esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 2 Materials and Methods
### Designing a System for Verification of AGV Routes Using Computer Emulation
#### 2.1.2 Structure of the Communication-Control Module  
Between the real part of the logistics system and the virtual model of the manufacturing system, it is necessary to create a communication-control module that decides on the need to perform logistics tasks, and assigns them in the sequence. Decision-making is made in compliance with the deadlines of customer requirements, taking into account the capacity possibilities of the system. The alignment of customer requirements and capacity options is carried out by the production planning and control (PPC) system (Figure 3).  
The first step is needed to load the data into the PPC system (Block 2.1). The system uses information from the internal factory information system, SAP (Block 2.2). Information about orders and inventory is retrieved from the SAP system database as needed. Subsequently, this information is processed by a manufacturing planning and control system through which a manufacturing plan is established. Based on this plan, we move to the decision-making module (Block 2.3) with the question of whether it is a standard manufacturing plan with a normal requirement for logistics processes or whether it is necessary to modify the manufacturing plan through a virtual model of the monitored system and to find a solution to the problem by changing the manufacturing plan or changing the logistics tasks. If the system evaluates the task as normal, the PPC system verifies the availability of logistics equipment (Block 2.4). Subsequently, the task is assigned to the logistical elements that perform it. If, through PPC, the task is evaluated as non-standard or based on customer requirements, it is a newly created problem. This task is sent to the virtual model of the system (Block 2.5). Once the problem has been resolved (Block 2.6), the solution is sent back to the PPC control system (Block 2.7). The system assigns the task to the selected logistical means based on the information received (Block 2.8). Subsequently, as with standard logistics requirements, the task execution process follows (Block 2.9). After execution, the resulting statistics are processed and subsequently interpreted (Block 2.10). The statistical information obtained is stored in the form of reports (Block 2.11), which serve as the basis for analyses and based on which the necessary decisions are made to optimise activities. If there are new requirements for logistics tasks after the tasks have been completed and the reports have been processed (Block 2.12), the next step is to return to the decision module and calculate the complexity of the logistic task. If there is no request for a new task, the processes in the control model can be terminated.
## Materials and Methods
### Methodological Flowchart  
As seen in Figure 2 the methodological flowchart included the collection and analysis of meteorological information, download and pre-processing of the Sentinel 1 and ALOS-2/PALSAR-2 images, analysis of the geology and selection of regions of interest. The overall process is detailed below.  
Sentinel 1A and B (C-band, 5.55 cm) and ALOS-2/PALSAR-2 (L-band, 23.4 cm) sensors provided the SAR imagery for this study. The former acquired in Interferometric Wide Swath (IW) mode, level 1 processing, and Ground Range Detected (GRD) with a spatial resolution of \(20\times 22\) m (range by azimuth) and a swath width of 250 km. The latter in StripMap (SM) Fine (10 m) mode through a project with the Japan Aerospace Exploration Agency (JAXA). The spatial resolution is 10 m with a 70 km-swath. The four-year Sentinel 1 dataset encompasses a total of 319 images from October 2014 to December 2018, mostly available in VV-VH polarization in ascending and descending orbits. The four-year Sentinel 1 dataset encompasses a total of 319 images from October 2014 to December 2018, mostly available in co-polarized vertical-vertical (VV) and cross-polarized vertical-horizontal (VH) polarizations in ascending and descending orbits. In the same four-year period, the ALOS-2/PALSAR-2 dataset has 16 images in horizontal-horizontal (HH) and horizontal-vertical (HV) polarizations in ascending orbit. In the same four-year period, the ALOS-2/PALSAR-2 dataset has 16 images in HH-HV polarizations in ascending orbit. The temporal coverage of these images is shown in Table 1.  
Figure 2: Methodological flowchart for the four-year data sets. 1 Digital Model Elevation.  
Overall, the Sentinel 1A data have some gaps during January, March, and June 2017 over the salar. Among 2014 and 2017, data availability was between 3 to 4 scenes/month, further increased to 5-13 scenes/month after the release of Sentinel 1B with data available from September 2016.  
The SAR Sentinel 1 and ALOS-2/PALSAR-2 datasets were pre-processed in the Sentinel Application Platform (SNAP). For Sentinel 1A-B, the chain process started with orbit correction, thermal noise removal, and radiometric calibration. Subsequently, a Refined Lee filter pixel window of \(7\times 7\) was used to improve the radiometric quality of the images. Finally, the geometric terrain correction was applied by assigning the digital elevation model SRTM 1 Sec HTG and bilinear interpolation, resulting in an image with a nominal pixel size of 10 m \(\times\) 10 m. As a final product, output bands of backscattering coefficients (\(\sigma^{0}\)) for Sentinel 1 (VV, VV-VH), for ALOS-2/PALSAR-2 (HH-HV), and the Local Incidence Angle bands were generated.
