esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Tests of the Proposed PRNG
### Linear Complexity  
The Berlekamp-Massey algorithm is used to estimate the linear complexity of a PRNG, and it is as tool to determine the shortest linear feedback shift register (LFSR) that produces a specific binary sequence [88]. Linear complexity in a PRNG is an important security condition when we want to know if such PRNG is suitable for cryptographic applications. A high linear complexity by itself does not guarantee any pseudorandomness property of the sequence under consideration, and therefore, it must also be known whether the sequences produced with the proposed PRNG pass the pseudo-randomness test suites of the NIST SP 800-22 and TestU01. Then we use the Berlekamp Massey algorithm to estimate the linear complexity, \(L_{c}(S)\), of pseudorandom sequences, \(S^{t}\), \(t=1,2,3,...,W\), which are produced by the proposed PRNG and they are read in binary format. Basically, this test determines the minimum degree of the polynomial that produces, in a linear feedback shift register (LFSR), a sequence like \(S\). Therefore, a PRNG with the highest possible linear complexity is desirable. For this test, we generate a set of \(W=1\times 10^{3}\) pseudorandom sequences that in binary format have 2 \(\times\)\(10^{5}\) bits. In this case, we use different initial conditions with slightly differences among them. Then, we compute the mean and the standard deviation of \(L_{c}(S^{t})\) for \(1\times 10^{3}\) sequences. Note in Figure 13 that \(L_{c}(S)\) reaches a maximum level of \(1\times 10^{5}\), and the variation of the standard deviation values are small. Furthermore, note that the linear complexity test helps us to confirm that the approaches we implemented in the proposed PRNG have worked in order to increase linear complexity and avoid the problems presented by PRNGs based on single chaotic map.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PRNG Configuration**} \\ \hline  & \multicolumn{2}{c}{**8-Bit**} & \multicolumn{2}{c}{**16-Bit**} & \multicolumn{2}{c}{**32-Bit**} \\ \hline
**Size** & **Alphabit** & **Rabbit** & **Alphabit** & **Rabbit** & **Alphabit** & **Rabbit** \\ \hline \(2^{26}\) & 17/17 & 40/40 & 17/17 & 40/40 & 17/17 & 40/40 \\ \(2^{28}\) & 17/17 & 40/40 & 17/17 & 40/40 & 17/17 & 40/40 \\ \(2^{30}\) & 17/17 & 40/40 & 17/17 & 40/40 & 17/17 & 40/40 \\
**BigCrush** & \multicolumn{2}{c}{160/160} & \multicolumn{2}{c}{160/160} & \multicolumn{2}{c}{160/160} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of pseudorandomness tests using TestU01 suite.
## 3 Results and Discussion  
Each slag sample was quenched and sent for laboratory tests. The results of the ICP-OES analysis, wet chemical analysis, and coulometric analysis are given in Table 2. The experiments are given as test numbers, which represent the sample number also. The elemental composition obtained from ICP-OES was converted into weight percentage. The composition of each component obtained in Table 2 was slightly different than the raw materials that were used, as shown in Table 1. An increase in the quantity of TiO\({}_{2}\) was observed in all samples compared to the values in Table 1, which indicates the oxidation of titanium from Ti\({}^{3+}\) to a Ti\({}^{4+}\) oxidation state. The amount of FeO and MgO remained almost the same, whereas the other impurities had some variation. Another reason for the difference can be attributed to the small sample size that was used for ICP-OES. Later, these values were normalized to represent a 100% composition value in the final slag composition values.  
