esa-sceva/satcom-chunk-collection huggingface dataset:
# Scheduling Optimization of Prefabricated Construction Projects by Genetic Algorithm
## 4 The Proposed Genetic Algorithm  
### Brief Introduction of Genetic Algorithm for Scheduling Problem of Prefabricated Building  
Figure 1 shows the solution process of the proposed GA in this research problem. In Figure 1, the first step is to transform the objective function of the model into a fitness function and use it as a basis for judging the adaptability of the population. With regard to the constraints of the predecessor relationship shown by the model, the Design Structure Matrix (DSM) is introduced to generate the initial population that satisfies the predecessor relationship required to execute the GA. In terms of the updatable resource constraints, the execution time of the sequence activities corresponding to each initial population individual will be arranged one by one according to the resource occupancy. Next, the GA selection operation is performed to screen and retain the better individuals according to the fitness calculation results of each initial population individual. To find better individuals, crossover and mutation operations of GA are performed on the selected parents to produce better offspring than the parents. The generated offspring also need to undergo a new round of fitness evaluation and selection. The designed algorithm takes the number of iterations as the criterion for loop termination. When the number of iterations reaches the maximum, the algorithm stops iterating and outputs the optimal individual of the group, that is, the optimal feasible solution under the current number of iterations. However, the obtained optimal scheduling plan needs to be further adjusted according to the supply of prefabricated components, and then the actual optimal scheduling plan of the prefabricated construction project can be obtained. In this process, the algorithm coding, fitness function, selection operator, crossover operator, and mutation operator design are the keys to determine whether the algorithm can be effectively applied to solve the prefabricated construction project scheduling problem.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Description** \\ \hline \(j\) & Sequence number of the activity contained in the project, \(j=1,\,2,\,\ldots,\,n\) \\ \(t\) & Time number, \(t=1,\,2,\,\ldots,\,T\) \\ \(k\) & Renewable resource serial number, \(k=1,\,2,\,\ldots,\,K\) \\ \(LT_{j}\) & On-site delivery time of prefabricated components required for activity \(j\) \\ \(S_{j}\) & Start time of activity \(j\) \\ \(d_{j}\) & Duration of activity \(j\) \\ \(F_{j}\) & Finish time of activity \(j,\,\,F_{j}=S_{j}+d_{j}\) \\ \(Prec_{j}\) & Predecessor set of activity \(j\) \\ \(r_{jk}\) & \(k\)th renewable resource required by activity \(j\) \\ \(R_{k}^{t}\) & Renewable resource supply of the \(k\)th resource at time \(t\) \\ \(T\) & Actual project duration \\ \(A_{t}\) & Collection of activities being executed at time \(t\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation description.
# Online EEG Seizure Detection and Localization
###### Abstract  
Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists--a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.  
EEG; seizure; epilepsy; on-line detection; non-patient-specific; network analysis; PSD +
Footnote †: journal:
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 4 Direct Method for the Estimation of the Rain Rate
### Rain Rate Estimation Algorithm
Thus, an estimate \(\hat{k}\) of the specific attenuation (in dB/km) is derived under the assumption of uniform rain rate along the whole length of slant path affected by rain, so that \(L=l^{(r)}=h_{B}/\sin\gamma\) and the estimate of the rain rate is obtained from (6) as  
\[R=\alpha\left(\Xi(\hat{\eta})/l^{(r)}\right)^{\beta}. \tag{9}\]  
The performance of the NEFOCAST system will be affected by the variability of precipitation in space. Several authors have studied the variability of rainfall and its effects on the ground based or satellite-based remote sensing of precipitation (we limit ourselves to mention [58] for horizontal variability, [15; 59] for vertical variability, the latter from the point of view of the impact on radar retrievals). The variability is related to the precipitation process. It is expected that, for stratiform rain, the variability of rainfall in time and space will be much lower than for convective rain. In our case, variability will affect: (1) the representativeness of the estimate of rainfall at ground from path-integrated measurements, and (2) the spatial density of LNB required to achieve a meaningful description of rainfall fields.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 2 State of the Art
### Protection Methods  
As evidenced by the success of prior work in fine-grained gesture recognition, there is a growing need for methods that secure privacy in the presence of Wi-Fi networks. Prior work on thwarting Wi-Fi sensing has introduced jamming signals into the wireless channel [5; 6; 7; 8; 21], causing a degradation in the sensing accuracy. In [5], interference was introduced on adjacent Wi-Fi channels in order to create competition for channel resources and reduce the CSI available to the sensing system. This subsequently caused a degradation in the sensing accuracy. Then, in [7], it was shown that the interference can be introduced in a time-varying manner in order to bias the measured CSI towards certain activity classes, thereby intentionally misleading the sensing system. This approach requires prior knowledge of the sensing systems ML parameters, which is unlikely. Thus, we focus on untargeted interference in order to degrade sensing accuracy. One aspect of interference injection that is overlooked is its impact on channel bandwidth. To the best of our knowledge, no prior work investigates the trade-off between improved security and loss in bandwidth due to interference injection. Thus, there is scope for more work to understand how the amount of interference can be chosen intelligently to preserve the communications channel for normal users.
