esa-sceva/satcom-chunk-collection huggingface dataset:
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 3 Application to Authenticate Turmeric Powder
### Subsurface Detection of Turmeric Powder and Metanil Yellow
In the five-layer capsule sample, the 1433 cm\({}^{-1}\) peak is not evident at no offset (Figure 12d). Increasing the offset distance enhanced the 1433 cm\({}^{-1}\) peak in the five-layer capsule sample. The turmeric peak at 1631 cm\({}^{-1}\) is not evident at no offset in the two-, three-, four-, and five-layer capsule samples (Figure 12a-d). Increasing the offset distance enhanced the 1631 cm\({}^{-1}\) peak in the multiple layer capsule samples. The 1631 cm\({}^{-1}\) peak can be used to identify the turmeric packed inside gelatin capsule. The results demonstrate this SORS measurement technique has a potential for non-destructive subsurface detection and the identification of gelatin-encapsulated pure and mixed samples of turmeric and metanil yellow powder. Figure 11: Spatially offset Raman spectra of turmeric-metanil yellow mixture packaged inside one-layer capsule.
## 6 Conclusions  
The main goal of this study was to build an accurate, easy, fast, inexpensive, and safe method to predict the retroreflectivity performance for road traffic signs mounted on roads. The logarithmic models suggested in this study provide an accurate and easy method to predict the retroreflectivity performance of road signs, to help decide if these signs should be replaced.  
Figure 7: Checking the assumptions for logarithmic regression of the NMF data.  
The color, age, class, GPS position, and direction of road signs are factors needed to predict retroreflectivity in the logarithmic regression models proposed by this study. These predictors can be collected easily, cheaply, safely, and quickly, with no expensive instruments needed to measure them. The proposed models in this paper are more accurate than other equations proposed in previous studies, which include only age as a predictor to evaluate the retroreflectivity of road signs.  
The suggested logarithmic regression models give R\({}^{2}\) values of 0.50 and 0.95 for VTI and NMF data, respectively. Even linear models showed a good performance, with the coefficients of determination (R\({}^{2}\) values) being 0.44 and 0.72.  
The suggested models differ from those suggested by the state of the art [1; 6; 12; 13; 15; 16; 17] and give higher R\({}^{2}\) values. Previous studies investigated the effect of road signs' age, region, orientation concerning the Sun, distance from the road, and visual condition (daytime appearance, message integrity, and general condition) on retroreflectivity. They found that only age and region were significant predictors of retroreflectivity, while this study determined that age, color, class, GPS latitude positions, and direction of the road traffic sign were significant. In addition, the statistical analysis in this study revealed that GPS longitude and luminance factors were insignificant to retroreflectivity.  
In this study, particular models included color and class as predictors, while previous studies used different sets of predicting equations according to color and class and included only age as a predictor. Furthermore, including colors and classes as predictors was found to increase the performance of the models compared to previous studies.  
Conceptualization, R.S. and H.F.; methodology, R.S. and M.A.; software, R.S. and M.A.; validation, R.S., M.A. and H.F.; formal analysis, R.S., H.F. and M.A.; investigation, R.S. and H.F.; resources, R.S. and H.F.; data curation R.S.; writing--original draft preparation, R.S.; writing--review and editing, H.F.; visualization, R.S.; supervision H.F.; project administration, R.S.; funding acquisition, R.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Swedish Road Administration, grant number TVR 2019/31837.  
Not applicable.  
The two datasets used to support the findings of this study were supplied by Road and Transport Research Institute (VTI) and NMF group and under license and so cannot be made freely available. Requests for access to these data should be made to VTI and NMF.  
The authors declare no conflict of interest.
## 5 Post Adsorption Results  
### FTIR Analysis  
FTIR analysis of AC-MOP compared the adsorption of MB and IBF before and after. This was undertaken to identify the functional groups responsible for the adsorption of MB and IBF. The FTIR spectra of MB and IBF after adsorption are shown in Figure 10. The absorption due to the -OH of AC-MOP shifted to 3387 cm\({}^{-1}\) after sorption of MB, whilst after the adsorption of IBF, the peak shifted to 3334 cm\({}^{-1}\). These peaks showed a decrease in intensity after adsorption. The ketonic group -C=O moved to 1701 cm\({}^{-1}\) after the adsorption of MB and to 1696 cm\({}^{-1}\) after the adsorption of IBF. The peak due to -COOH vibrations shifted to 1572 cm\({}^{-1}\) after the adsorption of MB and to 1567 cm\({}^{-1}\) after the sorption of IBF. The -C-O peak shifted to 1023 and 1018 cm\({}^{-1}\) after the adsorption of MB and IBF, respectively. The shifting of peaks to lower and higher wavenumbers suggests that the functional groups -OH, -C=O, -COOH and -C-O were involved in the adsorption processes [34].  
