esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Observation  
Two field observations including 21 stations were carried out in the East China Sea (ESC) in May 2021 and August 2021 (see Figure 4). The farthest station was about 90 km offshore. The filtered seawater was prepared for baseline measurement. To avoid the influence of ship shaking on the VSFlab, the angle and amplitude calibration of VSFlab were implemented every day. According to the \(c\) of samples, we diluted the samples to meet the requirements of single scattering. The samples were fully mixed in a magnetic stirrer  
Figure 3: Installation of LISST-VSF in lab. (A) represents the opaque rag. (B) represents the polycarbonate cartridge filter of pore size 0.2 \(\upmu\)m. (C) represents the Liqui-Cel Membrane Contactor. (D) represents the diaphragm pump. (E) represents LISST-VSF. The polycarbonate cartridge filter of pore size 0.2 \(\upmu\)m, the diaphragm pump, and the Liqui-Cel Membrane Contactor were connected through silica gel tube to form a filtration, degassing, and circulating system.  
before measurement. Since a complete measurement requires 8 s, it could be considered that the particles were evenly distributed in the solution during the measurement. Noting the influence of bubbles on the measurement results [33], we took extra care in the process of water collection, liquid transfer, and dilution to avoid bubbles as far as possible.  
A particle sizer (BT-3000) manufactured by Dandong Bettersize Instruments Ltd. (Dandong, China) was utilized to analyze particle size and morphology. It is equipped with a microscope to carry out microscopic imaging of particles in the water ample. The circulation system ensures the full dispersion of the sample. Then, according to the image, it calculates the circle equivalent diameter to describe the particle size [34] and circularity, which considers the relationship between the shape area and the shape perimeter, to describe the degree of irregularity of particle shape [35]. Note that recording microscopic images of particles is time consuming, so we only took images of particles at nine stations which are marked as red plus symbols in Figure 3.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Construction of Combined Model
#### 3.3.4 Construction of M-CM-GA-ELM Neural Network Integrated Model  
Figure 8 shows the calculation process of the M-CM-GA-ELM model, and the following are the specific steps:  
1. The multiple linear regression model of the relationship between the relevant parameters of the surface movement basin and conditions of geological and mining (such as mining height, dip, mining degree, advance speed, rock stratum lithology, thickness of unconsolidated layer, etc.) was constructed, and the trend term and residual term of the prediction model were calculated;
2. The GA-ELM neural network prediction model was constructed with geological and mining conditions as the input layer and the residual term as the output layer;
3. Errors of the GA-ELM prediction model obtained from prediction were eliminated, and the CM-GA-ELM prediction model was built;
4. At the same time, the multiple linear regression model and the CM-GA-ELM prediction model were used for prediction, and the final predicted value was obtained.  
In summary, the essence of the fusion model is to use the prediction method of the ELM neural network to compensate errors of the linear regression model. After the error compensation, M-CM-GA-ELM has both the experience of linear regression and the strong nonlinear mapping ability of neural networks.
