esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Thermal Storage Model  
As solar irradiation depends on the daylight and the clarity of the sky, CSP plants are usually non-dispatchable. In order to make them highly dispatchable, a TSS can be added to the CSP plants. Although the solar power source is intermittent, continuous electrical power can be produced due to the use of the TSS. A two-operation mode efficiencies  
Figure 8: HTF recirculation control valve (simplified).  
Figure 9: HTF protection control valve (simplified).  
(during charge and discharge modes) of more than 96 % was documented for TSS units consisting of hot and cold storage tanks of the molten salt [38].  
The storage system studied in this work will be explained based on two operation modes, including all control circuits.  
The excess solar irradiation is transferred to the TSS and provided as a substitute for the energy lost because of the clouds or during the night. This system allows for high operating flexibility, and produces more stable electricity. Hot and cold insulated tanks are connected by heat exchangers in APROS. The molten salt in both tanks is transferred to the heat exchangers by the series of thermal storage pumps that are located after the thermal storage tanks. The molten salt can be defined as a solution of potassium and sodium nitrates with known specifications.  
In the charge mode, the thermal oil from the SF heats the molten salt that flows from the cold tank to the hot storage tank, with a temperature of approximately 386 \({}^{\circ}\)C. It should be mentioned here that there will be losses in the heat exchangers between the thermal oil and the molten salt. This means that the hot molten salt will reach a temperature below the maximum value of HTF temperature of 393 \({}^{\circ}\)C, whereas the hot molten salt will reach a temperature of 386 \({}^{\circ}\)C. Note that the capacity of this system can reach a maximum value of about 1025 \(\mathrm{MW_{th}}\) h.  
The thermal energy in the thermal storage system can be used in the discharge mode, where the hot molten salt heats the thermal oil through the heat exchangers. Here, the thermal storage system provides the nominal mass flow of the HTF (600 kg/s) with a temperature of 377 \({}^{\circ}\)C in the evening period. This will affect the steam production performance, whereas the nominal amount of generated steam (55 kg/s) in the daylight will not be accomplished during the evening period. However, the hot thermal oil is transmitted to the PB. After that, the thermal oil is returned to a cold tank with a temperature of about 292 \({}^{\circ}\)C.
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 2 Methods  
The model presented in this paper consists of two main components: wav2 vec 2.0 and a feature fusion module. Our model architecture is illustrated in Figure 1. We first input the raw audio wave into wav2 vec 2.0 to extract corresponding audio features. Then, the obtained audio features were embedded with positional information through an embedding module. The resulting hidden layer features were subsequently fed into the audio feature fusion module, which consists of two parts: remix and fusion. Finally, the output results were obtained through a fully connected (FC) layer.
## 1 Introduction
We developed Application Programming Interfaces (APIs) of Broadcast, Gather, and AllReduce collective communication [10], which are frequently used as MPI data communication methods in distributed deep learning. In addition, we compare and analyze Python's two MPI communication methods, buffer communication, and object communication. * Second, we improve our Python-based MPI library by leveraging the advanced performance of On-package memory Multi-Channel Dynamic Random Access Memory (MCDRAM) of the Intel Xeon Phi Knights Landing processor [11], which is a typical many-core CPU used in supercomputing centers. To utilize MCDRAM, we adopted the Memkind library, a C-based library for memory allocation [12]. To use the Memkind library in a Python environment, Cython [13; 14] is used to implement wrapping in Python. * Third, for comparative analysis in various environments, we compare and analyze MPI communication performance by extensive experiments according to the Cluster mode and Memory mode of the Intel Xeon Phi Knights Landing processor. In addition, considering the practical distributed deep learning environments, we also conducted the experiment on the open source virtualization platform Docker [15] to see the performance difference in a Cloud computing environment. The remainder of this paper is organized as follows. Section 2 presents related works. Section 3 explains the background of our study. Section 4 explains how we developed a Python-based MPI library to exploit MCDRAM and how we bound it with Docker Container. Section 5 describes the experiment setup and compares the results to evaluate the proposed method with existing methods. Section 6 summarizes the experiment results and discussions. Section 7 concludes this paper.
## 3 Results
### Evaluation of Semantic Segmentation  
The performance of the semantic segmentation model was assessed based on DSC, IoU, and fps across five-fold cross-validation. The results for each fold, along with their mean and standard deviation (SD), are presented in Table 2. The model demonstrated a high segmentation accuracy, with an average DSC of 0.9615 \(\pm\) 0.0112 and an IoU of 0.9261 \(\pm\) 0.0206 across the five folds. These values indicate a strong agreement between the predicted segmentation masks and the ground-truth labels.  
