esa-sceva/satcom-chunk-collection huggingface dataset:
# From Mirrors to Free-Space Optical Communication--Historical Aspects in Data Transmission
## 7 The Communication Networks in the Future  
The crucial issue in Free-Space Communication is to expand the maximum usable distance between transceivers [107]. In Figure 7, FSO types are presented according to their maximum operational range. In the nearest future, the communication network infrastructure must adjust to the fifth-generation (5G) [108; 109] and the sixth-generation (6G) standards [110; 111]. This is connected with the designing and implementation of high-speed detectors. The main difficulty is the effective collection of the data-carrying optical beam. Some advances were made by the Facebook Team, including the use of fluorescent materials to collect and focus the light into a tiny optical detector [112].  
In long-range FSO, Deep-Space Optical Communications (DSOC) will take high bandwidth communications to the next level [113; 114]. They aim to provide high bandwidth optical downlinks from outside cis-lunar space. NASA also funded a Deep-Space Optical Terminals (DOT) project to develop a tool that allows live High Definition (HD) video, telepresence, and human exploration beyond cis-lunar space. The implementation of this system is expected to enable a ten-fold or even 100-fold increase in data returns compared to present Radio Frequency (RF) space communications.
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 2 Related Work
Specifically, we treat the wildfire delineation and the severity prediction as image segmentation tasks. Such a problem is highly present in the medical imaging field, in which the literature proposes a series of neural network architectures [39; 40; 41]. Many works in remote sensing make large use of networks such as U-Net [42], which is particularly suitable for analysis on small datasets. [PERSON] et al., in [43], use this architecture on Sentinel-2 images to map salt marsh along the coasts, while others apply similar techniques to the segmentation of forests [44] or cloud coverage [45]. Concerning wildfire detection, different works as those in [11; 46] address the task via CNN models, but they lack to answer the damage level assessment question. Among many other semantic segmentation architectures which have been proposed in literature [47; 48; 49], the works in [50; 51] demonstrated the U-Net architecture to be a valuable choice for the wildfire damage severity estimation problem. The state-of-the-art solution proposes a Double-Step U-Net architecture, addressing the wildfire delineation and the severity prediction tasks separately. This double-step configuration relies on the Binary Cross-Entropy (BCE) loss function on the first stage and on the Mean Squared Error (MSE) loss on the second stage to estimate the final severity level. It is a straightforward fact that a proper choice of model and loss functions can significantly improve the performance of the models, as shown in [52; 53]. For this reason, the goal of this work is to validate and extend the results in [54], testing the proposed models to a widely extended set of images, and introducing a multi-channel interpretability analysis by exploiting both domain knowledge and a newly added attention-based mechanism. Analyzing the contribution of different channels to the prediction provides insights into the contributions of each channel to the deep learning model reasoning, a task which is typically addressed in Explainable Artificial Intelligence.
## 2 Materials and Methods
### Antibiotic Analysis in Water and Plant Samples  
Liquid chromatography in combination with tandem mass spectrometry (LC-MS/MS) was used to analyze the antibiotics in the plant and water samples. Prior to extraction, the water was filtered through Whatman no. 1 filter paper to eliminate the suspended matter and then filtered through a 0.45 \(\upmu\)m mixed cellulose membrane. The pH of water samples was adjusted to 4.0 with 1 mol/L H\({}_{2}\)SO\({}_{4}\). The antibiotics were extracted in 250 mL of filtered water. The plant samples were freeze-dried and ground to 80 mesh. A mixed solution (5 mL) containing phosphate buffer, acetonitrile, and the plant samples was added to each centrifuge tube. The tubes were shaken in a shaker at 250 rpm for 30 min, sonicated for 10 min, and then centrifuged at 12,000\(\times\)\(g\) for 10 min.  