Figure 9: Reusability for AC-MOP.
## 3 Results and Discussion
### Industrial-Scale Phase
#### 3.2.1 Physico-Chemical Parameters and Consumption of Chemicals  
Parameters such as pH, conductivity, and temperature were constantly monitored to ensure constant operation of the flotation unit. BWW had elevated temperatures compared to urban WW, and its quality demonstrated significant variations, particularly in terms of influent pH, with values ranging from 5.6 to 10.2 (Table S1). These variations may be related to the production process that necessitates the implementation of CIP cycles to facilitate the preparation of tanks for the start-up of new products [30]. The brewery from which BWW was sampled is engaged in the production of multiple varieties of beer, resulting in regular variations in the quality of the effluent and the pH levels of the incoming inputs. It was feasible to limit the substantial fluctuations and establish a stable equilibrium of this value within the pH range of 6.5 to 7.5 providing the ideal conditions for the process of chemical precipitation using a coagulant [3]. In fact, to contrast the fluctuation of pH in the influent load, the industrial-scale plant was properly equipped with two tanks able to dose NaOH and HCl to adjust the pH levels to alkaline and acidic, respectively, depending on the necessity.  
The conductivity was constant during the testing process, both during the input and output phases, with no significant modifications detected. In terms of chemical consumption for pH adjustment, the mean dosage for NaOH was found to be 1004 mg:h\({}^{-1}\), while HCl was dosed at 658 mg:h\({}^{-1}\) on average. Additionally, the PAC showed an average dosage of 930 mg:h\({}^{-1}\), whereas the 0.1% AAF solution was dosed at 52 mg:h\({}^{-1}\) on average.  
The industrial scale tests also showed high temperatures due to the industrial production process, with an average temperature of approximately 35-40 \({}^{\circ}\)C, as confirmed in other studies [6; 7; 9]. It should be taken into account that the high temperature of the BWW could limit the adequate dissolution of oxygen in the water, reducing the effectiveness of clarification [31; 32]. Consequently, this might have adverse effects on the creation of microbubbles and subsequently decrease the efficiency of the separating process. This aspect should be further investigated in future studies given the possible influence on the process performance. However, preliminary results showed that the performances obtained with temperatures between 35 and 40 \({}^{\circ}\)C (industrial scale) are completely comparable to those obtained at 25 \({}^{\circ}\)C (laboratory scale), demonstrating that the impact of temperature on the process efficiency seems to be limited.
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 2 Dielectric Permittivity Measurements
### Microwave Characterization Methods
#### 2.1.2 Split-Cylinder Resonant Cavity  
The split-cylinder resonator is a nondestructive technique used to measure the permittivity and loss tangent of low-loss dielectric substrates. Initially proposed by [PERSON] [22], this method utilizes a circular-cylindrical cavity divided into two halves. A planar dielectric sample is positioned in the gap between the two shorted cylindrical waveguide sections. To excite the family of resonant modes, coupling loops in the cylindrical waveguide sections are connected to the input ports of a network analyzer. By measuring the resonant frequency (\(f_{0}\)) and quality factor (Q) of the \(TE_{0 np}\) resonant modes, the relative permittivity and loss tangent of the sample can be determined. This procedure is based on an analytical model first developed by [PERSON] and [PERSON] [23; 24].  
The equipment used is a Split-Cylinder Cavity, from the Institute of Information and Communication Technologies (ITACA) settled in Valencia, Spain, for measuring rectangular flat samples with a resonance frequency of 6.25 GHz (mode \(TE_{101}\) is excited in the cavity at vacuum) and a VNA, as shown in Figure 2b. Then, MATLAB R2025a software was used to calculate the permittivity of the materials following the circuit analysis described in [20], with an extension of the circuital model for the circular modes in the split cavity, which can be found in [25].
