esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of _Stropharia rugosanmulata_
## 2 Materials and Methods
### Experimental Vacuum Freeze-drying Scheme for Stropharia rugosaannulata  
#### 2.4.1 Freeze-drying Pretreatment Parameter Selection of Stropharia rugosaannulata  
The freeze-drying process can be divided into three main steps: (1) freezing (solidifying the sample), (2) primary drying (sublimation of frozen water), and (3) secondary drying (removal of unfrozen water) [13]. Pre-freezing is an essential step in the vacuum freeze-drying process. The frozen state and ice crystal morphology of fruits and vegetables significantly affect the quality of freeze-dried products [14].  
The _Stropharia rugosaannulata_ samples were then placed in ultra-low-temperature freezers at different temperatures for pre-cooling, and the _Stropharia rugosaannulata_ were sliced for tray loading. The pre-freezing temperatures were set at \(-20\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, and \(-80\)\({}^{\circ}\)C, and the pre-freezing times were set at 48, 60, and 72 h, respectively. Freeze-drying was performed at a vacuum degree of 0.70 mbar, a cold trap temperature of about \(-55.9\)\({}^{\circ}\)C, and a working time of approximately 72 h. The optimal pre-freezing temperature and pre-freezing time were then selected. Based on preliminary experiments, 0.70 mbar was selected as the vacuum pressure. For this, 2 mm was chosen as the slicing thickness. Freeze-drying time is also an important indicator for optimizing the process of _Stropharia rugosaannulata_. Based on preliminary experiments, the freeze-drying time was set to be 48, 60, and 72 h, and the final freeze-drying time was determined via subsequent comprehensive evaluations.
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 4 Experiment Results and Discussions  
In this section, to validate the optimization effect of our algorithm for the challenging task of carrier-based unmanned aerial vehicles landing on a ship deck in hazing conditions, we conducted numerous experiments in a simulated maritime environment. We separately verified the dehazing effect of the proposed algorithm in simulated hazeing maritime environments and evaluated the performance of runway detection and adjustment before and after dehazing, quantifying the errors.
# Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
## 4 Numerical Results  
In this section, the simulated performance results of the proposed beam pair selection algorithm is presented in terms of correct MIB, DCI and DL-SCH decoding percentages alongside with the bit error rate (BER) metric, shown as a function of \(E_{b}/N_{0}\), where \(E_{b}\) is the average bit energy and \(N_{0}\) is the one-sided noise power spectral density. The Quadrature phase-shift keying (QPSK) is the modulation scheme adopted in this section and the transport block size (TBS) is 176.  
The simulation was performed for three different algorithms, whose vectors specifying the antenna element array sizes comprised a two-element row vector specifying the number of antenna elements in the rows and columns. A URA vector specifying the antenna element array sizes is used when both values are greater than one, and a ULA is assumed if any of the values is one. The three different simulated algorithms were:  
* Beam pair selection based on the RSRP maximization algorithm proposed in [29];
* Optimum beam pair angle selection algorithm, for comparison purposes;
* Proposed BSAE algorithm.  
The considered optimum beam pair selection algorithm corresponds to the case where the real channel matrix is used to compute the optimal UE angle; however, since this matrix is not known in practical implementations, this algorithm is not feasible in real-time scenarios.  
In each simulation, for the sake of simplicity, a single cell scenario with only one BS and one UE was considered for both FR1- and FR2-mode operation (normal CP values). The considered transmit array was an URA with \(N_{Tx}=64\), whereas the receive array employed an ULA type configuration with \(N_{Rx}=16\). Additionally, there were eight SSBs per SS burst for the frequency-mode operation.  
