esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Biological Use Case 1: Comparative Genomics to Study Tomato Fruit Shape and Potato Tuber Shape
Lastly, for the QTL region selected for volatile compounds (i.e., 3-methylbuthanal and 3-methylbuthanol) there are no well-known candidate genes in the QTLs that had experimentally proven significance. Figure 7: A workflow to predict candidate genes in a QTL region for the traits of interest. The top three predicted candidate genes for each trait, with the help of our pipeline, are reported in Table 3.  
One of the most extensively studied metabolic traits in tomato is the total soluble solids content in fruits (i.e., TSS or Brix) [68]. We selected five GO terms related to these metabolites and Brix trait (see Table 3) and fed it to the QTLSearch pipeline in our workflow. Previously known studies have identified multiple QTLs that are associated with the Brix trait in tomatoes [58]. Out of the many known QTL locations, the most significant QTL is located on chromosome 9, containing Lin5 as the popularly known candidate gene for Brix trait [68]. Table 3 highlights the top three genes predicted from our workflow for the Brix QTL region from 3374710 to 3574710 on chromosome 9. Lin7 and Lin5 were the top predicted genes related to this trait. Both these genes are from a homologous family and are known to be associated with Brix. The scores of both genes are significantly higher than those of all other genes. We conclude that our pipeline performed well to predict the candidate genes for this QTL. Carotenoid compounds are the primary determinants of tomato fruit color [71]. Carotenoids exert a broad range of functions which associate to photosynthesis, the formation of pigments, antioxidant activities, and being precursors to signaling molecules, including volatiles [72]. Lycopene is a major carotenoid in tomato [73]. Lycopene occurrence with other bioactive compounds, like vitamin C, vitamin E, other carotenoids (alpha-carotene, beta-carotene, gamma-carotene and lutein), and flavonoids is primarily associated with the color of a tomatoes. Lycopene beta-cyclase is a key enzyme occurring at the branch point of the carotenoid biosynthesis pathway and responsible for converting lycopene to beta-carotene. Lycopene beta-cyclase activity is also related to the total carotenoid content accumulated in the tomato fruit. The major QTL region which is related to Lycopene beta-cyclase activity is found to be located on chromosome 6 between the region 45280179-49150528 [74]. Here we analyzed the prediction of candidate genes for Lycopene beta-cyclase activity with the help of our developed workflow. Two GO terms that relate to lycopene beta-cyclase activity were selected for inclusion in our workflow. Previous study suggests that lycopene cyclase (LCY) is a known candidate gene related to this trait. In some databases, lycopene cyclase (LCY) is also annotated as neoxanthin synthase (NSY) as these are genes that are closely related carotenogenic enzymes belonging to the same family. Table 3 shows the results from the workflow, containing the top three genes predicted for this QTL region on chromosome 6. NSY was ranked at the top of the list
# A Survey of CNN-Based Network Intrusion Detection
## 7 Other Factors of CNN-Based IDS Experiments
### Feature Reduction  
Feature selection entails selecting a subset of features to reduce the false alarm and time costs of IDSs. The feature with the greatest impact on the model performance is chosen in the feature selection stage. Embedded, wrapper, and filter techniques constitute the overall feature selection methods [37]. The discrete cuttlefish algorithm (D-CFA), non dominated sorting algorithm (NSGA), reptile search algorithm (RSA), and GA are examples of metaheuristic algorithms utilized in feature selection.  
Fuzzy C-means clustering (FCM) and GA-based exhaustive search have been utilized for feature subset selection in academic work [3]. The most efficacious \(1\times 100\)-dimension features were preserved via feature selection performed by the D-CFA in the work by [PERSON] et al. (2018) [1]. The parameters for updating the population, computing the fitness value, generating the initial population X, determining the best solution, and converting xi to binary were all initialized via the RSA for feature selection in another academic work [76]. An equation put forward in a separate academic paper served to compute minimum redundancy, and features with the greatest relevancy in forecasting the output variable were chosen in the feature subset selection in another study [35].  
