esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The abscisic acid (ABA) increase and auxin decline are both indicators of ripening initiation in grape berry, and norisoprenoid accumulation also starts at around the onset of ripening. However, the relationship between ABA, auxin, and norisoprenoids remains largely unknown, especially at the transcriptome level. To investigate the transcriptional and posttranscriptional regulation of the ABA and synthetic auxin 1-naphthaleneacetic acid (NAA) on norisoprenoid production, we performed time-series GC-MS and RNA-seq analyses on _Vitis vitiifera_ L. cv. Cabernet Sauvignon grape berries from pre-version to ripening. Higher levels of free norisoprenoids were found in ABA-treated mature berries in two consecutive seasons, and both free and total norisoprenoids were significantly increased by NAA in one season. The expression pattern of known norisoprenoid-associated genes in all samples and the up-regulation of specific alternative splicing isoforms of _VviDXS_ and _VviCRTISO_ in NAA-treated berries were predicted to contribute to the norisoprenoid accumulation in ABA and NAA-treated berries. Combined weighted gene co-expression network analysis (WGCNA) and DNA affinity purification sequencing (DAP-seq) analysis suggested that VviGATA26, and the previously identified switch genes of myb RADIALIS (VIT_207s0005g02730) and MAD-box (VIT_213s0158g00100) could be potential regulators of norisoprenoid accumulation. The positive effects of ABA on free norisoprenoids and NAA on total norisoprenoid accumulation were revealed in the commercially ripening berries. Since the endogenous ABA and auxin are sensitive to environmental factors, this finding provides new insights to develop viticultural practices for managing norisoprenoids in vineards in response to changing climates.  
Keywords:norisoprenoid; grape berry ripening; abscisic acid; naphthaleneacetic acid; gene expression; DAP-seq; alternative splicing +
Footnote †: journal: Molecular Sciences
## 7 Underwater Trials
In this situation, the signals suffer from more distortions than simulations, such as inter-carrier interference (ICI), which should be reduced by other algorithms. To show the generation of the NN-based detector, the networks were tested with the data transmitted in Xiamen Bay in June 2018. The signals were transmitted at 500 m with level 3 sea conditions. A pair of NI-6341 data acquisition cards were connected with 30 kHz transducers as the transmitter and the receiver, shown in Figure 12b. The data were modulated by QPSK and the numbers of subcarriers were set as 128 and 512. A comb-type pilot was applied for the LS channel estimation. The NN-based detector was applied to replace the original MMSE equalizer and Euclidean distance detector. The SNR at the receiver was estimated as 32 dB. Table 9 shows the BERs of different detectors. Both detectors could detect the symbols well and the NN-based detector outperformed the original detector with both 128 and 512 subcarriers.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 3 Results  
### Geogrid Strain  
Geogrid plays a crucial role in reinforcing pavement layers, which is often subjected to various stresses and strains under the weight of wheel loads. Figure 5a,b illustrates the geogrid strains along the wheel path, corresponding to 1% and 8% CBR values and several base course (BC) thicknesses. The wheel path, representing the total width of the pavement model, is normalized symmetrically from the centre. The figure reveals non-uniform  
Figure 4: Analysis approach adopted in this study.  
Figure 3: Comparison of the current FE model to another study [19].  
geogrid strain, peaking at the wheel locations. Near the wheel positions or the loading area, the geogrid experiences tensile forces as it expands, gradually decreasing as the normalized path increases, resulting in tensile strain near the loading area and compressive strain at the central portion. Notably, the tensile strain is significantly higher than the compressive strain. Moreover, increasing base thickness leads to a considerable reduction in geogrid strain due to enhanced grain-to-grain load dispersion in flexible pavement. This results in lower load and strain on the geogrid at higher base thicknesses. Analysis of subgrade strength, indicated by CBR values, reveals that geogrid strain is prominent in cases of lower subgrade CBR values. For instance, changing the CBR from 1% to 3% results in approximately a 21% reduction in geogrid strain for a 150 mm base thickness, whereas this reduction increases to about 43-45% when the CBR changes from 1% to 5% or 8%. This underscores that a strong soil subgrade minimally affects geogrid strain due to its stiffness and ability to withstand high loads, whereas a weak subgrade necessitates geogrid support. The strain and deflections observed in the geogrid material, extracted from the FE model, are also depicted in Figure 6.  
Figure 5: (**a,b**) Strain developed in the geogrid.