## 1 Introduction
Although PPG signals are characterized as an easy-to-acquire optical signal compared to other bodily signals (e.g., EEG (Electroencephalography), ECG (Electrocardiography), ABP (Arterial Blood Pressure)), there are specific methods and wavelength selection procedures that are required to obtain a good quality signal. Among the wavelength-dependent methods, SW-PPG, MW-PPG, and all-wavelength PPG (AW-PPG) are employed based on the purpose of the signal acquisition. As described previously, time-dependent properties usually require SW-PPG. On the other hand, MW-PPG and AW-PPG are employed in the measurement of quantitative properties. Since glycated hemoglobin is a quantitative property of blood, an MW-PPG or AW-PPG signal is required for estimation. The most widely used technique for acquiring an MW-PPG signal uses discrete LEDs of different wavelengths and single or multiple photodetectors (PDs) to record the PPG signals. In this method, the specimen or medium is placed between the LEDs and PDs for transmission-mode PPG, and the medium is placed on one side of the LED-PD arrangement for reflection-mode PPG signal acquisition. In the reflection mode PPG system, the LEDs and PDs are placed in a single plane facing at the medium. Figure 1 depicts the different arrangements of LEDs and PDs for PPG signal acquisition. The advent of wearable and mobile devices has led to an exponential increase in mobile-based healthcare systems. Smartphones with powerful processors, multiple sensors, and large data storage capabilities are well suited for healthcare systems. A smartphone camera can be a great candidate for a PPG signal acquisition device, where the camera sensor is the PD and the white built-in flashlight is the light source. In this case, the light source contains a wide range of wavelengths interacting with the medium, and the camera sensor filters the input light into three distinct wavelengths (red, green, and blue). The different signals from these three wavelengths can be utilized as an MW-PPG signal. Transmission- and reflection-mode PPG signals can also be acquired using smartphone cameras by placing the white LED on the opposite side of the medium or at the same plane of the camera sensor, respectively. Figure 2 illustrates the different modes of PPG signal for a camera-sensor-based PPG system. In this study, we compare the camera-sensor-based and discrete-LED/PD-based processes of MW-PPG acquisition to estimate in vivo glycated hemoglobin. We also perform comparative analyses on the two PPG-based HbA1c estimation methods described in [20]. In this manuscript, the study of [17] is described as a two-wavelength based method, and the study of [18] is described as a three-wavelength based method for estimating glycated hemoglobin.
# Parallel Accelerated Fifth-Order WENO Scheme-Based Pipeline Transient Flow Solution Model
## 4 GPU Acceleration Implementation Method  
Since the pipeline length can be hundreds of kilometers for large water transmission projects, traditional CPU computing architecture is time-consuming and inefficient. With the introduction of GPU acceleration technology, the model calculation efficiency can be significantly improved with minimal loss of model accuracy, ensuring both calculation accuracy and efficiency.
## 2 Single-Frequency Occultation Data Processing Method Based on BDS
The carrier-phase measurement of the reconstructed B\({}_{3}^{*}\) without ionospheric influence can be directly calculated through the carrier-phase measurement B\({}_{1}\) and pseudorange measurement. Follow-up calculations based on B\({}_{1}\) and B\({}_{3}^{*}\) (the single-frequency occultation processing method) to invert of occultation products and based on B\({}_{1}\) and B\({}_{3}\) (the dual-frequency occultation processing method) are the same [30; 31; 32; 33].
## 5 Discussion
### Advantages of Downscaled SIF in Drought Monitoring  
The current limitations of satellite technology pose a challenge in achieving high temporal and spatial resolutions simultaneously in existing satellite SIF products [67]. These coarse-resolution downscaled SIF data (0.05 degrees) present challenges for various research fields, including regional carbon cycle, crop growth studies, and drought assessments [2; 66; 68]. High spatial resolution satellite data are more suitable for regional agricultural drought monitoring. For instance, the spatial resolution of MODIS products has reached 1 km and 500 m, providing an advantage in regional drought monitoring [69; 70]. Moreover, the fragmentation of farmland in China is highly pronounced and low-resolution SIF products are insufficient for accurate regional agricultural drought monitoring. Therefore, the demand for higher resolution SIF products is urgent in order to achieve accurate regional drought monitoring and assessments. In this study, a lower SIF anomaly index value indicates more severe drought conditions while vice versa holds true. The downscaled SIF results (1 km spatial resolution) have been successfully applied to monitor drought in Henan Province (Figures 10 and 11). Henceforth, the downscaled 1 km spatial resolution SIF results presented in this study offer significant advantages for regional drought monitoring.