# Deep Learning Model with Sequential Features for Malware Classification
## 4 Experiments and Analysis of Results
### Experimental Environment and Data Set  
The experimental environment was a computer configured with Win10, Intel Core (TM)-9880H CPU @ 2.30 GHz, 64 GB RAM, Quadro RTX 4000 GPU; the programming environment was PyCharm2021.2.2, using the Python 3.7 language in a CUDA 11.0 accelerated environment. The neural network model used TensorFlow 2.4.1 and Keras 2.4.3 versions of the deep learning framework.  
The experimental datasets were from the open-source dataset provided by Microsoft [15], and the PE samples were from the Datacon Open Data Project provided by Qianxin (China) [25]. The malicious code families in the dataset provided by Microsoft were divided into 9 categories, with 10,868 malware samples. Each sample file had two formats:.asm and.bytes; the PE samples provided by Qianxin had two categories, containing a large amount of mining-type malicious code and non-mining samples. These are the latest real samples captured from the existing network; thus, these samples are likely to contain a large number of shelling samples and resource obfuscation samples. To prevent samples' mistaken execution from infecting the environment, the MZ and PE headers, as well as the import and export table parts, were removed. To ensure that the dataset samples have a certain level of diversity, similar samples were filtered. Therefore, in actual use, the MZ and PE headers were artificially added to extract the opcode features, and the samples were disassembled into.asm files using IDA tools. The family name, type number, number of samples, and expression number of the malware dataset used are shown in Figures 10 and 11.  
To fully evaluate our method, experiments were conducted on two different datasets according to different methods to fully validate the model. The first method used 9 malicious families in Kaggle malicious samples labeled 1-9, and the dataset was noted as 9-class-data. The second method used 0, 1 sub-table labeling on Datacon samples as sample labels, and the dataset was noted as 2-Class-Datacon; a five-fold cross-validation method was used to randomly divide the data into 10 parts, selecting 9 of these parts as the training set and 1 part as the test set.
## 1 Introduction
Authors in [23] explored the use of a Universal Software Radio Peripheral (USRP) X310 connected to a computer capable of calculating the direction of arrival (DoA) and generating beamforming in the sub-6 GHz frequency band for a satellite transponder structure. Nevertheless, this test bed presents considerable DoA estimation errors in a lower frequency of the operation scenario. Millimeter-wave RF systems are evolving rapidly, to some extent supported by software-defined radios. These systems provide a straightforward way to rapidly interact with hardware, and to test their use. Therefore, the combination of digital technology and versatile architectures, with adaptive antenna processing techniques, allows for powerful systems that can be used in communications and radar approaches. In this work, a versatile and reconfigurable \(4\times 4\) SDR digital beamforming phased array system operating in the Ka band at 28 GHz is proposed. This system has the capability of electronically and digitally manipulating and steering the radiation pattern of an antenna array, and adjusting it to the environment, maximizing its performance and efficiency. Besides that, this system also has the versatility to, with low complexity, change the frequency of the local oscillator (LO) and the IF to implement communication systems and radar in the millimeter waves (Ka band), enabling the rapid testing of new solutions based on software-defined radio. It has also the advantage of carrying out full-duplex communication. This architecture has the potential to be used in a great variety of future scenarios such as in 5G communications, radar applications, IoT or surveillance. It is an extension work regarding the previous papers [24; 25] with improvement in the digital processing unit and practical measurements validation. This paper is organized into five sections, starting with the Introduction in Section 1, stating the objectives and providing a brief state-of-the-art on SDR beamforming systems. Section 2 presents both digital and analog components that constitute the proposed architecture of the software-defined radio (SDR) phased array system. Section 3 describes the beamforming processing system while in Section 4 the experimental setup and the measurement results are presented. Finally, the last section reports the main conclusions of the work.