## 2 Literature Review on LMI Applications in Rehabilitation Robotics
### Applications in Rehabilitation Robotics  
LMI techniques have become a pivotal tool in rehabilitation robotics, addressing challenges associated with human-robot interactions, nonlinear dynamics, and adaptive control. Their application in lower-limb rehabilitation systems has demonstrated robustness and adaptability in diverse tasks. For instance, ref. [37] applied LMI-based control to lower-limb models for robust rehabilitation device control, while [38] introduced an LMI-based computed-torque controller to estimate human torque contributions and personalize tasks, validated using the OpenSim platform. In knee-joint control, ref. [39] developed a Takagi-Sugeno (T-S) fuzzy discrete-time regulator using LMIs, effectively adapting to varied patient conditions and enhancing rehabilitation for paraplegic and healthy individuals.  
Further advancements include integrating fuzzy logic with LMIs to manage complex system dynamics in rehabilitation robotics. For example, ref. [40] proposed a composite fuzzy control framework for a two-joint leg device, using LMIs to ensure stability and optimize control gains. Tracking controllers for exoskeletons, such as those designed using a T-S fuzzy model in [41], have also benefited from LMIs, enabling robust performance and improved human-robot synchronization. Additionally, ref. [42] demonstrated the versatility of LMI-based controllers in mitigating external disturbances and enhancing closed-loop performance through nonlinear control strategies. Collectively, these studies underscore the growing adoption of LMI methods in advancing rehabilitation robotics.
# How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?
## 2 Radiomics
### Differentiation Hemorrhage from Idonimated Contrast Extravasation after Thrombectomy  
Chen X et al. in their very interesting study of 101 patients, tried to differentiate intracranial contrast extravasation from intraparenchymal hemorrhage (IPH) after mechanical thrombectomy (MT). They used RFs from non-enhanced CT (NECT) images after MT. They used manual segmentation and extracted 1316 RFs from 166 intraparenchymal areas of hypertattenuation. They obtained very interesting results, with an AUC between 0.848 and 0.826 (training and validation cohorts). The accuracy (ACC), sensitivity (S), specificity (Spec), negative predictive value (NPV) and positive predictive value (PPV) were 0.776, 0.767, 0.789, 0.682 and 0.852, respectively. They concluded that radiomics can effectively differentiate IPH from intracranial contrast extravasation after MT [62]. Yuan Ma et al. also used a CT-based radiomics model to predict bleeding from iodinated extravasation after thrombectomy in AIS patients. They obtained an AUC of 0.972 and 0.926 in the training and validation cohorts, respectively [63]. Differentiating between hemorrhage and contrast extravasation is easy if dual-energy CT is available. However, this technology may not always be available when we need it. In this sense, radiomics could act as an aid to make this distinction in situations where dual-energy CT is not available.
## 2 System Architecture
### Threat Model and Security Considerations  
In our system architecture, we consider two types of attack. One is an attack by a compromised SM. In other words, we assume that SM is a honest-but-curious entity that will follow the protocol but may try to extract vehicle's driving trajectory data stored on the cloud for the purpose of collecting and profiling driver's preferences. The other is an attack by an outside attacker which has no access privilege to the cloud but try to learn vehicle's driving locations over VANET communications. However, when we assume that SM and RSUs are interconnected through a secure channel, it is hard for an outsider attacker to control the communications between SM and an RSU. In addition, we assume that all RSUs are trusted under the control of the TA so that SM is not allowed to collude with any RSU. Thus, it is assumed that TA can inspect all RSUs subordinated to it and the collusion or compromise of an RSU is detectable by the TA.  
Under the threat model and assumptions, we consider the following security requirements to design a secure vehicle trajectory data sharing system and message delivery protocol to guarantee the location privacy of vehicles in VANETs.  
* _Authorized access to trajectory data_: Access to driving trajectory data of a vehicle on the cloud must be restricted to only the RSUs authorized by the owner vehicle of the trajectory data. Even though vehicle's trajectory data are managed under the control of SM, driving trajectory data must be hidden from SM as well as unauthorized entities.
* _Self-control_: When a vehicle uploads its trajectory data to SM, it should be possible for the vehicle to control that which RSUs can or cannot access its trajectory data by the vehicle itself.
* _Authenticated communications_: For secure message delivery on VANETs, vehicles and RSUs involved in message delivery must be authenticated to each others. In message forwarding, a relaying RSU must authenticate a vehicle to check if the vehicle is a valid destination specified in the message. Besides, a vehicle must be convinced that the received message originated from a valid source claimed in the message.
* _Avoiding location tracking_: While a vehicle connects to RSUs on its driving routes to receive messages, driving trajectory of the vehicle must not be tracked by an outside attacker on VANETs. That is, it must be hard for an outside attacker to learn that a vehicle has moved from and to which of RSUs by overhearing vehicle-to-RSU communications.  