Additionally, the processing speed was evaluated by measuring fps during inference. The average fps across all folds was 16.61 \(\pm\) 0.92, with individual fold values ranging from 15.59 to 17.60. This suggests that the model is capable of performing segmentation efficiently, maintaining a high processing speed while achieving high segmentation accuracy. Table 2 provides a detailed breakdown of the DSC, IoU, and fps values for each fold.
# Adaptive Trajectory Tracking Control for
###### Abstract  
This paper proposes an adaptive trajectory tracking control strategy for underactuated unmanned surface vehicles subject to unknown dynamics and time-varing external disturbances. In short, the goal of this paper is to provide a control strategy that allows an underactuated unmanned surface vehicle to track a time dependent trajectory. First, a first-order sliding surface is introduced into the design of surge control law to converge to surge tracking error, and then a second-order sliding surface is hired to design yaw control law to deal with sway motion tracking error. Meanwhile, neural network minimum learning parameter method, which has a smaller amount of computation than a multilayer neural network, is employed to preserve the control law robustness against unknown dynamics and time-varing disturbances induced by wind, waves and ocean currents. Furthermore, much effort is made to obtain uniform ultimate bounded stability for the closed-loop control system. Finally, the numerical simulation experiments of straight line and circle trajectory tracking have been given to prove the correctness and feasibility of the proposed control strategy.  
adaptive; trajectory tracking; underactuated unmanned surface vehicle; sliding mode; neural network minimum learning parameter method +
Footnote †: journal: Applied Sciences
## 3 Control Strategy
### Particle Swarm Optimization (PSO)
#### 3.1.2 PSO-Based Optimizer-2  
The other optimizer is used to determine the \(\mathrm{K_{i}}\) value of the I controller, which controls the PWM generator inside the DC/DC buck converter, which reduces the voltage value at the output of the fuel cell to the voltage values needed for the motors, and the R, L and C values of the same converter. Optimizer-2 uses reference values of motor voltage, output voltages at the output of the DC/DC buck converters and the consumed power from the fuel cell for excitations of all motors according to control inputs, which are sent by Optimizer-1-optimized PID controllers. Optimizer-2 tries to find the best (optimal) values for the electronic components of DC/DC converters and \(\mathrm{K_{d_{c}}}\) values for controllers of four converters' PWM generation unit to adjust the switching factor by minimizing the errors between the reference and actual output voltages and consuming power from the fuel cell. The PSO-based DC/DC converters' tuning block diagram of Optimizer-2 is shown in Figure 6.  
Figure 5: (**a**) PSO-based PID tuning diagram of the AGV’s route-tracking control structure. (**b**) The optimization algorithm of the PSO method.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**j = 1, 2, 3, 4** & \(\mathrm{K_{P_{i}}}\) & \(\mathrm{K_{I_{j}}}\) & \(\mathrm{K_{D_{j}}}\) \\ \hline Maximum limit & 7500 & 5000 & 25 \\ Minimum limit & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The limits values of PID controllers’ parameters for Optimizer -1.  
Similarly, the limits of the components' coefficients and integral coefficients of the I controllers need to be defined for the optimization procedure. For Optimizer-2, the limits of these optimized parameters are defined as shown in Table 6.  
For both optimizers, the integral of time absolute error (ITAE) was chosen as the objective function for both power consumption and route-tracking optimization algorithms. The most ideal values were determined by minimizing the consumed power and the errors for the deviation in the route. The theoretical expression of the ITAE function is given in the equation below.  
\[\mathrm{ITAE}=\int_{0}^{\infty}\mathrm{t}|\mathrm{e}(\mathrm{t})|\mathrm{dt} \tag{20}\]  
\[\mathrm{e}(\mathrm{t})=\sum\mathrm{RV}_{\mathrm{i}}-\mathrm{AV}_{\mathrm{i}} \tag{21}\]  
where RV and AV denote the reference values and actual values of the parameters, respectively. For the route-tracking process, the total error is calculated by summation of the errors of the translational and rotation motions. Similarly, the voltage-tracking errors of each converter and total power consumption of the fuel cell are used in an attempt to minimize Optimizer-2, while the PSO algorithms try to eliminate or minimize the total error for both optimizers. The optimal values of the coefficients of controllers and converters are thus determined.
