esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator
## 3 Structure of Proposed Method
### Line Detection Model  
Line Detection Model (LDM) is used to extract contour of line from a color image. The purpose of LDM is to prevent line over-fitting and improve the line generalization performance. In our approach, we use LDM on real-world line-art and line-art generated from the draft model. The structure of LDM is shown in Figure 6, it uses auto-encoder as the Base model. The output filter number, kernel size, and stride are depicted in the blocks in the diagram. For example, c32k3s1 denotes output filter size of 32, kernel size of 3, and stride of 1. LDM receives the color image with resolution of 128 \(\times\) 128 and generates grayscale line-art with the same resolution. It is used only in the learning process of draft model. The loss function used for LDM is described in Equation (1), \(l\) denotes line extracted via either \"XDoG\" or \"Diate abs sub\", and \(c\) denotes color image. \(\mathbb{E}_{c,l}\) denotes the expected value of all \(l\) and \(c\) instances. \(\mathcal{L}_{L1}(ldm)\) measures \(L_{1}\) loss, which is the pixel difference of \(ldm(c)\) and \(l\). Adam [20] is used for the training, and the parameters used are \(\beta_{1}=0.5\), \(\beta_{2}=0.9\), and learning rate is 0.0001. The learning rate is decreased by one tenth of current rate at step 110K and 183K. The batch size is set to 16 and total of 256k steps are used in the training.  
\[\mathcal{L}_{L1}(ldm)=\mathbb{E}_{c,l}[\|l-ldm(c)\|_{1}] \tag{1}\]  
There are two benefits of using LDM. First, we can create optimized feature extraction model for the resolution of line-art used in the learning process. Second, it is used only in the learning phase; thus, it does not increase the overhead in the inference phase. The result of loss function is used as feedback to the draft model so that it can further improve the line generalization performance of the draft model.  
Figure 6: Line Detection Model (c: output filter number, k: kernel Size, s: stride, For example, c32k3s1 means inter convolution layer output filter number is 32, kernel size is 3, and stride is 1).
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 4 Simulation Experiments
### Simulation Result Data Analysis
#### 4.2.4 Path Planning under Collision Risk  
Through the simulation of airport flight operations, this study found that most of the associated collision risks are caused by taxiway path conflicts. Therefore, optimizing these paths by focusing on minimizing collision risks can serve as an effective strategy. The goal is to replan the taxi paths without affecting taxiing time. The algorithm selected for this purpose is the Dijkstra algorithm. As an example, Table 5, Figures 16 and 17 demonstrate its application to two flights that were identified as having a collision risk:The results of re-routing the flights that generated collision risks during peak hours using this algorithm are shown in Figure 18:  
As shown in the above, the cumulative collision risks have decreased following the re-routing of paths, though the extent of reduction varies. On normal days, the collision risk reduction can reach approximately 75%, whereas on peak days, re-routing only reduces the original collision risk by 40%, which is about 53% of the reduction achieved on normal days. The smaller decrease in collision risk on peak days compared to normal days can be attributed to two main factors: first, the higher number of conflicting flights results in a higher accumulated collision risk; second, some conflicts stem from positional conflicts at the start and end points of the conflicting flights, leading to fewer overlapping areas along the paths.
# Photoconductive Semiconductor Switches: Materials, Physics, and Applications
## 8 Application Case Study: A Medium-Voltage PCSS Breaker
### Testing of the Integrated SSCB  
To verify the circuit breaker design, experiments were conducted with a 6 kV prototype, as shown in Figure 15. The setup places the breaker between a DC power supply and a resistive load, starting with the breaker in the on-state. During a turn-off transient, the breaker transitions to a high-impedance state, interrupting the DC current. The focus is on the coordinated behavior of the normally-on and normally-off sub-circuits, controlled by timing signals to the PCSS's optical driver (Nd:YAG laser) and the JFET gate driver.  