A more complete list of system parameters used for the three algorithms mentioned above is presented in Table 1:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**KERRYPNX** & **Parameter** & **Value** \\ \hline \multirow{8}{*}{Main} & Subcarrier spacing & 30 kHz (FR1); 120 kHz (FR2) \\  & Number of symbols & 10,000 \\  & Number of SSBs & 8 \\  & Carrier frequency & 3.5 GHz (FR1); 28 GHz (FR2) \\  & Channel bandwidth & 100 MHz (FR1); 400 MHz (FR2) \\  & Modulation & QPSK \\ \hline \multirow{8}{*}{Transmitter} & gNB array size & 64 \\  & gNB azimuth range & \([-60;60]\) \\  & gNB elevation range & \([-90;90]\) \\  & Sample rate & 15.36 MHz (FR1); 61.44 MHz (FR2) \\  & SS Burst periodicity & 20 ms \\  & Array configuration & URA \\ \hline \multirow{2}{*}{Channel} & Number of clusters & 10 \\  & gNB position & \([0;0;0]\) \\  & UE position & \([100;50;0]\) \\ \hline \multirow{4}{*}{Receiver} & UE array size & 16 \\ \cline{2-2}  & UE azimuth range & \([-100;100]\) \\ \cline{1-1}  & UE elevation range & \([-90;90]\) \\ \cline{1-1}  & Array configuration & ULA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
###### Abstract  
Alternative fuels for internal combustion engines (ICE) emerge as a promising solution for a more sustainable operation. This work assesses combustion and performance of the dual-fuel operation in the spark ignition (SI) engine that simultaneously integrates acetone-butanol-ethanol (ABE) and hydroxy (HHO) doping. The study evaluates four fuel blends that combine ABE 5, ABE 10, and an HHO volumetric flow rate of 0.4 LPM. The standalone gasoline operation served as the baseline for comparison. We constructed an experimental test bench to assess operation conditions, fuel mode, and emissions characteristics of a 3.5 kW-YAMAHA engine coupled to an alkaline electrolyzer. The study proposes thermodynamic and combustion models to evaluate the performance of the dual-fuel operation based on in-cylinder pressure, heat release rate, combustion temperature, fuel properties, energy distribution, and emissions levels. Results indicate that ABE in the fuel blends reduces in-cylinder pressure by 10-15% compared to the baseline fuel. In contrast, HHO boosted in-cylinder pressure up to 20%. The heat release rate and combustion temperature follow the same trend, corroborating that oxygen enrichment enhances gasoline combustion. The standalone ABE operation raises fuel consumption by around 10-25 g \(\bullet\) kWh\({}^{-1}\) compared to gasoline depending on the load, whereas HHO decreases fuel consumption by around 25%. The dual-fuel operation shows potential for mitigating CO, HC, and smoke emissions, although NOx emissions increased. The implementation of dual-fuel operation in SI engines represents a valuable tool for controlling emissions and reducing fuel consumption while maintaining combustion performance and thermal efficiency.  
acctone-butanol-ethanol; dual-fuel operation; electrolyzer; emissions levels; hydroxy gas; spark ignition engine +
Footnote †: journal: Applied Sciences
###### Abstract  
The IberSpeech-RTVE Challenge presented at IberSpeech 2018 is a new Albayzin evaluation series supported by the Spanish Thematic Network on Speech Technologies (Red Tematica en Tecnologias del Habla (RTTH)). That series was focused on speech-to-text transcription, speaker diarization, and multimodal diarization of television programs. For this purpose, the Corporacion Radio Television Espanola (RTVE), the main public service broadcaster in Spain, and the RTVE Chair at the University of Zaragoza made more than 500 h of broadcast content and subtitles available for scientists. The dataset included about 20 programs of different kinds and topics produced and broadcast by RTVE between 2015 and 2018. The programs presented different challenges from the point of view of speech technologies such as: the diversity of Spanish accents, overlapping speech, spontaneous speech, acoustic variability, background noise, or specific vocabulary. This paper describes the database and the evaluation process and summarizes the results obtained.  
