esa-sceva/satcom-chunk-collection huggingface dataset:
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 3 Performance Analysis
### Acquisition Scheme
Additionally, the ACF illustration of \(FH-BOC(1,3,10)\) performs a complex distribution that changes rapidly, along with a greater MSR, compared to the \(FH-BOC(1,1,4)\) signal, which refers to a minimum frequency-hopping interval increase, which imposes worse ACF performance. Figure 4: Coarse acquisition procedure for FH-BOC signal. Additionally, since BOC modulation generally appends the subcarrier into ordinary spread spectrum modulation, where the subcarrier generation is of single frequency and can be regarded as a special case of FH-BOC with the hopset containing only one element, the power spectrum density transition between is plainly intelligible, as shown in Figure 6. It can be discerned that the \(BOC(1,1)\) signal possesses its PSD, as divided by two of the spread spectrum signals splitting into two main power spectral peaks, and the FH-BOC signals show either PSD extended further to split into more pieces than the BOC signal. The PSD of the \(FH-BOC(1,1,4)\) signal possesses continous peaks limited within about 5 MHz, and the \(FH-BOC(1,3,10)\) signals containing four interval peaks, where their spectral peaks are located at the same level of power/frequency, due to identical quantity of peaks. Meanwhile, the \(FH-BOC(1,1,10)\) signal's major power distribution has been extended over 10 MHz, similar to the \(FH-BOC(1,3,10)\) signal, although the former's power spectral peaks seem to be lower than the latter's. Generally speaking, the denser the subcarrier frequency presented in the hopset, the less distributed energy carrying there is on each power spectral peak. Such a characteristic conveys the manipulation way of adjusting power allocation in the frequency domain, and it provides a feasible solution for coordinating the OFDM signal in I/Q modulation with different data channels distribution.
## 2 System Model of NR-ODBR-DCSK
### Signal Slot Structure of NR-ODBR-DCSK  
In order to increase the information transmission rate of the NR-DCSK system, the NR-ODBR-DCSK utilizes a two-dimensional information transmission scheme. In this scheme, the one-dimensional information bits are composed of \(a_{k}\) k-bit information bits. The information signal part of the transmitter is equipped with a Walsh quadrature converter, which aims to perform quadrature conversion processing of the delayed chaotic signal, and then multiply it with another dimension consisting of k-bit information bits \(b_{k}\) and finally add the two mutually orthogonal chaotic signals, so as to achieve the purpose of transmitting two information bits in the information time slot.  
The NR-ODBR-DCSK frame structure is shown in Figure 2.  
Figure 1: Correlation analysis chart. (**a**) Autocorrelation of modified logistic map; (**b**) Cross-correltion of two modified logistic maps.  
Figure 2: NR-ODBR-DCSK frame.  
The reference signal of the NR-ODBR-DCSK system uses only \(N(N=\beta/P)\) different chaotic samples, and the chaotic samples are repeated \(P\) times consecutively to obtain a total of \(\beta\) samples of the reference signal, and the reference signal is processed by the Walsh function to modulate the two bits of data information, \(a_{k}\) and \(b_{k}\), respectively, in the information time slot. The purpose of the orthogonal transformation is to generate two chaotic signals that are orthogonal to each other so they can be easily separated at the demodulation side.  
At the receiving end, the received signal is averaged through a sliding average filter with a window length of \(P\), and the length of the averaged signal becomes \(N\). The principle of the sliding average filter in Figure 3 is as follows: starting from the 1 st code slice of the incoming signal, the sliding average filter will sum and average \(P\) code slices at a time according to the size of the window, until it has finished averaging \(\beta\) code slices, where \(g_{ik}\) is the \(g(t)\) obtained by sampling the discrete signal. Finally, the resulting averaged signal is correlated with the original reference signal one way to recover the information \(a_{k}\). The other way is correlated with the reference signal processed by the Walsh function to recover the information \(b_{k}\).
