esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
## 3 Algorithm Description
#### 3.1.1 Cycle Cross-Correlation (CCC) in Delay Domain
Therefore, the LTE signal has the characteristics of cyclostationarity, and the cyclostationarity period is 2048, i.e., the length of the OFDM useful signal. Figure 7 shows schematically the cyclic autocorrelation of the LTE signal with its delay replica, of which the delay is 2048 bins:  
Figure 6: LTE signal OFDM modulation schematic diagram. Figure 7: Cyclic autocorrelation of the LTE signal. It can be seen from the above figure that when performing the CAC with one of the signals delayed by 2048 points, the CP of the two signals match. The integrated gain of the CAC is proportional to \(144\times\text{D}\), where 144 is the length of CP in each OFDM symbols and D is the number of symbols contained in the LTE signal. Because noise has no cyclostationarity, it cannot integrate in the CAC results. Therefore, the INRs for \(m=1,2,\ldots,M\) are increased to the same amount, which is different from the case described in Equation (7). It can be seen from the above analysis that when \(k=0\), although the INRs for \(m=2\), \(3,\ldots,M\) can be improved, the INR for \(m=1\) will not be improved. Therefore, the cyclic cross-correlation result at \(k=0\) will have amplitude modulation for different antenna array element channels, which will have a great impact on the direction estimation accuracy. When \(k=2048\), the INR for each channel can be increased by the same amount. In addition, the other co-frequency and adjacent frequency interference signals that are not LTE signals will also be suppressed when \(k=2048\), as they have no cyclostationarity. This means that the impact of non LTE interference on DOA estimation of the LTE co-channnel interference can be eliminated. Therefore, the cyclic cross-correlation results at \(k=2048\) are mainly used in the following for the interference spatial spectrum estimation.
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### At Inter- and Intra-Organizational Level
#### 3.2.3 Conceptual Modeling Framework Using REA Ontology
Although the metadata entries are always stored using the identity strings (ID strings with PII) in organizations' ISs. Still, they lacked standardized information security management systems [35]. Nowadays, metadata privacy is protected by national and European regulations and involves legal commitments by the concerned authorities to avoid hefty money claims [6,7]. The legal and administrative requirements for privacy include privacy by policy, privacy by design, and patients' informed consent measures [6,7]. Therefore, the privacy preservation of care metadata share does not rely on the behavioral patterns of health insurers/caregivers anymore. Rather, legal benchmarks constrain the concerned authorities for shaping the respective organizational/services contracts accordingly. Consequently, the patients' privacy concerns now appear as Materialized Privacy Claims (MPCs) in the Dutch care metadata share landscape. Another approach (for a better generalizable REA model) was to incorporate privacy breach as a condition in the insurance policy. Furthermore, it could have added granularity regarding the levels of severity and respective cash disbursements. Nevertheless, the proposed REA model signifies the scenarios involving privacy claims by patients (or for patients by any other potent authority, i.e., DPO) against caregivers and insurers. In this regard, the possibility of these privacy claims being compensated with hefty cash payments by the concerned authorities (caregivers/insurers) is high; see Figure 7. The red dotted blocks show our contribution to the model. Financial Gain/Loss of Healthcare Enterprises and Healthcare Metadata Utility: If you see Figure 8, the healthcare providers such as insurers, GPs, hospitals, and diagnostic labs along with their sister pharmacies are financial enterprises. Enterprises that are privately run but are partially publicly funded and are sometimes nonprofitable. We discover that financial gain/loss depends directly upon the healthcare data utility. Data utility is also vital for patients' efficient and effective treatment and data spaces integration across domains in healthcare both nationally and internationally for collective gain. We are also working in this regard to find out both technical and managerial measures to locate optimal data utility while preserving patients' privacy while sharing patients' metadata.