IberSpeech Challenge; RTVE2018 database; Albayzin evaluation; speech-to-text transcription; speaker diarization; multimodal diarization +
Footnote †: journal: Applied Sciences
## 2 The Case for Maltese Web-Mapping  
### Spatial Analysis and Its Impact on Social Research: The Criminological Approach  
Crime analysis takes up different forms, from studying events by manually sticking pins on wall maps to identify crime locations, to the use of tabular data in spreadsheets and databases and eventually to the use of statistical tools and GIS, aided by theme-specific GIS modules in proprietary software that cater specifically for crime. Access to data is still a major issue, with access to sensitive crime-related data even more so. The 2011 online crimemap published by the UK police ([[http://www.police.uk/](http://www.police.uk/)]([http://www.police.uk/](http://www.police.uk/))) and that available on Malta since 2007 (www.crimemalta.com) show point data and aggregated data respectively  
Social-scientific research to date has depended on textual and tabular data that is rarely analysed in a spatio-temporal setting. The use of spatial and temporal analysis of crime data has enabled analysts to combine different resources stored in various formats into a coherent system. With GIS technology, multiple variable analyses is possible due to 'its ability to compare multiple geographic factors and investigate geographic correlations' and identify the root cause of a problem [15]. It also has the ability to unravel a complex issue that may be too difficult to investigate using conventional methods such as the wall-maps exercise where coloured pins identifying crime types were stuck to a wall making it too difficult to analyse as the number of crimes grew. GIS aids researchers to generate new knowledge through the use of cross-data analysis such as land zoning and offence location through the concept of layering. Better still, this analysis can be carried out in a time-series process to create a spatio-temporal output, effectively identifying trends and helping to recommend policy change. Although the development of these technologies has granted access to researchers to carry out comparative spatio-temporal analysis, caution must be taken to take up [PERSON]'s (2001) heed to understand the limitations of such systems and methodologies [16].  
Further developments in the area of crime mapping show an increased application use by police departments and crime analysts, with interest in crimes by such major entities as the Home Office [17] (UK) and the National Institute of Justice in the US [18], Interpol [19], Europol [20], University of Sheffield [21], University of Huddersfield [22], Jill Dando Institute [23], the University of Liverpool [24] and the University of Malta [25]. These initiatives were based on an ad hoc process which lacked a system-wide set of protocols that would establish convergence between the datasets. The INSPIRE exercise proved timely to the realisation of the requirements and work required to process crime data in line with the other domains.
# Reliability Optimization Design of Diesel Engine System Based on the GO Method
## 3 Problem Description and Model Building and Solution
### Establish the Optimization Model  
#### 3.2.1 Objective Function  
Considering both optimal performance (maximum system reliability and minimum total cost) and robustness (maximum unit reliability interval), this study aims to find a hyper-rectangle whose volume \(V(R)\) is as large as possible, reliability \(R_{S}(R)\) as high as possible, and total cost \(C_{S}(R)\) as little as possible.  
(1) Reliability hyper-rectangle volume function  
The \(m\) unit reliability interval of the diesel engine constitutes an \(m\)-dimensional hyper-rectangle whose volume function is taken as a logarithmic volume function in this study.  
\[V(R)=\sum\
olimits_{i=1}^{m}\ln\left(R_{i,up}-R_{i,low}\right)=\ln\left(\prod \
olimits_{i=1}^{m}\left(R_{i,up}-R_{i,low}\right)\right) \tag{2}\]  
(2) Reliability function  
The reliability function \(R_{S}(R)\) can be obtained by the GO method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Power** & **Cooling** & **Lubrication** & **Exhaust** \\ \hline Reliability & 0.9980 & 0.9834 & 0.9947 & 0.9391 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Functional reliability of the diesel engine system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Power** & **Cooling** & **Lubrication** & **Exhaust** \\ \hline Reliability & 0.9998 & 0.9980 & 0.9993 & 0.9925 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Functional reliability distribution of the diesel engine.  
Common reliability-cost function models comprise the logarithmic model, power function model, three-parameter cost function model, and four-parameter cost function model [21]. The reliability values of three-parameter and four-parameter cost function models cannot reach the upper limit. Thus, the cost-reliability model constructed in this paper is expressed in Equation (3) based on the general principles of cost-reliability function model construction.  
\[C_{S}(R)=\sum_{i=1}^{m}C_{i}(R)=\sum_{i=1}^{m}f_{i}\cdot\exp\big{(}\ln(R_{i}/R_ {i,low})/\ln\big{(}R_{i,up}/R_{i,low}\big{)}\big{)} \tag{3}\]  
where \(f_{i}\) denotes the cost factor evaluated according to engineering practice (such as material, manufacturing, and processing).