## 2 Materials and Methods
### Extract Characterization
#### 2.3.8. Color Parameters (CIELab)  
WinASPECT PLUS software (Jena, Germany) and a Specord 200 Plus spectrophotometer (Jena, Germany) were used to evaluate the color parameters (CIELab). Luminosity (L*), red/green component (a*), yellow/blue component (b*), chromaticity (C*) and hue angle (H*) are presented as results. These parameters were measured following the official method [26].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Compound** & **Max Absorption (nm)** & **Retention Time (min)** \\ \hline Gallic acid & 280 & 5.294 \\ Protocatechuic acid & 256 & 9.267 \\ \(p\)-Hydroxybenzoic acid & 256 & 13.918 \\ Procyanidin B1 & 280 & 16.704 \\ \(m\)-Hydroxybenzoic acid & 280 & 17.989 \\ Catechin & 280 & 18.53 \\ Vanillic acid & 324 & 22.871 \\ Procyanidin B2 & 280 & 23.433 \\ Syringic acid & 280 & 25.002 \\ Epicatechin & 280 & 26.836 \\ \(p\)-Cumaric acid & 324 & 29.695 \\ Ferulic acid & 324 & 36.233 \\ Salicylic acid & 280 & 36.995 \\ Polydatin & 280 & 38.234 \\ Sinapic acid & 324 & 38.564 \\ Hyperoside & 288 & 47.305 \\ _trans_-Resveratrol & 324 & 49.333 \\ _cis_-Resveratrol & 324 & 57.089 \\ Ferulic acid methyl ester & 365 & 57.754 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of polyphenol standards used in HPLC analysis and their retention times.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & Cyanidol-3- & Cyanidol-3- & Cyanidol-3- & Petunidol-3- \\  & galactozide & arabinozide & glucose & glucose \\ \hline
**Retention Time (min)** & 8.862 & 9.805 & 10.593 & 11.034 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Anthocyanins used as standards in HPLC analysis and their retention times.
# Smart-Median: A New Real-Time Algorithm for Smoothing Singing Pitch Contours
## 3 Current Contour Smoother Algorithms  
Several contour-smoother algorithms are commonly used to smooth pitch contours. This section provides a list of these algorithms.  
To refer to the smoother algorithms within this paper, a code has been assigned to each algorithm listed in Table 1.  