Energy dispersive X-ray microscopy (EDS) was performed to analyze the elemental composition of each phase and then compared with XRD to get a better idea about the phases present as XRD is not an accurate measuring technique for quantitative analysis. Figure 5 shows the XRD spectra of the cooled synthetic slag samples. Three samples were selected to show the peaks corresponding to the pseudobrookite, rutile, and silicate phases, as shown on the XRD pattern in Figure 5. The major and minor phases observed in each sample are listed in Table 3. It was observed that the pseudobrookite phase, having an M\({}_{3}\)O\({}_{5}\) composition of divalent and trivalent ions, was consistent in all samples. The major phases were seen as a solid solution of various titanates, such as pseudobrookite, armalcolite, and Mg\({}_{2}\)TiO\({}_{5}\), all having pseudobrookite-karrooite structure. Similar phases and structures were observed in a high titania industrial slag study [29]. The karrooite structure can accommodate a wide variety of mixed-valence metal cations. Other authors also reported that the solidified titania slag consisted mainly of a uniform M\({}_{3}\)O\({}_{5}\) solid solution with minor phases consisting of rutile, silicate glass phase, metallic iron, and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Weight Percentage of Each Component**} \\ \cline{2-9}  & **TiO\({}_{2}\)** & **Ti\({}_{2}\)O\({}_{3}\)** & **FeO** & **MgO** & **MnO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** \\ \hline
1 & 62.47 & 26.76 & 5.02 & 0.53 & 1.48 & 1.50 & 2.25 \\
2 & 67.16 & 21.40 & 6.48 & 0.71 & 1.63 & 1.11 & 1.51 \\
3 & 66.39 & 24.38 & 2  
magneli phases [30; 31]. The minor phases found in our samples consisted of rutile, magneli phases (titanium oxide), silicate phase, and anatase in a few samples.
# A Hierarchical Fusion SAR Image Change-Detection Method Based on HF-CRF Model
## 2 Materials and Methods
### D-DRUNet Fusion Semantic Segmentation Network  
UNet networks based on an encoder-decoder structure are commonly employed in medical imaging and change-detection segmentation tasks, due to their ability to learn from small datasets [56]. However, U-Net does not have multimodal feature fusion capability, due to the limitation of a single encoder structure. Additionally, the fixed convolutional kernel model limits its ability to generalize feature extraction, which results in difficulty in detecting change details and the increasing of missed alarms in detection.  
In order to solve the above two problems, we designed a novel D-DRUNet segmentation network model, which mainly includes three features: adopting a dual encoder-single decoder structure to solve the network-level multimodal fusion design problem; introducing a dynamic region convolution kernel (DRConv) and designing a multiscale guide mask module to improve the feature extraction capability of the network; a hierarchical fusion mechanism to realize the bottom and upsampling stages in turn of the multi-level network feature fusion. The specific network structure is shown in Figure 3, and we elaborate it in the following sections.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 1 Introduction
### Fault Diagnosis Using Alarm Data
Therefore, the established vector representations may deviate from the actual fault patterns in terms of capturing the relationships between the alarms. For the second issue, the process of averaging all alarm vectors in a sequence compresses much of the informative content in the word embedding space and fails to fully exploit its potential. In order to address the aforementioned issues, this paper presents an unsupervised clustering method for alarm sequences, aiming to group similar alarm sequences triggered by the same fault event. The clusters obtained through clustering can facilitate the identification of fault types triggering unknown alarm sequences, thereby achieving fault diagnosis for wind turbines. It becomes possible to assist on-site operational personnel at wind farms in promptly locating wind turbine malfunctions, thereby reducing maintenance time and downtime losses. The proposed method consists of three main steps: preprocessing alarm data, obtaining vector representations of alarms, and finally, clustering alarm sequences. To address the first issue, we propose an improved Word2 vec approach that fuses alarm category information obtained from domain knowledge. As for the second issue, we apply two novel distance metrics in clustering to enhance clustering performance. These two distance metrics have shown successful applications in sentence semantic similarity analysis, but they remain unexplored in alarm sequence comparison. This paper makes the following main contributions:  
1. Proposing an improved Word2 vec approach that fuses domain knowledge in the form of alarm category information, resulting in a more realistic vector representation of alarms aligned with actual fault patterns. 2. Employing word mover's distance (WMD) and word rotator's distance (WRD) separately for measuring the distance of alarm sequences, thereby improving clustering performance. 3. Conducting statistical analysis of alarms of a wind farm to gain insights into alarm data. The subsequent sections of this paper are structured as follows: In Section 2, an introduction to alarm data is provided, along with a statistical analysis of the data. Section 3 elaborates on the fundamental principles of Word2 vec and K-means clustering. Section 4 introduces the proposed method. The results and discussions are encompassed in Section 5, followed by the conclusions section.