## 2 Modified Ellipse Method
As shown in Figure 1b, two elliptical loci shown as dashed lines were plotted with the group velocities \(V_{g}\) and \(V_{d-s}\) in a sensing path, respectively, and the region between the two elliptical loci forms an elliptical ring, described by  
\[\frac{l_{Im}}{V_{g}}+\frac{l_{Im}}{V_{g}}<\frac{l_{Im}}{V_{g}}+\frac{l_{Im}}{V _{d-s}}<\frac{l_{Im}}{V_{d-s}}+\frac{l_{Im}}{V_{d-s}} \tag{3}\]  
Then, the elliptical ring, not just one ellipse-like locus, should be defined as the potential region for damage locations. If two or more sensing paths are available, the damage locations can be determined by the crossover area at the intersections of most of the elliptical rings. To present the identification results of damage localization, a grey-scale image was adopted to describe a two-dimensional structure with exactly the same dimension. The inspection area of the structure was virtually and evenly meshed. At each mesh node, the pixel value \(D_{Ij}(x_{m},y_{m})\) was quantified with respect to the region between the two elliptical loci. If the mesh node fell within the region, it had the high pixel value of 1 for damage occurrence. If not, it had the low pixel value of 0. By integrating information from all sensing paths in the sensor network, the ultimate identification image could be produced using an image fusion scheme as  
\[I(x_{m},y_{m})=\sum_{I=1}^{N}\sum_{J=1,J\
eq I}^{N}D_{II}(x_{m},y_{m})\]  
Figure 1: Schematic view of ToF-based (**a**) ellipse method and (**b**) modified ellipse method in a sensor network. with  
\[D_{IJ}(x_{m},y_{m})=\left\{ \begin{array}{l}1,\ (x_{m},y_{m})\ \text{falls in the region}\\ 0,\ \text{else}\end{array} \right. \tag{4}\]  
According to the above analysis, this modified ellipse method makes use of a region between two elliptical loci, instead of an elliptical or ellipse-like locus, for damage localization. Generally, it can improve the accuracy of damage localization, while considering the change in group velocity before and after interacting with the damage, and it has the potential to evaluate quantitatively the size and even the level of structural damage because it makes use of the region between two elliptical loci with respect to the certain dimension of the damage location. Moreover, it is suitable not only for locating micro-damages using nonlinear Lamb waves with quasi group-velocity matching (e.g., mode pair S0-s0) but also for damage localization with mode conversion at the damage.
# Online Streaming Feature Selection via Conditional Independence
## 3 Framework for Streaming Features Filtering
### Framework for Filtering Conditional Independence  
The feature selection process can be performed step by step, as illustrated in Figure 2, assuming that the feature space is the set of all features before the arrival of new features \(f_{i}\) at time \(t_{i}\). Of course, we do not save this space, but we filter only the new features.  
First, irrelevant features can be filtered through filtering of null-conditional dependence, leaving only relevant features. Second, parts of redundant features are further discarded from weakly relevant features through filtering of single-conditional dependence. Finally, the remaining redundant features are further filtered through filtering of multi-conditional independence. As a result, the remaining features are those that have been finally selected. The entire filtering process is repeated with the advent of new features. The arrival of new features changes the result of the selected features.  
In this section, we propose a framework for filtering conditional independence to deal with data with streaming features, as illustrated in Figure 3.  
The following features indicate the uniqueness of the _ConInd_ framework compared to existing algorithms. (1) _ConInd_ employs three-layer filtering strategies with conditional independence to filter streaming features. (2) _ConInd_ can mine an approximate _Markov blanket_ through filtering irrelevant  
Figure 3: The _ConInd_ framework for feature selection via conditional independence.  
Figure 2: Features filtered using conditional dependence.  
features and redundant features with a low time cost. At the same time, it can be used to prove the approximate _Markov blanket_ theoretically. (3) Among algorithms for feature selection from streaming features, _ConInd_ has significant performance improvements in terms of running time when the datasets have low redundancy and high relevance.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 4 Results and Discussion
### Setup and Implementation Details
#### 4.2.3 Methods for Comparison  
The following alternative methods were also employed in the experiment, for comparison with the proposed MTAD:  
* iForest, which defines anomalous samples as sparsely distributed points that can easily be isolated. On the basis of sampling, a random hyperplane is used to construct the feature space to build an isolation tree. In the experiment, the input length was set to 8 and the number of trees was set to 100.
