esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this work, cork was proposed as a UNO, part of the PUD system, along with its measurement kit (illumination device), and its recognition method. The effectiveness of capturing relevant information using the devised setup along with the proposed RIOTA's implementation was demonstrated in several tests: light test, repeatable features test, and databases test. In the tests carried out on the illumination device, the results clearly show the success of capturing stable/repeatable features needed for the recognition task. This achievement is also reflected in the perfect score achieved in the cork database, in the intra-distance measure \(\mu_{intra}\), which gives the notion of average noise between measures, and with inter-distance \(\mu_{inter}\), which provides hints about the randomness/uniqueness of the cork. Moreover, these results show that the three requirements that formulate a UNO--Disorder, Operability, and Unclonability--are fulfilled, regardless of the fact that cork stopper aging tests must be conducted to better support the operability requirement, as discussed in Section 4.4.1.  
The cork, measurement device and recognition method that suit the UNO definition [5] and the RIOTA's concept [1] has the following advantages over other anti-counterfeiting implementations: (i) it's non-invasive; (ii) it does not require any kind of marking and/or tagging; (iii) it does not use any kind of helper data (e.g., bar codes, serial numbers, etc.) to support or simplify the recognition task; (iv) it does not use any kind of superficial treatment to enhance the surface details (just use the object \"as is\"); (v) the information used for the recognition task only depends on the surface of the cork, making it hard to clone or falsify. In addition, this approach does not require a completely novel computer vision algorithm nor a huge dataset, which are usually necessary in machine learning-based approaches, since a state of the art local feature detector and extractor achieve a good performance. However, some limitations can also be identified from the application point of view: (i) the time needed to perform 1 vs. 1 match can make the application in a real-world scenario unachievable for a huge cork database if no other technique to reduce the search space and/or improve time performance is used; (ii) the quality of the images may alter the success of the RIOTA's implementation (as previously discussed, the blurrier the images are, the more likely it is that the recognition fails); (iii) the indispensability of using the illumination device to capture the images has, perhaps, a low degree of user friendless. The user is asked to put the illumination device in the bottle, turn the light on, and take the photo to validate a wine bottle.  
As previously pointed out, the necessity of reducing the search space and/or improving the time is mandatory. Therefore, future work should aim at addressing this question.
## 6 Conclusions  
In this paper, we proposed an underwater multi-DATA train protocol with multi-RTS reception and BACK for underwater acoustic sensor networks. Due to long underwater acoustic propagation delay, some RTS frames may not overlap each other at a sink node, even if the RTS frames were sent to the sink node simultaneously by different sensor nodes. We consider that our underwater sink node can recover these nonoverlapping RTS frames. Since our RTS frame contains ID of the RTS sending node and a timestamp, the sink node calculates the propagation delay between the RTS sending node and the sink node, then broadcasts a CTS frame. Since our CTS frame contains the time information about when each RTS sending node can transmit a DATA frame to the sink node, multiple DATA frames transmitted by different sensor nodes can be formed as a train at the sink node. Then, the sink node broadcasts a BACK frame which contains whether each DATA frame was successfully received or not. We also proposed an underwater BACK protocol which is analogous to our proposed underwater multi-DATA train protocol except the multi-DATA train and the information contained in RTS and CTS frames. Then, we analyzed the normalized throughput and the mean access delay of our proposed underwater multi-DATA train protocol, our proposed underwater BACK protocol, the underwater multi-RTS reception protocol, and the conventional CSMA/CA protocol, respectively. The analytical results agree very well with the simulation results for throughput and delay performances of our proposed protocols and the conventional protocols, respectively, with various numbers of underwater sensor nodes and input parameters. The analytical results and the simulation results show that our proposed protocols outperform the conventional protocols. As a further study, we will extend our proposed protocols and our performance analysis to consider the scenario that underwater sensor nodes may be hidden from each other. The extension of our proposed protocols and our performance analysis will be helpful to study the scenario in UWSNs and IoUT.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07049601). The work reported in this paper was conducted during the sabbatical year of Kwangwoon University in 2017.  
Figure 9: Normalized throughput versus the number of underwater sensor nodes for the proposed underwater multi-DATA train protocol, the proposed underwater BACK protocol, the underwater multi-RTS reception protocol, and the conventional CSMA/CA protocol.  