The extraction process was performed 3 times for each sample. The samples were extracted using Oasis HLB extraction cartridges (500 mg, 6 mL, Waters, Milford, MA, USA) in the following manner. Each cartridge was sequentially pre-conditioned with 6.0 mL acetone, 6.0 mL methanol, and 6.0 mL 0.1% formic acid solution. The samples were passed through an extraction cartridge at a rate of 3 mL/min. Subsequently, the cartridge was rinsed with 5 mL 0.1% formic acid solution containing 5 mmol/L ammonium acetate and then eluted with 5.0 mL methanol. Finally, the target fraction was re-dissolved in 10% methanol solution to a final volume of 1.0 mL for LC-MS/MS (ABI 3200 Q TRAP) analysis. The antibiotics were analyzed using a LC-MS/MS system with an Inertsil(r) ODS-SP column for separation. The chromatographic mobile phase consisted of methanol (A) and 0.1% formic acid solution dissolved with 5 mmol/L ammonium acetate (B). The column temperature was set to 40 \({}^{\circ}\)C. The target antibiotics were detected by electrospray tandem mass spectrometry in positive-ion mode. Data acquisition was performed by multiple reaction monitoring (MRM), recording two MRM sessions per compound. The optimal conditions are summarized in Table 1. The correlation coefficients (R\({}^{2}\)) of the calibration curves exceeded 0.99 for all analytes. Recoveries of the antibiotics in the plant and water samples were determined at different concentrations in triplicate and calculated as percentages of the measured concentration, relative to the spiked concentration. Limits of quantification (LOQ) of the antibiotics were calculated with signal-noise ratios of 10. Retention time, correlation coefficient, LOQ, and recovery of the samples are summarized in Table 2.
# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 2 Related Work
Because the Lhasa dialect is the standard pronunciation of Tibetan, the paper focuses on realizing Lhasa dialect speech synthesis. Although the above three Tibetan dialects have different pronunciations, they use the same Tibetan characters. A Tibetan character with horizontal and vertical spelling structure differs from English with fully linear spelling. A typical Tibetan character consists of seven parts, as shown in Figure 1. In our previous work, we proposed the HMM-based [39] and deep learning-based [40] Mandarin-Tibetan cross-lingual SPSS. In [39], we realized the Tibetan grapheme-to-phoneme conversion module to convert Tibetan characters into initial and final sequences and used speaker adaptive training to realize Mandarin-Tibetan cross-lingual speech synthesis. In [40], we proposed the deep learning-based Mandarin-Tibetan cross-lingual speech synthesis to realize both Mandarin and Tibetan speech synthesis under a unique framework. Similarly, [PERSON] et al. [41] proposed an HMM-based monolingual Tibetan SPSS. However, these Mandarin-Tibetan cross-lingual and Tibetan monolingual TTS still used SPSS methods. Therefore, the voice quality of the synthesized speech still needs to be further improved. Subsequently, [PERSON] et al. [42; 43] proposed the seq2 seq-based Tibetan TTS. In [42], authors explored the Tacotron2-based Lhasa dialect TTS combined with the WaveNet model. In [43], authors presented the Tacotron2-based Tibetan multi-dialect speech synthesis model to synthesize the Lhasa dialect and Amdo dialects. Although the above seq2 seq-based monolingual Tibetan TTS models have greatly improved the synthesized voice quality compared to that of SPSS, these methods still have not achieved seq2 seq-based Mandarin-Tibetan cross-lingual speech synthesis. In the paper, we implemented a meta-learning-based Mandarin-Tibetan cross-lingual speech synthesis. Our main contributions are as follows. 1. We build two kinds of Tacotron2-based Mandarin-Tibetan cross-lingual seq2 seq TTS: the shared encoder Mandarin-Tibetan cross-lingual TTS and the separate encoder Mandarin-Tibetan cross-lingual TTS, using the speaker classifier with gradient reversal layer to disentangle speaker-specific information from the text encoder. At the same time, we design a prosody generator to extract prosodic information from a sentence to explore the syntactic and semantic information adequately. 2. We propose a meta-learning-based Mandarin-Tibetan cross-lingual TTS to improve the synthesized speech quality of Mandarin and Tibetan. Based on the separate encoder Mandarin-Tibetan cross-lingual TTS, we use an additional dynamic network to predict the parameters of the language-dependent text encoder that could realize better cross-lingual knowledge sharing in the seq2 seq TTS.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### The Establishment of the Initial Model
#### 3.2.1 Loading Condition Analysis  
The load on the spaceborne optoelectronic tracking and aiming gimbal primarily stems from the optoelectronic detection equipment attached via the pitch axis. Given that the gimbal system experiences a hyper-gravity state during rocket launches, applying multiplied acceleration loads to the gimbal during ground mechanical tests is a standard practice to guarantee the structural reliability of the gimbal. Therefore, with a 70 kg load and 20 g acceleration as conditions, an equivalent load F for the U-shaped frame is determined.  