* LSTM-AE [44], which is a reconstruction-based method featuring LSTM layers on both the encoder and decoder.
* Fully connected neural network-based variation auto-encoder (FCNN-VAE), which is a VAE anomaly detection model implemented based on an FCNN.
## 2 Materials and Methods
### Artifact Removal Generative Adversarial Network (AR-GAN)  
The AR-GAN is constructed on the basis of Cycle-Consistent Generative Adversarial Networks [27], and the generator and discriminator use the same network parameters as the original text. As shown in the AR-GAN framework diagram of Figure 3, the AR-GAN is composed of two generators and two discriminators. The generator \(Gx2y\) maps the generation domain CT image \(r\_x\) to the \(f\_y\) domain through the loss of \(\mathcal{L}_{cycle}\), \(\mathcal{L}_{ide}\), \(\mathcal{L}_{err}\), and the \(\mathcal{L}_{GAN}\) loss constraint generated by the discriminator \(D\_y\). The generator \(Gy2x\) is similar.  
The loss path of \(\mathcal{L}_{GAN}\) loss is shown by the red lines in Figure 3. The mathematical expression can be expressed as:  
\[\mathcal{L}_{GAN}(Gx2y,D\_y,r\_x,r\_y)=\mathbb{E}_{y\sim P_{data}(r\_y)}[\log D \_y(y)]+\mathbb{E}_{x\sim P_{data}(r\_x)}[\log(1-D\_y(Gx2y(x)))] \tag{7}\]  
\[\mathcal{L}_{GAN}(Gy2x,D\_x,r\_x,r\_y)=\mathbb{E}_{x\sim P_{data}(r\_x)}[\log D \_x(x)]+\mathbb{E}_{y\sim P_{data}(r\_y)}[\log(1-D\_x(Gy2x(y)))] \tag{8}\]  
The loss of \(\mathcal{L}_{cycle}\) and \(\mathcal{L}_{idc}\) can further strengthen the ability of generators \(Gx2y\) and \(Gy2x\) to generate images of the corresponding domain. \(\mathcal{L}_{cycle}\) loss path is shown by the blue lines and \(\mathcal{L}_{idc}\) loss path is shown by the green lines in Figure 3.  
\[\mathcal{L}_{cycle}(Gx2y,Gy2x,r\_x,r\_y)=\mathbb{E}_{x\sim P_{data}(r\_x)}[ \left\|Gy2x(Gx2y(x))-x\right\|_{1}]+\mathbb{E}_{y\sim P_{data}(r\_y)}[\left\| Gx2y(Gy2x(y))-y\right\|_{1}] \tag{9}\]  
\[\mathcal{L}_{idc}(Gx2y,Gy2x,r\_x,r\_y)=\mathbb{E}_{x\sim P_{data}(r\_x)}[ \left\|Gy2x(x)-x\right\|_{1}]+\mathbb{E}_{y\sim P_{data}(r\_y)}[\left\|Gx2y(y) -y\right\|_{1}] \tag{10}\]  
When \(f\_x\) obeys the \(P_{data(r\_x)}\) distribution and \(f\_y\) obeys the \(P_{data(r\_y)}\) distribution, the difference distribution between \(r\_x\) and \(f\_x\) and the difference distribution between \(r\_y\) and \(f\_y\) should be very similar. Therefore, we used \(\mathcal{L}_{err}\) loss to limit the difference of artifacts. \(\mathcal{L}_{err}\) loss path is shown by the gray lines in Figure 3. The mathematical expression is as follows:  
\[\mathcal{L}_{err}(Gx2y,Gy2x,r\_x,r\_y)=\mathbb{E}_{x\sim P_{data}(r\_x)\sim P _{data}(r\_y)}[\left\|(Gx2y(x)-y)-(Gy2x(y)-x)\right\|_{1}] \tag{11}\]  
We defined the complete loss function as follows, and the parameters \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\), and \(\alpha_{4}\) were, respectively, 1, 8, 4, and 2:  
\[\mathcal{L}_{AR}=\alpha_{1}\mathcal{L}_{GAN}+\alpha_{2}\mathcal{L}_{cycle}+ \alpha_{3}\mathcal{L}_{idc}+\alpha_{4}\mathcal{L}_{err} \tag{12}\]