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 2 Materials and Methods
### Analytical Approaches for the Bending Resistance of Composite Beams
#### 2.1.1 Literature Overview
[4] investigated the behavior of partly stiffened CFS built-up beams and they compared the given experimental and numerical results with the analytical results. The characteristic bending strength of the beam was calculated in accordance with the design rules of EC3 [28] and the design rules of AISI-S100 [29]. It may be noted that the analytically determined strengths, calculated using the North American Specifications and the European Codes for CFS structures, were conservative compared with the numerical results. [PERSON] et al. [30] investigated the behavior of concrete/CFS-composite beams (experimental development of a novel structural system). This paper showed how a composite action could be achieved by attaching stand-off screws to the track and encapsulating the screw shank in the concrete slab. [PERSON] et al. [31] analyzed a large-scale test of symmetrical CFS-concrete composite beams with bent-up triangular tabs to improve shear transfer. A composite beam with shear transfer enhancements was found to have an increased capacity for ultimate bending moments compared with specimens without enhancements. In accordance with the research and their results mentioned above, it can be concluded that different approaches are used to calculate the bending resistance of composite beams made of CFS and concrete. The following section presents three commonly used approaches which will be used in this paper.
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 5 Conclusions  
This paper introduces a novel pedestrian trajectory prediction model based on a dual social graph attention network. Unlike previous approaches, we incorporate additional pedestrian features into the model and design a directed relationship attention function to compute the directed interactions between pedestrians. Additionally, we quantify group features in the crowd using a group attention function, which significantly improves the prediction of pedestrian intentions.  
The experimental results on the ETH and UCY datasets demonstrate the effectiveness of our approach. With the introduction of richer feature inputs, the model's predictive performance is further improved. Furthermore, this paper presents a spatiotemporal weighted attention network for directed weighted temporal graphs to enhance the model's ability to aggregate the information from such graphs. In our approach, the directed weighted graph extracted by using the relationship attention function works in conjunction with the spatiotemporal weighted attention network. This method better aligns with real-world movement patterns, reflecting pedestrians' decision-making logic and actual behaviors.  
Our proposed method demonstrates the effectiveness on the ETH and UCY datasets, suggesting its potential scalability to large-scale pedestrian scenarios. However, the validity of the threshold-based crowd segmentation approach in dense crowd scenarios or other datasets still requires further validation on additional test sets. Furthermore, this study primarily focuses on modeling the interactions among pedestrians, without incorporating physical scene information. The absence of scene context may limit the model's perception and decision-making capabilities when dealing with complex terrain scenarios in real-world environments. In future work, we plan to explore additional pedestrian feature information and validate our method across more diverse scenarios to comprehensively model pedestrian characteristics and enhance the accuracy of trajectory prediction.  
Conceptualization, X.L. and Y.L.; methodology, X.L.; software, X.L.; validation, X.L., Z.Y. and J.L.; formal analysis, X.L.; investigation, Z.Y.; resources, X.L.; data curation, J.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L.; visualization, Z.Y.; supervision, X.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Emission Characteristics
#### 3.3.3 NOx Emissions  
Figure 9 shows the variation of NOx emissions for all test fuels according to different engine loads. It can be clearly seen that NOx emissions of all fuels shows an obvious increasing trend with the engine load increasing from 30 to 90 Nm. At 60 Nm, the NOx emission of D100, D95E5, D90E10, and D85E15 is increased by 78.11%, 72.47%, 72.22%, and 92.11% compared with that at 30 Nm, respectively. At 90 Nm, the NOx emission of D100, D95E5, D90E10, and D85E15 is increased by 168.24%, 164.52%, 186.67%, and 207.89% compared with that at 30 Nm, respectively. However, on the other hand, with the addition of ethanol to the diesel fuel, the NOx emission of all diesel-ethanol blended fuels shows a decreasing trend under most load conditions. For example, at 30 Nm, the NOx emission of D95E5, D90E10, and D85E15 is decreased by 0.22%, 3.43%, and 18.46% compared with  
Figure 7: CO emissions for all test fuels according to various engine loads.  