Figure 5: U-frame optimized initial model.  
The force is applied at the intersection of the pitch axis hole and the azimuth axis hole, featuring a magnitude of 14,000 N and a direction vertically downward. The simplified mechanical model of the gimbal's U-shaped frame is depicted in Figure 6.
# Building Ensemble of Resnet for Dolphin Whistle Detection
## 2 Materials and Methods
### Proposed Approach
#### 2.3.1 ResNet50  
ResNet50 is a convolutional neural network (CNN) architecture introduced by Microsoft Research in 2015 that belongs to a family of models called Residual Networks, or ResNets [37], which are widely used for various computer vision tasks, including image classification, object detection, and image segmentation. The key innovation of ResNet is the introduction of residual, or skip, connections for optimal gradient flow. ResNet enables the training of much deeper networks with improved performance by using skip connections. The name \"ResNet50\" signifies that this particular model has 50 layers.  
The architecture of ResNet50 can be divided into several blocks. The input to the network is a 224 \(\times\) 224 RGB image. The initial layer is a standard convolutional layer followed by a batch normalization layer and a ReLU activation function. This layer is followed by a max-pooling layer that reduces the spatial dimensions of the input. The main building blocks of ResNet50 are the residual blocks. Each residual block consists of a series of convolutional layers with batch normalization and ReLU activation. The output of these convolutional layers is added to the original input of the block through a skip connection. This addition operation allows the network to learn the residual information, i.e., the difference between the desired output and the input, which can be thought of as the \"error\" to be corrected.  
ResNet50 contains several stacked residual blocks, with the number of blocks varying depending on the specific architecture. The model also includes bottleneck layers, which are 1 \(\times\) 1 convolutional layers used to reduce the dimensionality of the feature maps, making the network more computationally efficient.  
Towards the end of the network, a global average pooling layer spatially averages the feature maps, resulting in a fixed-length vector representation. This vector is fed into a fully connected layer with a softmax activation function, producing the final class probabilities.  
Overall, ResNet50 is a powerful and influential CNN architecture that has significantly advanced the field of computer vision. Its use of residual connections has paved the way for the development of even deeper and more accurate neural networks, and it continues to serve as a benchmark for many state-of-the-art models in the field.
## 5 Increase the Upper Limit of PV Capacity: Reactive Power Control  
Given the discussions in Sections 3 and 4, excessively high node voltage or system losses are clearly unacceptable, and the current effective solution is to reduce the participation of the PV units or increase their reactive power absorption. The latter allows for further increase in PV capacity while ensuring system stability and energy efficiency.