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
## 4 Conclusions  
A speech communication system based on a PZT-42 piezoelectric electret mechanical antenna was proposed. In the proposed communication system, a hardware system, a communication protocol of mechanical antennas, and a software system were reasonably designed for modularization and decoupling. Through a comprehensive examination of the system's workflow, each component and its functions were thoroughly demonstrated, including the layered communication protocol and the software system with a modular design. It was experimentally verified that the communication system achieves real-time communication and presents a user-friendly, highly compatible, and easily expandable design as a reference for the mechanical antenna-based communication system. We will conduct additional research in the future to improve the transmission distance and bandwidth of piezoelectric electret mechanical antennas. For speech communication systems, the signal recognition algorithm will be improved to make it adaptive to changes in operating frequency and modulation frequency, and additional functions like data compression and encryption will be integrated. This speech communication system provides a promising foundation for practical applications and advances the prospects of low-frequency communication systems based on mechanical antennas.  
Conceptualization, J.X.; methodology, J.X.; project administration, L.G., Y.H. and J.X.; software, S.W. and P.L.; supervision, K.H. and J.X.; validation, S.W., J.Y. and S.Y.; writing--original draft, S.W.; writing--review and editing, S.W. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 52102061 and 61905021) and Teaching Reform Projects at BUPT (Grant No. 2022 CXCY-B03).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Motivation and Introduction
[24] enhanced the EHLD by proposing the notion of the EHLD as a generator to produce the family of continuous distributions with the goal of making the distributions more practical. [PERSON] and [PERSON] [25] examined the moment and ML estimators of the EHLD parameters. EHLD's ML, inverse moment, and modified inverse moment estimators, as well as the joint confidence regions were investigated by [PERSON] [26]. [PERSON] et al. [27] created a reliability test strategy for the EHLD. [PERSON] and [PERSON] [28] used multiple Type-I hybrid censoring to look at estimators of the EHLD parameters. Adaptive progressive censoring was used by [PERSON] and [PERSON] [29] to investigate the parameter estimators of the EHLD. The goal of this essay is to construct a new probability, called the unit-EHLD (UEHLD) by using transformation \(T=e^{-Y}\), where \(Y\) is the EHLD. We were motivated to propose the UEHLD due to the following:  
1. To create various forms for the hazard rate function (HRF) and PDF. 2. With a range of 0 to 1, the UEHLD is versatile and may be used to describe a variety of datasets. It can be seen as a useful model for fitting skewed data that might not be effectively fit by other popular distributions. 3. To give a comprehensive comparison of three approaches for estimating the UEHLD parameters, as well as an examination of the performance of such estimators for various parameter values and sample sizes. Our investigations were limited to the ML, maximum product of spacing (MPS), and Bayesian methods. It is difficult to theoretically examine the behaviors of different estimating approaches; thus, we carried out extensive simulation studies to evaluate the behaviors of different estimators with the bias, mean-squared error (MSE), and length of the confidence interval (CI) criteria. * To describe some practical uses in many disciplines, such as economic growth data, tensile strength data, and COVID-19 data. This article's structure is as follows: In Section 2, we develop a brand-new model known as the UEHLD. Some moments' measures are deduced in Section 3. The stress-strength model and some information measures are described in Section 4. Section 5 discusses a few of the various techniques for estimating the model parameters. Section 6 carries out a numerical investigation using Monte Carlo simulations. Section 7 conducts a numerical examination of real datasets, and Section 8 gives the findings.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 6 Conclusions and Future Work  
The proposed solution demonstrates the ability to detect and respond to side-channel attacks in SDNs. Moreover, it possesses the capability to pinpoint the root cause of the timing probe activity. Its implementation exhibits minimal latency in an SDN environment. This study incorporated various novel aspects, such as the application of machine learning techniques, probe activity detection, and a data-driven automation approach for response. The results of this study demonstrate that the proposed techniques effectively minimise the occurrence of false positives and false negatives, thus attesting to the consistency and feasibility of these methods for detecting and responding to such attacks.  
Future research could concentrate on adapting the proposed techniques to larger and more complex network architectures and evaluating their effectiveness. Furthermore, augmenting the proposed response mechanism by testing different attack detection techniques can extend the scope of this study. This mechanism was created to respond to timing side-channel attacks; however, it can also be adapted to counter other types of attacks. For instance, the mechanism may be combined with intrusion detection systems to generate customised responses for detecting network intrusion attacks. Augmentation of the response mechanism can potentially increase the adaptability and practicality of SDN security solutions, thereby supporting the development of more robust and effective techniques.  