## 3 Results and Discussion
### The Fourier Transform Infrared (FTIR) Test
#### 3.2.3 Aromatic Compounds  
Aromatic compounds can be observed in five regions: 3100-3000 cm\({}^{-1}\), 2000-1700 cm\({}^{-1}\), 1650-1430 cm\({}^{-1}\), 1275-1000 cm\({}^{-1}\) and 900-690 cm\({}^{-1}\)[30]. However, the last region is under evaluation; FTIR spectrum results of bands are presented in Figure 6a. Many vibrations may vary by hundreds of wave numbers, as per the bands in the region 900-700 cm\({}^{-1}\); the variation in these bands showed differences in the absorbance for all modified bitumen after ageing, as can be seen in Figure 6b. These bands are called out-of-plane bending. The bands of the out-of-plane bending vibrations for compounds responsible for aromatics  
Figure 5: (a) FTIR spectrum results of bands at wave numbers 2920 and 2851 cm\({}^{-1}\); (b) absorbance value for carboxylic acid at wave numbers 2920 and 2851 cm\({}^{-1}\).  
were strong and could be observed easily. For 0 NCMB, the variation in the bands before and after STA and LTA was very clear. Reduction in the absorbance value for the bands after ageing was observed. The absorbance of C-H out-of-plane stretching was reduced due to the effect of ageing, and the reduction was increased when ageing was more severe (LTA compared to unaged). The variations in the bands for 2 NCMB before and after ageing were different compared to 0 NCMB. The bands were more closed, and the effect of ageing was less as compared to 0 NCMB. In addition, the variations in the bands for 4 NCMB before and after ageing were more closed and similar. The results for unaged and STA were almost same. The results for LTA showed a slightly smaller reduction in the absorbance.  
Figure 6: (**a**) FTIR spectrum results of bands at wave numbers 860,809 and 724 cm\({}^{-1}\); (**b**) absorbance values for carboxylic acid at wave numbers 860,809 and 724 cm\({}^{-1}\).  
It can be concluded that the band of C-H out-of-plane stretching, which reflects the existence of aromatics, was affected by ageing for all the samples. The reduction in this band was expected, as the ageing effect reduces the aromatic in the bitumen binder [1; 6; 14; 31; 38]. It was stated and accepted by researchers that the aromatics generate resins, which in turn generate asphaltenes [1; 7; 11; 39]. The most sensitive to ageing was 0 NCMB, followed by 2 NCMB. The 4 NCMB showed less sensitivity, confirming that hard bitumen shows less sensitivity to ageing [1].
# Rule-Based Scan-to-BIM Mapping Pipeline in the Plumbing System
## 5 Scan-to-BIM Mapping Pipeline Component Design
### Scan-to-BIM Data Structure  
Scan-to-BIM is a process of converting a given PCD according to the purpose of use and finally mapping it to a shape and a BIM object. Therefore, PCD becomes the core data structure in the pipeline components. The point data structure constituting the PCD may include RGB and intensity values in addition to x, y, and z coordinate values. Points are added as values and attributes calculated for segmentation, etc. and are passed through the pipeline process. For example, in order to calculate the curvature of a point, a normal vector must be calculated. Figure 8 shows a proposed data structure. The roles of major classes are summarized in Table 4.  
Figure 8: Scan-to-BIM Data Structure.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Input** & **Output** & **Responsibility** \\ \hline ProjectSetup & Name description position & project setup file & Scan-to-BIM project setting parameter definition. \\ \hline LoD & PCD file resolution (mm) & PCD file & Point cloud resolution is adjusted to a level appropriate for the purpose of use for processing large amounts of scan data. \\ \hline PointCloudToGrid & PCD grid = [x, y, z] & PCD files & Split point cloud data into grids for processing large-scale scan data. \\ \hline Filtering & PCD & & Removes noise from point groups within the divided grid. The removal method depends on the purpose of use and the scanning environment. The definition of the noise removal parameter specifies the distance between the low-density point group and the other point groups by kNN algorithm. The target point cloud area to be used is designated as a boundary box. \\ \hline Segmentation & PCD cylinder = [min radius, max radius, min length, min curvature, max curvature] & Segmented PCD files parameter & To extract the cylinder shape from the point group, this study defines the features for the point group. \\ \hline ScanToGeometry & Segmented PCD & & Specify the minimum radius, maximum radius and length of the cylinder, which are the characteristics of the pipe shape. Also, the curvature of the points constituting the cylinder is specified. \\ \hline \multirow{2}{*}{\begin{tabular}{c} MergeGeometry \\ \end{tabular} } & Segmented PCD & & Convert pipe segment point cloud to geometry. Pipe shape dimensions are adjusted to a value corresponding to a radius within a given tolerance considering noise. \\ \hline ScanToGeometry & Segmented Pream & & Segmented pipe shapes are merged with a given tolerance and merged into geometry. \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} GeometryToBIM \\ \end{tabular} } & Segmented Pream & & Converts geometry files to BIM object files. \\ \cline{1-1}  &
\begin{tabular}{c} Property*] property = \\ [name, value] \\ \end{tabular} & BIM file & & Defines type and property information to convert to BIM object file. Property information consists of a name and a value. \\ \hline \end{tabular}
\end{table}
Table 3: Scan-to-BIM pipeline component definition (* = multiple).