## 2 Materials and Methods
### The Optical Transmission Spectrum of Polycrystalline PMN-PT  
For the transparent dielectric slab with a thickness of d, a refractive index of \(n\) with considering a normally incident s-polarized plane wave with a vacuum wavelength of \(\lambda_{0}\), using [PERSON]'s equation, the complex amplitude of the reflected and transmitted beam at the slab's frontside (\(\rho\) and \(\tau\)) and the ones at the slab's backside (\(\rho^{\prime}\) and \(\tau^{\prime}\)) are calculated as [2]  
\[\rho=(1-n)/(1+n) \tag{11}\]  
\[\tau=2/(1+n) \tag{12}\]  
\[\rho^{\prime}=(n-1)/(n+1) \tag{13}\]  
\[\tau^{\prime}=2n/(n+1) \tag{14}\]  
If \(\phi\) is the phase shift associated with a single path of the beam through the slab (\(\phi=\text{knd}=2\pi\text{md}\lambda_{0}\)), the total reflection \(r\) and total transmission \(t\) of the slab considering all partial reflections and transmissions at the two sides will be  
\[r=\rho+\tau\tau^{\prime}\rho^{\prime}e^{2i\phi}\sum_{m=0}^{\infty}\left(\rho^ {\prime}e^{i\phi}\right)^{2m}=\rho+\frac{\tau\tau^{\prime}\rho^{\prime}e^{2i \phi}}{1-\rho^{\prime}e^{2i\phi}} \tag{15}\]  
\[t=\tau\tau^{\prime}e^{i\phi}\sum_{m=0}^{\infty}\left(\rho^{\prime}e^{i\phi} \right)^{2m}=\frac{\tau\tau^{\prime}e^{i\phi}}{1-\rho^{\prime 2}e^{2i\phi}} \tag{16}\]  
If the slab is thin enough to ignore absorption, the total transmission of the slab can be calculated as  
\[T=1-\left(\rho+\frac{\tau\tau^{\prime}\rho^{\prime}e^{2i\phi}}{1-\rho^{\prime 2 }e^{2i\phi}}\right)^{2} \tag{17}\]  
By determining the refractive index spectrum using FTIR, the optical transmission spectrum of PMN-PT ceramic can be calculated by Equation (17). In order to verify the calculation results, the experimental set-up shown in Figure 7 was developed in the lab. A 1.5 mm \(\times\) 5 mm \(\times\) 5 mm polycrystalline PMN-PT slab was polished and used as the sample in that set-up. The transmission of that PMN-PT slab is measured by dividing the intensity of 532 nm, 632.8 nm, and 1550 nm lights, measured by the power meter, with the slab in its place by the intensity without the slab in the pathway.  
Figure 6: The measured fringe position was used to extrapolate the zeroth-order fringes (at 0 cm\({}^{-1}\)) to assign absolute fringe order numbers to each fringe position.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 2 Background
### Data Fusion in ITS  
Data fusion is applied in many areas: intelligent transport systems, bioinformatics, cheminformatics, geospatial information systems, oceanography, wireless sensor networks. There are several papers giving a review of DF in ITS. [PERSON] in [23] provided a survey of how DF is used in different areas of ITS, such as: Automatic Terminal Information Service, Automatic Incident Detection, Advanced Driver Assistance, Network Control, Crash Analysis and Prevention, Traffic Demand Estimation, Traffic Forecasting, and Monitoring. [PERSON] introduced two levels of DF: the first level involves fusion to provide only raw and uncorrelated data to the end-user, and the main methods are data association and positional estimation using Kalman filter. The second level delivers meaningful information from raw data for guiding human decision-making, and the methods include pattern recognition using adaptive neural network and clustering methods and identify fusion using Bayesian Decision Theory and Dempster-Schafer evidential reasoning.  
[PERSON] et al. in [24] separated the DF process through the six-level hierarchy based on DF goals. Level 0 encompasses source preprocessing to address estimation and compression of input data. Level 1 includes data analysis from all appropriate sources, e.g., point, point-to-point, and area-wide sensors. Level 2 includes a complete and timely assessment of the observed data and information from external sources (weather reports, seasonal traffic patterns, special events and construction schedules, and others) by incorporating relations among the entities of interest. Level 3 evaluates traffic flow patterns and external sources for assessing the occurrence of an incident, travel time delays, and other events that influence traffic flow. Level 4 improves the effect of the fusion process through traffic planning and control. Level 5 processing is focused on issues related to human support in cognitive decision making and action taking based on the fused information.  
[PERSON] et al. in [25] made a review of the state of practice and prospects for DF in the management of the travel demand. The proposed system architecture requirements, several DF models, and a brief review of major relevant industry players in data provision, data aggregation, and delivery to end users. [PERSON] and [PERSON] in [26] warn that in this field, there are still some remaining challenges. This includes the need for obtaining data with the necessary accuracy to make dynamic and real-time ITS applications. They see the potential in the development of methods to combine traffic sensors and human-generated data.