Conceptualisation, F.S.; experiments, F.S.; result analysis, F.S.; paper write-up, F.S.; supervision, Y.-W.C., E.V.-G. and C.N.; critical revision Y.-W.C., E.V.-G. and C.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors thank the School of Computing and Information Technology, University of Wollongong, Australia for technical support.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SDN & Software-defined networking \\ DDoS & Distributed Denial-of-Service \\ TTL & Time-to-live \\ \end{tabular}
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 2 Flow Field Signal Acquisition  
### Numerical Scheme  
In this paper, the flow field signal around a square target was chosen as the original signal, as shown in Figure 1. The side length of the square target was named \(H\). The computational domain was located in a Cartesian coordinate system, within a range of \(30H\times 10H\). The virtual lateral line with the pressure sensors was under the center of the targets at a distance of \(D\). CFD was used to obtain the signal series, according to a numerical method described in our previous work [47].  
In the numerical scheme, the two-step Taylor characteristic-based Galerkin (TCBG) method was used to solve the flow governing equation, which was presented by [PERSON] et al. [48]. The progress of the TCBG scheme for the momentum equations is shown in flowing equations. The accuracy of the numerical scheme was validated previously [49]. The Strouhal numbers \(S_{1}\) of the square target with different postures \(\theta\) was computed, as shown in Figure 2. The results were in accord with previous reports, and the computational algorithm was adequate to solve the flow field around the underwater target.  
Figure 1: Schematic diagram of the computational domain for a square target.  
\[u_{i}^{n+1/2}=u_{i}^{n}-\frac{\Delta t}{2}(u_{i}^{n}\frac{\partial u_{i}^{n}}{ \partial x_{j}}+\frac{\partial p^{n}}{\partial x_{i}}-\frac{1}{Re}\frac{ \partial\tau_{ij}^{n}}{\partial x_{j}})+\frac{\Delta t^{2}}{8}u_{k}^{n}\frac{ \partial}{\partial x_{k}}(u_{j}^{n}\frac{\partial u_{i}^{n}}{\partial x_{i}}+2( \frac{\partial p^{n}}{\partial x_{j}}-\frac{1}{Re}\frac{\partial\tau_{ij}^{n}} {\partial x_{j}})) \tag{1}\]  
\[u_{i}^{n+1}=u_{i}^{n}-\Delta t(u_{j}^{n+1/2}\frac{\partial u_{i}^{n}}{ \partial x_{j}}+\frac{\partial p^{n+1}}{\partial x_{i}}-\frac{1}{Re}\frac{ \partial\tau_{ij}^{n+1/2}}{\partial x_{j}})+\frac{\Delta t^{2}}{2}u_{k}^{n+1/ 2}\frac{\partial}{\partial x_{k}}(u_{j}^{n+1/2}\frac{\partial u_{i}^{n}}{ \partial x_{i}}+\frac{\partial p^{n+1}}{\partial x_{i}}-\frac{1}{Re}\frac{ \partial\tau_{ij}^{n+1/2}}{\partial x_{j}})) \tag{2}\]  
\[\frac{\partial^{2}p^{n+1}}{\partial x_{i}\partial x_{i}}=\frac{1}{\Delta t} \frac{\partial}{\partial x_{i}}(u_{i}^{n}-u_{i}^{n+1})-\frac{\partial}{ \partial x_{i}}(u_{j}^{n+1/2}\frac{\partial u_{i}^{n+1/2}}{\partial x_{j}}- \frac{1}{Re}\frac{\partial\tau_{ij}^{n+1/2}}{\partial x_{j}}) \tag{3}\]  
where \(u_{i}\) is the \(i\)-component velocity, \(\rho\) is the water density, \(p\) is the pressure, \(\tau_{ij}\) is the deviatoric stresses, \(Re\) is the Reynolds number, \(n\), \(n+1/2\), and \(n+1\) denote the time points of \(t_{n}\), \(t_{n+1/2}\) and \(t_{n+1}\), respectively.
