esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Training Signal Optimization  
In this section, we optimize the training signal by solving problem (14). Specifically, a closed-form structure of the worst-case MSE minimizing training signal is initially obtained by deriving the structure of the optimal matrix solution for the problem (14). From the structure, the original problem (14) involving the complex-valued matrices becomes a simple power allocation problem involving the real-valued scalar variables only. Thereafter, optimal and suboptimal power allocation schemes are proposed. The optimal power allocation is determined numerically and the suboptimal power allocation is obtained in a closed-form with low complexity.
# Research and Development of Inland Green and Smart Ship Technologies in China
## 4 Inland Green Smart Test Ship
### Multi-Level Test Verification  
The test ship is a smart organism composed of the ship's hull and distributed system modules. First, a closed-waterway test was conducted on the hull to meet various navigation requirements of inland ships. Independent product tests were then conducted on each smart module system to verify that the specifications met the boarding requirement, and that each independent module matched the reserved interface standards of the ship's hull. Next, the major modules were installed on the test ship platform to form a complete system, and the system was debugged to ensure that all subsystems met independent operation specifications and the coordinated operation specifications. Further, the stability and reliability of the integrated operation of each module were verified. Finally, the entire smart ship system was tested in an all-round and multi-scale manner using the dynamic test system to assure that each module met independent test specifications as well as the overall system operation specifications.  
Through the digital twin, a technology that combines virtual and real systems, a virtual navigation environment was built to test the real ship systems under various scenarios in different waterways. A closed waterway test field was established and evaluation of the ship's smart systems was conducted to verify the stability of each system and various product performances when operating in a simple waterway. Multi-level navigation tests of the smart ship were conducted on specific routes, such as the Han River and the Yangtze River, including actual operation tests on various smart system modules. Tests completed included the smart perception of the navigation environment for specific routes, the autonomous evasion of multi-ship counterparts, the smart host combined propulsion, the autonomous planning of navigation trajectories, and the smart operation of engine room deck machinery. The \"virtual navigation environment\" is a ship navigation environment model formed by multi-source perception of the ship's surrounding environment information, image stitching, enhancement, and fusion.
## 5 Joint Iterative Decoding for the Proposed Scheme  
The joint iterative SISO turbo decoding for the proposed scheme is depicted in the Figure 6. The design of SISO de-mapper for iterative turbo codes has been reported in [18]. The joint decoder is primarily composed of two different SISO decoders, one for each recursive systematic convolutional encoder. Each SISO decoder accepts three inputs and delivers one output. The input of the SISO decoder consists of LLRs of information bit, parity bits and LLRs of a priori information bits and it outputs an extrinsic information \(E_{i1}\) and \(E_{i1p}\) for systematic and parity received bits, respectively.  
In first time slot, the soft de-mapper produces LLRs \(\lambda_{ip}^{1}\), these LLRs are soft-demodulated sequence of bits that corresponds to information and parity sequence of bits, after OFDM and Alamouti space time block encoding, which were initially originated from source node by RSC-1 encoder. The de-multiplexer divides the LLRs \(\lambda_{ip}^{1}\) into two distinct streams such as sequence of information bit LLRs \(\lambda_{ip}^{1}\) and sequence of parity bit LLRs \(\lambda_{i1p}\). These LLRs are delivered to SISO decoder 1 as its both inputs that generates extrinsic information sequence of bits \(E_{i1}\). The extrinsic information sequence \(E_{i1}\) of systematic bits is passed to CMI. This interleaved extrinsic information bits \(\pi(E_{i1})\) is given as a-priori input to SISO-decoder-2.  
In second time slot, the second soft de-mapper outputs the soft LLRs \(\lambda_{ip}^{2}\). These LLRs are soft-bits that correspond to parity bits of RSC-2 encoder, which were transmitted from relay node. SISO-decoder-2 produces extrinsic information such as \(\overline{E}_{i2}\) and \(\overline{E}_{i2p}\) that corresponds to sequence of information and parity bits, respectively. The extrinsic information \(\overline{E}_{i2}\) of information bits is de-interleaved and is given back to SISO-decoder-1 while the extrinsic information of parity bits \(\overline{E}_{i2p}\) is ignored. The extrinsic information of corresponding information sequence of bits are interchanged between the constituent SISO-turbo decoders for a desired number of iterations. Finally, the extrinsic information \(\overline{E}_{i1}\) and \(\overline{E}_{i2}\) are added in summer block and is provided to the slicer through a de-interleaver. The slicer will perform hard decoding operation to estimate the information sequence of bits \(\hat{i_{1}}\) that completes the transmission process.