Information Gain (IG) was utilized for feature selection in the work by [PERSON] et al. (2021) [37]. Discovering a subset of the original variables is attempted by IG, which indicates the most effective training set of feature vectors to discriminate between the classes to be learned. The mining time or the precision of the system is enhanced via the use of IG. The features that influence data classification are ranked, while communication is determined by entropy. The value of the relationship between every feature and the value of the information is regulated by entropy.
## 5 Case Studies
### Case 1: Wind Speed of 8.0 m/s, Wind Penetration of 20%, Disturbance of 100 MW  
Figure 10 illustrates the simulation results for Case 1. When the DFIG operates in de-loaded operation without frequency response, the maximum ROCOF and frequency nadir are \(-\)0.766 Hz/s and 58.984 Hz, respectively, since there is no active power injection from the DFIG. The maximum ROCOF and the frequency nadir with the conventional frequency response are \(-\)0.734 Hz/s and 59.257 Hz, respectively, because an amount of active power is injected from the DFIG to the power grid. The frequency nadir and the maximum ROCOF with the proposed enhanced frequency response are improved to 59.396 Hz and \(-\)0.723 Hz/s, respectively, because more power is injected from the DFIG to the electric power system due to the suggested power reference (see Figure 10a,b). Furthermore, the setting frequency with no frequency response, the conventional frequency response, and the proposed enhanced frequency response are 59.594 Hz, 59.607 Hz, and 59.610 Hz, respectively, since the power reference switches to the MPPT curve at operating point 3 (which is more than those of the other frequency responses.)  
Figure 8: Model system.  
Figure 9: IEEEG1 steam governor model.  
As shown in Figure 10b, the output power of the enhanced frequency response increases the torque limit referred to power that is larger than the conventional frequency response so that the frequency nadir and the maximum ROCOF are better. Furthermore, the DFIG releases more energy so that the operating range of \(\omega_{r}\) for the enhanced frequency response is wider, as illustrated in Figure 10c.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Performance Evaluation for Optical Communications  
The proposed nanostructure can be used for filtering applications at optical communication by modifying ground layer of the plasmonic nano structure. Optical fibre communications use the spectral region from 800 to 1675 nm. For usage in intermediate-range and long-distance optical fibre communications in the 1260-1675 nm range, the International Telecommunications Union (ITU) has designated six spectral bands [35]. The regions are identified by the letters O, E, S, C, L, and U, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Band** & **Wavelengths (nm)** & **Band** & **Wavelengths (nm)** \\ \hline O & 1260 to 1360 & C & 1530 to 1565 \\ E & 1360 to 1460 & L & 1565 to 1625 \\ S & 1460 to 1530 & U & 1625 to 1675 \\ \hline \end{tabular}
\end{table}
Table 2: The ITU designated six spectral bands for optical communication.  
Figure 14: Metal nanoparticles show an intense electric-field close to the surface.  
Figure 13: MA-based STPV system design.  
To evaluate the performance of the nanostructure as a filter, the Tu ground layer has been removed from the MA structure. As a result, the EM waves starts to transmit through the nanostructure. From the Figure 15, it can be seen that the nanostructure blocks the wavelengths of visible band and allows the optical bands to transmit. The wavelengths lower then 800 nm are experiencing high transmission loss while the optical communication bands are transmitting with a relatively lower transmission loss. So, the proposed nanostructure can be used as a filter for optical communication to filter out noises from visible wavelengths.