The goal is to confirm the behavior depicted in Figure 16. Successful operation requires the precise timing of the PCSS activation for lock-on and current redirection from the JFET circuit. The critical parameter is the delay between the JFET trigger (\(t_{1}\) in Figure 16) and  
Figure 17: (**a**) HV switch is a 6 kV, normally-on device composed of a series connection of 1.2 kV SiC JFETs. (**b**) Static voltage balancing of the HV switch. (**c**) Dynamic voltage balancing of the HV switch during the turn-off transition [95].  
the PCSS firing (\(t_{2}\) in Figure 16). The PCSS needs a minimum voltage at activation; if it fires too early, it will not lock on, and if too late, the JFET circuit bears full fault current, risking thermal stress.  
The experiments differ from Figure 16 by initiating the turn-off transient from a constant current, allowing control over current magnitude by varying load impedance. Results for a successful turn-off at 6 kV are shown in Figures 18 and 19, with a line current of 5 A and the optical driver delivering 1.35 mJ of laser energy. Voltages across the breaker, shunt capacitor, and load were measured, along with currents through the circuit legs using Hall-effect probes. Figure 18 shows that the turn-off duration is dominated by the shunt capacitor's charging time.  
Figure 19 illustrates the coordinated behavior of the circuit legs. As the transient begins, the voltage across the JFETs rises, and once it reaches a critical threshold, the optical driver fires the PCSS. These results confirm the breaker's functionality: lock-on is achieved, diverting current into the shunt capacitor, and reducing thermal stress on the JFETs. In contrast, Figure 20 shows a 5 kV test where premature PCSS firing fails to achieve lock-on, leading to energy dissipation in the JFETs.  
Figure 19: Shorter duration turn-off transient voltage and current behavior for the 6 kV hardware demonstration [88].  
Figure 18: Longer duration turn-off transient voltage and current behavior for 6 kV hardware demonstration [96].  
Key implications for practical implementation include the need for a precise optical driver with consistent delay for timely PCSS activation and the importance of the voltage threshold for lock-on. A larger threshold relative to the system's rated line voltage narrows the window for achieving lock-on and limits JFET stress reduction, while higher line voltages improve the chances of successful lock-on.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion
### Mechanical Strength  
The mechanical strength of hydrogels is an important physicochemical property, which plays a crucial role in the utilization of the hydrogel for biomedical applications. The mechanical strength of the DNA loaded 3 LM was characterized using rheological profiles. Figure 6 shows the storage (\(G^{\prime}\)) and loss (\(G^{\prime\prime}\)) moduli of the 3 LM hydrogels at _inner.outer_ ratios of 1:1 and 1:10, and the control hydrogel from the simple PLLA-PEG-PLLA micelle; plotted immediately after mixing with the PDLA-PEG-PDLA micelle solution. All measurements were performed at 37 \({}^{\circ}\)C with a constant solution concentration of 20 wt %. The L+D blank micelles show the highest storage modulus, indicating that the presence of 3 LM in the hydrogel network leads to a decrease in mechanical strength. The hydrodynamic sizes of the co-gelling 3 LM and PDLA-PEG-PDLA micelles are considerably different. Thus, the inter-micelle distance, micelle packing density, micelle aggregation behaviors and chain exchange kinetics would be different than the L+D blank micelles. In our previous study, we utilized time-resolved small angle neutron scattering (TR-SANS) to investigate the effects of chain exchange kinetics on the formation of L+D stereocomplexed hydrogels [35]. Model dependent analysis of the scattering data suggested, (1) micelle volume increased due to increase in aggregation number following non-equilibrium chain exchange, (2) chain exchange rate was influenced by the size of the PEG corona, with larger PEG corona showing a delayed response in aggregation number increase and (3) micelles initially formed small and tight clusters, which then grew to larger clusters and eventually formed the hydrogel network. The storage modulus is considerably lower for the _inner.outer_ ratio 1:1 formulation than the control in Figure 6. When the simple PLLA-PEG-PLLA micelles are incorporated into the gelling solution by increasing the _inner.outer_ ratio, a clear increase in \(G^{\prime}\) is observed. The improvement in mechanical strength of the 1:10 _inner.outer_ formulation can be attributed to increase in micelle packing density. The simple PLLA-PEG-PLLA micelles increase the crosslinking points, effectively bridging the gap that existed between 3 LM and PDLA-PEG-PDLA micellar populations in the 1:1 _inner.outer_ formulation.  