**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 4 Subtypes of Sphingolipidoses
### Gaucher Disease
#### 4.2.2 Retinal Manifestations  
Ocular manifestations in GD can affect all ocular structures. While the exact pathophysiology is still under investigation, the accumulation of Gaucher cells macrophages with a distinctive \"crumpled silk\" morphology due to lysosomal expansion appears to be a key factor [29, 78, 80]. These cells not only deposit in ocular tissues but also act as proinflammatory-tory mediators, leading to fibrosis and microvascular compromise [29, 80]. Although ocular involvement is most frequently described in Type IIIc, all GD subtypes have reported cases of ocular pathology. Notably, the retinal manifestations, when present, are highly variable, underscoring the rarity of GD and the heterogeneity of its ocular presentations [77]. Neurological ocular findings are more prevalent in neuronopathic forms and may serve as early indicators. Among the most commonly reported findings, saccadic eye movement abnormalities, including horizontal saccadic slowing and horizontal saccadic failure, typically manifest during early childhood [74, 77, 82]. Over time, vertical saccades may also be affected, mimicking patterns observed in progressive supranuclear palsy. Additional ocular abnormalities include the pre-retinal white dots [77, 80, 81, 89, 90]. Such white dots appear ophthalmoscopically as amorphous whitish deposits diffusely distributed in the posterior pole and retinal periphery, appearing on SD-OCT as hyperreflective pre-retinal deposits localized at the interface between the ILM and the vitreous. Their recognition has been associated with posterior vitreous detachment in correspondence of their sites of formation [91, 92]. Occasionally, hyperreflective deposits are also observed within the vitreous, mimicking vitreitis. Rarely, tractional retinal detachments originating from these pre-retinal deposits have been reported [93]. However, such findings are sporadic, reflecting the limited focus on ocular evaluations in the clinical management of GD. A schematic representation of hyperreflective pre-retinal deposits is shown in Figure 2.
###### Abstract  
The refinement level of bio-based fertilisers (BBFs) can influence environmental and agronomic performance. This study analyses the environmental and agronomic effect of different BBFs on potato growing in sandy soil. A less refined product (liquid fraction of digestate (LFD)), two refined products (ammonium sulphate (AS) and potassium concentrate (KC)), and mineral fertilizer (MF) are compared by conducting: (i) a nitrogen (N) incubation experiment where the N release rate of the BBFs is determined, (ii) a greenhouse gas emission experiment where N\({}_{2}\)O, CO\({}_{2}\), and CH\({}_{4}\) emissions after BBF application are measured, (iii) a pot experiment where the nutrient fertiliser replacement value (NFRF) of the BBF is calculated, and (iv) a full-scale field trial where the potato quality and quantity and the remaining N residues in the soil after harvest are assessed. The N release rate and the NFRF of AS (142 \(\pm\) 19% and 1.13, respectively) was higher compared with the LFD (113 \(\pm\) 24% and 1.04) and MF (105 \(\pm\) 16% and 1.00). Lowest N\({}_{2}\)O emissions were observed after the application of the less refined product (0.02 \(\pm\) 0.01 per 100 g N applied) and highest for MF urea (0.11 \(\pm\) 0.02 per 100 g N applied). In the full-scale field trial, no significant difference in potato yield was observed in the plots that received manure in combination with BBF or MF. This study showed that all three BBFs can safely be used in potato growing on sandy soils. However, the adoption of BBFs can be stimulated by (i) solving the practical issues that occurred during the application of LFD, (ii) making sure BBFs are on the list of RENURE materials so they can legally replace mineral fertiliser, and (iii) reducing the surplus of slurry manure to stimulate the use and fair pricing of BBF products.  
agricultural circularity; sustainable agriculture; environmental impact; manure processing; CHG emissions; fertiliser replacement value +
Footnote †: journal: Applied Sciences
# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 5 Results and Discussion
### Ray Space Analysis  
The cube surface light field is presented by a set of cube maps arranged by the texture coordinates, i.e., the sampling points. Each sampling point in the viewpoint space represents all the light rays starting from this point to the whole image space. All the sampling points in the viewpoint space amalgamated to form a viewpoint cube map. Therefore, the coverage of the viewpoint cube map has an important impact on the DoF of the subsequent rendering.  
Considering that it is easier to understand the light ray's distribution in ray space, we convert the light rays in 3D space (Cartesian coordinate system, CCS) to ray space (Polar coordinate system, PCS). Without losing generality, the light ray's projection on the XOY plane is taken as an example to analyze the ray space distributions. Given an arbitrary light ray R in 3D space, its projection on the XOY plane can be marked as Figure 6a. The angle with the +X axis is denoted as \(\theta\) and the distance between this projection line to the origin of CCS is denoted as \(r\). Taking \(\theta\) and \(r\) to be the two coordinate axes of the new coordinate system, this light ray R is converted from CCS to PCS, as shown in Figure 6b. We define the domain of \(\theta\) as from \(-\pi\) to \(\pi\). When a light ray intersects with the positive of the X-axis in CCS, \(r\) is positive. On the contrary, \(r\) is negative. Each light ray is marked by a black dot and all the light rays constitute the shaded area in PCS.  