that of D100, respectively. At 60 Nm, the NOx emission of D95E5, D90E10, and D85E15 is decreased by 3.37%, 6.63%, and 12.05% compared with that of D100, respectively. This is because the formation of NOx largely depends on the high temperature, oxygen enrichment, and sufficient duration in the cylinder. Under low load, short combustion duration and low cylinder temperature are not conducive to the formation of NOx. With the increase of engine load, the fuel injected into the cylinder increases, and the combustion temperature and combustion duration increase in the cylinder, resulting in an increase in NOx emissions [36]. In addition, the main reason for the reduction of NOx caused by the addition of ethanol may be related to the high evaporation latent heat of ethanol. The cooling effect of ethanol may reduce the combustion temperature in the cylinder, thereby reducing the formation of NOx [27].
## 2 Material and Methods  
The sample consisted of 16 CBCT images of patients in permanent dentition, between 8 and 13 years old (mean 11.42 years, SD 1.31), in need of Rapid Maximilary Expansion (RME), with good quality CBCT, obtained with the same protocol, treated at the Orthodontics Clinic at the School of Health and Life Sciences of Pontificia Universidade Catolica do Rio Grande do Sul. All CBCT images were acquired with a volumetric scanner (model I-Cat Next-Generation Imaging Science International, Hatfield, PA, USA) using the following settings: 120 kV, 8 mA, exposure time 40 s, protocol FULL, and a voxel resolution varying from 0.25 to 0.30 mm\({}^{3}\). The Institutional Research Ethics Committee approved this retrospective study under protocol number 10/05093. In this study, the goal was to evaluate the Acta plane and not the growth or treatment outcome. Therefore, the same scan was compared with and without orientation. The scans before and after the RME were neither compared nor superimposed. Three examiners participated in this study: two dental students and one orthodontist. The dental students underwent a calibration period under supervision, and the orthodontist had already worked extensively with CBCT images. Examiners 1 (dental student) and 2 (orthodontist) located the landmarks in the multiplanar axial, sagittal, and coronal slices during the same period of time. To determine intra-examiner reliability, the location of the landmarks was repeated at a 1-week interval. Subsequently, examiner 3 (dental student) reoriented the scans, identifying the landmarks of the plane himself. Examiners 1 and 2 then located the landmarks on the reoriented images. To assess intra-examiner reliability, the landmarks were randomly relocated two days later, and the examiners were not blinded for this landmark identification. Data involving landmark distances and measurements were later calculated by examiner 2. The examiners were not involved with CBCT scanning procedures or orthodontic treatment. Landmarks and measurements were selected based on the previous study by [PERSON] et al. [30] that added to the landmarks that define the Acta plane. The following landmarks were chosen for this research: Nasion (Na), Sella (Se), Basion (Ba), Pogonion (Pog), Gonion (Go), Mandibular Fossa (MF), Orbita (Or), Lateral Orbita (Orbital.Lat), Porion (Po), and Root Tip (TL6). The definition of the landmarks and measurements are listed in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Landmark** & **Anatomic Area** & **Sagittal Plane** & **Transversal Plane** & **Frontal Plane** \\ \hline \multirow{3}{*}{Nasofrontal structure in the midline} & Anterior-most point of the & Middle & \multirow{3}{*}{Midpoint} \\  & & & nasofrontal structure & anterior-most point \\ \cline{1-1} \cline{3-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of landmarks.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 6 Results and Analysis
### Scalability  
The proposed methodology not only enhances the resilience of ML-based network traffic classification models but also highlights significant scalability. Through the integration of DVars into training, our approach achieves a reduction in computational overhead, imposes minimal resource demands, and demonstrates adaptability across various classifiers. Its seamless compatibility with existing systems, coupled with a straightforward integration process, makes it a lightweight, efficient, and easily scalable solution that is particularly useful for deployment in real-world scenarios. Moreover, the adaptability of DVars to changing network dynamics and their ability to maintain robust performance underscores their scalability, positioning them as a versatile and practical solution for securing communication networks of varying scales against adversarial attacks. Additionally, it is imperative to highlight that even for clean data, the baseline classifiers exhibit commendable performance. This observation emphasizes the reliability of our approach, affirming that the introduction of DVars does not compromise the efficacy of the baseline classifiers during routine operational scenarios.
