esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 An Adaptive Attitude Estimation Method Based on Multi-Sensor Fusion
### Adaptive Threshold Criterion  
The accelerometer output data are used as the basis to judge the influence of maneuvering acceleration for the attitude estimation of roll angle \(\gamma\) and pitch angle \(\theta\), respectively. Collect the output data of \(X\)-axis and \(Y\)-axis of accelerometer in the conditions of the maneuvering acceleration exists and does not exist. If there are \(N\) groups of data within the sampling interval, the standard deviation(STD) of them can be expressed as  
\[q_{STD}=\sqrt{\frac{1}{N-1}\sum\limits_{i=1}^{N}\left(A_{i}-\mu\right)^{2}}, \tag{7}\]  
where \(A_{i}\) is the \(i\)-th output of the single axis of the accelerometer, and \(\mu\) is the average value of \(N\) groups data of the corresponding axis.  
Take the \(X\)-axis as an example and collect the output data of \(X\)-axis of accelerometer in two conditions of the same interval. The STD of two conditions are shown in Figure 3. It shows that the STD of acceleration changes slowly when the maneuvering acceleration does not exist and the STD of the acceleration fluctuates greatly and changes rapidly when the maneuvering acceleration exists.  
Based on the above analysis, an adaptive threshold criterion method is adopted. The threshold value \(\lambda_{sx}\) is calculated according to the STD of \(x\)-axis and \(y\)-axis acceleration obtained in the fixed length sliding window, which can be expressed  
\[\lambda_{sx}=\sum\limits_{i=1}^{n}\left(Q_{STDxi}\cdot p_{xi}\right)\cdot k_{x}, \tag{8}\]  
where \(n\) is the fixed window length which used to calculate the threshold. \(k_{x}\) is the magnification factor. \(Q_{STDxi}\) is the STD value calculated of each sampling interval in the window time. \(p_{xi}\) is the weight factor and \(p_{x1}\leq p_{x2}\leq\cdots\leq p_{xnt}\).  
\[p_{xi}=\frac{i^{m}}{\sum\limits_{i=1}^{n}i^{m}},m=2, \tag{9}\]  
Figure 3: STD of the output of X-axis accelerometer. (**a**) maneuvering acceleration does not exist. (**b**) maneuvering acceleration exists.  
where \(i\) is the serial number in the window time. Compared with \(m=1\), it can increase the influence of approaching time; compared with \(m>2\), it will not highlight the approaching time too much to suppress the influence of gross error on the threshold calculation.  
If the STD of acceleration calculated in the current period is less than the threshold \(\lambda_{sx}\), it could be considered that the influence of the maneuvering acceleration in the current period can be ignored. Otherwise, it is considered that the influence of the acceleration in the current period cannot be ignored. The threshold setting and the method of judging whether the maneuvering acceleration exists of \(Y\)-axis are similar to that of \(X\)-axis.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 3 Method
### Network Architecture
#### 3.2.2 Discriminators  
The structure of the D\({}_{1}\) discriminator is inspired by the classical VGG [46] network, but simplified and adapted to suit specific input sizes and feature extraction needs. The network consists of a series of convolutional layers, each followed by a Leaky ReLU [47] activation function that introduces nonlinearity to facilitate training. The convolutional layers are designed to gradually increase the number of channels and decrease the spatial dimensions, thus capturing image features from low to high levels. Specifically, the network first expands the number of channels of the input image through two convolutional layers, followed by three additional convolutional layers to further expand the number of channels and gradually reduce the spatial dimensions of the image. The processed feature maps are spread and passed through two fully connected layers, finally outputting a single value for judging the authenticity of the image. In this way, D\({}_{1}\) is able to effectively assess the quality of the generated image and provide feedback to the generative model to guide it in generating more realistic images.  
The D\({}_{2}\) discriminator is based on the classical deep convolutional neural network architecture ResNet-50 [48], with customized modifications: the last two layers of the original model are removed to reduce the depth of the feature map, and a new convolutional layer is introduced to further reduce the number of channels in the feature map to meet the task requirements. During the input phase, the data were normalized to ensure that the inputs matched the expected distribution, a step that is crucial to improve model performance and stability. Subsequently, the normalized data are fed into the D\({}_{2}\) discriminator for feature extraction and the final output feature map reflects the high-level semantic information of the image. Through this well-designed feature extraction process, the D\({}_{2}\) discriminator is able to effectively capture the key features of the image, providing strong support for the discrimination task.