## 3 Results and Discussion
### Computational Fluid Dynamics Simulations
#### 3.2.2 Unsteady Inner Iteration Study  
The maximum inner iteration stopping criterion is determined by the number of inner iterations performed by the solver during a CFD simulation. This stopping criterion can be used to constrain the number of inner iterations that must be performed at each physical time step. To determine the optimum number of unsteady inner iterations for the numerical investigation, unsteady inner iteration independence testing was carried out. Three distinct inner iteration numbers, in increments of 5, were examined to determine their influence on the distribution of water droplet diameters. Unsteady inner iterations did not significantly  
Figure 6: (**a**) Mesh independence study. (**b**) Normal distribution fitting (Q—Q plot) with linear fit of \(R^{2}\) for N1H1.  
affect the final results, as demonstrated in Figure 7. For all CFD simulations, 15 unsteady inner iterations were selected as the standard because they yielded identical results to 20 iterations. This choice was made to optimize computational efficiency, as higher iteration numbers require increased computational time without providing additional accuracy or insights.
## 2 Results and Discussion  
The results presented in this paper concern the study of the biological activity of polyisoprene based surfaces. The starting material used to prepare the films was obtained from natural rubber. After dissolution in tetrahydrofuran, NR was submitted to an oxidative treatment with periodic acid, at low temperature (30 \({}^{\circ}\)C) and atmospheric pressure, to cut some of the polyisoprene carbon-carbon double bonds and to obtain shorter oligoisoprenes possessing an aldehyde and a ketone at their chain ends [17]. The amount of periodic acid was added considering the number of moles of double bonds to cut statistically in order to have oligomers with a targeted molecular weight (two molecules of H\({}_{5}\)IO\({}_{6}\) are required for one double bond). This procedure allowed the preparation of oligomers of different molecular weights in the range of 700-15,000 g\(\cdot\)mol\({}^{-1}\), with dispersities around 2. Successive reactions could be carried out to modify the extremities in order to insert different chemical groups. The strategy used for the antimicrobial materials was to insert one or two acrylate groups in the oligomer structure to perform radical polymerization and to have linear or cross-linked polymeric films, and to insert also a biocide functionality covalently linked, in order to obtain non-releasing antibacterial surfaces. The previous tests with both pathogenic and marine bacteria [16,18], showed that the presence of a single or of three quaternary ammonium groups (QA) per oligomer provides a surface that has both antifouling and antibacterial effect. However, as surfaces containing only the polyisoprene backbone, prepared from acrylate oligomer **(1)** without any biocide group, showed a low but noticeable activity, the present study focused on the synthesis of this kind of \"simple\" film and on the modification of a few features in order to have indications about the reasons of the intrinsic activity, which can be enhanced by grafting biocide groups.  
For most of the biological tests, regarding the biomedical environment, the three pathogenic bacteria used previously [16] were chosen, one Gram-negative (_Pseudomonas aeruginosa_, PA), and two Gram-positive (_Staphylococcus aureus_, SA, and _Staphylococcus epidermidis_, SE). Concerning antifouling in the marine environment context, different wild bacteria strains were tested: four Gram-negative strains (_Flavobacterium_ II2003, _Shewanella_ IV3014, _Alteromonas_ IVA014, and the pathogenic _Vibrio splendidus_), and two Gram-positive strains (_Bacillus_ IV3004, _Bacillus_ IVA016). They were selected for their ability to form biofilm and/or be involved in biocorrosion processes.  
An attachment assay was performed for each strain and surface. The assay was looking at the initial stage of biofilm formation, where within a short period of time cells have already committed to attachment. A quantitative plate count was performed as opposed to simply looking at the optical density in order to determine living cell numbers and viability.
# On the Trade-Off between the Main Parameters of Planar Antenna Arrays
## 2 Planar Arrays with Regular Lattices
### Calculation of the Excitations  
Once the lattice has been chosen, it is possible to calculate the excitations of the elements.  