## 4 Mobility and Environmental Data Integration: System Overview  
In this section, we show the main components that together constitute the EMDI system. Specifically, it is composed of mobility, pollution and meteorological data collector, spatial join processor and data aggregator. They work synergically to achieve a QoS processing of mobility, pollution and meteorological data combined. We first start by formulating the problem in mathematical terms.
## 5 Results and Discussion  
### Datasets  
This paper employs the RML2016.10a dataset [5] for algorithm validation. The RML2016.10a dataset is a renowned public dataset, frequently adopted for machine learning investigations within the wireless signal modulation classification domain. Introduced by the DeepSig group in 2016, this dataset comprises over a million samples of radio signals. These signals are 1/Q-recorded, where \"I/Q\" stands for in-phase and quadrature. I and Q components represent the real and imaginary parts of the complex samples of a signal, respectively, which are essential for representing the amplitude and phase of a radio signal. For our study, the dataset was partitioned into training, testing, and validation sets with a distribution ratio of 0.8, 0.1, and 0.1.
## 4 Experiments
### Semantic Segmentation
#### 4.2.7 Qualitative Results  
Figure 4 shows qualitative results of our short-term and mid-term forecast. The rows show: the last observed frame, the future frame (unobserved), ground truth, and our forecast. We overlay the ground truth and our forecast over the future frame for the sake of more informative visualization. We also measure the ratio of correctly classified pixels for each of the examples individually to get a better sense of model accuracy. The results are as follows (col. 1-4): 93.3%, 90.0%, 95.0%, and 94.1%. The results show that our forecast is mainly accurate. In some cases, we are even able to recover some fascinating details. For example, in the second example of the mid-term forecast, our model accurately forecasts the pose of the pedestrians legs, which is extremely hard.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Mid-Term**} \\ \cline{2-5}  & **All** & **MO** & **All** & **MO** \\ \hline RN-18 w/o d.a. & 67.6 & 66.7 & 55.3 & 50.7 \\ RN-18 w/ d.a. & 68.3 & 67.4 & 55.9 & 51.8 \\ \hline DN-121 w/o d.a. & 69.6 & 68.4 & 57.4 & 54.0 \\ DN-121 w/ d.a. & 70.2 & 69.0 & 58.5 & 55.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation of data augmentation during forecasting training.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Short-Term**} & \multicolumn{2}{c}{**Mid-Term**} \\ \cline{2-5}  & **All** & **MO** & **All** & **MO** \\ \hline ML-F2 MF-DN-121 & 70.2 & 69.0 & 58.5 & 55.9 \\ \hline w/ oracle optical flow & 72.4 & 70.8 & 57.9 & 55.4 \\ w/ oracle skip conn. & 74.9 & 73.8 & 64.2 & 61.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of the forecasting model with oracle skip connections.
## 3 Exploring Speaker-Independent Dysarthric Speech Recognition
### Datasets and Data Split
#### 3.2.1 Rich-Resource Canonical Speech  
For rich-resource canonical speech, we use the **Common Voice** dataset [53] (version 13.0), which is a well-known dataset consisting of speech recordings of sentences, noted for its diversity in accents, languages, and demographics, making it ideal for training robust and inclusive speech recognition models. To ensure a comprehensive knowledge base and better alignment with target speech languages, we selected data from five languages: English, Spanish, Italian, Dutch, and Tamil.  
For each language, we limited the training set to a maximum of 100 h and the validation set to 1 h. This resulted in 100 h of training data for English, Spanish, and Italian, 75 h for Tamil, and 38 h for Dutch. Training data that combines all five languages are referred to as **C_rich** while data using only the target language's data are referred to as **C_rich_tgt**.