## 2 Methodology
### Fixed-Bed Gasifiers
#### 2.1.2 Downdraft Gasifiers  
In this configuration, the feedstock is loaded in the upper section of the gasifier, while the gasification agent is introduced in the lower portion. The operational mechanism of a downdraft gasifier follows a stepwise process involving drying, pyrolysis, oxidation, and reduction. Initially, solid biomass is introduced into the gasifier, initiating the drying phase when heat is applied to reduce the moisture content. Subsequently, during the pyrolysis stage, the partially dried biomass undergoes thermal decomposition without oxygen, yielding volatile gases, tar, and char. As the mixture descends, it enters the oxidation zone, where a carefully controlled amount of oxygen is introduced. In this area, the volatile gases generated during pyrolysis undergo combustion, releasing additional heat and forming combustion products. Finally, the mixture enters the lower reduction zone, characterized by a bed of hot carbonized material from the initial combustion. Here, any remaining oxygen reacts with the volatile gases, leading to the production of syngas that is both clean and energy-rich, primarily composed of carbon monoxide, hydrogen, and methane.  
Both the fuel and gas move downwards during the gasification process. Gasification reactions predominantly take place in the lower region of the gasifier, and the resultant gas is discharged from the upper section, and gasification reactions occur from top to bottom, passing through drying, pyrolysis, combustion, and reduction stages as shown in Figure 1b. The downdraft gasifier exhibits a quicker start-up time, low quantity of tar, and easy operation. However, it struggles with high moisture and feedstock and ash content, cannot operate effectively with certain unprocessed fuels, produces a lower heating value product gas when compared to updraft systems, and has limited scalability [17]. A study on the experimental analysis of a combined downdraft gasifier and multi-integrated gasification combustion system for MSW in remote areas provided information on the gasification efficiency, carbon conversion efficiency, specific gasification rate, quality of syngas, and combustible gas mixtures obtained by gasification in slow-moving updraft gasifiers [18]. Cooking with the gas produced using downdraft gasifiers was studied experimentally in the laboratory to study the gasification process, fuel effect, and its efficiency [19].
###### Abstract  
We have performed studies and comparative analysis of the biosynthesis characteristics of intracellular recombinant enzyme, such as hexahistidine-containing organophosphorus hydrolase (His\({}_{6}\)-OPH) in _Escherichia coli_ SG13009[pREP4] cells when various perfluorocarbon compounds (PFC) were introduced into the medium for cell cultivation. The PFC were found to facilitate the biosynthesis of His\({}_{6}\)-OPH: increased levels of the total OPH-activity (up to 37%) were measured upon introduction of 1,1,1,2,2,3,3,4,4,5,5,6,6,6-tetradacafluorohexane (PFH) and 4,7,10,13,16,19,22,25,28,31-decaoaxaperfluoro-5,8,11,14,17,18,21,24,27,30-decamethyl tetratriacontane (Polyether II) into culture medium. We have demonstrated the possibility of effective and multiple (at least five-fold) use of PFH for biosynthesis of intracellular recombinant protein His\({}_{6}\)-OPH, which catalyzes the hydrolysis of organophosphorus pesticides (OP), is widely used in agriculture and can be applied as new antidote for OP-detoxification in vivo. The multiple use of PFH was achieved through recycling of this substance: sediment of _Escherichia coli_ SG13009[pREP4] cell biomass was collected at the end of each culture growing step and disintegrated with ultrasound, and obtained residue containing almost all of the initially introduced PFC was then added to the medium at the start of the following culture growing step.  
