esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Identification of Strain NH as Anerinibacillus aneurinibticus  
In order to assign a species identity to strain NH, we amplified the NH genomic DNA with PCR primers previously described in the literature [52] that bound to conserved sequences flanking the V4 region of the 16s rRNA gene. This PCR product was DNA-sequenced, and the resulting sequence was used as the query sequence to search the RDP classifier database of Michigan State University, and also in a Smith-Waterman local alignment to a strain NH whole-genome sequence. The RDP database result indicated that NH is a member of the genus _Aneurinibacillus_. The Smith-Waterman alignment identified the corresponding region in the NH whole-genome sequence. Comparison of the surrounding sequence to the published 16s rRNA gene [54] allowed us to obtain the entire 16s rRNA gene sequence of NH. This full-length 16s sequence was subsequently used as the query sequence to search for matching sequences using the BLAST algorithm search of all non-redundant nucleotide sequences in the NCBI database. The resulting BLAST search revealed a percentage nucleotide sequence identity between _Aneurinibacillus aneurinibticus_ strain Murayama (accession no. NR_036798) and NH of 99.9%. This value exceeds the 98.7% cutoff established by [PERSON] [55] for bacterial species identification. Lastly, a MAFFT multiple-sequence alignment and a phylogenetic tree were constructed (Figure S1). The heat map and phylogenetic tree both indicated that NH is highly related to species _Aneurinibacillus aneurinibticus_ and diverges from species _A. migulanus_. We conclude that strain NH is a new strain of _Aneurinibacillus aneurinibticus_. Thus, we propose that strain NH be called \"_Aneurinibacillus aneurinibticus_ strain NH\". From these results, one would predict that other _A. aneurinibticus_ strains should exhibit the same bactericidal effect on _C. jejuni_ and _L. monocytogenes_. This was indeed found to be the case for CFCSAS precipitate dialysate of _A. aneurinibticus_ strain ATCC 12856 when partially purified by cation-exchange chromatography.
## 2 Materials and Methods
### Post-Processing
#### 2.3.1 Axial Velocity Estimation  
Axial velocities \(\hat{v}_{z}\) were obtained by applying the autocorrelation technique on the IQ-data as follows [34; 35]:  
\[\hat{v}_{z}=\frac{c_{L}\big{(}\frac{PRF}{n_{T}}\big{)}}{4\pi f_{0}}\angle\hat{ R}_{x}(1) \tag{3}\]  
where \(\angle\hat{R}_{x}(1)\) represents the phase angle of the autocorrelation function of lag one which is estimated from the received signal sequence, and \(n_{T}\) the number of transmit beams to obtain one image. The axial velocity estimate was further improved by spatial averaging the autocorrelation estimate over an area of approximately 0.6 \(\times\) 0.6 mm both in simulations and in vitro.  
Note that this post-processing step is not applied on the mechanical shear wave simulations, as these immediately provide access to all components of the particle velocities in the 3D spatial domain. Additionally, the mechanical wave simulations have a slow time sampling rate of 40 kHz, whereas the sampling rate of the real and virtual SWE imaging measurements depends on the acquisition, i.e., 6.9 kHz for single plane wave emissions and 2.3 kHz for plane wave compounding.
# Improving a PGNAA Technique to Detect Heavy Metals in Solid Samples
## 1 Introduction
Following the NIS reaction, chrome (Cr), zinc (Zn), and titanium (Ti) generate measurable gamma rays at 1.43 MeV, 1.005 MeV, and 0.984 MeV that coincide with Br peaks at 0.97 and 1.00 MeV and  
Figure 1: Four prompt gamma-ray spectra were collected from an empty sample container and a CeBr\({}_{3}\) detector. These spectra were related to the direct background (BkGd), background with a moderator before the detector (BkGd-Mod), background with angling the neutron generator tube (BkGd-Angle), and background with angling the neutron generator tube and adding a moderator directly before the detector (BkGd-Angle-Mod). Ce peaks at 1.35 MeV. These elements were chosen to demonstrate the effectiveness of the new optimization for the existing setup, as well as its suitability for determining the lowest level of elemental detection in solid samples. Adopting PGNAA to detect Cr, Ti, and Zn in solid samples by applying fast neutron activation is rare. Available data were reported a few decades ago [22; 23], and their achievements regarding the above elements will be reviewed in the results and discussion section below. Currently, exploring the resources near the earth's surface requires developing the PGNAA technique for borehole logging purposes, where information is needed to investigate the rocks or soils surrounding the hole. In this vein, the majority of recent articles [24; 25] have focused on Monte Carlo simulations. Hence, measurements employing portable PGNAA setups will provide important information about the technique's quality.