The traditional 2 PP light field has been widely studied, and its light ray's spatial distributions are shown in Figure 6c,d. It can be seen that the 2 PP light field can only represent a subset of light rays, and cannot completely describe the light rays in the whole observation space. Therefore, only a part of viewpoint images can be provided in the rendering stage.  
Figure 6 shows the light ray's distribution of the cube surface light field in ray space. It can be seen that the angular domain \(\theta\) is symmetric in \((-\pi,\pi)\), which covers the whole observation space outside the cube surface. The domain of the light ray's distance \(r\) is symmetric on \((-a,\,a)\) where \(a\) is half of the square of the diagonal length, and the light rays are uniformly distributed in the closed area composed of \(\theta\) and \(r\).
## 3 Results and Discussion
### Impact of Heating Source Distance to the Tank on the Defect Detection  
Similar to the concern about how the IR camera distance impacts the accuracy of defect detection, as discussed in Section 3.3, there is a concern about the distance of the external heating source to the tank wall. The thermogram in Figure 14 was produced by placing the heater very close to the tank wall surface at a distance of less than 10 cm (4 in). Such localized heating deposited more heat in a small area, which saturated the area under inspection and reduced the heat deposited in the adjacent areas. Therefore, when the heat source is too close to the object (i.e., the tank wall in this study), the induced thermal gradient in the area under study is not uniform, resulting in a poor defect detection. Thus, the authors recommend stacking as many external heat sources and increasing the distance of the heat source from the tank wall to cover the entire area under evaluation. Increasing the distance of the heating source to the tank wall causes a more uniform heat deposition, which helps to prevent heat diffusion on the surface. This results in better heat penetration into the tank wall in the area under evaluation, which consequently results in better defect detection.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 2 Environmental Regulation of the _P. syringae_ T3 SS
### Induction of the P. syringae T3 SS in Response to Synthetic Media Conditions  
Initial experiments to assess the regulation of the T3 SS revealed that _hrp/hrc_ genes are repressed when _P. syringae_ are cultured in nutrient-rich media [73; 74; 75; 76; 42; 76]. Various genes within the _hrp/hrc_ pathogenicity cluster, including _hrpV_ and _hrpT_, are required for maintaining repression of the T3 SS in rich media, suggesting a low level of _hrp/hrc_ expression occurs even under repressive conditions [77; 36]. Based on a model proposed by [PERSON] et al., this basal level of expression may \"prime\" the T3 SS for rapid activation upon the removal of repressive environmental conditions [77; 57]. In contrast to repression by rich media, _hrp/hrc_ genes are induced when _P. syringae_ are cultured in defined minimal media [73; 78]. These media, variously referred to as _hrp_-inducing minimal medium (HIM or HMM), _hrp_-de-repressing medium (HDM) and/or inducing medium (IM), are characterized by a slightly acidic pH, a low nitrogen-to-carbon ratio, and the presence of a simple sugar such as fructose or mannitol as a carbon source. It should be noted that slight variations exist in the composition and pH of _hrp_-inducing minimal media described in various published works. Relative to a nutrient-rich medium such as King's B, these minimal media conditions more closely mirror general features of the environment within the leaf apoplast. Together, these early findings suggested that T3 SS-associated gene expression may be stimulated by general conditions encountered by _P. syringae_ during infection rather than by specific plant signals.
## 4 Dataset Generation and Simulation Environment  
This section details the simulation-based approach employed to rigorously validate the MHMM. Our methodology emphasizes grounding a high-fidelity synthetic dataset in empirical literature, ensuring it mirrors real-world human proficiency dynamics. This controlled environment provides an unambiguous ground truth, enabling precise and quantitative evaluation of the proposed MHMM approach's ability to recover latent proficiency states from multimodal data streams under various conditions. Furthermore, this section outlines a sensitivity analysis to assess the robustness of the MHMM's parameter settings and describes an expanded comparative framework including advanced neural models and methodological transparency.
# Detection of Welding Defects Tracked by YOLOv4 Algorithm
## 5 Conclusions  
The modified YOLO model demonstrates an enhanced automatic detection performance for three types of internal defects in welds. The main conclusions are as follows:  
1. An improved model, YOLOv4-cs1, is proposed. This model primarily modifies the fusion method involving residual blocks, the feature extraction approach of the PANet network, and the activation functions. As a result, the model can better learn edge information.
2. YOLOv4-cs2 further improves upon YOLOv4-cs1. In YOLOv4-cs2, the residual block structures and activation functions corresponding to different convolution kernels are modified to accelerate the learning of rare features. Two SPP (Spatial Pyramid Pooling) modules are added after the third and fourth residual blocks to expand the model's receptive field.