Figure 5: Summary of so-to-gel phase diagram.  
Figure 6: Rheological properties with (**a**) time and (**b**) frequency plots for the stereocomplexed hydrogels prepared using 3 LM with different _inner.outer_ polymer ratios and the PLLA-PEG-PLLA micelle.
# Trends and Challenges in Network Covert Channels Countermeasures
## 6 Trends and Challenges
To spot this class of emerging threats, the warden should collect the traffic in different portions of the network and do some form of comparison. This poses several challenges, not only limited to technical aspects. For instance, the needed traffic could be collected in environments of different operators leading to technical and legal issues;
* **resistance by design**: the ability of storing arbitrary data or manipulating a protocol are usually consequence of an ambiguous design or an imperfect implementation. An interesting research activity deals with the identification of patterns at the basis of the creation of network covert channels [7] and should be considered when designing the next-generation of secure-by-design protocols. Nevertheless, the application of formal methods to endow a warden with the ability of performing runtime checks or model unknown threats is another promising trend to explore.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
###### Abstract  
Digital image authentication has become a hot topic in the last few years. In this paper, a pixel-based fragile watermarking method is presented for image tamper identification and localization. By analyzing the left and right singular matrices of SVD, it is found that the matrix product between the first column of the left singular matrix and the transposition of the first column in the right singular matrix is closely related to the image texture features. Based on this characteristic, a binary watermark consisting of image texture information is generated and inserted into the least significant bit (LSB) of the original host image. To improve the security of the presented algorithm, the Arnold transform is applied twice in the watermark embedding process. Experimental results indicate that the proposed watermarking algorithm has high security and perceptual invisibility. Moreover, it can detect and locate the tampered region effectively for various malicious attacks.  
Keywords:fragile watermarking; image authentication; singular value decomposition (SVD); Arnold transform +
Footnote †: journal:
## 4 Discussion
### Oxidative Stability  
The oxidative stability of oils and fats is one of the most important parameters for assessing their quality. Many methods have been developed for this evaluation, some of which are based on the measurement of the induction time of oil oxidation (e.g., differential scanning calorimetry or the Rancimat method). Vegetable oils, in addition to being used in the food, cosmetics, pharmaceutical, and chemical industries, have found a huge market in the biofuel industry. Oils and fats are also extremely important in human nutrition. However, oils and fats are very susceptible to oxidation processes in which essential fatty acids are degraded, resulting in an unpleasant smell and taste as well as the generation of compounds that are potentially toxic to humans [43, 44].  
In the case of the oxidative stability analysis, the primary principle is that the longer OIT, the greater the oxidative stability of the oil. The rate of oxidation processes, on the other hand, may depend on the presence of anti- or prooxidants.  
[PERSON] et al. [45], in their research into the oxidative stability of vegetable oils, stated that oils and fats that contain PUFA are the most exposed to oxidative changes because they undergo faster oxidation processes. Results obtained in this study showed that the lowest oxidative stability was in the sample containing the most tomato seed oil (A75B25), which is known to be rich in polyunsaturated fatty acids.  
OIT for crude coconut oil showed very high oxidative stability (OIT over 300 min). This result is in agreement with the work of [PERSON] et al. [46], who obtained OIT above 550 min.  
