esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Speech Perception and Hearing Aids  
### Hearing and Perception  
The speech signal is a kind of time-varying signal, whose signal characteristics represent the different speech produced--an audible signal transmitted to the human ear by the vibration of particles in the air. There are three ways to mark events in speech. The first is silence, where there is no speech. The second is the unvoiced state, where the vocal cord does not vibrate; therefore, the output speech waveform is aperiodic and random [26]. The last is the vocal state, where the vocal cords vibrate periodically as air is expelled from the lungs. The membrane is displaced in different positions depending on the frequencies; for example, the membrane is displaced near its apex with low frequencies while stimulating the membrane at its base with high frequencies. The displacement ability of the membrane at a specific point is directly proportional to the amplitude of the frequency at which it is excited. When a sound contains multiple frequencies, the basement membrane displaces at multiple points. The cochlea then divides complex sounds into frequency components [23] (Figure 4).  
Each pronounced word consists of the phonetic combination of a limited set of the vowel (which carry the power of the sound), and consonant (which provide intelligibility) speech sound units [1,3]. Lower frequency sounds (between 250 and 500 Hz) correspond to the vowel sounds, while higher frequencies (between 2000 and 4000 Hz) correspond to the consonant sounds. Recent studies have shown that healthy ears can decode separate consonants efficiently [27,28]. However, with sensorineural hearing loss where hair cells and synaptic connections are not correctly functioning, speech can be heard but not understood. The most considerable difficulty may include communication in background noise (predominantly of low frequency), which results in difficulty understanding talkers with soft voices and hearing speech at a distance.
###### Abstract  
This paper shows soliditude for the path following control issues of underactuated unmanned surface vehicles subject to unknown external disturbances, deviation of vessel model parameters and actuator saturation. Initially, an improved adaptive integral line-of-sight (IALOS) guidance law is introduced to estimate the sideslip angle, which helps to promote the precision of path following. Furthermore, a finite-time convergent disturbance observer is utilized to size up time-varying disturbances and the single-parameter neural network strategy is utilized to reduce the impact of model deviation. Meanwhile, by introducing a finite-time auxiliary dynamic system to improve the impact of actuator saturation (input saturation), the higher-order tracking differentiator (TDS) is introduced into the backstepping controller for reducing the number of derivations. It is shown that all error signals of the control system, employing Lyapunov stability theory, are uniformly ultimately bounded. Finally, the validity of the put forward scheme is validated by numerical simulations.  
underactuated unmanned surface vehicle; IALOS; path following; model deviation; input saturation; finite-time convergent disturbance observer; TDS +
Footnote †: journal: Applied Sciences
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 3 Model Design and Implementation
### Improvement of Non-Maximum Suppression NMS Algorithm  
Non-Maximum Suppression (NMS) is an important part of the target detection algorithm, and its main function is to eliminate redundant candidate boxes generated in the RPN network. The elimination criterion is shown in Formula (6).  
\[S_{i}=\left\{\begin{array}{l}S_{i},IOU(M,b_{i})<N_{t}\\ 0,IOU(M,b_{i})\geq N_{t}\end{array}\right. \tag{6}\]  
where \(S_{i}\) is the score of the \(i\) th candidate box; \(M\) indicates the candidate box with the largest score; \(b_{i}\) is the candidate box to be scored; \(IOU\) is the combined ratio of \(b_{i}\); and \(N_{t}\) is the threshold value of \(IOU\) set in NMS.  
When the ratio between the candidate box to be scored and the candidate box with the largest score exceeds the threshold, the NMS deletes the candidate box to be scored. As a result, NMS mistakenly deletes overlapping candidate boxes in partially crowded  
Figure 7: FPN Feature Pyramid Structure.  
and truncated complex scenarios. To solve this problem, CF-RCNN uses the Soft-NMS algorithm. The Soft-NMS score function is shown in Formula (7).  
\[S_{i}=\left\{\begin{matrix}S_{i},IOU(M,b_{i})\,<\,N_{t}\\ S_{i}(1-IOU(M,b_{i})),IOU(M,b_{i})\,\geq\,N_{t}\end{matrix}\right. \tag{7}\]  
If the combined ratio is less than the threshold, the score remains unchanged. If it is greater than or equal to the threshold, \(S_{i}\) is the difference between \(S_{i}\) multiplied by 1 and the combined ratio. Compared with NMS, Soft-NMS does not directly overlap candidate boxes, but resets \(S_{i}\) to a smaller score.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
###### Abstract  
Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.  