Figure 2 illustrates the effect of the smoother algorithms on a single estimated pitch contour. A female singer sang an arpeggio in the C major scale, and the FComb algorithm  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code** & **Algorithm** \\ \hline
00 & Smart-Median \\
01 & Gaussian (sigma = 1) \\
02 & Savitzky-Golay filter \\
03 & Exponential \\
04 & Window-based (window\_type = ’rectangular’) \\
05 & Window-based (window\_type = ’hanning’) \\
06 & Window-based (window\_type = ’hamming’) \\
07 & Window-based (window\_type = ’bartlett’) \\
08 & Window-based (window\_type = ’blackman’) \\
09 & Direct Spectral \\
10 & Polynomial \\
11 & Spline (type = ’linear\_spline’) \\
12 & Spline (type = ’cubic\_spline’) \\
13 & Spline (type = ’natural\_cubic\_spline’) \\
14 & Gaussian (sigma = 0.2, n\_knots = 10) \\
15 & Binner \\
16 & LOWESS \\
17 & Decompose (type = ’Window-based’, method = ’additive’) \\
18 & Decompose (type = ’lowess’, method = ’additive’) \\
19 & Decompose (type = ’natural\_cubic\_spline’, method = ’additive’) \\
20 & Decompose (type = ’natural\_cubic\_spline’, method = ’multiplicative’) \\
21 & Decompose (type = ’lowess’, method = ’multiplicative’) \\
22 & Decompose (type = ’natural\_cubic\_spline’, method = ’multiplicative’) \\
23 & Kalman (component = ’level’) \\
24 & Kalman (component = ’level\_trend’) \\
25 & Kalman (component = ’level\_season’) \\
26 & Kalman (component = ’level\_tend\_season’) \\
27 & Kalman (component = ’level\_tend\_bestimated the pitches. The smoothed contours are plotted in eight different panels. Each panel includes ground truth (GT), the original estimated (ST) contours, and the smoothed contours generated by some of the smoother algorithms.  
Figure 2: The effect of each contour-smoother algorithm on a pitch contour from a female singer producing arpeggios in the C major scale. The pitch estimator algorithm was FComb. GT = Ground Truth (PYin), ST = Estimated pitch contour. For more straightforward observation, the smoothed contours are plotted in parts (**a**–**h**). Each panel (**a**–**f**) plots three smoothed contours, while panels (**g**,**h**) have four contours each. Descriptions of the algorithms’ codes are provided in Table 1.  
In addition, the Python libraries employed to implement these smoothers are listed in Appendix A.  
Each of the algorithms is described below.
###### Abstract  
This work presents a comprehensive investigation into the interfacial charge storage mechanisms and lithium-ion transport behavior of Li-metal all-solid-state batteries (ASBs) employing LiFePO\({}_{4}\) (LFP) cathodes fabricated via alternating current electrophoretic deposition (AC-EPD) and Li\({}_{1.3}\)Al\({}_{0.3}\)Ti\({}_{1.7}\)(PO\({}_{4}\))\({}_{3}\) (LATP) as the solid-state electrolyte. We demonstrate that optimal sintering improves the LATP-LFP interfacial contact, leading to higher lithium diffusivity (\(\sim\)10\({}^{-9}\) cm\({}^{2}\cdot\)s\({}^{-1}\)) and diffusion-controlled kinetics (\(b\approx 0.5\)), which directly translate to better rate capability. Structural and electrochemical analyses--including X-ray diffraction, scanning electron microscopy, cyclic voltammetry, and rate capability tests--demonstrate that the cell with LATP sintered at 900 \({}^{\circ}\)C delivers the highest Li-ion diffusivity (\(\sim\)10\({}^{-9}\) cm\({}^{2}\cdot\)s\({}^{-1}\)), near-ideal diffusion-controlled behavior (\(b\)-values \(\sim\)0.5), and superior rate capability. In contrast, excessive sintering at 1000 \({}^{\circ}\)C led to reduced diffusivity (\(\sim\)10\({}^{-10}\) cm\({}^{2}\cdot\)s\({}^{-1}\)). The liquid electrolyte system showed higher \(b\)-values (\(\sim\)0.58), indicating the inclusion of surface capacitive behavior. The correlation between \(b\)-values, diffusivity, and morphology underscores the critical role of interface engineering and electrolyte processing in determining the performance of solid-state batteries. This study establishes AC-EPD as a viable and scalable method for fabricating additive-free LFP cathodes and offers new insights into the structure-property relationships governing the interfacial transport in ASSBs.  
s +
Footnote †: journal: Applied Sciences
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation  
In this section, we will use standard test tools to evaluate the network performance of the SR-IOV-based virtual resource allocation framework, and gradually increase the number of VMs to test system performance in terms of bandwidth throughput and latency. To simulate different load types and load changes of VMs, we introduced different types of benchmarks to simulate CPU-intensive VMs and network-intensive VMs. In addition, based on this, we compared the default scheduling strategy of KVM and the network performance based on SR-IOV virtual resource allocation framework.