## 6 Abuse Attack Techniques
### Component Object Model (COM) Hijacking  
CPLS as a Primary C&C Communication Channel  
Researchers at ESET conducted a thorough analysis of the Turla backdoor malware [69]. Turla exploits the victim's Outlook mailbox for C&C communication, receiving instructions, and exfiltrating data. After infecting the host, Turla utilizes the legitimate messaging application programming interface (MAPI) to interact with Outlook, granting complete control over the target mailbox and utilizing additional MAPI functionalities. To ensure persistence and concealment, the operators employ the COM to modify the Windows registry. This Microsoft technology enables developers to manipulate objects in various applications. The communication between the botmaster and the infected bot takes place through email, utilizing specially encrypted PDF attachments to transmit operational commands and exfiltrated information between compromised Outlook emails and the botmaster's email.
## 3 Data and Methodology  
### Data Source and Processing  
#### 3.1.1 Modis Data  
In this study, considering the difference of temporal and spatial resolution, we apply the NDVI data in our study that were derived from the MODND1D product (the temporal resolution is one day and the spatial resolution is 500 m) acquired by the Terra satellites in 2012, which is processed by daily MOD09 GA (Collection 6 and the spatial resolution is 500 m) through splitting, cutting, projection conversion, unit conversion and other processes. The NDVI data (MODND1D product) from 2001 to 2019 were derived from the Geospatial Data Cloud website ([[http://www.gscloud.cn/](http://www.gscloud.cn/)]([http://www.gscloud.cn/](http://www.gscloud.cn/))). They were used to obtain the monthly maximum NDVI dataset, at a spatial resolution of 500 m, in the selected years. By contrast, the NDVI product MOD13A1 (Collection 6, the spatial resolution is 500 m), which is acquired by the Terra satellites, has a temporal of 16 days, and the Global MYD13A1 product is also provided every 16 days, at 500-m spatial resolution, as a gridded level-3 product in the Sinusoidal projection, which is acquired by the Aqua satellites ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))). In addition, this study used other MODIS products with a spatial resolution of 500 m and a time resolution of 8 days, such as fraction of absorbed photosynthetically active radiation (FPAR) from Terra Product (MOD15A2H) and surface reflectance data from Terra Product (MOD09A1). Moreover, surface reflectance data were used to calculate the Land Surface Water Index (LSWI) to require the limiting coefficient of water on photosynthesis of vegetation. These data were obtained from Nasa Earth Science Data ([[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/))) and were spliced and projected using the MRT tool.  
In order to verify the accuracy of GLO-PEM in simulating NPP, the NPP at the spatial resolution of 500 m in 2003 and in 2008 (MOD17A3 HGF Version 6 product) was derived from the NASA website ([[https://cmr.earthdata.nasa.gov/](https://cmr.earthdata.nasa.gov/)]([https://cmr.earthdata.nasa.gov/](https://cmr.earthdata.nasa.gov/))). Here annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products ([MOD17A2H] ([[https://doi.org/10.5067/MODIS/MOD17A2H.006](https://doi.org/10.5067/MODIS/MOD17A2H.006)]([https://doi.org/10.5067/MODIS/MOD17A2H.006](https://doi.org/10.5067/MODIS/MOD17A2H.006)))) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR).  
Figure 1: Location of the study area in Northeast China.