non-terrestrial network; unmanned autonomous intelligent systems; 5G/6G network; unmanned aerial vehicle (UAV); mobile edge computing (MEC); cloud computing; distributed computing; artificial intelligent (AI)
# Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
## 1 Introduction
In addition, the researcher has to deal with many possible options of algorithms, quantization parameters, parallelisms, code rates, and frame lengths. Furthermore, a reduced area and power are particularly compulsory for mobile devices. Therefore, designs of the area and energy-efficient FEC chips are excessively desirable. This brief presents low-complexity and high-throughput QC-LDPC decoder architectures for emerging 5G wireless communications standards. The remainder of this paper is structured as follows. In Section 2, a brief overview of the 5G NR LDPC codes is presented. Section 3 proposes a combined min-sum (CMS) decoding algorithm, which is a combination of the offset min-sum (OMS) and the original min-sum (MS) algorithm. Section 4 details the proposed low-complexity high-throughput pipelined layered QC-LDPC decoder for 5G NR wireless communication standards. Section 5 provides implementation and comparison results. Finally, conclusions are drawn in Section 6.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 5 Simulation Results
### Data Description  
In this paper, the small power system consists of micro-diesel, photovoltaic array, lithium battery cabinet and load demand. The simulations are performed to verify the operation of the proposed algorithm. The data used in ASPF comes from the actual factory which locates in Hekou town, Nantong city, China(\(32.49^{\circ}N,120.83^{\circ}E\)). For PV power forecasting, the prediction can be performed every 2 h, 1 h, 0.5 h, or 15 min according to users' demand. All the simulations complete in the MATLAB, and in our ASPF the computation time mainly depends on the BPNN which only takes several seconds  
Figure 7: The flow chart for ASPF in power systems.  
one time in Matlab. So the results of ASPF can be used in day-ahead energy management, and it can be applied to the real-time energy management too. In this paper, the parameter for the PV power adaptive compensation time interval is set to 1 h based on our system. In this system, solar power is mainly supplied to the canteen and the water pumps in the factory. According to the historical data, we obtain the original forecasting PV power in NN and MLR respectively, and store them in EMS.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 3 Outage Probability Analysis  
In this section, the system performance analysis in terms of OPs is presented. The OPs are defined as the probability that the received SINR falls below a certain threshold limit. We derived closed-form expressions for the outage at each destination as well as the overall system outage. Then, we derive an asymptotic expression for each of them at a high SNR regime. To gain more insight into the system performance, the outage diversity order is further derived.
# Distributed Interference-Aware Power Optimization for Multi-Task Over-the-Air Federated Learning
## 4 Proposed Method
### Centralized Scheme
#### 4.1.1 Denoising Factor Optimization  
Under fixed device transmit power, problem \((\mathrm{P1})\) is decomposed into \(M\) independent subproblems, each corresponding to one cell, expressed as  
\[(\mathrm{P2}):\ \ \min_{\{0<\theta_{m}\}}\Bigg{\{}\sum\limits_{k\in\mathcal{K}_{m}} \left(\frac{\sqrt{p_{k}}|h_{m,k}|}{\sqrt{\theta_{m}}}-v_{k}\right)^{2}+\sum \limits_{j\in\mathcal{M}\backslash\{m\}}\sum\limits_{i\in\mathcal{K}_{j}} \frac{p_{i}\Re\{h_{m,i}(h_{j,i})^{H}\}^{2}}{\theta_{m}|h_{j,i}|^{2}}+\frac{ \sigma_{m}^{2}}{2\theta_{m}}\Bigg{\}}. \tag{29}\]  
By introducing auxiliary variables \(\gamma_{m}=1/\sqrt{\theta_{m}}\), the aforementioned problem can be reformulated into a quadratic equation, thereby enabling the straightforward derivation of the closed-form solution for the denoising factor, given as  
\[\theta_{m}=\left(\frac{\sum\limits_{k\in\mathcal{K}_{m}}p_{k}|h_{m,k}|^{2}+ \sum\limits_{j\in\mathcal{M}\backslash\{m\}}\sum\limits_{i\in\mathcal{K}_{j}} \frac{p_{i}\Re\{h_{m,i}(h_{j,i})^{H}\}^{2}}{|h_{j,i}|^{2}}+\frac{\sigma_{m}^{ 2}}{2}}{\sum\limits_{k\in\mathcal{K}_{m}}\sqrt{p_{k}}|h_{m,k}|v_{k}}\right)^{2}. \tag{30}\]