Figure 2: System Architecture.
## 3 Proposed Method
### Estimation of Visibility Coefficient  
To estimate the visibility coefficient \(\alpha\) in our model, we employ the probability density function (PDF) of the high-frequency band. Figure 6 shows the typical PDF of a high-frequency band of an image. In a general image unaffected by the scattering medium, the PDF of its high-frequency band exhibits a broad Gaussian shape, as shown in Figure 6b. However, in an image affected by the scattering medium, the presence of strong low-frequency components results in a narrower shape for the PDF of the high-frequency band. This is illustrated in Figure 6d. These characteristics can be utilized to approximate the PDF of the image affected by the scattering medium to that of the normal image. Thus, the coefficient \(\alpha\) can be determined by a range ratio of two PDFs, which is written as  
\[\alpha=\frac{R_{1}}{R_{2}}=\frac{
# Pollen-Food Allergy Syndrome: Allergens, Clinical Insights, Diagnostic and Therapeutic Challenges
###### Abstract  
Pollen-food allergy syndrome (PFAS), also known as oral allergy syndrome, is a common condition affecting individuals sensitized to pollens such as birch, ragweet, and grass. This syndrome arises from immunological cross-reactivity between pollen allergens and structurally similar proteins found in various fruits, vegetables, and nuts. Although typically presenting with mild oral and pharyngeal symptoms, PFAS can occasionally result in severe allergic reactions, underscoring its clinical significance. This review explores the pathophysiology of PFAS, highlighting the molecular mechanisms underlying cross-reactivity and examining the main protein families involved, including those contributing to variations in symptom severity. Current diagnostic approaches, including skin prick testing, specific immunoglobulin E measurements, and component-resolved diagnostics, are discussed. Emerging diagnostic tools and biomarkers with potential to enhance accuracy are also examined. Therapeutic strategies for PFAS primarily focus on symptom management and avoidance of trigger foods. However, novel approaches such as allergen immunotherapy and biologics targeting key immune pathways are gaining traction as potential interventions for more severe or refractory cases. By addressing the diagnostic and therapeutic challenges of PFAS, this paper aims to provide clinicians and researchers with a comprehensive understanding of this condition, fostering improved patient care and the development of innovative treatment strategies.  
**Keywords: pollen-food allergy syndrome; oral allergy syndrome; cross-reactivity; allergen immunotherapy; food allergens; diagnostic strategies; component-resolved diagnostics; biologics in allergy; food-pollen syndrome; allergy management**
# A Survey on Secure WiFi Sensing Technology: Attacks and Defenses
## 3 Attacks Against WiFi Sensing
### Discussion
In this type of attack, the attacker actively manipulates the wireless environment to intercept or decode sensitive information rather than passively monitoring the channel. In the context of WiFi sensing, proactive eavesdropping could be adapted to target the sensing process itself. Forinstance, an attacker might transmit interfering signals to disrupt the sensing system's ability to accurately detect motion or environmental changes. Alternatively, the attacker could manipulate the wireless channel to extract sensitive information about the sensing target, such as identifying their presence, activities, or even physiological characteristics (e.g., breathing or heart rate).
# Distributed Combinatorial Maps for Parallel Mesh Processing
## 5 Experiments
### Comparison in Memory Space  
In this third experiment, we studied the number of hexahedra and the memory space of the 3-dmap depending on the number of processes. In this experiment, we fixed a memory limit to 3 gigabytes per process (the maximum value allowed by the IN2P3 computing center), and ran our software by increasing the number of processes from one to 32.  
Results can be seen in Figure 12. As we can observe in Figure 12a, the number of hexahedra obtained in the final 3-dmap increases with the number of processes. Indeed, the limit of 3 gigabytes fixed by the IN2P3 is per process: using a distributed combinatorial map in \(k\) processes allows us to use \(3\times k\) gigabytes of memory, and thus allows us to store much more hexahedra.  
On average for the six meshes, we could store 599,720 hexahedra with one process, and 18,925,061 with 32 processes: we were able to store about 31.5 more hexahedra. This shows that the memory space overhead due to additional storage for the distributed combinatorial map is negligible comparing to the memory space for the different 3-maps.  
Comparing the memory space of the final 3-dmap, given in Figure 12b, confirms this result: with 32 processes, we are able to use \(32\times 3=96\) gigabytes of memory.  