## 4 Discussion
### Discussion on the Risk of Overfitting the PSO-LSTM Prediction Model  
The PSO-LSTM (Particle Swarm Optimization-Long Short-Term Memory) model is a deep learning model that combines particle swarm optimization (PSO) and a long short-term memory network (LSTM). The goal of this model is to improve the performance of the model on the task by utilizing PSO to adjust the parameters of the LSTM. However, like other deep learning models [35; 36; 37], PSO-LSTM may also face the risk of overfitting, especially in tasks such as settlement displacement prediction. The following are the overfitting risks you may face when using the PSO-LSTM model: (1) If there is noise in the training data, the PSO-LSTM model may strive to adapt to the noise, resulting in overfitting to the training data. In this case, the model may perform well on the training set but poorly on unseen data. (2) The number of parameters in the PSO-LSTM model may be relatively large, especially when using PSO for parameter adjustment. If the model's capacity is too large, it may learn details from the training data and fail to generalize to new data. (3) If the amount of available training data are insufficient, the model may rely too much on individual samples in the training set and fail to capture the distribution of the overall data. (4) During the training process, if the model goes through too many iterations, it may perform better and better on the training set, but there is no corresponding improvement in performance on the validation set or test set. This is a typical example of overfitting. Performance. (5) If too many or inappropriate features are selected during the modeling process, the model may overfit these features instead of the true data pattern. In order to mitigate the risk of overfitting the PSO-LSTM model, the following strategies can be adopted: (1) This article uses the regularization technology Dropout on the LSTM layer to reduce overfitting. (2) Use cross-validation to evaluate the performance of the model to ensure its generalization ability on different data sets. (3) Stop training when the model's performance on the validation set no longer improves to prevent overfitting. (4) Increasing the amount of training data or introducing some changes through some data enhancement techniques can help improve the generalization ability of the model. (5) Carefully select features relevant to the task and avoid using too many irrelevant features. When establishing and adjusting the PSO-LSTM model, this article comprehensively considers the above factors to help the model reduce the risk of overfitting and improve its robustness and generalization ability.
## 3 Illustrative Example
### State Space Representations for Reference and True Systems  
In order to implement the proposed control scheme, an initial model plant \((A_{m})\) must exist. Allow the model system, as defined in Equation (3), to have the following properties:  
\[A_{m}=\begin{bmatrix}-7&2&4\\ -2&-1&2\\ -2&2&-1\end{bmatrix};B=\begin{bmatrix}0&1\\.7&0\\ 2&0\end{bmatrix};C=\begin{bmatrix}0.5&0&1\\ 1.2&0&.25\end{bmatrix};x(0)=0. \tag{72}\]  
For the control approach to be viable, as defined in Theorem 1, allow \(A\in\text{sp}\{A_{m},BL_{*}C\}\
i A=A_{m}+BL_{*}C\). For this example, allow the fixed correction matrix \((L_{*})\) to follow:  
\[L_{*}=\begin{bmatrix}L_{11}&L_{12}\\ L_{21}&L_{22}\end{bmatrix}=\begin{bmatrix}-3&4\\ -1&-5\end{bmatrix}. \tag{73}\]  
Therefore, the true system, as defined in Equation (2), is written as follows:  
\[A=A_{m}+BL_{*}C=\begin{bmatrix}-13&2&1.75\\ 0.31&-1&0.6\\ 4.6&2&-5\end{bmatrix};B=\begin{bmatrix}0&1\\.7&0\\ 2&0\end{bmatrix};C=\begin{bmatrix}0.5&0&1\\ 1.2&0&.25\end{bmatrix};x(0)=0. \tag{74}\]  
Allow the stochasticities to be bounded such _supremum_ of \(\|v_{x}\|=0.25\). Recall that internal interactions and constitutive constants of the true system's plant \((A)\) are unknown, but an initial model exists \((A_{m})\) such that plant dimensions correspond. Following the input and output matrices \(\{B,C\}\) are known.  