## 3 Results and Discussion
### Electrical Response
\begin{table}
\begin{tabular}{c c c c} \hline
**Ideality Factor** & **Saturation Current (eV)** & **Barrier Height** & **Series Resistance** \\ \hline \(\eta\) & \(I_{\rm s}\) (10\({}^{-7}\) mA) & \(\Phi_{B}\) (eV) & \(R_{\rm s}\) (\(\Omega\)) \\
2.47 & 0.36 & 1.08 & 712 \\ \hline \end{tabular}
\end{table} 
Table 1: Diode parameters were obtained by fitting the experimental I vs. V curve. Figure 7: I vs. V characterization under darkness conditions for the ITO/CdS/PbSe heterostructure: (**a**) ohmic response (without annealing); and (**b**) diode response (annealing at 150 \({}^{\circ}\)C at 5 min). narrow potential barrier at the interface, thus allowing the electrons to tunnel through the thin barrier [34]. While rectifying behavior was achieved by thermal annealing, this was attributed to better diffusion between the layers of the ITO/CdS/PbSe heterostructure, resulting in well-interlaced interfaces (Figure 8b). On the other hand, [PERSON] et al. [20] reported a PbSe-QDs/CdS/CdSe:In/Si heterojunction. They proposed a possible application of their heterojunction as an uncooled midwave infrared device. However, the electrical characterization was limited to dark current density-voltage annealed at various temperatures and the Shockley model was not used to analyze the diode parameters; therefore, their results cannot be comparable with the results of this work. In the same manner, [PERSON] et al. [19] built a PbSe/CdS/nitrogen-doped TiO\({}_{2}\) nanorod array photoelectrode. They did not synthesize a homogeneous layer CdS/PbSe heterojunction while they used CdS and PbSe nanoparticles deposited by CVD on TiO\({}_{2}\):N nanorods, and their electrical characterization was related to the properties of photoelectrodes; consequently, their results are also not comparable with ours.
# EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
## 1 Introduction
### Main Contributions
Through cooperative communication of vehicle nodes, the data dissemination process will speed up; thus, the delay for the network to converge to a balanced status can be reduced. The number of communication stages is also reduced as the computing and communication burdens can be distributed among the nodes, which can be seen as a reflection of reduced communication overhead. To summarize, the main contributions are described as follows. 1. We apply graph theory to different scenarios in VANETs and divide the network into arbitrary graph, linear graph and complete graph. The cases of arbitrary graph and linear graph are discussed in this study. We propose a general system model for disseminating a bounded number of message copies in the network. Under this model, we develop EDDA, an efficient data replication algorithm that can be applied to both arbitrary graph and linear graph, in which the number of messages that can be replicated is limited and a network balanced status will be achieved. 2. We derive the theoretical analysis to obtain the approximate number of nodes that would receive the message when the system achieves an \(e\)-balanced status. The convergence speed of the algorithm is also presented. As a special case of arbitrary graph, detailed analysis of the upper bound and lower bound for linear graph is provided, to show the efficiency of the proposed algorithm. The effectiveness of our algorithm has been validated by extensive simulations.
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 1 Introduction  
Three-dimensional (3D) printing technology is rapidly advancing in the medical field [1; 2; 3; 4; 5]. In orthopedic surgery, 3D printing is now widely performed to customize implants [6; 7; 8; 9; 10]. This novel additive manufacturing method facilitates the rapid and cost-effective fabrication of customized implants [3; 11; 12; 13; 14; 15; 16]. Unlike the traditional metal casting method, 3D printing technology facilitates the production of implants with a constant internal pore. A few in vitro studies and in vivo animal experiments have demonstrated the potential of a titanium alloy implant with appropriate porosity to enhance the osseointegration of implants [11; 12; 15; 17; 18; 19; 20; 21]. Some studies have developed titanium alloy implants with a bioactive coating--however, animal and in vitro studies on these implants are still underway [22; 23; 24].  
Soft tissue adhesion is essential for limb salvage surgery in treating orthopedic tumors to effectively reestablish the muscle origins or insertions that are detached during surgical tumor resection. Furthermore, soft tissue adhesion can reduce the dead space to prevent postoperative infection. TheTrevira tube is a specially designed surgical envelope that has been widely used for this purpose to enhance the attachment of soft tissue around megaprostheses [25]. However, to our knowledge, no study has evaluated the integration of a 3D-printed titanium implant in human bones and soft tissues.  
Here, we report the successful osseointegration and soft tissue integration ability of a 3D-printed titanium implant during limb salvage surgery in treatment of a bone tumor and demonstrate the potential of this implant to appropriately design the host bone junction and implant surface.