The overall computation times of our method were respectively 115, 145, 158, 176, 291 and 292 s (average for the six meshes). The program run with 32 processes was about 2.5 slower than the program run with one process; however, this is a very good improvement knowing that we computed about 31.5 more hexahedra. This can be verified by comparing the computation time per 100,000 hexahedra. These times were respectively 19, 12, 7, 4, 3 and 2 s, showing a speedup of about 9.  
These results show that our method can be used to generate very big meshes due to our distributed combinatorial maps, and we will probably be able to deal with much bigger meshes by running more processes.  
Figure 12: Comparison of maximal number of hexahedra and memory space occupied when using a 3-dmap for different number of processes. (**a**) Final number of hexahedra. (**b**) Maximal memory space occupied (in gigabytes). The six curves are superposed, because all runs occupy the same maximum memory space.  
This experiment shows that the main goal of this paper is clearly reached: with our distributed combinatorial maps, it is now possible to handle big objects that do not fit in the memory of a single computer. We are able to apply an operation on the 3-dmap and obtain the same result as if we had used a classical 3-map on a single computer with a huge memory space.
# Synergy of Blockchain Technology and Data Mining Techniques for Anomaly Detection
## 7 Where Are We Now, and What Follows?
### Challenges  
Research of papers, on the one hand, revealed interesting trends which suggest that most of the solutions are prototypes and proofs-of-concept. Some research papers also proposed several solutions which are basically only ideas without their practical evaluation using proof-of-concept. Therefore, there is still a long road to ensure quick flow or transition from prototypes to real applications.  
On the other hand, research exposed several challenges where much more devotion should be given in the future, especially in the design of datasets, experiments, test cases or scenarios and implementation of algorithms. Researchers should also explore further ways for automatization of some preprocessing steps [148], while expanding and enlarging the datasets [56; 90; 106; 109; 118]. This aspect should also be at the centre of the research, since more complex and expanded datasets should definitely contribute to more accurate anomaly detection and potentially result in faster decisions. Also, the datasets should be kept up to date to include new frauds and types of attack. Some researchers reported that the developed methods also use a lot of computational power [126] or communication cost of propagating a new block to all participants in the network [95]. The need for simulation in real world scenarios was also reported in paper [90], as well as the creation of realistic test cases and experiments [140]. Additionally, enhancing methods with different types of fraud detection is also one important research direction [117]. Finally, using additional optimisation methods, i.e., metaheuristics [121], should also be a fruitful direction in improving the existing algorithms.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 5 Evaluation
### OWL-Based Matcher--DeVISor  
DeVISor [44] is a semantic matcher used in the OWL-based approach that is able to run on the network as a service and infer which devices are capable of satisfying a given request based on knowledge of the devices. Figure 4 presents the architecture of DeVISor. It is implemented as a web service and extended to support SPARQL. The design follows client-server architecture. On the client side, the DeVISor client provides an Application Programming Interface (API) to interact with application requests, and device descriptions needed to be registered. On the server side (shown in the rectangle with dashed line), it controls the process of the device (de)registration and matching device capabilities against the application requests. The support for data storage is provided by Apache Jena Fuseki [45]. However, since the triple store is not equipped with the OWL inference, the DeVISor server uses BaseVISor [46], a forward-chaining inference engine specialized to handle facts in the form of RDF triples with support for OWL 2 RL [47] and XML Schema Datatypes, to make automatic inferences about any changes to the triple store. The following illustrates how DeVISor gets involved in the matching process with the associated implementations.  
The first step taken during the matching process is to register devices. DeVISor supports such a feature by implementing an associate function (_runSparqlUpdateFromString(updateScript:String):void_), a function of the DeVISor client API which takes a SPARQL Update script as an input argument. Whenever there are RDF device descriptions needed to be registered, DeVISor wraps the dataset as a SPARQL Update script and invokes this function. After the script has been executed by the triple store, the dataset is inserted into the triple store as a set of triples. After that, the triple store returns all of the facts (including background knowledge) to the triple store client. BaseVISor loads the facts and executes the inference engine to derive inferred facts. The inferred facts are then inserted into the triple store.  
After the devices have been registered, the next step is to send the application requests to DeVISor for matching. This feature is implemented by _runSparqlQueryFromFile(queryPath:String):String_, a function of the DeVISor client API, which is able to process a request as a SPARQL query script and return the query results in JSON format. When the function is invoked, DeVISor forwards the script to the triple store, and the triple store executes the script with the function and returns the results back. Note that BaseVISor is not involved in this process, since no inference is needed, and all of the inferred facts are already stored in the triple store. Since the results are in JSON format, the last step is to extract the matching devices from the results. The devices are identified by the Internationalized Resource Identifier (IRI) and are extracted by the binding value of the device variable in variable name/value pairs of the results.