esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Research Gaps and Future Opportunities
### Protection against Communication Attacks
#### 7.1.1 Lack of Transparency and Privacy  
Current blockchain-based solutions provide a full trace of interactions (i.e., data exchange) among various stakeholders involved in the blockchain. Yet, they fail to provide adequate transparency to its interacting stakeholders and customers, mainly because the blockchain based solutions consider all of the interacting components (i.e., implementation of computing/business process) as \"blockbox\" with very little information about their actual operations such as implementations of business/computing processes and APIs. Also, the only information the blockchain contains about stakeholders is the smart contract that is transaction specific. Thus, the blockchain-based solutions typically provide limited protection to supply chain systems. Resultantly, without the information of stakeholder's entire supply chain business process implementation and based on the full trace of supply interactions, such solutions fail to identify concrete information about \"why\" a certain incident/compromise took place. Furthermore, getting full trace of supply interactions actually depends on the system design: some recent systems adopt the so called on chain and off-chain transactions to keep the maximum amount of tracing data. Due to the lack of transparency, partners and customers become more concerned about their data privacy and finally, may loose their trust in system operations.
## 2 QTL Mapping Problem Description  
Let \(P\) be a population of size \(n\) individuals that exhibit a quantitative trait. For individual \(i\), the magnitude of the trait (measured value of the trait) is defined as \(t_{i}\). There exist a set of \(k\) genetic markers implemented with molecular tags, that denote the presence of locus on the chromosomes of specific alleles in the individuals. An experiment yields a vector \(M_{i}\) of genetic markers present in individual \(i\) and the magnitude of the individual's trait \(t_{i}\). For each specific experiment \(M_{i,j}\) denotes the value of marker \(j,1\leq j\leq k\) in individual \(i\).  
The model sought by geneticists is to identify the loci of DNA that control the quantitative trait, the contribution of each locus to the magnitude of the trait, and the interaction among the loci. As an example, a phenotype may be shaped by many independent loci distributed over multiple chromosomes, or by a few loci within a single chromosome. There may be little or no interaction among the genes at those loci so that their effect on the trait can be treated as independent and additive; or interaction may be epistatic where multiple genes interact to control the trait. The complexity of the QTL mapping problem depends whether the genes are treated as independent or epistatic. First consider the case when genes are independent, then the model can be represented as a weighted sum and determined by partial least squares (PLS) linear regression [15]:  
\[\hat{t}_{i}=\beta_{0}+\sum_{j=1}^{k}\beta_{j}M_{i,j} \tag{1}\]  
where \(\hat{t}_{i}\) is the predicted value of the trait, \(\beta_{0}\) is the error term (bias) and the \(\beta_{j}\) values are the weights or regression coefficients of the model over all individual genes. The loci present in individual \(i\), \(M_{i,j}\) determine which weights contribute to the magnitude of the trait. The PLS method determines the model parameters \(\beta_{j}\) by minimizing the squared error \((t_{i}-\hat{t}_{i})^{2}\) over the population of \(n\) individuals. To extend the method to epistatic interactions we consider all pairwise markers in addition to individual markers:  
\[\hat{t}_{i}=\beta_{0}+\sum_{j=1}^{k}\beta_{j}M_{i,j}+\sum_{u=1}^{k}\sum_{v=u+ 1}^{k}\beta_{u,v}M_{i,u}M_{i,v} \tag{2}\]  
In this model, there are a total of \((k^{2}+k)/2\) regression coefficients, a number that will most likely exceed the number of samples \(n\). The \(\beta\) values for additive or epistatic interactions may be positive, signalling synergistic influence or negative signalling antagonistic influence.
## 4 Conclusions  
The main findings of the present study are:  
1. The pretreatment influenced the nickel nitrate-glycine complexes that were formed in the precursor solution in the form of dendrites, which played a key role in the SCS reaction mechanism and the formation of nickel.
2. The existence of dendrites led to the inhibition of oxygen diffusion on the foam-like structure due to the resulting denser structure (i.e., lower height of dendrites in the final product).
3. In accordance, the oxidation of glycine was not complete, yielding carbon and carbon monoxide, which reduced nickel oxide to nickel. This \"double thermal treatment\" led to the occurrence of a sintering process and an increase of crystallite size.