Keywords:perfluorocarbon compounds; perfluorohexane; recombinant intracellular protein; hexahisidine-containing organophosphorus hydrolase; _Escherichia coli_
## 5 Conclusions  
In this paper, a novel particle filter track-before-detect algorithm based on hybrid differential evolution was developed. Firstly, we exploit a hybrid differential evolution as a novel particle updating strategy for overcoming the shortcoming of the particle impoverishment. Secondly, in order to reduce the influence of the particle degeneracy, the systematic resampling method is chosen for the proposed method. Finally, the proposed algorithm combines a more effective particle initiation method, which only places the particles in the highest intensity bins of the data. The proposed algorithm not only can make full use of the data information, but also decreases the particle degeneracy and enhances the diversity of particles. The detection and tracking of the performance advantage of the proposed algorithm have been demonstrated by two cases of a low signal-to-noise point target against a background of Gaussian noise with the CV and CT motion models, respectively. What is more, the proposed method is much more efficient than previous methods. Future work will continue to enhance the detection accuracy of the proposed algorithm and to explore the implementation of multiple targets within this framework.  
Figure 6: (**a**) RMSE in position at SNR = 9 dB for the CT model; (**b**) RMSE in position at SNR = 6 dB for the CT model; (**c**) RMSE in position at SNR = 3 dB for the CT model; (**d**) RMSE in the position of existence at SNR = 1 dB for the CT model.
# Integration of _Salmonella_ into Combination Cancer Therapy
## 3 _Salmonella_ in Combination with Other Approaches to Cancer Therapy
### Combination with Immune Checkpoint Inhibitors
It is worth noting that a comparison with anti-PD-1 alone was not performed. In another study of colon cancer, CT26 or MC38 tumor-bearing mice treated with a combination of _Salmonella_ carrying IDO siRNA and anti-PD-1 showed delayed tumor growth in comparison to anti PD-1 monotherapy [145]. However, the combination showed no additional tumor growth inhibition compared to _Salmonella_ treatment alone [145]. A recent study also used sub-therapeutic doses of IDO-targeting _S. typhimurium_ along with anti-PD-1/CTLA-4 antibodies to treat LLC1-tumor bearing mice [137]. They observed a significant delay in tumor growth in mice that received the treatment combination in comparison to those received either treatment alone, and this inhibition was associated with an increase in tumor infiltration of CD4+ and CD8+ T cells [137]. These results collectively highlighted _Salmonella_'s capacity to enhance the efficacy of checkpoint inhibitors in treating tumor-bearing mice. However, the immunological response underlying the enhanced outcome of _Salmonella_-checkpoint inhibitors combination did not receive sufficient attention and need to be further investigated.
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 2 Conventional 3 GPP Enhanced Voice Services  
The EVS is a 3 GPP speech codec designed for the VoLTE network. The EVS codec supports two modes: ACELP (for low and intermediate bitrates) and MDCT (for intermediate and high bitrates). The mode is selected depending on channel capacity and speech quality requirements.  
The EVS speech/music classifier operates when the EVS codec is detected as \"active\" by voice activity detection (VAD) every 20 ms frame.
## 3 Mathematical Approach Using Homomorphic Encryption and Zero-Knowledge Proofs
### Results
#### 3.7.3 SafeSplit Performance Against Backdoor Attacks  
We evaluated the SafeSplit mechanism for its effectiveness in detecting backdoor attacks and restoring model integrity. Table 4 represents sets, demonstrating SafeSplit's resilience in identifying suspicious behavior and initiating rollbacks as necessary.  