## 2 Experiment One: Localisation Thresholds for Natural Sound Sources
### Results
#### 2.2.4 Localisation Thresholds for Combined Sources  
As it was shown that the effect of sound source on the localisation threshold was not significant, the results for each sound source were combined. This is shown in Figure 7. The median threshold for sources with 0 ms ICTD was \(-\)9.5 dB. Based on the notch edges, the threshold for this ICTD appears to be significantly lower than the \(-\)7 dB median threshold found for the 1 and 10 ms ICTDs. This significance was confirmed with the results of both Friedman (\(p\) = 0.000), and Wilcoxon (\(p\) = 0.01 between 0 ms and 1 ms, \(p\) = 0.00 between 0 ms and 10 ms) tests. It can therefore be concluded that the only variable whose effect was significant on the localisation thresholds obtained in the present study was ICTD. The effects of sound source and presentation method were not significant.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 2 Related Work
PhaseU [31] explores the phase values of CSI by eliminating the random phase offset through the signal differential. The authors compute the variance of phase difference and adopt a threshold-based classifier to achieve NLOS identification, whose accuracy is 94%. Although PhaseU allows users to move during identification, it can only collect samples after the receiver remain static. An extra inertial sensor is required to detect the user's motion status, making PhaseU unable to achieve real-time identification. To further investigate the characteristic of CSI, other WiFi-based approaches also adopt deep learning models. [PERSON] et al. [32] utilizes a neural network to analyze CSI magnitude. [PERSON] et al. [33] leverage a Recurrent Neural Network (RNN) to extract temporal features of CSI and RSS sequence. [PERSON] et al. [37] propose a one-dimensional CNN model to learn spatial features from CIR obtained by iFFT. Although the above works have incorporated various new techniques, none of them explore the time features of CIR, which could be a potential candidate for fast and accurate LOS/NLOS identification. Apart from magnitude and phased information, some recent methods also exploit the Fine Time Measurement (FTM), a fine-grained ranging method supported by WiFi standard [34; 36]. It measures the distance between two access points with meter-level accuracy and thus reflects patterns of NLOS paths [35]. However, since FTM-based approaches raise a requirement for specific WiFi protocols, they may not be supported by all commodity devices.
## 2 Internet of Things (IoT): Specifications & Security Challenges
### Architecture of IoT
#### 2.1.2 Network Layer  
This layer is located between the perception and application layers as a mediator to transmit the data and information. This layer works on transmitting and processing the network traffic and data alongside connecting IoT infrastructure components including smart objects, network devices, and servers. Multiple protocols are adopted such as Wi-Fi, IPv6, GSM, and others for the purpose of linking devices with smart services. Moreover, specific protocols are created and used such as the 6 LoWPAN protocol due to the low computational power of IoT devices. The criteria for selecting protocols for specific applications can be summarized as follows: the network's capacity, the node's computational power, and the required transmission speed.  
The wireless protocols are more adopted since the wireless sensor network has unique features that ease its installation and expansion in different environments. In the case of wired networks, it offers better transmission rates with more reliable connections which is recommended to be used in crucial environments of IoT [17; 18; 19].  
Figure 3: Typical IoT architecture. (**a**) shows three-layer models of IoT architecture and (**b**) shows five-layer models of IoT architecture.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 6 Conclusions  
This paper's experimental analysis strongly suggests that a shift towards post-quantum key generation cryptography is desirable for new deployments, due to its comparable or superior energy efficiency and inherent resilience to future quantum threats. In  
Figure 5: Similar plot to the energy utilisation in Figure 4, but detailing time to generate 1000 keys for each algorithm on the Pi 5 device under test.  
Figure 4, it is clear that as the NIST security level increased, the lattice-based key generation methods outperformed ECC, and significantly outperformed RSA, whilst adding resilience to known and projected post-quantum computing attacks.