4. Larger crystallites had less surface energy and as a result they tended to agglomerate less, leading to enhanced specific surface area.  
These results help to explain phenomena which have been observed during SCS, such as dendrite formation. The influence of pretreatment may explain many of the difficulties reported in repeating synthesis, since this parameter generally was not taken into consideration during the investigation of nanomaterials synthesis.  
Thus, the consideration of pretreatment's influence has significant potential applications in the preparation of other nanocomposite catalysts for engineering highly efficient catalysts and also for researchers that are occupied with nanomaterials.  
Figure 10: The dependence of maleic acid conversion on the SCS catalysts, calculated from the quantity of absorbed hydrogen.  
**Author Contributions:** Conceptualization, G.X. and O.T.; methodology, O.T.; software, O.T.; validation, G.X. and G.V.; formal analysis, O.T.; investigation, O.T. and G.X.; data curation, G.X.; writing--original draft preparation, O.T.; writing--review and editing, O.T. and G.X.; visualization, O.T. and G.X.; supervision, G.X. and A.C.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank [PERSON] (National University of Science and Technology \"MISIS\", Moscow, Russia) for the captions during SCS with the IR-high speed camera.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Face Recognition System  
A general face-recognition system, as presented Figure 4, consists of three main modules [15]:  
* Face detection separates the area of the face from the image background. Face detection's purpose is to focus in a smaller image area for analysis, rather than analyzing the whole image.
* Face feature extraction is used to extract such information from the face image, which helps differentiate between faces of different individuals and is robust to photometric and geometric changes. The extracted face features are used to verify the identity of the face.
* Face recognition occurs when the extracted face features are compared to the saved data of one or more faces in the database. This module's output is \"yes\" or \"no\" for 1:1 verification; otherwise, when doing a 1:N identification, the output is the face's identity.  
The purpose of this work is the development of an application that does authentication based on face recognition in Android mobile devices. Face recognition is not a problem belonging only to the field of computer vision. It is a relevant topic in pattern recognition, neural networks, computer graphics, image processing and psychology.  
The three main problems that face recognition aims to solve are [15]:  
* Verification (authentication): Am I who I pretend to be? The face recognition system decides whether the face in the acquired image belongs to the identity the person pretends to have.
* Identification (recognition): Who am I? The face recognition system matches the face from the acquired image with the face models saved in the database.
* Watch list: Are they looking for me? The acquired image is compared to the face models of people in the watch list saved in the database. If the face in the image belongs to a person in the watch list, that individual is identified.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## 3 Methodology and Results
### Communication Range
#### 3.1.2 Method of Evaluation  
* Step 1: Extraction of registration points. In the first step, the registration points for each traffic light were extracted from the entire route of the U5 area of study. For this purpose, the log files of the AVM onboard computer of the public transport provider VGF were evaluated.
* Step 2: Localization of the registration points. The registration points were located on the basis of the determined GNSS positions from the onboard computer system of the VGF AVM. To compensate for the positioning errors in the GNSS system, the arithmetic average values of all messages sent from a vehicle were estimated and used.
* Step 3: Positioning the Roadside Unit (RSU). The positioning of the RSU was based on on-site inspections. The positioning was carried out carefully, considering the cable feed from the signal control module to the RSU, which must not exceed 100 m due to the usage of PoE (Power over Ethernet), a common method of powering RSUs.
* Step 4: Radio propagation planning. Based on the positions of the registration points, the C-ITS G5 radio propagation planning tool was used. The results were displayed as radio levels on a color scale from dark red to light yellow. The brighter an area, the better the communication performance. It can be assumed that sufficient radio coverage is guaranteed in all red and yellow areas for the transmission of the registration points to the traffic lights. The presentation of the radio propagation tool results on the one hand and the localization of the registration points on the other hand clearly show, on a visual basis, whether the registration points are within the communication range or outside the communication range.