It is worth recalling that there could be many different set of excitations, satisfying the same pattern specifications with the same elements' positions, so we are going to choose the excitations to maximize the broadside directivity of the array of isotropic elements:  
\[D=\frac{|F(0,0)|^{2}}{\int_{0}^{2\pi}\int_{0}^{\pi}|F(\theta,\phi)|^{2}\sin \theta\,d\theta\,d\phi}=\frac{1}{\mathbf{a}^{\dagger}\mathbf{S}\mathbf{a}} \tag{10}\]  
where \(\mathbf{a}\) is the vector of the elements' excitations, and \(\mathbf{S}\) is a square matrix in which the entries are:  
\[s_{m,n}=\frac{\sin(\beta\rho_{m,n})}{\beta\rho_{m,n}} \tag{11}\]  
with \(\rho_{m,n}=\sqrt{(x_{m}-x_{n})^{2}+(y_{m}-y_{n})^{2}}\) the distance between two elements, and we have applied Equation (2). The relationship in Equation (11) can be obtained, exploiting the symmetry of isotropic radiators, from the well-known formula for linear arrays [10], through a simple rotation of the coordinate system.  
According to Equation (10), the maximization of the directivity requires the minimization of the term \(\mathbf{a}^{\dagger}\mathbf{S}\mathbf{a}\). The pencil beam synthesis, with the maximization of the directivity in Equation (10) and constraints on BW and SLL (Equations (2) and (3)), turns out to be a convex problem, which can be efficiently solved by one of the efficient convex programming packages, like CVX for Matlab [56].
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 3 Design, Fabrication, and Validation of Graded Micro-Porous Material System  
This section focuses on the design, fabrication, and validation of the graded pore-size distribution material system, drawing upon key insights from previous works [25; 33; 35] as well as findings from the present study. Based on the conclusions, combinations of graded \(\upmu\)PHB composite material will be designed and fabricated, with details of the material fabrication process outlined herein.
# Training and Inference of Optical Neural Networks with Noise and Low-Bits Control
## 2 Architecture
### Quantization  
As we mention in Section 3.2, quantization is needed to ensure higher accuracy when converting analog and digital circuits. Integer quantization is an optimization strategy that converts a 32-bit floating-point number (FP32), such as weights and activation outputs, to the nearest 8-bit fixed-point number (INT8). This leads to smaller models and faster reasoning, which is valuable for low-power devices such as microcontrollers [18].  
Two main methods are used in the quantification process: (1) post-training integer quantification--using FP32 weight and input to train the model, and then quantifying the weight [20]. The main advantage of this is that it is easy to use. The drawback is the decrease in accuracy. (2) Quantization-aware training--weights are quantified in the training process, and calculated for quantization [21]. This is the best result when using INT8 quantization, but is much more complex than other methods.  
A large amount of work has shown that a more efficient deep neural network (DNN) can be achieved through low bit quantization [22; 23]. Experimental results using low precision numerical representations indicate that these experiments require higher precision than eight bits to deal with backward propagation and gradient [2; 24; 25]. This will make the implementation of the training more complicated. Therefore, after training the model, it is reasonable to only use the quantized weight for reasoning [21].  
The quantization equation from FP to INT is shown as Equation (4):  
\[Q=\frac{R}{S}+Z \tag{4}\]  
The inverse quantization equation from INT to FP is shown as Equation (5):  
\[R=(Q-Z)\times S \tag{5}\]  
where \(R\) represents the real FP value, \(Q\) represents the quantized INT value, \(Z\) represents the quantized INT value corresponding to the FP value, and S is the minimum scale that can be represented after the quantization of INT. The evaluation equations of \(S\) and \(Z\) are shown in Equations (6) and (7), respectively:  
\[S=\frac{R_{max}-R_{min}}{Q_{max}-Q_{min}} \tag{6}\]  
Figure 4: Error rate vs. \(photon/n_{MAC}\).  
where \(R_{max}\) represents the maximum FP value, \(R_{min}\) represents the minimum, \(Q_{max}\) represents the maximum INT value, and \(Q_{min}\) represents the minimum.  
\[Z=Q_{max}-R_{max}\div S \tag{7}\]  
where each symbol represents the meaning as in the description above.  