# New Nonlinear Second-Order Phase-Locked Loop with Adaptive Bandwidth Regulation
###### Abstract  
Synchronization of large acquisition bandwidth brings great challenges to the traditional second-order phase-locked loop (PLL). To address the contradiction between acquisition bandwidth and noise suppression capability of the traditional PLL, a new second-order PLL coupled with a nonlinear element is proposed. The proposed nonlinear second-order PLL regulates the loop noise bandwidth adaptively by the nonlinear module. When a large input-output phase error occurs, this PLL reduces the frequency offset quickly by taking advantage of the large bandwidth. When the phase error is reduced by the loop control, the proposed PLL suppresses noises by using the small bandwidth to increase the tracking accuracy. Simulation results demonstrate that the tracking speed of the proposed PLL is increased considerably, and its acquisition bandwidth is increased to 18.8 kHz compared with that of the traditional second-order PLL (4 kHz).  
Keywords:phase-locked loop; acquisition bandwidth; tracking accuracy; adaptive; loop noise bandwidth +
Footnote †: journal: Electronics
## 3 Results
### The Serovars Had Phenotype Differences in Nutrient Utilization  
A phenotype microarray analysis with pre-configured 96-well plates was used to assess the effect of and/or utilization of 950 compounds for carbon, nitrogen, phosphorus, and sulfur sources, as well as nutritional supplements and osmotic and pH tolerance. The phenotypes demonstrated in the wells of the plates was reflected by redox dye intensity as explained in the Section 2 and were measured in Omnilog Units (OUs), which range from 0-500. For analysis purposes, OUs were differentiated into three categories: little to no growth (<20 OUs), intermediate growth (20-100 OUs), and high growth (>100 OUs). The data were examined according to the individual strains and average of each serovar. Under these criteria, there were 157 wells among the 960 analyzed that showed differential OU categories between the servours (Supplementary Table S2). These could be further differentiated by of the number of OUs between the different levels. There were 125 wells where there was >100 OUs between the highest utilizing serovar for a compound and one of the other servours. A total of 84 of these wells measured nitrogen utilization of dipeptides, 70 of which had the highest levels of utilization (OUs ranging from 100-130) in serovar Enteritidis strains. The remaining 41 phenotype wells that showed differential utilization levels according to serovar are shown in Figure 2. The data for the individual strains are given in Supplementary Table S2. Of the 41 traits, 21 were carbon sources, 6 were nitrogen sources, 9 were phosphorus sources, 2 were sulfur sources, and 3 measured osmotic sensitivity. \(S\). Enteritidis strains had the highest OU numbers among the carbon sources, showing utilization of several compounds that were not used by at least one of the other servours. These included plant-derived sugars such as xylose, mannitol, rhamn, fructose, and melibiose. \(S\). Typhimurium strains showed little utilization of 8 of the carbon sources among this set of 21. However, \(S\). Typhimurium were the only strains positive for utilization of D-tagatose, which is found in dairy products and can be derived from lactose. There was variability between the servours regarding the non-peptide sources of nitrogen in the compounds shown in Figure 2. The average values of the \(S\). Kentucky strains were the most diverse among the four servours in the use of phosphorus sources, showing more or faster growth with inorganic phosphate, thiophosphate, phosphoenol pyruvate, and several nucleotide phosphates as the sole phosphorus sources. The \(S\). Kentucky strain RM16068 had higher utilization of the phosphorus compounds than did the \(S\). Kentucky strain RM17366, but both strains performed better than strains of the other servours for these compounds (Supplementary Table S2). Due to the better performance of the \(S\). Kentucky strains in the phosphate wells and the importance of the Pi utilization to several aspects of metabolism, we examined the phosphorus assimilation genes in the strains of different servours.