# Blockchain-Based Evidence Trustworthiness System in Certification
## 6 Conclusions  
This paper discusses the importance of evidence in certification, as it is crucial to verify compliance, foster trust, and ensure transparency. However, current digital evidence is vulnerable to several security attacks. Thus, a risk assessment has been carried out to identify the main security needs of evidence in the certification domain. Digital evidence is vulnerable to manipulation, loss, and integrity issues. To address these challenges, the authors have adapted an existing general-purpose Blockchain-based audit trail solution to the certification domain, as Blockchain features such as decentralization, transparency and immutability make it suitable for securing evidence for certification.  
The trustworthiness system is based on storing hashes of evidence on the Blockchain while keeping the raw evidence in a local and private storage. In this way, data integrity is guaranteed without revealing sensitive information. In addition, the system includes an automatic integrity verification service to enhance usability, allowing auditors to verify evidence integrity efficiently. The proposed solution has been validated within the EMERALD framework for cloud services cybersecurity certification, demonstrating its effectiveness in improving the trust and transparency of evidence and assessment results for cybersecurity certification processes.  
In conclusion, this paper highlights the critical role of secure and reliable evidence management in certification and presents a robust solution that uses Blockchain technology to ensure the trustworthiness of evidence.  
As future work, the evidence trustworthiness system should be deployed in a general-purpose Blockchain network, such as EBSI, to create the European evidence trustworthiness system for auditors where different types of evidence integrity proofs can be stored. The benefits over current certification processes should be measured for both auditors and certified entities. In addition, the system should be considered in other certification domains in addition to cybersecurity to validate its generalizability and improve its impact.  
Conceptualization C.R. and B.U.; Methodology C.R. and B.U.; Software C.R. and B.U.; Validation C.R. and B.U.; Formal analysis C.R. and B.U.; Investigation C.R. and B.U.; Resources C.R. and B.U.; Data-curation C.R. and B.U.; Writing--original draft preparation C.R.; Writing--review and editing C.R. and B.U.; Visualization C.R. and B.U.; Supervision C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Commission, H2020 MEDINA project, Security framework to achieve a continuous audit-based certification in compliance with the EU-wide cloud security certification scheme (GA 952633); and Horizon Europe EMERALD project, Evidence Management for Continuous Certification as a Service in the Cloud (GA 101120688).  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
# Investigating and Modeling of Cooperative Vehicle-to-Vehicle Safety Stopping Distance
## 7 Joint Distance and Density Losses  
The final part of our analysis is to employ a joint loss system between both distance reliant and density-based losses. This will be a brief comparison as we know that density losses contribute significantly more to losses than those of distance. To do this, we took both previous results for distance independent losses and the density-based losses and combined them in four different scenarios. These scenarios are all shown at 70 mph and for  
Figure 19: Seventy miles per hour (112.63 km/h) reduction in stopping distance—PATH.  
Figure 18: Seventy miles per hour (112.63 km/h) reduction in stopping distance.  
four different data rates of 10, 5, 100, and 140 pps. The 4 differences used in each simulation are 0%, 1%, 10%, and 100% of the density-based losses. The algorithm used is the same as with distance independent; however, we modified it to consider two different packet loss scenarios. The results can be seen in Figures 20-23.  
From the figures produced, it can again be seen the biggest contributor to unsafe stopping distances is at any point over 1% density-based packet losses, and the second highest is the use of low data rates, such as 10 pps. In the 10% and 100% figures, the only data rates that do not become unsafe are those of 100 and 140 pps, and this is where the stopping distance does not decrease highly, further proving the requirement of high data rates. The 10 pps performs extremely poorly when subjected to high density collisions, which would be expected.  
The final point to note is that, with the requirement of 99.9% reliability, our examples at density 1% and maximum distance loss of 18 packets consecutively, which totaled approximately 20 consecutive packet losses, 140, 100, and 50 pps, did not approach unsafe levels of distance.  
Figure 21: Joint loss—1% density losses.  
Figure 22: Joint loss—10% density losses.