## 1 Introduction
An introduction to the standardization efforts is reported in Section 2.4, whereas a more comprehensive, cloud- and service-oriented presentation, and comparison of both reference architectures can be found in [16]. This evolutionary process is not straightforward, and an analysis reported also in [10] investigated the evolution of _distributed intelligence architectures_ and their expected path of development, what many today identify with Multi-Agent Systems (MAS) [17]. While IIoT is a growth factor for MES in some industries, the availability of newer technologies and approaches can hinder MES deployments. Companies are trying to reduce their future risks and understand future directions in the plethora of I4.0 standards [18]. They are willing to adopt a technology which will be part of the new standards, which is easy to integrate, use, and expand, as well as which can flexibly accommodate new technologies and applications as they are developed. Research work outlined in this paper aimed ultimately at identifying the minimum set of service concepts and technologies that every CPPS should commonly understand and leverage in their standardized service provision. In line with the argumentation reported in [19], I4.0 is requiring also a new style of MES for several reasons. Firstly, modern consumers are demanding customization. I4.0 offers an unprecedented opportunity for this, but companies need a plant floor software that is ready to adapt to the underlying technologies. Secondly, I4.0 is a concept revolving around CPSs, which is about the merging of virtual and real worlds and the creation of links between information and operations technologies. Thirdly, reference architectures are not always easily nor efficiently applicable in small to mid sized development projects due to their complexity and design requirements. However, all the above issues cannot be resolved overnight and MES needs to be upgraded alongside the challenges and opportunities of I4.0. In particular, the shop-floor has to be optimized through decentralized management, where autonomous agents may be seen as a marketplace with demand (the products being built) and supply (the equipment). Future MES needs to accommodate connectivity, mobile, cloud, and advanced service-oriented computing, as well as human collaboration in order to face the challenges of a new industrialization era [19]. Supporting Human-Robot Collaboration (HRC) in a multi-agent setting is a current issue of special relevance [20]. Furthermore, MES needs context-resolution capabilities in order to enable components of a CPPS to act autonomously and release their own intelligence even in a decentralized setting. In this sense, an MES is an orchestrator of complex production processes, typically over multiple layers of abstraction. Finally, IIoT sensory activity and CPPS communications create new data flows and their vertical integration is crucial to ensure an effective response in the enterprise.
# Predicting Ceramic Wool Diameter by Motor Frequency Using Improved BP Neural Network
###### Abstract  
Ceramic wool was prepared by the melt-spinning method, and the diameter was the main factor severely affecting the performance of the final product which was difficult to check online. The current study discusses the approximate simulation of the fiber formation and presents a fast precision measuring method to predict the ceramic wool diameter using an improved Back-Propagation (BP) neural network. Particle Swarm Optimizer (PSO) was employed to optimize the neural network structure for its presentation of the relationship between the motor frequency of the spinning wheel and the ceramic wool diameter. The superiority of this method was demonstrated by experiment compared with the least square method (LSM). The mean measurement error of PSO-BP was 0.471%, which was lower than that of LSM. The presented PSO-BP method was very valuable for predicting the wool diameter, and the neural networks could solve nonlinear problems successfully, which was certified by the actual prediction of ceramic wool diameter.  
ceramic wool diameter; motor frequency; PSO-BP; wool diameter prediction Article
# Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
## 5 Guided Image Filtering
### OpenCL Code
#### 5.2.1 Streaming Data I/O  
The guided image filter algorithm operates on data that is read from global memory. This data contains either different color channels or 3D-coordinates. The different dimensions of a specific pixel, however, are stored together in an interleaved fashion as is shown in Figure 6. This figure shows a 1D-array, with image data stored sequentially row-wise in memory, containing per pixel the concatenated color channels, Red (R), Green (G) and Blue (B). In order to cope with the interleaved storage of color channels, all computations are duplicated three times, once for each color channel. In this way, data can be read sequentially without discarding color channels. This leads to a SIMD-implementation in which three color channels are processed simultaneously. SIMD operations in a single work-item are realized using the OpenCL vector type. The Intel FPGA SDK for OpenCL does not support native vectorized data types containing three elements [13]. Therefore, a custom struct was defined that contains the three different channels (Listing 2). The filtering input and output  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **FLOPS** & **Radius = 3** & **Radius = 6** \\ \hline
1 & \(6(2r+1)(2r+1)\) & 294 & 1014 \\
2 & 6 & 6 & 6 \\
3 & 5 & 5 & 5 \\
4 & \(2(2r+1)(2r+1)\) & 98 & 338 \\
5 & 1 & 1 & 1 \\ Total/px & \(32r^{2}+32r+22\) & 406 & 1366 \\ Total/image & \(3(32r^{2}+32r+22)^{*}1920^{*}1080\) & \(2.51\times 10^{9}\) & \(8.49\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: FLOPs calculation for a full-HD RGB image with radii \(r=3\) and \(r=6\).  
images consist of real number values, while the guidance image consists of color values between 0 and 255. Therefore, two different structs were defined, respectively with 3 floats and 3 char fields.  