# Wide-Aperture Bimorph Deformable Mirror for Beam Focusing in 4.2 PW Ti:Sa Laser
## 3 Results
### Wavefront Correction in a 4.2-Petrawatt Ti: Sa Laser  
The main parts of the CoReLS laser [3] and elements in the optical path from the end of power amplifiers to the target chamber are shown in Figure 7[10]. The 127-channel bimorph mirror DM2 with a diameter of 320 mm was installed after the pulse compressor to compensate for additional WF aberrations arising in the wide-aperture optical elements and focusing optics in the compressor, the beam transport system and the interaction chamber. Wavefront correction was performed at a pulse energy of 89.7 J after booster amplifiers, while it decreased after the compressor to 55.6 J. The peak power in the target chamber was 2.7 PW. When measuring the wavefront and focal spot of the full power laser beam, the pulse energy was attenuated by the four high-quality (surface flatness below 20 nm RMS) partial reflection mirrors AM1-AM4 with 1%, 1%, 1% and 5% reflection coating. It was also assumed that the distortion of the wavefront from the thermal load in the pulse compressor was minimal due to the use of the fused quartz gratings. Thus, it would be expected that the divergence of the attenuated beam used for the measurement is not much different from the one at full energy.  
Figure 5: Examples of the response functions of the electrodes under numbers ##1, 10, 23, 43, 71 and 100 in the form of the interferograms.  
Figure 6: Induced piezoelectric signal (solid line) and its phase shift (dashed line) vs. exitation frequency.  
The phase conjugation algorithm [25] was used to control the DM2 based on the signals obtained from the Shack-Hartmann wavefront sensor WFS3. The 1-inch CMOS camera with a frame rate of up to 90 frames/s and the lenslet array with a size of 13.5 \(\times\) 13.5 mm\({}^{2}\) were used in WFS3. Thus, it contained more than 6500 microlenses with a focal length of 3.2 mm and size of 0.136 \(\times\) 0.136 mm\({}^{2}\). After focusing with an off-axis parabolic mirror (OAP), the beam was collimated using micro-objective MO. The part of the beam reflected from the beam splitter BS was directed to the sensor WFS3 (Figure 7). The video camera CCD recorded the intensity distribution in the focal plane of the OAP.  
The wavefront correction reduced the initial value of the aberrations to 0.07 \(\upmu\)m (RMS). After such a procedure in the focal plane of an f/1.1 parabolic mirror (focal length 300 mm), a significant increase in intensity was achieved. Figure 8 shows the images of the focal spots before and after correction. The corrected focal spot size was 1.1 \(\upmu\)m (FWHM), and the calculated diffraction limit was 0.92 \(\times\) 0.89 \(\upmu\)m\({}^{2}\). The peak intensity calculated for the focused 19.6 fs pulse (Figure 8b) at an energy of 55.6 \(\mathrm{J}\) was equal to 1.1 \(\times\) 10\({}^{23}\) W/cm\({}^{2}\), which is a record among the values measured on high-power laser systems existing in the world [10].
## 9 Performance Analysis and Results  
It is necessary to store the relevant data in order to make use of the KMS. These data should include a variety of keys, counters, and other values. The information that needs to be kept in MS and SXs is outlined in Table 6. In Table 7, the calculation techniques for the storage cost of the communication endpoints are provided. These approaches are based on the information in Table 6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Refreshing Period** & **Refreshing Algorithm** \\ \hline \(gk_{j}\) & Periodically & \(gk^{\prime}_{j}=H(gk_{j})\) \\ \hline \(GCount_{j}\) & Every session & \(GCount^{\prime}_{j}=GCount_{j}+1\) \\ \hline \(GC_{j}\) & Every session & \(GC_{j}=H(GCount_{j})\) \\ \hline \(gsk_{j}\) & Every session & \(gsk_{j}=H(sk_{j}\oplus j)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fixed DR project group’s primary refreshing policy for multicast communications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Refreshing Period** & **Refreshing Algorithm** \\ \hline \(gk_{j}\) & Periodically & \(gk^{\prime}_{j}=Kgen(1^{b})\) \\ \hline \(GCount_{j}\) & Periodically & \(GCount^{\prime}_{j}=Random(b)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The DR project group’s key refreshing policy when new users are introduced via multicast communication. The length of the key used in symmetric cryptography methods typically ranges between 128 and 256 bytes in real-world applications (such as AES and IDEA). In this particular piece of research, we chose a key that was 128 bits long, with a counter that was the same length and an extra value. For MS, the storage of keys and the data associated with them can be managed by specialized key management servers. There is no issue with the expense of storage. In contrast, SXs have a restricted capacity for storing data. As a result, it is necessary to determine the storage cost that is the absolute maximum and achievable for each SX in accordance with the number of SX and the number of DR projects. Table 8, which is derived from Table 6, summarizes the storage cost according to the number of DR projects and SXs. Figure 11 shows the relation between the number of DR projects and the storage cost in Kbytes. From this figure, it is shown that when the number of DR projects increases, the storage cost also increases. As a result of this, we have determined that the storage cost incurred by each SX inside the AMI system would not rise as the number of SXs increased. Only an increase in the total number of DR projects would result in an increase in the storage cost for each SX. We assumed that the number of DR projects did not exceed fifteen and that each SX's maximum related storage cost was 1.088 KB when everything operated as it should. The outcome might be considered satisfactory.