## 1 Introduction
### Background and Motivation
#### 1.1.1 Uses of Eye Tracking in Educational Research  
Recent educational research has increasingly utilized eye-tracking technology to examine and enhance individual learners' engagement and cognitive processes related to learning [17]. In the field of education, eye tracking technology has primarily been used to analyze reading behavior [11; 12], mathematics learning [13; 14], multimedia learning [5; 7], and visual attention [18; 19; 20]. Most of these studies have investigated various aspects of visual attention, including fixation counts, the frequency with which gaze is directed toward an area of interest (AOI), and fixation duration, which indicates the time a fixation is allocated to an AOI. Finally, the patterns of eye movements are known as visual scanpaths, which record eye movement trajectories consisting of a series of fixations and saccades while viewing a visual stimulus. This metric is reported as a valuable measurement for understanding the relationship between eye movement patterns and visual attention among individuals [21]. All these metrics serve as indicators for analyzing cognitive processes during the learning process.  
Some studies have investigated how learner characteristics mediate visual attentional behaviors during the learning process, utilizing scanpath data for analysis. For example, [PERSON] et al. (2002) identified that readers performed better on reading comprehension tasks when presented with materials with a clear topic structure [22]. A comparative study investigated the reading behaviors of average and poor readers in the context of digital books [23]. By analyzing gaze transitions, the study revealed that poor readers engaged in more extensive visual exploration of highlighted text and demonstrated a higher frequency of transitions between text and illustrations. Conversely, this pattern was not evident among average readers, suggesting differing strategies in visual attention and engagement with digital content. Taken together, the findings underscore the significance of analyzing eye movement trajectories in educational research and give a direction for eye-tracking applications in the classroom.  
Eye tracking has frequently been used in highly controlled laboratory settings [17]. Consequently, it is essential to conduct studies to identify scanpath metric utilization that could be applied in the classroom. It is imperative to investigate the advancements in eye-tracking technologies and their potential integration into everyday educational practices and naturalistic learning environments.
## 2 Materials and Methods  
This is a retrospective study including all consecutive patients suffering from idiopathic ERM that underwent PPV with 23 or 27 g and ERM \(\pm\) ILM peeling at the Triemli City Hospital Zurich between 2014 and 2018 with a minimum follow-up of 3 months. The study was approved by the local Ethics Committee (BASEC ID 2017-02306) and adhered to the tenets of the Declaration of Helsinki. All subjects signed an informed consent statement.  
Patients were excluded if they had previous a PPV, a retinal photocoagulation procedure, history of ocular trauma, secondary ERM, myopia with an axial length >26 mm, amblyopia, diabetic retinopathy, retinal vascular occlusion or macular disorders potentially affecting surgical outcomes or a follow-up of less than 3 months. Patients with corneal disorders or other ocular pathologic features that could have interfered with the quality of SD-OCT images were also excluded.  
All patients received a complete ophthalmologic assessment, including best-corrected visual acuity (BCVA), slit-lamp biomicroscopy, intraocular pressure, dilated fundus examination and SD-OCT at baseline and at each visit during follow-up.  
BCVA was measured through Snellen charts by a single, well-trained experienced ophthalmologist (M.B.), and converted into the logarithm of the minimum angle of resolution (LogMAR).
# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 2 Vehicle Modeling
### Guidance Law
#### 3.2.1 Constant Angle Glide Trajectory  
Equation (19) proposes a law which is chosen to increase range. It adjusts the longitudinal axis of the vehicle (\(x_{b}\)) with a vertical flight plane, orthogonal to ground, parallel to the line joining the gravity center of the vehicle and the destination target and containing the gravity center of the vehicle. The line of sight is expressed in working axes is given by vector \(\overrightarrow{LOS_{w}}=[LOS_{1_{w}},LOS_{2_{w}},LOS_{3_{w}}]\). \(x_{b}\) in working axes is represented by vector \(\overrightarrow{x_{b_{w}}}=[x_{b1_{w}},x_{b2_{w}},x_{b3_{w}}]\). Consequently, the lateral correction to be applied (\(\psi_{dem}\)) is calculated by the first component of Equation (19), while the correction in the vertical plane (\(\theta_{dem}\)) with respect to a constant glide angle trajectory given by \(C_{1}\)[1] is given by the second component. Note that guidance effectively starts after apogee, which is determined by the pitch angle (\(\theta\)) and after fuel burn time.  
