanatory variables and their responses. However, if the relationship exhibited significantly non-linear features and spatial heterogeneity, such as the relation of the SOM content with VNIR spectra in the study area, then the PLS-MARS method produced more accurate results than the rest of the prediction methods tested. This superiority is generally due to the consideration of the natural local difference of data sets, and the construction of a local non-linear optimization model in the PLS-MARS method rather than the global non-linear optimization characteristics of the other prediction methods. The adaptive strategy of the PLS-MARS method also facilitated the automatic estimation of natural relationships and supported their promotion. Thus, the PLS-MARS method has immense potential for estimating the complex nonlinear relationships of geographical data with numerous explanatory variables, and the PLS-MARS method calibrated by the samples obtained utilizing the MVARC-R-KS method shows great potential in estimating significantly nonlinear relationships in spatial and non-spatial domains.
## 4 Discussion
### Implications for Ecosystem Management  
Sustainable development required for conserving Nature's Contributions to People and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate, and development goals [46]. The priority conservation areas identified in our study are crucial for conservation planning and must be integrated into current ecological management strategies. This integration is essential as 70% of China's nature reserves, which are intricately linked to local livelihoods, are located on the Qinghai-Tibet Plateau (QTP).  
The challenge is evident in areas like Changtang, Mount Qomolangma, and Selincuo, where overgrazing by local residents around the reserves has led to a negative cumulative impact on ecological conservation areas [47]. This degradation often results from conflicts between conservation measures and local community practices, which can antagonize the relationship between residents and managers [28]. Conversely, initiatives such as payments for ecosystem services and ecological migration have proven successful in regions like Sanjiangyuan and Hoh Xil National Nature Reserve. These programs not only achieve conservation targets but also boost local incomes and enhance human well-being.  
Despite these successes, both past studies and our current research highlight a persistent gap in enhancing ecological benefits alongside improving human well-being [10]. The existing ecological management frameworks struggle to balance nature's contributions with human demands [39]. Therefore, it is imperative to leverage our understanding of the importance and vulnerability of NCPs to implement zonal management that can guide policy decisions effectively. By tailoring conservation strategies to the specific needs and vulnerabilities of each area, we can mitigate conflicts and foster a more sustainable coexistence between human activities and ecological preservation. This approach will not only safeguard biodiversity but also align development goals with ecological sustainability on the QTP and beyond.
## 3 Results  
The proposed method was applied to two real dwellings located in Valencia (Spain). The hourly demand curves were recorded during 2019 (avoiding the pandemic situation of 2020, as mentioned above). Likewise, a solar photovoltaic generation curve was registered in the GEDERLAB, a real facility next to the dwellings in which the generation and management of various renewable energy sources is studied. The recorded data was randomized swapping nearby days to develop the SMC simulation, so that the demand and generation curves maintained their seasonal patterns, but irregular situations such as a special event in a dwelling or a cloudy day did not match in each simulation. Figure 5 shows the typical patterns of demand for both dwellings and generation in each season of the year in per unit values.  
Using the rules explained from Equation (1) to Equation (14), the SMC simulation was carried out. In each simulation, the results of all scenarios were evaluated. The simulation was repeated 100 times, checking that the average indices stabilized in order to analyse the results and draw conclusions.  
Figure 4: Stages of the proposed method.  
To simulate the different scenarios, we started from the optimal design of both dwellings shown in Table 1, obtained after analysing the demand and generation of each dwelling using the method described in [38].