The study performed by [PERSON] et al. [47] indicated that the oxidation induction time of interesterified palm-based oil blends decreases in comparison with unmodified blends, which supports our observations. Blending and interesterification reduce the oxidative stability of coconut oil, as blends composed of 75% coconut oil are characterized by a much shorter oxidation induction time than raw coconut oil.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 3 IberSpeech-RTVE 2022 Evaluation Tasks
### Speaker Diarization and Identity Assignment Challenge  
The Speaker Diarization and Identity Assignment evaluation consists of segmenting broadcast audio documents according to different speakers and linking those segments which originate from the same speaker. On top of that, for a limited number of speakers, the evaluation asked for assigning the name of these people to the correct diarization labels. No prior knowledge is provided about the number of speakers participating in the audio to be analyzed. Participants are free to use any dataset for training their diarization systems provided that these data were fully documented in the system's description paper. The organization provides all the RTVE datasets ([[http://catedartve.unizar.es/rtvedatabase.html](http://catedartve.unizar.es/rtvedatabase.html)]([http://catedartve.unizar.es/rtvedatabase.html](http://catedartve.unizar.es/rtvedatabase.html)) (accessed on 24 July 2023)), the Catalan broadcast news database from the 3/24 TV channel proposed for the 2010 Albayzin Audio Segmentation Evaluation [15; 16] and the Corporacion Aragonesa de Radio y Television (CARTV) database proposed for the 2016 Albayzin Speaker Diarization evaluation. For the identity assignment task, the RTVE2022 dataset provides enrolment audio files for 74 speakers (38 male, 36 female). At least 30 s of speech from each speaker to identify are included in the dataset.
# Artificial Intelligence Imagery Analysis Fostering Big Data Analytics
## 2 Artificial Intelligence Imagery Analysis: Background, Evolution, and Illustration  
### Background  
Artificial Intelligence Imagery Analysis, belongs to the field of computer vision, and it is a sub-discipline of AI. It deals with the segmentation, analysis, and understanding of the content of stationary and moving images by computers [9]. Using machine learning and AI methods, models are trained on \"training datasets\", for which images and digital formats are given. The output, on which the machine learning model is intended to be trained on is known, for instance, through prior rating by humans.  
Scientific research has developed and improved Artificial Intelligence Imagery Analysis methods and the application of these methods. Work on Artificial Intelligence Imagery Analysis methods originates from various fields including computer science, applied mathematics, neuroscience, and fundamental psychology research [10] and recent research has mainly been concerned with the ongoing refinement of these methods and their extension to new applications. Areas for the groundwork on Artificial Intelligence Imagery Analysis are diverse and include video tracking or taking, object recognition, for instance, for the driverless car, image database indexing, scene reconstruction, and event detection [9].  
Research on concrete applications related to Artificial Intelligence Imagery Analysis spreads across many application sectors. We find early research and applications in medicine, in relation to driverless cars and in (industrial) robotics. For instance, in medicine, research has targeted the detection of skin cancer [11] and the detection of cut marks in bones [12]. Other works have focused on analyzing images of the human eye to detect the state and progression of certain eye diseases [13]. In the context of the driverless car, research has been concerned with road boundary detection [14], traffic sign detection [15], and visual localization [16].  
Finally, in (industrial) robotics, researchers have employed Artificial Intelligence Imagery Analysis for object detection that supports indoor robot navigation [17], for detecting forest trails for a mobile robot travelling through the forest [18], and for spotting and identifying apples in an automated apple harvesting system [19].
# HI-Sky: Hash Index-Based Skyline Query Processing
## 2 Related Study
### Index Based Skyline Computation  
The main problem with skyline queries is the need to perform dominance tests for the entire set of data. Index structure-based methods have been proposed to solve this problem, such as branch-and-bound skyline (BBS) [6; 7] and Z-SKY [10; 11].  
The BBS was proposed by [PERSON] et al. [6; 7]. It generates an R-tree [8; 9] index structure for the given data through preprocessing. The skyline query is then requested using the spatial features of the minimum bounding rectangle (MBR), which is an index component of the R-tree. The main feature of BBS is that it removes MBRs dominated by other MBRs or skyline candidates through dominance testing. Thus, the removal of MBRs can be used to prune large amounts of data using a single comparison. However, the R-tree also has the problem of overlapping being increasingly likely among the MBRs as dimensionality increases. Because this overlapping significantly reduces the probability of domination in the dominance test, it also reduces the efficiency of the skyline computation.  