## Materials
### Validation of GPP Datasets  
Validating a new global dataset is a challenging process, as ideal validation would involve measuring daily GPP across a diverse range of biomes and climates. An algorithm successful in a coniferous forest might be inadequate in a tropical savanna. Therefore, a global array of validation sites is necessary. More fundamentally, each available measurement for validating GPP represents a different spatial scale and presents unique limitations [71]. Three types of data appear useful for validating global GPP data: direct measurements of biomass [72], tower flux measurements [73], and measurements of atmospheric CO\({}_{2}\) concentrations [74]. However, each data type has limited precision or scope. Direct biomass measurements of GPP are typically measured on an annual basis (or longer) and the sample area is often less than 1 hectare [53], with uncertain carbon losses due to respiration. Tower flux measurements are continuous over time and sample a footprint that may be of a similar spatial extent to a 1 km pixel of satellite images, but towers only directly measure NEE, such that deriving GPP requires additional modelling and subsequent assumptions. Measurements of atmospheric CO\({}_{2}\) concentrations integrate over a large, difficult-to-define land area and include anthropogenic CO\({}_{2}\) emissions and are often collected through a monthly repeat snapshot sampling process. In short, no measure matches the detail represented by GPP in terms of time, space, and ecosystem characteristics.  
A network called FLUXNET, comprising hundreds of tower sites across all continents and biome types, provides accurate seasonal measurements about vegetation carbon uptake and local land CO\({}_{2}\) balances [75]. These towers enable validation of GPP values derived from remote sensing products.  
The MODIS GPP dataset was validated using all station data in the FLUXNET network. The MuSyQ dataset was validated using measurements from stations in the BigFoot and FLUXNET networks [56]. For the PMLV2 GPP dataset, 26 EC towers selected in China covering 9 PFTs were used and a Genetic Algorithm was employed during the calibration process [76]. The TL-LUE model utilized 68 towers from the FLUXNET network for calibration and 25 for validation [50]. The GOSIF dataset was developed using 91 FLUXNET sites and has been tested for 10 different biome types with satisfactory results [67]. The FLUXCOM dataset was developed using current machine learning techniques and information from 224 towers in the FLUXNET system to represent 15 different PFTs [69]. Detailed technical information on how the GPP datasets used in the study were calculated by the developers is shared in the Supplementary Materials.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 2 Materials and Methods
### The multi-band ARC  
In the framework of future SAR ground calibration campaigns, three equal ARCs will be deployed, covering the four operational bands (L\(\backslash\)C\(\backslash\)X\(\backslash\)Ka) of the SAR sensors. Figure 1 shows the block diagram of the ARC. It consists of an antenna and servo subsystem, a radio frequency (RF) subsystem, a control and data collection subsystem, and a remote-controlled subsystem.  
The antenna and servo subsystem comprises two similar multi-band antennas used for reception and transmission, and the servo allows the ARC to move mechanically in the elevation and azimuth directions. The RF subsystem includes a wideband RF receiver, a Ka-band receiver, an optical fiber delayer, a gain adjustor, a wideband RF amplifier, a Ka-band amplifier, an internal calibration module, and a switched filter module (SFM). It amplifies the signal level that complies with the required RCS value and selects the work frequency, delay, and so on. The control and data collection subsystem is used to control the whole ARC and collect the SAR signal from the radio frequency subsystem. The remote-controlled subsystem is in charge of monitoring, controlling, and scheduling the ARC. This subsystem is located away from the ARC, and its communication method is the 4G communication network. In the following text, we emphasize the design of the antenna and radio frequency system.  
Figure 1: The block diagram of the active radar calibrator (ARC).