Here, \(S\) and \(Z\) are quantized parameters, while \(Q\) and \(R\) can be evaluated by the equation. Truncation would be needed where the quantized \(Q\) or the FP value \(R\) obtained by backward derivation are beyond their maximum range.
## 5 Conclusions  
In order to avoid repeated calculation of structural modes and to improve the optimization efficiency, an improved objective function for damage identification based on the SVDM is proposed. A numerical model of an eight-story frame structure is used to verify the effectiveness of the proposed method. The main conclusions are as follows:  
1. Considering the characteristic that the amplitude of the frequency response attains a local maximum at the position of structural natural frequencies, an improved objective function is developed which avoids the repeated calculation of structural modes in the optimization procedure and improves the computational efficiency.
2. Utilizing the fast structural re-analysis approach of the SVDM, frequency response in the improved objective function can be computed quickly, and the gradient expression of the objective function can be derived which further improves the optimization efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **The First Three Mode** & **The First Two Mode** & **The First Mode** \\  & **Shapes Used** & **Shapes Used** & **Shape Used** \\ \hline
**The First Six** & 0.0154 & 0.0184 & 0.0365 \\
**Frequencies Used** & 0.0320 & 0.0383 & 0.2147 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maximum difference between the identified damage factor and the theoretical value in the improved method using different modes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **The First Three Mode** & **The First Two Mode** & **The First Mode** \\  & **Shapes Used** & **Shapes Used** & **Shape Used** \\ \hline
**The First Six** & 0.0103 & 0.0162 & 0.0421 \\
**Frequencies Used** & 0.0172 & 0.0206 & 0.1755 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Maximum difference between the identified damage factor and the theoretical value in the traditional method using different modes.  
The optimization efficiency using the traditional objective function is related to the number of DOFs of the entire structure, while the optimization efficiency based on the improved objective function is related to the number of damage factors to be identified and the number of selected virtual distortions.  
Conceptualization, J.H., Q.Z. and L.J.; methodology, J.H. and Q.Z.; software, S.W. and J.H.; validation, S.W. and J.H.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and J.H., Q.Z. and L.J.; supervision, J.H.  
This research was funded by National Key Research and Development Program of China (2018 YFC075604), National Natural Science Foundation of China (NSFC) (51878118), Liaoning Provincial Natural Science Foundation of China (20180551205), Key Laboratory of Structures Dynamic Behavior and Control (Ministry of Education) in Harbin Institute of Technology of China (HITCE201707) and the project DEC- 2017/25/B/ST8/01800 of the National Science Centre, Poland.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Building Response Prediction Models
Even though the percentage variations associated with the results of the parametric models (LR, SLR, and RR) were comparatively lower than those of the non-parametric models, the values were quantitatively higher than those of the values reported by non-parametric models. 7. The performance of the ML models can be influenced by the underlying database, which in turn is impacted by factors such as building type, input variables, dataset size, and output variables in structural-engineering applications. The existing literature contains consistent findings which demonstrate that non-parametric regression models outperform parametric regression models. Specifically, RF, SVM, and DT have demonstrated superior performance in accurately estimating the structural responses of steel moment-resisting frame buildings [6].