# Novel Approaches to Combat Medical Device-Associated BioFilms
## 3 Novel Approaches to Combat Medical Device-Associated Biofilms
### Agents for Degradation of the Extracellular Matrix of Biofilms
These biofilm-dispersing agents can be used to functionalize the surface of medical devices to inhibit the formation of biofilms and can also be combined with other antibiofilm agents or technologies to improve antibiofilm activity [234; 244; 245; 235]. Moreover, several new compounds have also been shown to have antibiofilm activity (Figure 1). These compounds can be extracted from organisms or produced by chemical synthesis. Farnesol, which is a QS molecule, can be secreted by a variety of organisms, mainly _Candida_, and can also be produced by chemical synthesis [246]. A recent studydemonstrated that farnesol has significant antibiofilm activity, disrupting the mycelia of _Fusarium keratoplasticum_ and the biofilm extracellular matrix and preventing conidial adhesion, filamentation, and biofilm formation [247]. In addition, a study on the effects of farnesol on _Candida dubliniensis_ biofilms indicated synergy between farnesol and fluconazoleazole in resistant strains. This led to a reversal of fluconazole resistance, which is a crucial result that suggests a possible application of farnesol as an adjuvant therapeutic agent [248]. Another study showed that caffeine, which is a xanthine alkaloid compound, significantly inhibited the formation of _P. aeruginosa_ biofilms at sub-MIC concentrations by interfering with QS [249]. \(\beta\)-Lapachone and lapacho oxime are semisynthetic derivatives of lapachol that were originally isolated from species of the _Bignoniaceae_ family and have also been proven to be effective in inhibiting biofilm formation [250]. In addition, _Warburgia ugandensis_ Sprague subspecies _ugandensis_ is a plant that is widely distributed in Eastern, Central, and Southern Africa. Warburganal, polygodial, and alpha-linolenic acid (ALA) were the major bioactive compounds isolated from the acetone extract of _W. ugandensis_. These compounds have been shown to inhibit the formation of _C. albicans_ and _S. aureus_ biofilms and exhibit synergistic effects with azole compounds [251]. However, some compounds have also been shown to enhance biofilm formation. Nicotine, for example, promotes _S. aureus_ biofilm formation by promoting initial bacterial attachment and extracellular DNA release [252]. These new compounds provide new perspectives and possibilities for antibiofilm agents, requiring further verification of the efficacy and safety in clinical applications.
## 4 Experimental Results
### Evaluation of Feature Extraction and Matching Modules  
To extract palmprint ROI features, we passed palmprint ROI images, obtained by REM, through the network and optimized the final vector of \(n\times 4096\) feature matrix, where \(n\) is the overall number of images in each database. In other words, the values of each row in this vector correspond to the features of each individual's image. For example, considering \(n=3480\) for the contact-based HKPU database, the first 18 images of overall 3480 images are related to the first person, the second batch of 18 images is for the second person, and so on. A learning rate of \(L_{r}=10^{-5}\) was fixed for the FEM network. The cost function of the fine-tuning process in FEM is depicted for three databases in Figure 11.  
Figure 11: Fine-tuning cost versus epoch in Feature Extraction Module for contact-based Hong Kong Polytechnic University (HKPU), 2D contact-free HKPU, and Indian Institute of Technology Delhi (IITD) Touchless Palmprint Databases.  
Figure 10: Comparison of extracted palmprint regions with the ground truth ROI.  
As we show, the network parameters could be well optimized during the fine-tuning process. Moreover, the cost function had the lowest value for contact-based HKPU because of more discriminative features in the database images. To have a better perspective on the issue, we visualize the feature vector of the FC7 layer in FEM as an image shown in Figure 12. In other words, we reshaped the 4096D matrix into a 64 \(\times\) 64 image. These extracted deep features were used as the input of the SVM classifier in the verification task.  
To evaluate the matching module, we compared the performance of different methods via verification accuracy, ROC curves, and EER using four main concepts: true positives (TPs) (data items correctly predicted as positive), false positives (FPs) (data items incorrectly predicted as positive), true negatives (TNs) (data items correctly predicted as negative), and false negatives (FNs) (data items incorrectly predicted as negative.). Furthermore, the verification performance of each classifier was calculated by matching palmprint ROIs from two sessions. The first classifier was a linear SVM with parameters \(C\) = 1 and max iter = 1000. We also applied KNN, which classifies feature vectors by the number of neighbors 3 via the Minkowski metric. The last was the RF classifier, for which the number of estimators was 200.