# Non-Intrusive Load Disaggregation Based on a Multi-Scale Attention Residual Network
## 3 Data Selection and Preprocessing
### Data Preprocessing  
Firstly, the NILM-TK toolkit was used to export the power data of the selected home appliances from the WikiEnergy database and UK-DALE database. We then created aggregate power profiles and used them as the experimental data. Secondly, different evaluation indexes often have different dimensions, and their values are quite wide ranged, and this may affect the analysis results. In order to eliminate the influence of these differences, data standardization is needed. Here, the maximum and minimum value normalization method was used to normalize the result between \([0,1]\). The normalization equation is  
\[x_{t}^{*}=\frac{x_{t}-X_{min}}{X_{max}-X_{min}}, \tag{12}\]  
Figure 5: The Comparison of Relu and Leaky-Relu.  
where \(x_{t}\) is the total unstandardized power at time \(t\), \(X_{max}\) is the maximum of the total power sequence, \(X_{min}\) is the minimum of the total power sequence, and \(x_{t}^{*}\) is the standardized result at time \(t\).
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 3 Transfer Learning
### Transfer Learning Workflow for Hurricane Damage Detection  
Figure 2 shows the object detection workflow for training ResNet, MobileNet, and EfficientNet. All three networks were pre-trained on the same image resolutions (640 \(\times\) 640 pixels) and the COCO 2017 dataset [30] which contains 330,000 images and 80 object categories. Each of the three models were also configurable to begin with the same training parameters. Therefore, the batch size was set to four images, and each model training instance was terminated after the twenty-thousandth epoch. Our damage detection dataset was configured with a 50/50 split for training and testing purposes when pre-processed into each of our models. This left 400 images and their corresponding annotations for model training and the other 400 images and their corresponding annotations for testing each of the models' predictions. Our transfer learning work leverages the pre-trained model weights for features extracted from the typical objects contained within the COCO 2017 dataset. More specifically, our hurricane damage detection transfer learning work consists of the following steps.  
* Initialize training with pre-trained neural network model and extracted feature weights;
* Configure a new pipeline with specified training parameters for our model;
* Use the pre-trained model checkpoint as the starting point for adding new, trainable layers that contain predictions of the four distinct object categories in our dataset;
* Train the new layers on our in-house hurricane damage dataset related to building damage.  
Because the four object categories from Table 2 do not appear in the COCO 2017 dataset, the primary objective for using the pre-trained models is feature extraction.
# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
###### Abstract  
The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to [PERSON]'s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.  
flood detection; deep learning; water level estimation; water segmentation; CCTV; CNN +
Footnote †: journal: Medical Imaging
# Preliminary Study of New Sustainable, Alkali-Activated
###### Abstract  
During the glass selection process by optical sorting equipment, a rejection material called CSP (ceramic, stone, and porcelain) is generated, which is lower than 2 wt % of the glass cullet collected in Catalonia (Spain). Although this process should only separate non-glass impurities from the glass cullet, around 84 wt % of glass is found in the CSP. The CSP characterization reveals that CSP is mainly compound by SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), alkali metals, and CaO, which are key components for the alkali-activated cement (AAC) development. Consequently, this study is focused on the potential of CSP as a precursor to synthesize AAC. The concentration of the alkali activator (NaOH: 1 M, 4 M, and 8 M) and the liquid-to-solid (L/S) ratio were tested in the formulation of the AAC. The AAC specimens at 28 days cured were evaluated using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FIR), scanning electron microscopy (SEM), apparent density (\(\rho_{app}\)), and compressive strength (\(\sigma_{s}\)). The results obtained showed that the L/S of 0.5 and 4.0 M for NaOH concentration are the best conditions, due to the mechanical properties (\(\rho_{app}=1.75\) g-cm\({}^{-3}\); \(\sigma_{s}=52.8\) MPa), cohesion (SEM), and formed phases (XRD and FT-IR). Therefore, CSP can be a precursor for developing new, sustainable binders.  
sustainable binder; alkali-activated cement; residue; CSP; municipal waste management 4.0/. +
Footnote †: journal: Applied  
Article  
Preliminary Study of New Sustainable, Alkali-Activated  
Cements Using the Residual Fraction of the Glass Cullet  
Recycling as Precursor  
[PERSON]  
Departament de Ciencia de Materials i Quimica Fisica, Universitat de Barcelona, C/[PERSON], 1, 08028 Barcelona, Spain; [EMAIL_ADDRESS] (J.G.-P.); [EMAIL_ADDRESS] (A.M.-A.);  
[EMAIL_ADDRESS] (A.A.-R.); [EMAIL_ADDRESS] (J.M.C.); [EMAIL_ADDRESS] (J.M.C.)  
Correspondence: [EMAIL_ADDRESS]; Tel.: +34-93-402-1316