## 2 Experimental Platform  
An arc fault experimental platform which is based on the low-voltage electric standards is constructed as shown in Figure 1. Electrical standards include UL1699, IEC 62606: 2013 and GB/T 31143-2014 which are used as reference standards to construct the experimental platform. Typicalexperimental loads are composed of six 50 W halogen lamps, a 0.75 kW electrical hand drill, two 40 W fluorescent lamps, a 0.35 kW computer, a 1.2 kW electric stove, a 1 kW dimming lamp, a 1.2 kW vacuum cleaner, a 1.1 kW air conditioning unit and a 2.2 kW air compressor. The arc generator used for simulations of arc faults in circuits consists of a stationary electrode and a moving electrode. A PXI (the abbreviated form of the peripheral component interconnection extension for instrumentation) data acquisition system and a high frequency transducer are used to acquire a large number of high frequency signals in circuits. The band width of PXI system is 100 MHz. Different types of load signals in arc faults and normal states are acquired through this experimental platform, and they will be used to analyze the common features of arc faults in the following section.
## 1 Introduction  
### Background  
The pressure in a building is generated by the wind, the softening effect, and the air conditioning system based on the heating and cooling load of each room [1]. Pressure differences in the building envelope cause unintended airflow through gaps in the building, and this is known as leakage airflow [2]. When the pressure inside a building is lower than the pressure outside, air infiltration occurs, wherein unfiltered air from the building envelope enters the interior, and this has garnered significant research interest on the effects of infiltration on buildings. For example, [PERSON] [3] demonstrated that infiltration increases the total heating and cooling energy consumption of a building by approximately 10-30%, depending on factors such as the location of the building, external climatic conditions, and airtightness. Another study on houses in Finland and Estonia observed that heating and cooling energy due to leakage through the building envelope accounts for 30 to 50% of the building heating and cooling energy [4; 5]. [PERSON] [6] analyzed the effect of infiltration on radon gas concentrations and reported that in areas with high radon concentrations, a 0.5 ACH increase in the infiltration rate can increase the radon concentrations by approximately 30-40%. [PERSON] [7] emphasized the importance of building pressure management for moisture control, highlighting the promotion of moisture infiltration by indoor sound pressure, and the subsequent effects, such as mold formation, degradation in building material performance, and structural defects. Further, many previous studies have demonstrated that leakage airflow from the building envelope causes problems, such as increased indoor heating and cooling energy, poor indoor air quality, and moisture diffusion [8; 9; 10; 11; 12].  
To address these issues, airflow must be managed to account for pressure differences in the air conditioning system control, and an accurate understanding of leakage is critical. Previous studies on the design of Heating, Ventilating, and Air Conditioning (HVAC) systems for building pressure control have demonstrated that knowledge of the leakage rate can help in minimizing the indoor-outdoor pressure difference, thus saving energy and improving the overall performance of the HVAC system [13]. Ventilation system control that considers the building pressure is important for optimizing the indoor airflow and pressure balance, preventing pollutants from entering the building, and maintaining a comfortable indoor environment [14]. Therefore, knowing the airflow of leakage is essential for developing an efficient pressure control strategy within the air conditioning system.
# Robust Adaptive Synchronization of a Class of Uncertain Chaotic Systems with Unknown Time-Delay
## 5 Conclusions  
In this paper, a novel adaptive control strategy for robust synchronization of two uncertain chaotic systems with unknown time delays is presented. System uncertainties have been applied in the form of external disturbances and bounded non-linear uncertainties in master and slave chaotic systems with unknown time delays. In the proposed control mechanism, two controllers are utilized, and the robust stability of the controllers is proved by Lyapunov theory in the form of theorems. Adaptation rules for estimation of the uncertain parameters and unknown time delays in master and slave chaotic systems have also been proposed, and the capability of these rules to estimate uncertain parameters and unknown time delays has been proved using Lyapunov theory. Finally, to evaluate the proposed adaptive control mechanism, two uncertain Jerk chaotic systems with unknown fixed and variable time delays have been simulated. The simulation results reveal the proficiency of the proposed method in robust synchronization of the aforementioned systems and desired estimation of uncertain parameters and unknown fixed and variable time delays of the systems.  