* Step 5: Radio measurement. The radio propagation planning tool works on the basis of learned and referenced data sets. It must always be ensured that the radio propagation is modeled sufficiently and precisely. Therefore, if there are registration points on the border regions for the radio propagation shown, they must be validated with real measurements. For the radio measurement, however, only the driving relation of the U5 was recorded. Tests showed (compare Figure 1) that the results from the radio propagation planning tool matched the radio measurements. This means that the communication ranges can also be validated and estimated at locations that are not actually measured on the basis of the results of the radio propagation planning tool.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 2 Unlicensed Medium Access Mechanisms  
This section summarizes the functioning of the medium access mechanisms implemented in each of the coexisting technologies in the 5-GHz band, describing the parameters that regulate each of them.
# Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers
## 1 Introduction  
For the public safety management, the use of a structural health evaluation system based on seismic accelerometers is in increasing demand. Due to the recent development of sensor and measurement technologies, the number of monitoring systems installed on important structures such as cable bridges is rapidly increasing [1; 2; 3]. The range of the application of the structural health monitoring is very wide. Various researches have been performed to examine the condition of the infrastructure for durability issues [4; 5; 6]. Besides the issues of the condition assessment, structural health monitoring systems can be deployed to ensure that the built structure satisfies the design requirements and assumptions [7]. For example, the effect of spatial variation of seismic ground motions on the super structures of long-span bridges has been studied using the measured data during the earthquake [8]. Investigation of damping mechanisms and estimation of damping values based on the real structure behavior are performed [9]. Researches on damage detection of bridge structures have been continuously developed on the basis of the sensor and communication technologies. Structural health monitoring technologies based on imaging and vision sensors [10; 11], and microelectromechanical systems (MEMS) sensors [12; 13] can be considered as emerging technologies [14]. Application of wireless sensor network [15; 16] for the condition assessment has evolved to internet of things (IOT) [17; 18] based structural health monitoring system.  
While the state-of-the-art research and novel sensors lead the progress of technologies in structural health monitoring of bridges, it should be noted that detailed information on the analysis and interpretation of data is essential for engineers to construct the monitoring system. The originality of this study lies in providing details of monitoring algorithms for interpretation of the measured data inthe post-earthquake condition assessment based on seismic accelerometers. This study focuses on practical information to deal with the issues encountered in data processing procedures and calculation of response indices, which have not been treated enough in the previous studies.  
The condition assessment system is useful for pre-earthquake disaster planning and post-earthquake recovery programs [19]. The majority of the monitoring software in the field are developed by the equipment providers and hence have not been verified their reliability in structural condition assessment. Therefore, it is of importance to develop reliable health monitoring software that can generate appropriate response indices of the structure [20; 21; 22]. In this paper, major components of a health monitoring software and a data matrix to generate response indices are discussed. The data matrix represents the interrelationship between the measured data from the seismic accelerometers and the response indices.
## 4 Experiments
### The Performance of Similar User Recommendation
#### 4.2.5 Effects of User Clustering  
To verify if user clustering is effective in finding similar users, we further took the feature map and categories from either early fusion or late fusion. Since user clustering can be done offline, only the similarity calculations are needed for those users in the same cluster as the given user. From our experimental results, the best number of clusters was 3 with the average top-k accuracy of 0.367. Since the effectiveness was not as good as a direct recommendation, we further verify the efficiency of recommendation using user clustering in Table 2.  
As shown in Table 2, although the total amount of time needed in the user clustering model was longer than late fusion due to the time for clustering, the actual amount of time for recommendation was less than that for late fusion. In practical application scenarios, user clustering only needs to be calculated offline once when there are certain amounts of new content or new users. This makes it much faster in the recommendation stage.  
To investigate the effects of our proposed approach on a larger scale, we further conducted experiments on Dataset_2. The performance comparison of different models on both datasets is summarized below.  
As shown in Table 3, multimodal feature fusion is useful since we can combine clues from both text and image contents. The best performance could be obtained for the late fusion model, with the average top-k accuracy of 0.491 and 0.281 for Dataset_1 and Dataset_2, respectively. Although the accuracy could not be further improved by autoencoders, we could obtain comparable performance with the benefits of dimensionality reduction since a lower dimension means less storage needed, as well as less time required for recommendation systems.