Giving both model and true systems, defined by proprieties in Equations (72) and (74), respectively, a unit step input, results in discrepancies in steady-state output responses, shown in Figure 2. The variation in the output response becomes particularly evident when comparing the eigenvalues of the model and the true plant:  
\[\begin{split}\sigma\{A_{m}\}=\{-1,-3,-5\}\\ \sigma\{A\}\approx\{-0.5,-4.6,-14.4\}.\end{split} \tag{75}\]
# A Dual-Branch Speech Enhancement Model with Harmonic Repair
## 1 Introduction
In the work of [14], a dedicated path encoder-decoder is designed to restore phase information and generate the phase spectrum for predicting speech. Experimental results have shown that the neural network's receptive field significantly affects the efficiency of model parameter utilization. Therefore, expanding the model's receptive field to be more sensitive to contextual information can achieve better phase understanding. Additionally, processing amplitude and phase spectra information as separate branches in neural networks can better utilize phase information in speech signals, offering better interpretability. Therefore, in this paper, we propose the harmonic repair large frame enhancement model, HRLF-Net, which is a dual-branch speech enhancement model designed with specialized modules to predict the harmonic distribution of speech. In the real-part branch of the network, we utilize fast Fourier convolutional operators instead of traditional 2D convolutions for amplitude spectrum repair, which effectively expands the model's receptive field and significantly improves the performance of speech harmonics. An architecture with dilated DenseNet and deconvolution blocks is deployed in the imaginary branch to fully utilize speech phase information while preserving the temporal characteristics of the speech signal, making the enhanced speech more accurately reflect the dynamic changes of the original speech. HRLF-Net is tested on two public datasets, VoiceBank + DEMAND [15] and DNS Challenge 2020 [16]. Experimental results show that it outperforms most existing models and achieves state-of-the-art results in terms of Perceptual Evaluation of Speech Quality (PESQ).
### Research Contributions  
The main contributions of the paper are as follows:  
Figure 1: Basic human central nervous system monitoring process with a CPS.  
Efficient data balancing has been maintained in a CPS using three exciting processes: a node advertisement, a node selection and recruitment, and an optimal distance determination with mid-point. These modules enable the physical domain to efficiently maintain a data balance when collecting data from the human central nervous system. Furthermore, using EDB-CPS, hop-to-hope delay is reduced to obtain better throughput.
* An optimal distance determination with mid-point algorithm is introduced to allow the sensor node to identify the nearest actuators to share data efficiently and avoid any potential data loss or data delivery.
# Temperature Estimation during Pulsed Laser Sintering of Silver Nanoparticles
## 2 Experimental Details  
In this study, a commercially available silver (Ag) nanoparticle (NPs) ink (DGP 40 LT-15C, Advanced Nano Products ANP Inc., Sejong, Korea) is used. The ink contains approximately 34 wt% of Ag NPs with an average of 50 nm particle size in triethylene glycol monomethyl ether (TGME). The viscosity and surface tension of the ink was known to vary from 10 to 17 cPs and 35 to 38 mNm\({}^{-1}\), respectively according to the information provided by the supplier. The NPs ink was inkjet-printed (DMP-2831, Dimatix, Santa Clara, CA, USA) onto glass substrates (Eagle XG 2000, Samsung-Corning, Glendale, AZ, USA) and dried at 60 \({}^{\circ}\)C for 20 min. Two identical square pads were printed on each side of the printed line for precise line resistivity measurement. The pads were baked at 250 \({}^{\circ}\)C for 30 min prior to printing of the Ag NPs line to minimize the pad resistivity and reduce errors in the resistivity measurement.  
The prepared sample was mounted on an \(X\)-\(Y\) translational stage (SGSP20-35, Sigma Koki Co. Ltd., Sumida-ku, Tokyo, Japan). The translational stage could operate up to a maximum scanning speed of 4000 \(\upmu\)m/s. An acousto-optic modulator (AOM) connected to a fixed frequency driver (AA Optoelectronics Laboratory version, [PERSON], Orsay, France) was used to modulate an Nd: YAG continuous wave laser beam (Coherent Verdi V5, 532 nm, Coherent Inc., Santa Clara, CA, USA) into the desired pulsed laser operation. Scanning speeds of the modulated laser were varied at 14, 27, 81, 135, and 189 \(\upmu\)m/s. During laser scanning the beams overlapped at 95%, 90%, 70%, 50%, and 30%, with above mentioned scanning speeds of 14, 27, 81, 135, and 189 \(\upmu\)m/s, respectively. The Bragg angle of the AOM was adjusted at 3\({}^{\circ}\) for maximized laser power. The laser power was maintained at 276 mW from the AOM outlet. The repetition rate and the pulse duration were controlled via an arbitrary waveform generator (Agilent 33250A, Agilent Technologies, Santa Clara, CA, USA). Among various parameters, 100 ms pulse duration and 1 Hz repetition rate were especially adopted for the numerical calculations. The schematic of the experimental setup along with the printed ink line is shown in Figure 1. A convex lens was positioned in front of the AOM at a focal length of 1000 mm from the sample. The beam diameter at 1000 mm focal length was approximately 270.9 \(\upmu\)m (at \(1/e^{2}\) of Gaussian distribution), which was designed to cover the total width of the printed Ag NPs line (135 \(\upmu\)m). Approximately 50% of the Gaussian beam profile covered the entire width of the printed line as illustrated in Figure S1 (Supplementary Materials). This value was equal to the full width at half maximum (50%) of the Gaussian beam profile allowing the intense portion of the Gaussian beam to irradiate on the surface of the Ag NPs line. Finally, the power of the pulsating beam was measured with a laser power meter (FieldMax11-TO, Coherent Inc., Santa Clara, CA, USA).