Backdoor Accuracy (BA) represents the success rate of adversarial modifications in bypassing security defenses. Lower BA values indicate stronger defense mechanisms, with HavenSL demonstrating a significant reduction in adversarial effectiveness. Detection Efficiency (%) measures the framework's ability to identify and mitigate unauthorized parameter modifications in real time. A higher Detection Efficiency percentage signifies robust protection against backdoor attacks. To further evaluate the efficiency of HavenSL, we conducted additional experiments measuring overall training time under different  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & \begin{tabular}{c} **Rollback** \\ **Count** \\ \end{tabular} & \
\begin{tabular}{c} **Backdoor Accuracy** \\ **(BA) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Detection** \\ **Efficiency (\%)** \\ \end{tabular} \\ \hline CIFAR-10 & ResNet-18 & 4 & 9 & 96 \\ \hline MNIST & Simple CNN & 2 & 5 & 98 \\ \hline Fashion-MNIST & Simple CNN & 3 & 7 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SafeSplit mechanism for its effectiveness in detecting backdoor attacks and restoring model integrity.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & \begin{tabular}{c} **Encryption** \\ **Time (s/image)** \\ \end{tabular} & \
\begin{tabular}{c} **Processing** \\ **Time (s/image)** \\ \end{tabular} &
\begin{tabular}{c} **Main Task** \\ **Accuracy (MA) (\%)** \\ \end{tabular} \\ \hline CIFAR-10 & ResNet-18 & 5.8 & 13.5 & 87 \\ \hline MNIST & Simple CNN & 3.2 & 10.8 & 98 \\ \hline Fashion-MNIST & Simple CNN & 4.0 & 11.3 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Encryption and processing times remain efficient across datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & \
\begin{tabular}{c} **ZKP Generation** \\ **Time (s/epoch)** \\ \end{tabular} &
\begin{tabular}{c} **ZKP Verification Time** \\ **(s/epoch) (s/image)** \\ \end{tabular} \\ \hline CIFAR-10 & ResNet-18 & 4.8 & 3.5 \\ \hline MNIST & Simple CNN & 2.5 & 2.0 \\ \hline Fashion-MNIST & Simple CNN & 3.0 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Incorporating ZKP into Split Learning.  
configurations. Table 5 presents a comparative analysis between HavenSL and baseline Split Learning models, considering end-to-end training time, communication overhead, and cryptographic overhead.  
Results indicate that while HavenSL incurs a moderate increase in computational cost, it significantly enhances security by mitigating backdoor attacks with a 96% detection efficiency.
## 5 Discussion
### Future Work  
The findings of this study pave the way for several avenues of future research. First, a deeper literature review and more experiments are needed to complement the proposed classification method with latent presence factors, as well as to validate potential presence factors. Moreover, we suggest that some presence factors, such as immersion and aesthetic design, could be split into more concrete presence factors that can be better utilized in the design and implementation processes. A comprehensive, multi-level taxonomy of presence factors could be one way to achieve this in a future study.  
Second, inspired by the mixed results on some of the presence factors in our study, we encourage further research on exploring the manners in which the personal characteristics of the user and other contextual factors affect the experienced presence. This is a challenge for designers of immersive VR experiences, as the design must be personalized to meet the needs and preferences of diverse users. Further research on this could lead into concrete recommendations, frameworks, and tools for personalizing VR experiences to better facilitate presence across diverse individuals.  
Third, we hypothesize that users will be willing to forgive minor inconveniences and issues if VR experience is otherwise of high quality and the positive effects of the experience triumph the negative aspects. Future research is needed to test this hypothesis, identify various types of inconveniences, and investigate the effects of them on user experience, including presence.  
Fourth, although this study did not focus on exploring the aspects of emotional engagement in VR experiences, the user study results demonstrated emotional engagement among some of the participants. To the best of our knowledge, emotional engagement in immersive VR using gesture-based interaction is a largely unexplored area. Potential investigations include understanding the difference between VR experience with and without gesture-based interaction in terms of emotional engagement, investigating which emotional responses are associated with a gesture-based interaction, and exploring how a gesture-based interaction effects emotional engagement in multi-user scenarios, such as in metaverse environments.  
Fifth, as our study only investigated Four Seasons as a manifestation of the gesture-based interaction paradigm, the potential presence factors and other findings should be validated in a future comparative study that would consider different types of gesture-based UIs, as well as different variations in diegetic [64] and non-diegetic interfaces, to see their effects on presence and what kinds of presence factors could be associated with each one.