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
## 1 Introduction
### Related Work
experimentally demonstrated an MDM-TDM-PON system using two LP modes over 10 km SMF and 10 km FMF at 10 Gbit/s data rate [23]. [PERSON], [PERSON] et al. reported a chaotic power division multiplexing-based MDM-PON system over 5 km FMF at a 30 Gbit/s transmission rate [24]. [PERSON], [PERSON] et al. presented a radio over free space optics (RoFSO) employing an MDM-WDM system over 80 km FSO at 120 Gbit/s transmission rate [25]. Furthermore, spectral amplitude coding (SAC) based OCDMA operates at bit rate instead of chip rate and balanced detection scheme to reduce the multiple access interference (MAI). SAC-OCDMA also offers a large code set at a lower cost as compared to the OCDMA scheme, which is appropriate for PON applications, where the number of users and cost are important. Moreover, it is known that by incorporating balanced detection techniques and spectral encoding in MDM-PON systems, MAI can be fully restrained. Lately, researchers have introduced various SAC-based OCDMA codes such as random diagonal (RD) [26], modified double weight (MDW) [27], optical orthogonal [28], modified frequency hopping (MFH) [29], multi diagonal (MD) [30], zero cross correlation (ZCC), etc. A Hermite-Gaussian (HG) modes OCDMA-MDM system using RD code over 11 km MMF at 100 Gbit/s transmission rate was successfully introduced in [31]. Moreover, an integrated MDM-OCDM system incorporating LP modes over 2 km two-mode fiber (TMF) at 80 Gbit/s data rate was demonstrated [32]. In another work, LP modes incorporated MDM-based OCDMA-PON system at 2 km and 40 km TMF and single mode fiber, respectively, at 80 Gbit/s transmission rate, was demonstrated [33].
## 6 Discussion
### Practical Implications  
The findings of this study can improve the ability of SaaS providers to make better decisions on tenant customization requirements. For example, SaaS providers can outweigh between subscription fees received from tenants and the effect of implementing the new requirement on SaaS quality. If the implementation of the new feature has a negative impact on SaaS quality, then they lower its priority and try to communicate with the customers to inform them that this is not the direction they would like to take. Otherwise, the findings this study could help SaaS providers persuade customers to consider a more favorable approach (e.g., configuration approach) over other approaches (e.g., modification approach) that could cost more in the long run, especially their arguments would be backed with numbers and statistics.  
Furthermore, rather than doing assumptions, the empirical results of this study will help SaaS developers and architects to understand the relationship between software customization and SaaS quality. Moreover, instead of assessing all the customization approaches and all quality attributes, the developed model mainly focuses on the most generic types of customization approaches and most-related quality attributes of SaaS applications, which make it easier for SaaS implementation teams to conduct control assessment. Being able to accurately identify the customization type that will affect SaaS quality will aid developers to make the decision on suitable customization type and practice without degrading the quality of the SaaS application.
## 4 Results
### Performance Analysis of PPP-RTK
#### 4.2.2 Large-Scale Networks  
The SD ionospheric residuals of GPS and Galileo for the 98-km reference network are shown in Figure 8. The average RMS values of GPS and Galileo residuals that were interpolated by DSM were 0.032 m and 0.036 m, respectively. The average RMS values of GPS and Galileo residuals that were interpolated by DIM were 0.043 m and 0.045 m, respectively. The mean squared error of the SD ionospheric delays of 5.7 cm by experience is slightly higher than the interpolation accuracy. The SD ionospheric residual was poor during 9:00-14:00 local time throughout the observation period. The ionospheric correlation was weakened by the large distance between the user and reference station. Therefore, the interpolation accuracy was significantly reduced, especially during the active period of the ionosphere. The convergence of the two models in the horizontal and three-dimensional directions are summarized in Table 4. The TTFF of PPP-RTK for users in the 98-km reference network was 33.1 s. Using DSM, 77% of the periods converged within 1 min, 89% converged within 2 min, and 97% converged within 3 min. The convergence speed of DSM in the horizontal direction is similar to that in the three-dimensional direction, and so is the DIM. The ambiguity fixing rate for DSM was much higher than that for DIM, especially in the three-dimensional direction. For DSM, the ambiguity fixing rate in the horizontal direction was approximately 84%, and that in the three-dimensional direction was 82%. These represent 15.2% and 17.5% improvements, respectively, over those for DIM. The TTFF of DSM is faster than that of DIM. Figure 9 shows the positioning bias in the E/N/U direction at TTFF and the RMS value of the positioning bias after convergence in all periods. It can be seen from Figure 8 that the interpolation accuracy of DSM is better than that of DIM during the period of greater ionospheric activity. The maximum differences in the ambiguity between the two models occurred in the groups in the 165-195, that is, during the period with larger ionospheric residuals. During the same period, the positioning bias at TTFF was concentrated within 2 cm in the horizontal direction and 10 cm in the vertical direction. According to statistics, the average RMS values of DSM in the three directions (E/N/U) were 1.0, 1.1, and 4.0 cm, respectively compared with DIM, which represents a reduction of 8.2%, 29.2%, and 11.2%, respectively.  