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 5 Conclusions  
Rate adaptation algorithms help wireless networks achieve better performance by selecting the most suitable MCS, depending on the channel state. These algorithms are rarely specified in the wireless standards and they are left for the constructors to implement. In this paper, we have presented several rate adaptation algorithms and grouped them into different categories according to the way they function. We also provided an analysis for these algorithms highlighting their functionalities and drawbacks. We implemented algorithms of different categories in the NS-3 simulator and performed a comparison study for Ad hoc and infrastructure modes.  
Our study consisted of evaluating the impact of interference and mobility on RAAs in different scenarios. We analyzed several metrics, such as Throughput, Frame Loss Ratio, and selected MCS. The simulation results show that using metrics that give a good indication of the channel conditions such as SINR and reacting instantaneously, which is the case of the Ideal RAA, lead to selecting the rate which provides the best achievable throughput. Otherwise, relying on frame loss ratio or making rate decisions without distinguishing the cause of frame losses lead to a slow process to reach the best rate.  
Analysis of simulation results shows that we need to be able to estimate the channel state to improve the delivery success of the next transmission in order to have a robust RAA. This is achieved by selecting an MCS that maximizes the throughput under the current channel state. In addition, having a process that predicts future channel state may help preventing frame losses.  
A lot of research has been done on rate adaptation algorithms addressing different scenarios and environments, relying on a variety of metrics, only few RAAs have been evaluated on real devices. Hence, the field is still open to many possible improvements. In our future works, we will work on the enhancements that we proposed in Section 4, namely extending Ideal RAA by adding implicit metrics for better decision making. We will also study the impact of routing algorithms on the overall performance of the network in Ad hoc scenarios. In this paper, we focused on dense deployment and high mobility in infrastructure and ad hoc modes; in our future works, we will also study the impact of mobility scenarios and application profiles in terms of data rate and packet size on the performance of RAAs.  
Conceptualization, I.S. and G.C.; methodology, I.S. and G.C.; software, I.S.; validation, I.S. and G.C.; investigation, I.S. and G.C.; resources, I.S. and G.C.; data curation, I.S.; writing--original draft preparation, I.S. and G.C.; writing--review and editing, I.S. and G.C.; visualization, I.S.; supervision, G.C.; project administration, G.C.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25).  
The authors declare no conflict of interest.
## 1 Introduction
Therefore, older adults are often excluded in the development of VR and AR technologies. There is a lack of solid evidence that adopting AR and VR technologies is effective in the long run. The biggest barrier to wide adoption of these technologies is the lack of good user experience design and ergonomic issues. Many designers and researchers realize that AR and VR technologies have the potential for improving older adults' social relationships, wellbeing, and quality of care and facilitating their independence [10; 14; 15]. For successful aging and wellbeing, a variety of factors should act together, such as avoiding disability or disease, maintaining physical and cognitive functions to be healthy, retaining intimate human relationships, participating in diverse activities actively, and maintaining interest in social activities [16]. Older adults are a very diverse group covering a wide age range and have varied characteristics, behaviors, and needs. Typically, when people age, they experience a significant decrease in their physical, cognitive, and sensory capabilities, and many of them might have negative attitudes toward technological innovations [17; 18]. One of the many stereotypes around aging is that seniors are resistant to new ideas and advances in technology. Older adults express less comfort in using technology and less confidence in their ability to successfully use these systems. These unique physical and cognitive characteristics could act as interference factors for their engagement in using AR and VR technologies. Many challenges exist in the process by which they develop interest in, give attention to, and therefore, easily use, AR and VR technologies. Current studies of AR and VR technologies for older adults are limited because they focus on how to enhance innovative designs and usability [9; 19; 20] with little regard for the potential older adult users. Efforts to conduct research on them are often hampered by lack of understanding of the abilities, requirements, and preferences of this population. The present study suggests that it necessary to explore further the challenges older adults face currently and the related design issues of VR and AR, considering the balanced physical, emotional, and psychological wellbeing of this group. This study critically reviews AR and VR technologies, using an evaluation framework consisting of three categories: physical, emotional, and psychological wellbeing. Through this review, we examine the challenges and barriers in the use of AR and VR based on the characteristics of older adults and discuss emerging design issues and future directions. Because of the natural characteristics of these individuals, accurate understanding of likely challenges would make it possible to provide them better design and user experiences. Ultimately, AR and VR technologies should be designed to promote wellbeing by improving their overall quality of life.