Figure 8: The robust synchronization pressures of systems (25), (26) with unknown variable time delays based on the proposed adaptive control strategy where it is activated at \(t=5\,\mathrm{s}\). Solid linesare the dynamics of the master system for \(\tau_{1}=0.45\), dashed-dotted lines are the dynamics of the master system based on step-change \(\tau_{1}=0.45\) to \(\tau_{1}=0.5\) at \(t=38.45\) s, and dotted lines are the dynamics of the slave system based on step-change \(\tau_{2}=0.1\) to \(\tau_{2}=0.7\) at \(t=38.45\) s.  
Figure 9: Controller signals based on the proposed robust adaptive control strategy (11) where it is activated at \(t=5\,\mathrm{s}\). The solid line is the control signal based on the fixed unknown time delays \(\tau_{1}=0.45\), \(\tau_{2}=0.1\) and the dotted line isthe control signalbased on step-change of unknown variable time delays \(\tau_{1}=0.45\) to \(\tau_{1}=0.5\) at \(t=38.45\) s and \(\tau_{2}=0.1\) to \(\tau_{2}=0.7\) at \(t=38.45\) s.  
**Author Contributions**: Conceptualization, A.Z. and S.Z.M.; methodology, A.Z., S.Z.M. and M.H.; software, N.G. and A.S.; validation, A.Z., M.H., and A.M.; formal analysis, A.S., R.A., N.G., and M.J.; resources, A.Z. and S.Z.M.; writing--original draft preparation, A.K., A.S., R.A and N.G.; writing--review and editing, A.S., A.Z., A.M., U.R.A., A.K. and M.J.; visualization, A.Z., M.H., and A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding**: The author(s) received no financial support for the research, authorship, and/or publication of this article.  
**Conflicts of Interest**: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# INS/Vision Integrated Navigation System Based on a Navigation Cell Model of the [PERSON]
## 3 Results Construction of INS/Vision Brain-like Navigation Hardware System
### Construction of the INS Hardware System
#### 3.1.2 Serial Data Collection  
In the process of serial data collection, the RS422 interface is used as the transmission mode of STIM202. As we all know, RS422 has high anti-interference ability and avoids the interference of noise in the transmission process, and the transmission rate can up to 460,800 bits/s. The serial port interrupt mode is applied to receive data. It is noted that the interrupt priority level is set to the second level. In addition, in order to avoid mutual interference between the data, the frame head and frame tail of the data are set to trigger the receiving mode of serial data communication. Furthermore, we can determine whether the received data has an error code or not by judging the number of significant bits of the received data.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.1 Traceability  
Before looking at the accuracy data, the traceability of the instruments must be understood. Figure 10 shows the various instrument pathways to the NIST. The top half of Figure 10 shows the traceability pathway for the reflective instruments. Basically, there were two paths as follows: the USGS EROS ASD system was calibrated using a Labsphere spectralon calibration panel which was calibrated via the Labsphere calibration laboratory and adheres to the NIST Volunteer Laboratory Accreditation Program. The SDSU ASD system is traceable through its spectralon panel to calibration by the University of Arizona's calibration laboratory. The RIT UAS reflective instruments were calibrated using the ELM based on observations of the Permaflect panels, as measured by the SDSU ASD. As a result, the traceability path is longer for the RIT UAS and has greater uncertainty attached to it.  
Targets which had an absolute calibration attached to them included the Permaflect panels, which were calibrated at Labsphere, and the Mystery panels, which were calibrated at the University of Arizona. University of Arizona's laboratory uncertainty varies from 0.0035 to 0.0065 absolute reflectance units depending on the wavelength. Labsphere's uncertainty is similar for the Permaflect 55% panel at 0.0064 absolute reflectance units, on average, from the visible through the SWIR and up to 0.035 at 2200 nm. Because of the low signal level from the Permaflect 5% panel, traceability uncertainties were from 0.012 to 0.043 absolute reflectance units.  