Figure 8: Time series of SD ionospheric residuals of DIM and DSM in large-scale networks. Different colors represent SD ionospheric residuals of different satellite pairs.
## 3 Results and Discussion
### Assessment of Shelf Life of Eggs from Hens Fed with Supplemented Diet
It should be noted that eggs with higher shelf life were shifted towards lower values of egg protein quality, although this trend was more pronounced in control samples. \begin{table}
\begin{tabular}{c c c c c} \hline  & & **Control-0** & **Control-28** & **W4-0** & **W4-28** \\ \hline \multirow{4}{*}{**Category**} & ALA (mg/100 g) & 57.7 \(\pm\) 6.9 & 58.2 \(\pm\) 8.0 & 51.9 \(\pm\) 9.4 & 49.3 \(\pm\) 3.3 \\  & EPA (mg/100 g) & 2.1 \(\pm\) 0.1 & 2.2 \(\pm\) 0.1 & 5.2 \(\pm\) 1.2 * & 4.7 \(\pm\) 0.7 * \\  & DHA (mg/100 g) & 50.3 \(\pm\) 4.0 & 51.7 \(\pm\) 6.5 & 105.2 \(\pm\) 18.2 * & 87.1 \(\pm\) 9.0 * \\ \hline \multirow{4}{*}{**Category**} & Lipids (\%) & 9.90 \(\pm\) 0.26 & 9.79 \(\pm\) 0.40 & 9.62 \(\pm\) 0.39 & 9.63 \(\pm\) 0.12 \\  & Protein (\%) & 11.95 \(\pm\) 0.50 & 12.28 \(\pm\) 0.48 & 11.81 \(\pm\) 0.30 & 11.73 \(\pm\) 0.36 \\  & Ash (\%) & 0.83 \(\pm\) 0.01 * & 0.86 \(\pm\) 0.04 *,* & 0.84 \(\pm\) 0.02 *,* & 0.89 \(\pm\) 0.02 * \\ \hline \multirow{4}{*}{**Category**} & Eggs weight (g) & 65.2 \(\pm\) 4.0 & 61.5 \(\pm\) 6.0 & 63.1 \(\pm\) 5.3 & 61.8 \(\pm\) 2.1 \\  & Yolk colour (Roche scale) & 9.6 \(\pm\) 0.8 & 8.5 \(\pm\) 1.0 & 11.4 \(\pm\) 0.8 * & 11.7 \(\pm\) 1.0 * \\ \cline{1-1}  & Shell colour (\%) & 28.4 \(\pm\) 7.2 & 30.0 \(\pm\) 4.6 & 31.7 \(\pm\) 4.4 & 30.0 \(\pm\) 4.6 \\ \cline{1-1}  & Protein quality (UH) & 80.8 \(\pm\) 7.9 & 40.2 \(\pm\) 9.0 * & 82.1 \(\pm\) 8.6 & 42.3 \(\pm\) 5.2 * \\ \cline{1-1}  & Shell thickness (\(\mu\)m) & 372 \(\pm\) 19 & 349 \(\pm\) 25 & 371 \(\pm\) 27 & 387 \(\pm\) 24 \\ \hline \end{tabular} For each parameter (same row), distinct symbols (*,*) mean statistically significant differences (\(p<0.05\)) between groups (N = 5 for fatty acids and nutritional profile data; N = 12 for physical parameters data).  
\end{table} 
Table 3: Comparison of fatty acids contents, nutritional profile and physical parameters of control eggs those from hens fed with a supplemented diet (W4), at zero (0) and 28 days of storage.