3. The results indicate that the recall rates for pores and slag inclusion are significantly improved in both optimized models, which is attributed to their enhanced ability to learn edge information. In the future, we will continue to focus on designing an  
Figure 16: Images containing three defects simultaneously. (**a**) YOLOv4-cs2, (**b**) YOLOv4-cs1, (**c**) YOLOv4, (**d**) YOLOv3, (**e**) YOLOv4-Tiny, (**f**) Centernet, (**g**) SSD.  
Figure 15: Incomplete penetration (green frames). (**a**) YOLOv4-cs2, (**b**) YOLOv4-cs1, (**c**) YOLOv4, (**d**) YOLOv3, (**e**) YOLOv4-Tiny, (**f**) Centernet, (**g**) SSD.  
advanced intelligent detection system for aluminum alloy weld defects that is aimed at improving the safety and automation of equipment.  
Conceptualization, validation, writing--original draft preparation: Y.C. and Y.W.; writing--review and editing: Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 51809161).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare that they have no competing financial interests that could affect this research.
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
###### Abstract  
(1) Background: Concentric ring artifacts in reconstructed SPECT images indicate the presence of detector non-uniformity in gamma camera systems. The identification of these artifacts is generally visual and not quantitative. The aim of our study was to evaluate observer assessments of the presence of concentric rings in reconstructed SPECT phantom images and to verify whether quantitative texture analysis can detect such artifacts, which are detrimental to accurate tumor detection. (2) Methods: Test data were acquired as part of the quarterly quality assurance program using a standardized SPECT phantom containing solid spheres, solid rods, and a water solution of \({}^{99m}\)Tc. Forty separate SPECT acquisitions were analyzed to assess the presence of ring artifacts. Two experienced medical physicists independently reviewed transaxial images and graded the severity of artifacts on a five-point scale. Quantitative radiometric features were computed for volumes of interest located in the uniform phantom section. In addition to these, radial contrast (\(R_{Contrast}\)) and radial root-mean-square contrast (\(R_{RMSC}\)) were also calculated and derived from the radial profile of summed slices transformed into polar coordinates. (3) Results: Artifacts were considered sufficiently severe to warrant camera re-tuning in 10 rod sections, 17 sphere sections, and 16 uniform sections. In the uniform sections, there was \"good agreement\" for inter-observer and intra-rater assessments (\(\kappa\) = 0.66, Fisher exact \(p\) < 0.0001 and \(\kappa\) = 0.61, and Fisher exact \(p\) = 0.001, respectively). The two radial features agreed significantly (\(p\) < 0.001) with visual severity judgment of ring artifacts in uniform sections and were selected as informative about the presence of ring artifacts by LASSO approach. The increased magnitude of \(R_{Contrast}\) and \(R_{RMSC}\) correlated significantly with increasingly severe artifact scores (\(p\) = 0.65-0.66, \(p\) < 0.0001). (4) Conclusions: There was good agreement between the physicists with respect to the presence of circular ring artifacts in uniform sections of SPECT quality assurance scans, with the artifacts accurately detected by radial contrast and noise-to-signal ratio measurements.  
Keywords:image artifacts; radiomics; SPECT system; gamma camera; quality assurance; image texture analysis; inter-observer agreement; phantom +
Footnote †: journal: Applied Sciences
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 3 Smart Grid Cyber-Physical Attacks
### Advanced Cyber-Physical Attacks  
Random attacks are a kind of attack that especially aims at the sensor readings rather than bypassing the detection system [26]. The random attack vector can be generated at any time by the attacker. According to the attacking period, an attack can be presented as either a short-term or long-term random attack.  
[PERSON] et al. [27] mentioned a unique cyber-physical attack called an Aurora-like attack, which refers to attacks specifically aiming at the breaker near a power generator. The attack leads to extreme torque by opening and closing the breaker rapidly, which may cause physical damage to the critical assets in smart grid power generators according to the Idaho National Lab [28]. The purpose of an Aurora-like attack is to disconnect as many generators as possible from the smart grid and then reconnect them to the grid out of synchronism. In [29], the author simulated the Aurora-like attack under the IEEE 9-buses 3-generators testbed.  
Adversarial Machine Learning (AML) attacks have also been introduced in many works to exploit the vulnerabilities in the pre-trained data-driven IDS model. AML attacks can manipulate the measurement data like FDI attacks but aim at bypassing ML/DL-based IDSs [30]. In [30], [PERSON] demonstrated the adversarial samples designed for the Random Forest and J48 Decision Tree model. In [31], the author proposed an AML attack method against the RNN model that works under the black box scenario in the power system.