```
1__attribute__((packed ))
2 structguide_image
3{
4<type> R;
5<type> G;
6<type> B;
7};
```  
Listing 2: Structure used to vectorize image channels. Attribute \"packed\" creates a continuous data stream without padding.  
Besides vectorizing the color channels, the mean value calculation \(f_{mean,r}\) is identical for each of its arguments. Therefore, we use vector types float4 and float2 respectively in Step 1 and 4 to compute the mean values in parallel.
# On the Utilization of Reversible Colour Transforms for Lossless 2-D Data Compression
## 4 Experimental Setup And Results
### Lossless Compression of 2-D EEG Data  
Inspection and storage of EEG produce large data containing patient's significant information. Compression algorithms reduce the storage capacity of the data and provide effective methods for preventing overwriting due to the small capacity of EEG machines.  
[PERSON] et al. in [52] proposed a method based on securing the derivation of EEG instead of the unprocessed data. Lossless compression was achieved by the amalgamation of various techniques including Huffman encoding and vector quantization. [PERSON] et al. in [53] devised a methodology utilizing wavelets to achieve compression over multi-channel EEG. The effective compression is achieved by the utilization of wavelet-based volumetric coding, energy-based lossless compression of the resulting wavelet bands followed by coding based on the tensor. The latter scheme recorded with the best compression results.  
[PERSON] et al. in [54] proposed a multi-channel EEG compression scheme based on the Independent Component Analysis (ICA) followed by Set Partitioning in Hierarchical Trees (SPIHT). Prior to the application of ICA, the algorithm utilizes Principal Component Analysis (PCA) followed by the application of SPIHT compressing any residues adding effective compression in comparison to the independent components. [PERSON] et al. in [55] proposed a compression method based on No List Set Partitioning in Hierarchical Trees (NLSPIIHT) algorithm for 1.5 D EEG. It considers the I-D DWT instead of SPIHT based 2-D compression requiring lower computational complexity and power. [PERSON] et al. in [56] proposed another scheme utilizing the same techniques as mentioned above i.e., DWT and SPIIHT.  
Experimentation was conducted utilizing the EEG data samples from the University of Bonn [57]. Figure 10 depicts the compression results achieved for the EEG data in terms of size and ratios. Five datasets labelled (A-E) constitute the data where each label presents an ailment. The proposed YA6 UA6 VA6 RCT based BBWCA shows a significant improvement in compression for the source signals.  
Figure 10: Comparative analysis of compression size and ratios for lossless compression of EEG data (size in KB).
## 1 Introduction
In [9], the reliability of wireless sensor networks is analyzed by considering random failures, energy consumption, and environmental randomness, and their calculation methods are provided based on the algebraic graph theory and Monte Carlo simulations. In the analysis of the influence of the sense-control terminal on reliability, we need to consider the influence of the sense-control terminal on the fault handling process. In Reference [10], by introducing the state factor of switch automation, the traditional failure mode consequence analysis method is improved, and the importance of the sense-control terminal in the improvement of the power supply reliability of the distribution network is verified by an actual example. In Reference [11], the model of an integrated distribution system is established by the fault tree method, which illustrates the impact of substations on the primary distribution system under various automation configurations. However, due to the massive distribution of low-voltage power supply stations, the reliability evaluation model is difficult to apply to the reliability analysis of low-voltage distribution networks. In Reference [12], the influence of the sense-control terminal on the fault outage time is analyzed, and the reliability calculation model considering the location time in the fault section is established. Based on the component failure rate curve, the reliability index of the distribution network is calculated under different distribution terminal configuration schemes, and the whole equipment life cycle is considered [13]. Existing research has shown that cyber system failures can have a significant impact on the reliability of distribution networks. Although relevant literature has included consideration of sense-control terminals, the impact of sensing terminal failure on the reliability of the distribution network has not been studied. At the same time, the voltage level involved in the reliability assessment of distribution networks is mostly a single voltage level; the reliability assessment of medium and low voltage levels has not been studied. The main contributions of this paper are twofold. (1) The impact of sensing terminal failure on reliability is considered in this paper. We analyze the influence of the fault of the sense-control terminal on the outage time and the outage range from the three stages of fault location, fault isolation, and power restoration. Meanwhile, the fault influence table of the sense-control terminal is established. (2) Compared to the pioneer work, we present a reliability evaluation method enable to realize the overall evaluation of medium and low voltage levels. According to the features of the distribution network, the sequential Monte Carlo simulation method and the failure mode effects analysis method have been proposed.