## 1 Introduction
### Motivation and Objective  
Due to the growth of the OTT market, the revenue generated by the consumer growth is positive from the standpoint of OTT service providers, but content delivery network (CDN) service costs also have grown [7]. To generate profits, OTT companies must analyze and capitalize network usage. Network operators are also struggling because of the growth in OTT traffic. A problem occurs in terms of profitability if the service fees are not commensurate to the provided services [8]. In particular, because it is important for an OTT company to ensure quality of service (QoS), it is essential to manage the network effectively [9]. In the case of live streaming, which now accounts for a considerable portion of OTT, QoS expectations have placed a large burden on telecommunication carriers [10]. To address these challenges, carriers offer a variety of service contracts. Normally, if allocated bandwidth is exceeded, a service degradation is often applied [11]. This allows telecommunication carriers to better manage large and complex network resources. However, throttling often causes complaints from customers with changing needs. Therefore, both providers and consumers have a learning curve. As such, companies participating in the OTT market need to be able to more accurately classify the OTT service patterns of consumers to maintain contracts and create profits.  
Many studies have been conducted to analyze the traffic of traditional networks. In particular, there have been many studies recently that have applied artificial intelligence and machine learning (ML) based methods for analyzing common Internet traffic. However, these methods generally target general-purpose traffic, and very few studies have analyzed OTT service traffic [12; 13]. While these studies are significant in that they dealt with ML methods specialized for OTT traffic classification, they neglected real-world problems that were common to the OTT market. OTT consumers range from heavy users who watch tremendous amounts of OTT content, to light users who rarely watch, and the proportions are not the same [12; 14]. If ML is applied without considering these imbalances, the classification accuracy drops for the smaller user group [15]. OTT usage patterns change rapidly over time, such as during the COVID-19 pandemic. Because new OTT services are continually offered, and telecommunication carriers offer various subscription-fee schemes, consumer usage patterns also change. Conventional studies on OTT traffic have not considered the dataset changes caused by these phenomena. They generally perform ML and classification for the data once, which does not reflect continuous changes. Hence, classification accuracy declines over time. Our research objective is to propose a method to analyze OTT consumers well, based on actual market environment conditions.
## 4 Case Study
### Flexibility Evaluation
Figure 6: Dynamic prediction model of production system design scheme. (**a**) Dynamic prediction model for A. (**b**) Dynamic prediction model for B. In the flexibility dynamic evaluation of the PSDS, Scheme A has seven processing machines and Scheme B has eight processing machines. The failure rate of each processing equipment is set as 0-20%, and the failure rate increment is set as 0.5. Therefore, 41 groups of experiments are carried out for each processing equipment, and each group is observed five times to obtain the simulation experiment of the production system production volume, and then the dynamic prediction results of two design schemes are obtained. Finally, the production capacity of each processing equipment at different failure rates is processed according to Equations (28)-(30), which obtains the comprehensive flexibility evaluation index of the production system design scheme in Table 5.
###### Abstract  
The joint control problem of the underwater manipulator is addressed in this paper, under the influence of uncertainty factors such as model uncertainty, external disturbance, and manipulator joint lag. In general, for the uncertainty factors, it is usually approximated online, but it is difficult to select a reasonable value for the approximation error boundary, too conservative estimated values would cause chattering problem easily. And the influence of joint lag on the manipulator control should be considered in actual work. Unlike most previous control method, in this paper, the function approximation technique (FAT), which uses the Legendre polynomial, is adopted to approximate the uncertainty factors online. Then, based on the proportion integral differential (PID) sliding manifold with the integral and differential of tracking error, a sliding model PID controller is designed to speed up the response and reduce the effects of joint lag. For the error boundary, the adaptive law is proposed, and it will reduce chattering of the control quantity under the steady state of the system. It was proved that the joint error of the control system is uniformly asymptotic convergence through the stability analysis. Finally, the effectiveness of the proposed approach is demonstrated with pool comparison experiments of the underwater manipulator installed in the autonomous underwater vehicles (AUVs).  
underwater manipulator; sliding mode control; function approximation technique; joint lag; error boundary; uncertainty estimation [label=]