## 3 Results and Discussions
### Suppression of NO\({}_{x}\) Emissions
A shorter flame after jetting indicates an increased flame combustion speed and reduced flame residence time in the combustion chamber [32; 33]. Figure 12 compares the changes in flame morphology at jet flow rates of 3, 5, and 7 L/min. The evolution of flame shapes before and after jet introduction was vertically captured using a Nikon camera. As the jet flow rate increases, the flame becomes more flattened, and the overall flame structure disperses, forming multiple flame lobes (Figure 12). The Ar/He volume ratio influences the flame structure. As the Ar/He volume increases, the flame color gradually darkens. This change in flame color is related to the decrease in the temperature field (Figure 11). The variations in flame color and temperature field may be associated with the decrease in the equivalence ratio caused by the increased injection medium. Moreover, an increase in the injection medium improves the combustion reaction rate, which reduces the flame length and color intensity [25; 26]. The flame shape is influenced by the jet flow rate and gas density, which determines the momentum ratio of the transverse jet. Figure 13 shows the momentum ratio of jet-in-crossflow at different flow rates and gas densities. The momentum ratio of jet-in-crossflow is given by \(J=\frac{\rho_{\rm t}U_{\rm t}^{2}}{\rho_{\rm x}U_{\rm cs}^{2}}\). As the momentum ratio of the jet-in-crossflow exceeds 1.0, thermoacoustic oscillations are suppressed. At a momentum ratio of 4.0, the intensity of these oscillations further weakens, and the flame is blown out. This is consistent with the findings in recent research by [PERSON] et al. [34], indicating that a too high jet momentum ratio will blow out the flame. Figure 11: Flame temperature distribution before/after the stratified Ar/He jet-in-crossflow with the injection flow rate set to 5.0 L/min: (**a**) no jets; (**b**) Ar-0% + He-100%; (**c**) Ar-100% + He-0%. Figure 12: Flame shape before and after the introduction of stratified Ar/He jet-in-crossflow with the injection flow rate set to: (**a**) 3.0 L/min; (**b**) 5.0 L/min; (**c**) 7.0 L/min.
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 3 Results
### Second DOE Configuration  
The second configuration of the holographic waveguide, proposed and tested, is shown in Figure 7. Such an architecture can be defined as no segmentation, because in the input and relay areas with DOE1, DOE2a and DOE2b have no formal division, and, consequently, no overlapping boundaries. Here, a single area formed by superposing DOE1, DOE2a, and DOE2b areas (Figure 6a) performs as two-dimensional diffraction gratings with a 60\({}^{\circ}\) angle between the line directions of the diffraction gratings. DOE1 in Figure 6a is expanded to the areas of DOE2a and DOE2b, as Figure 7a represents.  
The input pupil corresponds with the aperture directly illuminated by the optical engine, and the function of the relay region is performed by its left and right parts outside the input area, respectively.  
In such a structure there are no overlapping frames of the input and relay areas with DOEs, and thus one of the factors of possible quality degradation of the image is absent. Due to the superposition of gratings, the resulting diffraction structure begins to outcouple  
Figure 7: The second configuration of the holographic waveguide: (a) DOE configuration scheme; (b) photo of a single holographic waveguide; (c) resulting AR image.  
the radiation from the waveguide while horizontal pupil expansion. On the one hand, this contributes to a decrease in the brightness of the central band, since the radiation of the parasitic part does not completely reach the DOE2 region. On the other hand, this leads to direct optical loss, which reduces the overall brightness of the observed image. The optical losses in the side parts of DOE1 are caused by the presence of additional periodicity which is symmetrical to the main one. As Figure 7c shows, the second waveguide configuration has a noticeable decrease in brightness at the edges of the field, but despite all of the above, satisfactory quality of the observed image is achieved. For the second configuration of the holographic waveguide the achieved level of brightness non-uniformity was about 40% because light and dark stripes are explicit in the image.