The absolute calibration placed on the Permaflect panels uses the diffuse reflectance method [35]. The absolute calibration placed on the Mystery panels included an illumination/viewing geometry that was almost identical to that which existed during the BigMAC 10:30 AM acquisitions. Because of this, the accuracy analysis was limited to only the morning acquisition.
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 5 Hardware Architecture of the FPGA-Based System: An Application Example
### The Proposed FPGA-Based Architecture
#### 5.2.1 The Timing Block
The modules k_clock_generator and k_generator in Figure 9 have the aim to produce a clock pulse (named k_clock) every 1024 pulses of the system clock and to update the numerical value of the k index every _k_clock, respectively. The module twid_address_gen produces the sequence of ROM addresses in which twiddle factors are stored for each \(k\) index value.
## 1 Introduction  
### Motivation and Incitement  
For long distances and huge amounts of power transfer, HVDC technology has proven to be the most efficient, cost-effective, and established technology. There are several technological and environmental advantages of the HVDC system compared with HVAC, such as less transmission loss, lesser required transmission tower, improved system stability, narrowed transmission corridor, higher efficiency, better power quality, and being eco-friendly [1; 2; 3]. However, HVDC systems face various challenges in terms of protection viewpoints, more specifically relating to fault detection, classification, and location [3]. The DC fault current has a large peak and steady values within a few milliseconds. This requires high-speed fault detection and isolation methods to handle the critical implications in an HVDC grid. In this regard, intelligent fault protection schemes are required against the chance of damage triggered by short circuit faults and to provide continuous power to connected loads. The schemes should pose self-diagnosing and self-healing capabilities that can detect and isolate the faulty section as soon as possible [3].
## 3 Results and Discussion
### Characterization of the Nanoparticles Containing Zinc Complexes
#### 3.2.4 DR UV-Visible Analysis  
UV-visible absorption spectroscopy is a widely used technique to examine the optical properties of nano-sized particles. The absorption capacity depends on the size, concentration, level of aggregation and refractive index of the nanoparticles. Substances with a higher molecular weight tend to have a lower absorption capacity than those with a lower molecular weight. Substances that are more polar have a higher absorption capacity than  
Figure 3: Mid-infrared spectra of the ZnNPs and VLSENPs samples.  
those that are less polar. The DR UV-vis absorption spectra recorded for both the VLSENPs and ZnNP samples are shown in Figure 4.  
The VLESNP sample exhibits a higher absorption capacity than the ZnNP sample since the complexes obtained by the formation of the polar covalent/coordinative covalent bonds between the zinc ions and the ligands present in the vine leaves aqueous extract have a higher molecular weight and a lower polarity compared to the free ligands contained by vine leaves. The DR UV-visible spectrum of ZnNPs consists of a broad band with a maximum absorption located in the domain of 300-400 nm. The two absorption peaks that occur at 245 nm and 290 nm highlight the formation of the zinc-phenolic acids and zinc-flavonoids complexes. After the complexation of polyphenols with zinc ions, the electron density at the O atoms from the hydroxyl groups of the polyphenols increased, the degree of conjugation increased, the electron delocalization in the system enhanced and the energy difference between the excited state and the ground state has decreased, so that the intensity of the absorption band corresponding to the \(\pi\rightarrow\pi^{\star}\) transitions decreased. These results agree with the scientific literature data according to which the zinc-polyphenols complexes exhibit a broad UV absorption band in the range comprised from 280-410 nm [8,21,39,40]. The absorption band recorded in the visible region (400-800 nm) is due to the \(n\rightarrow\pi^{\star}\) electronic transitions wherein the electron pairs are involved from the \(n\) non-bonding orbitals of the oxygen atoms from the carbonyl groups and the \(\pi^{\star}\) antibonding molecular orbitals of the carbon atoms from the aromatic rings as well as to the \(\pi\rightarrow\pi^{\star}\) electronic transitions.