Z-SKY was proposed by [PERSON] et al. [10; 11]. It computes the skyline through a ZBtree by combining a B+-tree and a Z-order curve index structure [12; 13]. The Z-order curve generates an address for the given multidimensional data called a Z-address, and the Z-address is used as a basis to map the data to a one-dimensional space. Z-addresses have the characteristic of monotonic ordering, which allows the location information of the given data to be extracted. This in turn allows Z-SKY to compute the skyline without access to the actual data stored on the disk. For more efficient skyline computations, Z-SKY also uses RZ-regions, which is are clusters that employ the B+-tree and Z-order curve segments. An RZ-region is generated by searching the Z-order curve segments that can be included in all Z-addresses belonging to the corresponding node using the internal node of the B+-tree. Therefore, as the generated RZ-region shows the data space with various lattices, it enables easy data space pruning with a small number of comparisons. However, when data at fine intervals (i.e., real values) are used in Z-SKY, the Z-address becomes very long. Because of this, Z-SKY can only be used with a limited range of integer data or with real values normalized to a limited range of integers. Even if real values are normalized, the exact skyline cannot be computed by the Z-address alone, because the actual data may be different. Thus, Z-SKY is highly dependent on the data type. Moreover, if the RZ-region of the skyline does not dominate the given data, all the RZ-regions belonging to this region are stored in a queue. Consequently, when the threshold of the node is high, a large number of nodes is entered into the queue, and a dominance test is performed on them that may decrease computation performance. Thus, Z-SKY cannot guarantee fast performance in all cases due to the potentially massive storage and memory space requirement of the Z-address.
## 1 Introduction
The protruding height refers to the distance from the peak of the riblet to the apparent starting point of the riblet. In the groove below the apparent starting point, most of the flow is viscous, which is equivalent to increasing the thickness of the adhesive bottom layer in the boundary layer and reduces the average velocity gradient near the wall surface. So, this theory suggests that when the fluid flows over the surface of the riblet structure, instantaneous lateral flow caused by turbulent motion is impeded and the turbulent kinetic energy change in the entire boundary layer is weakened, thereby reducing the frictional resistance on the riblet surface. [PERSON]'s [17] wind tunnel experiment confirmed that the riblet plate has less drag than the smooth plate at \(s^{+}~{}=~{}25.2\) (\(s^{+}\) is dimensionless spacing of grooves or riblets). It is attributed to the suppression of the spanwise vortex by the top of the riblet. [PERSON] [18] proposed the \"mini air bearing\" theory. It is believed that if small lateral riblets of appropriate size are placed at a certain distance at the bottom of the turbulent boundary layer, it is possible to \"lock\" the small vortex of the flow. After being blocked by the riblets, it stays in a suitable pit, like air bearings continuing to rotate or not move. Because the rolling friction is much smaller than the sliding friction, the flow at the level of the bottom layer of the turbulent boundary layer and the riblet height is affected by the rolling friction, which reduces the friction. [PERSON] et al. [19] used PHOENICS 3.6 (computational fluids and computational heat transfer commercial software) calculation software to simulate the flow field after the horizontal concave groove is arranged in the middle of the flat plate, and verified the drag reduction mechanism proposed by [PERSON]'s [18] \"mini air bearing\". The drag reduction mechanism of riblets is directly related to the turbulent flow structure, and the true features of the turbulent structure have not been fully understood so far. Therefore, the theory of each researcher can only be considered as conjecture to a certain extent [20]. The true mechanism remains to be investigated. In the current research on drag reduction of riblets, there is a deep understanding of the riblets arranged along the flow direction, while the research results of the lateral riblets are still few. In this paper, numerical simulations are used to study the lateral riblets of different shapes at different speeds. The effects of lateral riblets on the boundary layer and drag reduction are explored by comparing the results with the smooth plate and the plate with riblets of different shapes. Most of the current research on riblets' drag reduction is triangular riblets or grooves along the direction of flow. In this paper, lateral triangular grooves are chosen to be placed on the airfoil to explore the influence of the grooves on the airfoil.