## 6 Trigger Algorithm Testing  
Additionally, the correct operation of the OBC-triggering algorithm and the processing and protection of the TTEs around the time of the trigger were tested during the characterisation campaign. GMOD was configured for experiment-running and the OBC's triggering algorithm was enabled. During these tests, the source was brought close to each face of the spacecraft (without touching the satellite) for a fixed amount of time, i.e., 10 s. GMOD triggered on the source at multiple positions, and it was confirmed that the trigger time and duration were correct, contributing to the verification of the functionality of the onboard triggering algorithm. The trigger data processing and protection in the OBSW around the time of the trigger were also validated as the data analysed from these triggers were downlinked from the protected trigger data storage channels on the OBC. The OBC successfully recorded 21 triggers. Figure 15 presents six triggers, where each trigger had the source located at a different face of the spacecraft (+X, -X, +Y, -Y, +Z, -Z) for ten seconds. Panel A of Figure 2 presents the EIRSAT-1 coordinate system.  
In Figure 15, the sources were approximately the same distance from each face of the spacecraft for the same duration, so the intensity of the triggers reflects the impact of the crystal geometry and surrounding spacecraft on the performance of GMOD. The highest number of counts in a trigger (\(\sim\) 3500) was when the source was at the +Z face of the spacecraft; the only components between the source and GMOD are EMOD and the GTRB. As expected, the counts in the trigger are at a minimum when the source is at the \(-Z\) face of the spacecraft. The trigger only creates \(\sim\) 230 counts, as the CubeSat stack of PCBs attenuates the gamma-ray photons before they arrive at the GMOD detector. Similarly, the +X trigger has relatively low counts, as the gamma-ray photons need to pass through the components in the detector assembly (SIPHRA, Interface PCB) before reaching the scintillator. The +Y trigger has \(\sim\)2000 counts, as the source is further away from the GMOD detector. The \(-\)Y and \(-\)X triggers have a similar number of \(\sim\) 3000 counts in the trigger, as there was only a solar panel between them and the source.  
Figure 14: Angular effective area of GMOD FM at 59.4 keV.
## 4 Discussion
### The Role of Chronological and Relative Age in Injury Risk Identification  
Chronological age seems to be a relevant factor in traumatic injury risk identification. A statistically significant higher risk for traumatic injuries was found in 10-year-old athletes (OR = 3.36). The highest rate of injured observations was found during the competitive season after the first investigation of the athletes, when they were in mean 10.6 \(\pm\) 0.3 years old. This finding might be explained by the different preconditions of the young ski racers before frequenting the ski boarding school. After having passed the entrance exam for the school, which normally takes place in the fall, they start with their dual career (school and ski racing) in September (first examination of the athletes in present study). Some athletes had already trained with experienced coaches and were already well-prepared, whereas others did not have any planned training sessions except for participating in ski races; thus, an inhomogeneous group of athletes might start with the first school year. However, all of them have then the same training loads of skiing-specific and athletic-specific training. It might be assumed that these loads are too much for some athletes during the first months in the new school; together with the new life in the boarding school and psychological stress to perform well, it might lead to the high traumatic injury rates in this age group. This result is in line with a study in youth soccer, in which an association between traumatic injuries and chronological (younger) age was found [32]. In this context, in the future, it might be necessary to adapt the training loads based on the diverse preconditions of the pupils when entering the school.  
Chronological age might be more relevant in identifying athletes at higher risk for sustaining traumatic injuries than relative age. In line with a previous study in youth ski racing [4], in which no difference in frequencies of traumatic injuries between the four relative age quarters was found, the relative age quarter was not identified to be a significant risk factor in the present study. However, the fourth relative age quarter was considered as a reference in the statistical analyses, which means that the other three quarters were compared with Q4. As it was also assumed by [PERSON] et al. [4], relatively younger athletes, who might be expected to be at a higher injury risk based on previous findings in different sports (a.o., [32]), may have counteracted their relative age disadvantage if they were more mature and possibly at a higher level of physical fitness. Thus, they might present the same physical conditions as their relatively older counterparts and therefore, they might not be at a higher injury risk.