## 1 Introduction
Most previous studies use the average NDVI of the entire Loess Plateau to reflect the overall change [4; 6; 17; 20], discounting spatial variations within the region, or temporal variation over time. Moreover, climate change is not only a simple vegetation state change caused by temperature change. As an important component of the lower atmosphere, precipitable water vapor (PWV) changes dramatically with variations in temperature. PWV is among the conditions necessary for precipitation weather evolution and an important indicator influencing the occurrence of extreme weather [21; 22; 23; 24]. Several studies have shown that the true rate of nowcasting precipitation forecasting with PWV can reach 85% [24], which reveals the strong correlation between precipitation and PWV. According to the Clausius-Clapeyron equation, a high temperature corresponds to a strong atmospheric storage capacity [25], which reveals the close relationship between temperature and atmospheric water vapor. [PERSON] et al. [26] also proved that the relative humidity (RHU) and SSD are two primary factors influencing precipitation. In this work, we investigated the potential relationship of vegetation growth with extreme weather events and meteorological parameters (PWV, T, RHU, and SSD). Variations in the relationship at different periods were considered to better understand the time-varying characteristics of the multiple meteorological factors and extreme climatic indices and vegetation over Loess Plateau. In addition, we evaluated the time-lag effect of extreme weather changes and meteorological factors on vegetation growth in detail. We aim to further understand the extreme weather change impact on the ecological environment in the Loess Plateau.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 5 Discussion
### Comparison between Techniques in Geophysical Exploration
#### 5.3.1 Differential Geometry  
These methods offer a robust framework for dealing with the intrinsic curvature of geological formations. Its application extends to modelling complex structures like salt domes, which are challenging to represent accurately with Euclidean methods.  
Strengths
* **Curved Space Analysis.** Differential geometry can naturally handle the curvature and complexity of geological structures, leading to more accurate models.
* **Advanced Seismic Imaging.** Techniques derived from differential geometry, such as Riemannian wavefield extrapolation, improve seismic imaging by accounting for the curvature and heterogeneity of the subsurface.  
Weaknesses
* **Computationally Intensive.** These methods often require significant computational resources, making them less practical for real-time applications.
* **Complex Implementation.** The mathematical complexity of differential geometry necessitates specialised expertise and training.
## 2 Data and Theory Description  
### The CPRAP Usage Data Description  
In this paper, the CPRAP was established by combining the ground-based (GNSS, RS), space-based (FY-3A, Sentinel-3A) and reanalysis-based (ERA5) techniques to guarantee high accuracy and spatio-temporal resolution in deriving PWV simultaneously. The performance of the established CPRAP is first validated and the applicability was then performed in drought and rainfall monitoring. Figure 1 shows the flowchart of the established CPRAP and its applications.  
In this paper, five kinds of datasets, which are GNSS, RS, ERA5, FY-3A and Sentinel-3A, were selected to perform the experiment. Table 1 provides the specific information of the data used for the experiment.  
Figure 1: Flowchart of establishing CPRAP and its applications for drought and rainfall monitoring.
## 4 Experiments and Discussion  
### Experimental Dataset  
This paper conducts experiments on ShipsEar that consist of recordings from different regions of the Spanish Atlantic coastline in northwestern Spain during the autumn of 2012 and the summer of 2013, most of the data was collected at Porto Vigo (42\({}^{\circ}\)14.5\({}^{\prime}\) N 008\({}^{\circ}\)43.4\({}^{\prime}\) W) or nearby. The Port of Vigo is located within the Vigo River, a submerged river valley 35 km long, 10 km wide at its widest point, and has a maximum depth of less than 45 m. The recording equipment is the Hyd SR-1 hydroacoustic recorder. The core of this recorder is a hydrophone with a sensitivity of \(-\)193.5 dB re 1 V/1 uPa and a frequency response range of 1 Hz-28 kHz.  
Vigo port is one of the largest fishing ports in the world, and there is a huge flow of fishing vessels on the waterway. The data-target categories collected in this area are diverse. Researchers deploy the hydrophones under the water and schedule labels according to vessel movement information obtained from the port authority and the Automatic Identification System for vessels. Original recordings were clipped to preserve information from the beginning to the end of the event or pass-by. ShipsEar comes from these edited recordings, which included 90 recordings in wav format lasting from 15 s to 10 min. The recordings contain 11 types of ship radiated noise signals, of which types of ship radiated noise signals can be divided into five categories based on vessel size according to [36], as is shown in Table 4. Each recording of ShipsEar contains only one type of vessel. The records in different recordings may come from different voyages, even if they are the same vessel type.