## 4 Results and Discussion  
### Geophysical Survey  
#### 4.1.1 Influence of Seasonal Variations on Geophysical Data  
The rainfall distribution in the region includes a rainy season from November to March (values over 150 mm/month) and a dry season from July to September (values less than 20 mm/month) [40]. These data, in conjunction with the high porosity and permeability of shallow geological formations, induce changes in the unconfined aquifer storage and groundwater quality. Therefore, it was necessary to investigate seasonal effects in shallow geophysical measurements if readings taken at different times of the year are to be compared.  
The electrical conductivity measurements for two profiles (1 and 2; Figure 3A) obtained in the dry (September 2016) and in the wet (February 2016) seasons at sites with distinct features (south of the mud dam and close to the ditch Vala da Breja, respectively) and with different directions (W-E and NW-SE, respectively) are shown in Figure 3B.  
Since the reported estimated depths of exploration for the vertical and horizontal coils surveys were 15 m and 30 m, respectively [44], data from the vertical coils survey was expected to be influenced by the unconfined aquifer, while data obtained from the horizontal coils survey should be related to a deeper area, including the semi-confined aquifer (Figure 2).  
According to the data, no striking differences between the surveys carried out in February and September of 2016 were obtained, proving that geophysical data are not markedly influenced by seasonal variations in this area.
# Determination of Intrinsic Sodium in Grass-Fed Bonsmara (_Bos taurus indiacus_) Meat
## 4 Discussion  
Animal gender, age, meat temperature, and pH did not affect the intrinsic sodium and mineral concentration patterns in the selected cuts. This is supported by [PERSON] et al. [36] that animal gender has no bearing in beef mineral meat prolife. However, the intrinsic sodium concentration in T60/40 (formulated with a 60% meat and 40% fat ratio) is lower than in whole muscles. This was consistent in all samples of different carcasses. This suggests that fat influences the level of intrinsic sodium in grass-fed beef. This finding contrasts with a study by [PERSON] et al. [37], which found no significant influence of fat on beef minerals. While intrinsic sodium in whole muscle Ts and Ss showed no statistical difference when compared, the intrinsic sodium was higher across all Tf meat samples. Similarly, [PERSON] et al. [38] highlighted the influence of organic minerals on the physiologically active muscles of beef animals.  
However, low-fat content in meat and processed meats is ordinarily encouraged for the sake of consumer health [39]. This outcome creates an impasse when the higher fat content in meat is the result of low intrinsic sodium and mineral deposits. This, therefore, is a balanced combination approach to the fat and meat mix ratio for processed beef product formulation. Although meat quality and safety are enhanced by lower concentrations of organic minerals, it is not known whether or not they are desirable to the human body [40]. Both meat quality and safety are crucial for consumer health when consumed over an extended period [41]. Importantly, the level of physiological deposit of mineral composition in beef that has a direct bearing on human health cannot be taken lightly or overlooked. This study acknowledges the results by [PERSON] et al. [42], that essential minerals (of which sodium is part) in ruminal meat animals were lower than most documented predictions. From this investigation, it is evident that whole muscle meat cuts have higher intrinsic sodium than physiologically unrecognizable meat mixes such as trimmings. Furthermore, this study revealed that the salinity of raw beef can be influenced not only by intrinsic sodium but also by saline minerals naturally occurring in beef, such as K, Mg, Ca, and Cu. For human safety, it is, therefore, vital to consider that high levels of minerals such as potassium and copper can result in kidney defects, high blood pressure, cardiac conditions, and irregular growth patterns in infants [43; 44]. [PERSON] et al. [45] concluded that minerals with saline characteristics are generally in lower concentration in grass-fed beef. Conversely, the investigation found that similar trace elements are in noticeably higher concentrations in grass-fed beef than predicted. Therefore, the results of this investigation revealed a correlation between the physicochemical properties of intrinsic sodium and the tested macro-elements in different carcass meat cuts.
# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
###### Abstract  
Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level.  
surface-enhanced Raman scattering; biomarkers; multivariate analysis; metabolic fingerprinting; disease diagnosis; cancer; microbes; volatile organic compounds +
Footnote †: journal: Applied Physics Letters
## 3 Results  
### Performance of the Algorithm  
Compared to expert manually segmented SHSs (n = 896) in 100 images from 10 cases (10 images/case), the algorithm achieved a sensitivity (TP/(TP + FN)) of 82.6%, and a positive predictive value (PPV) (TP/(TP + FP)) of 86.7% on a per-SHS basis. For correctly detected SHSs, the boundary segmentation accuracy was 90% \(\pm\) 3%. The computation time of the algorithm was 3.9 s/image, tested on a 64-bit computer with a 3.2 GHz intel i7 CPU.