## 2 Related Work
### JPEG-1 Image Coding Standard  
JPEG stands for Joint Photographic Experts Group and is a joint WG of ISO and the International Electrotechnical Commission (IEC). The JPEG-1 celebrated its twenty-fifth anniversary in 2017 and is still dominant in the market (2017). The JPEG-1 image coding standard is the most prominent codec and de facto image format for lossy compressed photographic imagery in digital cameras and on the World Wide Web. Many researchers agree that JPEG-1 leads the art and science of reducing the number of bits required to describe an image. The conventional compression process of JPEG-1 is as follows (2018; 2019):  
First, the input image's color space, the RGB color space, is converted into luminance (Y) and chrominance (Cb and Cr). Chrominance channels are subsampled. This conversion aims to achieve the maximum energy of the image into the luminance channel, as human eyes are more sensitive to luminance than chrominance. The defined sampling factors are 4:4:4, 4:2:2 horizontal, 4:2:2 vertical, and 4:2:0 for the luminance and chrominance channels. The most used subsampling factor is 4:2:0, in which the chrominance channel resolution is reduced to 1/4. That is, for four luminance samples, one chrominance sample is maintained, and the remaining chrominance samples are removed. Discrete cosine transform (DCT) is applied to the partitioned 8 \(\times\) 8 blocks of each channel, and transform coefficients are calculated. Each coefficient of 8 \(\times\) 8 blocks of each channel is quantized. Data reduction and consequent data loss occur in this step. Luminance and chrominance channels are quantized separately, with different quantization factors deciding the tradeoff between the data reduction rate and the consequent degree of data loss. The run-length and Huffman entropy coding scheme or arithmetic encoding scheme are the final steps after quantization. In this step, the quantized DCT coefficients are encoded losslessly.  
In the decoding process, these steps are performed in reverse order. The encoded data are first decoded into 8 \(\times\) 8 blocks of quantized DCT coefficients. Dequantization and inverse 2D DCT are followed, and upsampling of Cb and Cr channels is performed based on the sampling factors. Finally, a decoded image is obtained by recovering the color space from YCbCr to the original color space.
## 6 Discussion
### Discussion for Scenario 2: Fetch  
In the fetch scenario, the cobot retrieves tools or materials efficiently, guided by robust mapping and object identification systems. With the following implementation hardware and software tools, cobots can rapidly locate, fetch, and deliver tools or parts, minimizing human effort.  
Hardware:  
The fetch scenario is suited for flexible cobots like the ABB YuMi or KUKA LBR iwa, ideal for environments with spatial constraints. The vision system must be able to identify objects in cluttered storage. Implementation is best in high-resolution 2D or 3D cameras, like the Keyence CV-X or Intel RealSense. Fetch requires a gripper, designed for secure object handling. This is best achieved by multi-grip robotic hands, such as the Schunk Co-act EGP-C gripper.  
Software:  
Fetch implicitly assumes autonomous navigation and mapping: this could be achieved by simultaneous localization and mapping (SLAM) algorithms for dynamic workstation navigation. Implementing the fetch task may require using Python-based frameworks to integrate workstation maps with cobot actions, automating tool fetch routines. Finally, in case something goes wrong, or if further input is needed, real-time communication becomes important and could be achieved by NLP tools such as Wit.ai or Rasa.
# Women's Singles Tennis Match Analysis and Probability of Winning a Point
## 4 Discussion
### Practical Implications Related to the Match Strategy of the Returning Player  
According to the data from this study, the receiver should focus on several key aspects. Firstly, they should practice anticipating the opponent's serve and improving reaction speed, utilising video analysis to study the opponent's serving patterns. Returns should be deep and angled to force the server to play from uncomfortable positions. Additionally, extending the rally beyond four shots increases the probability that the opponent will make unforced errors; thus, it is beneficial to direct the ball to areas where the opponent is more likely to falter, such as the baseline and the sidelines, rather than aiming for winners. Diversifying return strategies by using a variety of spins, such as flat, topspin, and slice, helps to destabilise the server and force them to hit from unfavourable positions.  
Coaching the return of serve to the opponent's feet or body is a highly effective strategy. This tactic extends a rally, as the server will find it difficult to hit a winner, thus increasing the chances of winning the point. Placing the return close to the opponent's feet reduces their ability to attack effectively and forces them into a more defensive position. Additionally, prolonging a rally increases the likelihood of unforced errors by the server, who is under constant pressure to maintain the initiative.  
In terms of general training recommendations, it is essential to simulate match situations, focusing on different scenarios of short and long points. Video analysis is a valuable tool for identifying both your own and your opponent's patterns of play, allowing for strategic adjustments. Additionally, endurance training is crucial for maintaining high performance during long rallies and extended matches, while quick recovery techniques between points and games help sustain physical and mental freshness. Incorporating stress management techniques into training can enhance concentration and calmness during matches, and establishing consistent routines before and during points helps maintain focus and reduce anxiety.