# Application of Machine Learning Tools for Long-Term Diagnostic Feature Data Segmentation
## 4 Methodology
### Principal Component Analysis  
Principal component analysis (PCA) in the mathematical definition [51] is an orthogonal linear transformation that takes data to a new coordinate system. Assuming that a given multidimensional data set is arranged as \(N\) samples with \(M\) variables, it can be expressed as a point cloud in \(M\)-dimensional space. The aim of PCA is to translate the location and orientation of the new coordinate system so that the variance is maximized along the new dimensions. It is performed in a way that the most significant variance of the data is on the first coordinate axis, and the second-largest variance is on the second coordinate axis, and so on. The idea is visualized in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Formula** \\ \hline Max value & \(M=\max(x_{i})\) \\ Sample median & \(\begin{cases}x_{(n+1)/2^{n}}&\text{$\eta\circ 2=1$},\\ \frac{x_{(n+1)/2}+x_{(n/2)/1}}{2}&\text{$\eta\circ 2=0$},\\ \end{cases}\) \\ Sample mean value & \(\overline{x}=\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}\) \\ Sample standard deviation (STD) & \(\hat{\sigma}=\sqrt{\frac{1}{N-1}\sum\limits_{n=1}^{N}(x_{i}-\overline{x})^{2}}\) \\ Sample kurtosis & \(\text{K}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{(x_{i}-\overline{x})^{4}}{\hat{ \sigma}^{4}}\) \\ Sample skewness & \(\text{Sk}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{(x_{i}-\overline{x})^{3}}{\hat{ \sigma}^{3}}\) \\ root mean square & \(\text{RMS}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}x_{i}^{2}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time domain features.  
Figure 4: The geometric interpretation of PCA [52].  
Principal component analysis can reduce the data dimension, thus preserving the components of the data set that have the most significant effect on variance. For example, Figure 4 presents that the first principal component (the longer arrow) describes most of the variance in the data. Hence, if one extracts only the coordinates projected on this axis, one would obtain a majority of information described by the entire data set (or rather the percentage of the information corresponding to the relative content of the variance described by this new dimension).  
By utilizing the singular value decomposition (SVD), the principal components can be calculated based on N observations of M-dimensional data stacked into a matrix \(X\in\mathbb{R}^{N\times M}\)  
\[\frac{1}{\sqrt{N-1}}\mathbf{X}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{T}, \tag{1}\]  
where \(U\in\mathbb{R}^{N\times N}\) and \(V\in\mathbb{R}^{M\times M}\) are unitary matrices and \(\Sigma\in\mathbb{R}^{N\times M}\) contains the nonnegative real singular values of non-increasing magnitude (\(\sigma_{pc1}\geq\sigma_{pc2}\geq\cdots\geq\sigma_{pcM}\geq 0\)). Principal components are the orthonormal column vectors of the matrix \(\mathbf{V}\), and the variance of the \(i\)-th component is equal to \(\sigma_{pci}^{2}\).