\[\left[\begin{array}{c}\psi_{dem}\\ \theta_{dem}\end{array}\right]=\left\{\begin{array}{c}\left[\begin{array}[] {c}(atan\frac{LOS_{3_{w}}}{LOS_{1_{w}}}-atan\frac{x_{b_{w}}}{x_{b_{w}}})\\ C_{1}\end{array}\right]\text{ if }t>5\text{ and }\theta\leq 0\\ \left[\begin{array}{c}0\\ 0\end{array}\right]\text{ else }\end{array}\right. \tag{19}\]
# Failure of Rock Slope with Heterogeneous Locked Patches: Insights from Numerical Modelling
## 2 Brief Description of RFPA and Numerical Model
To simplify the calculation, the rock slope was simplified in two intact locked patches sandwiched by a bedrock and a slide body. The mechanical parameters of the left locked patch are constant and the right locked patch has identical magnitude of the elastic modulus but with different strength, length, height, depth, and homogeneity. The locked patches share the same node elements with bedrock and slide body on the interface. It should be noted that the failure of the interface is mainly affected by the small deformation failure of the element and the mechanical behaviour of the locked patch. In the meantime, we focus on the effect of the mechanical parameters of the locked patch on the model stability while do not study large displacement friction behaviour of the interface. Therefore, based on the continuum mechanics, the mechanical behaviour of the interface is equivalent to the deformation and failure behaviour of the surface elements. Table 1 lists the mechanical and physical properties of the model. The normal stress was prescribed at 0.5 MPa, according to the fact that the maximum effective normal stress ranges from 0.1 to 2.0 MPa in many rock engineering problems [33]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Rock Mass** & **Left Locked Patch** & **Right Locked Patch** \\ \hline Elastic modulus & 50 & 40 & 40 \\ (GPa) & 0.25 & 0.25 & 0.25 \\ Poisson’s ratio & 10 & 10 & 10 \\ Ratio of compressive & 30 & 30 & 30 \\ friction (\({}^{\circ}\)) & 300 & 200 & 100, 150, 200, 250, 300 \\ Mean element & & & \\ uniaxial compressive & 300 & 200 & 100, 150, 200, 250, 300 \\ strength \({}^{\ast}\) (MPa) & & & \\ Length (m) & 10 & 0.5 & 0.1, 0.25, 0.5, 1.0, 1.5 \\ Spacing (m) & - & 0.5, 2, 4, 5, 6 & 0.5, 2, 4, 5, 6 \\ Depth (m) & 5 & 0.5 & 0.5, 0.6, 0.7, 0.8, 0.9, \\ Height (m) & - & 0.5 & 0.5, 0.6, 0.7, 0.8, 0.9, \\ Homogeneity index, & & & 1.0 \\ \(m\) & 3 & 3 & 1, 2, 3, 4, 5 \\ \hline \hline \end{tabular}  
* The relationship between mean uniaxial compressive strength of element and macroscopic uniaxial compression strength of sample is shown in Equation (6).  
\end{table} 
Table 1: Input parameters for numerical modelling. Figure 2: Illustration of the numerical model.
## 5 Experimental Results and Analysis
### Credibility Analysis
The possibility of collaboration failure is high when the ratio of malicious nodes to normal nodes is 50%, and the number of failures in the first round of experiments is about 35 times. After 50 rounds of collaboration, the collaboration is stable, and the number of failures in collaboration is around 3 times. The higher the number of malicious nodes, the higher the possibility of enterprise collaboration failure, and the more serious the actual loss brought about, so it is important to discover malicious nodes and mark them in time. Under the timely discovery of the system, the final  
Figure 13: Credibility experiment flow chart. number of collaboration tends to stabilize, reaching the threshold of industrial collaboration. The specific results of the experiment are shown in the following Figure 16.  
In this experiment, the industrial collaboration architecture can well protect the interests of honest enterprises, detect malicious nodes in time, and reduce the loss of legitimate nodes. At the same time, because of the principle of roulette when selecting enterprises, enterprises with high credibility have a greater enterprise selection rate than those with low credibility, encouraging enterprises to cooperate in good faith and improve product quality,  
Figure 16: Impact of varying number of malicious nodes as a percentage. Figure 14: Trend chart of malicious node changes. Figure 15: Trends in the number of successful collaborations. which to a certain extent can motivate enterprises to improve the qualification rate of their products, actively participate in collaboration in good faith, and safeguard the sustainable development of the industrial chain.