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 2 Log Parsing
### Accuracy of Parsing
#### 2.2.2 Accuracy
\begin{tabular}{c c c c c c c} \hline \hline
**Metrics** & **Log Parser** & **Year** & **HDFS** & **BGL** & **Zookeeper** & **OpenStack** & **Hadoop** \\ \hline PA & LogCluster & 2015 & 0.546 & 0.835 & 0.732 & 0.696 & 0.563 \\ PA & Spell & 2016 & 0.999 & 0.787 & 0.964 & 0.764 & 0778 \\ PA & Drain & 2017 & 0.999 & 0.963 & 0.967 & 0.733 & 0.948 \\ PA & MoLFI & 2018 & 0.998 & 0.960 & 0.839 & 0.213 * & 0.957 \\ PA & Paddy & 2020 & 0.940 & 0.963 & 0.986 & **0.839** & 0.952 \\ PA & QuickLogS & 2021 & 0.998 & - & 0.965 & 0.837 & - \\ PA & USTEP & 2021 & 0.998 & **0.964** & **0.988** & 0.764 & 0.951 \\ PA & LTImatch & 2021 & **1** & 0.933 & 0.987 & 0.835 & **0.987** \\ PA & Drain+ & 2022 & **1** & 0.941 & 0.967 & 0.807 & 0.954 \\ \hline RI & Spell & 2016 & 0.999 & 0.880 & 0.999 & **0.917** & **0.999** \\ RI & Drain & 2017 & **1** & 0.912 & **1** & 0.904 & 0.982 \\ RI & Prefix-Graph & 2021 & 0.989 & **0.993** & **1** & 0.890 & **0.999** \\ RI & LogStamp & 2022 & **1** & 0.98 & 0.95 & - & 0.95 \\ \hline \hline \end{tabular}   
For datasets such as OpenStack with long log event lengths and complex log event formats, the potential feasible methods are as follows: (1) Multi-layer grouping of log events--group log events by length, and, then, divide the grouped content layer by layer so as to reduce the impact of \"length sensitivity\"; (2) Do not use the method of relying on sequence comparison--for example, use the idea of LogStamp to sequence label each word in the log event, and combine parts marked as templates into log templates; (3) Take preprocessing measures for the logs to be parsed. Log events are semi-structured texts, which contain a large number of parameters and time fields that carry less information, and some information can be discarded in the preprocessing stage. In fact, just like other machine learning application scenarios, a good preprocessing step can greatly improve the parsing effect. In addition, we also noticed that most of the parsing methods proposed in recent years are online log analysis solutions, such as Prefix-Graph, Drain+, USTEP, etc., which is related to the huge potential of online log parsing methods. Online log parsing methods can extract available information from real-time log streams, thus providing the possibility for downstream intrusion detection and situational awareness.
###### Abstract
Modelling advancements now enable detailed simulation of stator windings and their complex geometries, improving accuracy and design iteration speed. Despite progress, challenges remain in accurately capturing conjugate heat transfer, interfacial effects, and the influence of intricate winding features on airflow patterns. In prior research on Computational Fluid Dynamics (CFD) concerning the thermal efficiency of electrical motors, significant challenges have been identified. These encompass the implementation of conjugate heat transfer principles, especially in relation to the utilisation of slot liners and the refinement of mesh structures; the detailed representation and computational analysis of stator windings, with a particular emphasis on the terminal windings; and the precision of numerical outcomes derived from CFD simulations, as discussed in [19]. This study addresses these ongoing challenges by focusing on stator winding thermal management in permanent magnet motors. Concentrating on the thermal management of stator windings within aerospace electrical systems, our investigation evaluates three critical elements influencing the thermal regulation of these windings. The aim is to discover a practical strategy for improving the thermal management of stator windings, thereby enhancing their cooling efficiency without introducing complicated supplementary mechanisms. The paper is structured into several core sections following this introduction. The Modelling Description section outlines the technical specifications and simulation assumptions of the permanent magnet machine. Within this, Approximation of Stator Windings and Description of CFD Simulations detail the modelling approaches for simulating stator winding thermal dynamics. Wind Tunnel Validation for CFD Simulations presents an empirical validation of the CFD model, ensuring its accuracy. Thermal Design Investigations with the Validated CFD Model examines the impacts of airflow velocity, turbulence intensity, and winding spacing on cooling efficiency, highlighting effective thermal management strategies. Section 5 summarises the study's key findings, underscoring the enhanced heat transfer efficiency in high-power permanent magnet motors and offering practical industrial applications.