## 2 Materials and Methods
### Precipitation Estimates
\begin{table}
\begin{tabular}{c c c c} \hline
**Products** & **Version** & **Spatial Resolution** & **Temporal Resolution** \\ \hline GLDAS & GLDAS\_NOAH025 v2.1 & 0.25\({}^{\circ}\) (25 km) & Daily/Monthly/Annual \\ MERRA & M2T1 NXFLX v5.12.4 & 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) (50 \(\times\) 65 km) & Daily/Monthly/Annual \\ TRMM & TRMM\_3B42 v7 & 0.25\({}^{\circ}\) (25 km) & Daily/Monthly/Annual \\ GPM & GPM\_3 IMERGDF v06 & 0.1\({}^{\circ}\) (10 km) & Daily/Monthly/Annual \\ GPCP & GPCPMON v3.0 & 0.5\({}^{\circ}\) (50 km) & Monthly/Annual \\ \hline \end{tabular}
\end{table} 
Table 2: Spatial and temporal resolutions and versions of each of the precipitation products used over the state of Mato Grosso, Brazil. GPM is 30 min and was accumulated and processed by the GES DISC to the daily time scale. It should be noted that while GPM and TRMM mostly use the same input datasets, their different algorithms used in producing these estimates allowed GPM to provide its high frequency precipitation values (30 min). This allows to capture the temporal evolution of precipitation events more accurately; thus, it is expected to obtain more accurate time-averaged daily values. The GPCP is part of the Global Climate and Energy Exchange Project (GEWEX) of the World Climate Research Program (WCRP), to maintain homogeneous global record of long-term precipitation estimates and information for climate studies. GPCP, which has data available since 1983, is based on five dataset (i.e., passive microwave, IR, and gauges) mainly from geostationary satellites and surface measurements of precipitation [24]. One of these five datasets include TRMM Composite Climatology (TCC) which was developed to be used for global scale modeling such as GCM [25]. The highest temporal resolution to GPCP is the monthly time scale; thus, the measurements were aggregated to monthly in order to be compared with GPCP.
## 4 Discussion
### Preclinical Models and Translational Potential  
Perclinal models provide valuable insights into the translational potential of GTR technologies [51]. [PERSON] et al. [29] demonstrated that TEC constructs successfully differentiated into mature colon epithelium when implanted in immunocompromised mice. These findings align with research indicating that biodegradable scaffolds can support the functional differentiation and integration of tissue-engineered constructs in vivo [29; 52].  
While preclinical models highlight the feasibility of applying GTR technologies to complex tissues, they also underscore the challenges of translating these findings to human populations. Differences in immune system dynamics and tissue-specific responsesbetween animal models and humans necessitate rigorous clinical trials to validate efficacy and scalability.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 2 Related Works  
To enhance visual SLAM performance in dynamic environments, experts in dynamic SLAM concentrate on two principal approaches: The first approach focuses on detecting and eliminating dynamic feature points to prevent their adverse effects on camera pose estimation. The second strategy jointly models dynamic objects and camera motion, achieving global optimization through multi-object tracking and motion constraints [10; 11; 12; 13; 14; 15]. While the latter demonstrates superior perception and modeling capabilities in highly dynamic scenarios, it typically incurs substantial computational overhead and requires robust data association algorithms. This paper focuses on dynamic feature removal, which offers simpler implementation and faster computation while effectively mitigating dynamic interference. Existing removal methods can be broadly classified into three categories: motion segmentation-based filtering methods, semantic information-based approaches, and the fusion methods.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Challenges and Obstacles in the Implementation of ITSs in Europe
#### 5.2.3 Regulatory and Administrative Barriers  
Businesses and service providers face restrictive regulations that differ greatly between Member States and hinder cross-border ITS operations. The use of these divergent regulatory strategies could lead to the unnecessary duplication of compliance checks; this can complicate the market entry of new technologies and innovations [65].  
Despite the European Commission's efforts to address these regulatory obstacles, many issues remain unresolved, in particular regarding the rapid availability of permits required for the deployment of new technologies [66].  
To overcome these regulatory obstacles and accommodate emerging technologies, policymakers should consider several best practices. One approach is to adopt technology-neutral regulatory frameworks that neither impose nor favor specific technical solutions, providing flexibility to integrate innovations like blockchain and AI without constant legal overhauls [67].  
An additional approach is the use of regulatory test scenarios, which provide a controlled environment for testing innovative ITS solutions under regulatory oversight. This reduces uncertainty for innovators and provides evidence-based information for future regulatory design [68]. To prevent regulatory fragmentation and enable seamless transnational ITS operations, it is equally important to pursue cross-border legal alignment, i.e., the harmonization of standards and recognition of regulations across jurisdictions. Finally, the incorporation of robust data and AI governance frameworks (e.g., clear guidelines on algorithmic safety, liability, privacy and ethics) is essential for managing the risks of AI-powered mobility and for strengthening public trust in ITS innovations [69]. Overall, these measures ensure that regulatory frameworks promote, rather than hinder, advances in Intelligent Transport Systems, while protecting the public interest.