## 2 Ice Load Prediction Model
### Stochasticity and Discretization in the Present Model  
Ice-hull impact is a highly stochastic process. Normally, ice collision happens along the ship waterline plane randomly. Still, most impacts occur at the ship bow part which means that the stem and shoulder are more effective in terms of ice breaking. To estimate the ice breaking force, the periphery of waterline plane is discretized into small elements and the ice breaking force is computed in the time domain. In order to simplify the ice-hull interaction model and keep the dynamic feature, the ship stem and bow parts are highlighted in the model and discretized with infinite number of elements (points). Thus, several contact locations within the target area are evaluated in the model, as shown in Figure 3. In this paper, only five points are selected to demonstrate the concept. More points (elements) can be introduced to increase the model accuracy. In the model, the number of collisions is also randomized from one location to four locations for shoulder collision event.  
Figure 3: Bow discretization and locations considered in the paper.  
When ice sheet breaks, it may break into different number of pieces. In this paper, the ice sheet is assumed to break into small ice floes with the same angle and size. Here the number of ice floes evaluated are 1, 2, 3 and they correspond to wedge angle of 180\({}^{\circ}\), 90\({}^{\circ}\), 60\({}^{\circ}\). Those parameters are randomly selected in the simulation. A random wedge angle, e.g., 35\({}^{\circ}\), can be explored in further study. All the scenarios mentioned above are considered to happen randomly in the model.
# Effects of Matrix Modification on the Mechanical Properties of Wood-Polypropylene Composites
###### Abstract  
Polypropylene (PP) modified with two reactive monomers, divinyl benzene (DVB) and maleic anhydride (MAH), was used as the matrix to prepare wood-polypropylene composites to improve interfacial compatibility. The effects of the co-modified PP matrices with different DVB concentrations on the mechanical properties of the composites were evaluated. Compared with unmodified composites and the composites containing a coupling agent, the composites modified with MAH only, and that with both MAH and DVB, improved the tensile, flexural, and impact strengths. Interestingly, adding a small amount of DVB (0.4%) resulted in significant increase in impact strength, relative to that of the composites modified with MAH only. Dynamic mechanical analysis and fracture morphology analysis of the modified composites also suggested an improvement in interfacial adhesion owing to the matrix modification.  
matrix modification; chemical graft; polymer-matrix composites; fiber-matrix interface; mechanical properties +
Footnote †: journal:  
Article
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 2 DTU Fault Analysis
### DTU Fault Types  
The DTU has several types of faults as summarized in Table 1. Functional faults can be divided into telemetering faults, remote signaling faults, and remote control faults [33]. A telemetering fault means the DTU is unable to measure the analog signals of the distribution network (e.g., voltage, current, and power). A remote signaling fault means that the DTU fails to measure signals of breakers and protection devices. A remote control fault means the DTU cannot send signals to control breakers and switches [34]. Faults that cause the DTU to stop working are called device faults. Among them, DTU offline means that the DTU is disconnected from the main station, and frequent online-offline means that the DTU is not working in a stable state.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**DTU Fault Types**} \\ \hline \multirow{3}{*}{**Functional Faults**} & Telemetering Fault & \multirow{3}{*}{**Device Faults**} & \multirow{3}{*}{**DTU Offline**} \\ \cline{2-2} \cline{4-4}  & Remote Signaling Fault & & \\ \cline{2-2} \cline{4-4}  & Remote Control Fault & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: DTU fault types.  
Figure 1: DTU in the power system.  
Figure 2: (**a**) Picture of DTU; (**b**) configuration of DTU.