esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd
### Design
#### 3.1.1 Optimizations for Koo\(N\) Computation
```
Input: Reliability \(R_{i}\) of each component Input: \(j\)-th combination of \(iooN\) components \(C(N,i,j)\) Function ReliabilityStep(\(N,i,j\)) \(R_{step}=1\); for\(l\in[1,N]\)do if\(l\in C(N,i,j)\)then \(R_{step}=R_{step}\cdot R_{i}\); else \(R_{step}=R_{step}\cdot(1-R_{l})\); return\(R_{step}\); Function ReliabilityRecursive(\(i,j\)) if\(j=0\)then return\(1\); if\(j>i\)then return\(0\); return\((1-R_{i})\cdot ReliabilityRecursive(i-1,j)+R_{i}\cdot ReliabilityRecursive(i-1,j-1)\);
```  
**Algorithm 2**Auxiliary functions for computation of RBD \(KooN\) block with generic components. ```
Input: Reliability \(R_{c}\) of each component Result: Reliability \(R\) of \(KooN\) block begin if\((N-K)\leq(K-1)\)then \(R=0\); for\(i\in[K,N]\)do \(R=R+\binom{N}{i}\cdot R_{c}^{i}\cdot(1-R_{c})^{N-i}\); else \(R=1\); for\(i\in[0,(K-1)]\)do \(R=R-\binom{N}{i}\cdot R_{c}^{i}\cdot(1-R_{c})^{N-i}\);
```  
**Algorithm 3**Computation of RBD \(KooN\) block with identical components.
## 5 Experimental Results
### Comparison with Manual Feature Extraction Methods  
In the study of encrypted traffic feature representation learning, the efficacy of feature extraction significantly impacts the performance of subsequent classification models. To thoroughly assess the effectiveness of the proposed AFF_CGE method, we conducted comparative experiments against several manual feature extraction techniques. These techniques include base features (BS-F), Flowmeter features (FM-F), encrypted content features (EC-F), and all features (ALL-F), which combines all three methods. * Base Features (BS-F): This method selects 17 session-based features. During the IP-level sample aggregation, four statistical treatments--mean, maximum, minimum, and variance--are applied to 8 of these numerical features, resulting in 32 features. Additionally, TF-IDF is used for enumerated feature extraction, capped at a feature dimension of 48. Combining these two treatments yields an 80-dimensional base feature set. * Flowmeter Features (FM-F): This approach utilizes 80-dimensional statistical session features extracted using the ClCFlowmeter tool, an open-source network traffic analysis tool. The ClCFlowmeter efficiently collects a wide range of statistical information from network traffic, including flow duration, packet header and payload information, flow time intervals, flow rates, and window sizes. IP-level sample aggregation is performed similarly to BS-F, resulting in a 320-dimensional feature set. * Encrypted Content Features (EC-F): These features are extracted from SSL/TLS encrypted traffic, capturing security attributes such as connection status, protocol version, encryption suite, certificate chain, and alerts. After processing with the TF-IDF model, a 48-dimensional feature set is produced. * ALL Features (ALL-F): This method combines the base feature (BS-F), flowmeter feature (FM-F), and encrypted content feature (EC-F) into a unified 448-dimensional feature set. To ensure fairness and objectivity in our comparisons, we employ a five-fold cross-validation method, which mitigates random errors through repeated training and testing and calculates the mean value of each metric as the final result. Our experiments encompass various traditional machine learning classifiers, including logistic regression, Naive Bayes, KNN, decision tree, and XGBoost, to comprehensively evaluate the performance of different feature representation methods across classifiers. The comparative experimental results are shown in Table 4. The results reveal significant performance differences among BS-F, FM-F, and EC-F across classifiers, underscoring the crucial impact of feature selection on detection effectiveness and highlighting the necessity of feature learning for encrypted malicious traffic. Limitations of Manual Feature Extraction: As presented in Figure 7, the analysis shows that no single feature set (i.e., BS-F, FM-F, EC-F) consistently outperforms the others across diverse classifiers. The performance of these feature sets varies unpredictably depending on the classifier. Specifically, BS-F emphasizes basic statistical dimens
## 4 Design Methodology of Composite-Anchor Uplift Piles
### Regulations for Crack Control  
Crack control for uplift piles is categorized into three levels: Level 1, Level 2, and Level 3. The calculation for crack control must adhere to the following regulations.  
(1) Level 1 Crack Control:Level 1 crack control requires the absence of any cracks. Additionally, the pile shaft should not be subjected to tensile stress under the specified combination of load effects. The pile shaft must meet the following criteria:  
\[\sigma_{ck}=N_{\text{k}}/A_{0} \tag{12}\]  
where \(\sigma_{ck}\) is vertical tensile stress at pile top under standard load combination (kPa) and \(A_{0}\) is converted sectional area (m\({}^{2}\)), including the concrete section area (excluding the section area of ordinary steel bars) and the section area of all longitudinal ordinary steel bars converted into concrete.  
(2) Level 2 Crack Control:  
Under Level 2 crack control and the standard combination of load effects, the tensile stress experienced by the pile shaft must not exceed the axial tensile strength of the pile concrete. The following criteria should be met:  
\[\sigma_{ck}-\sigma_{\text{pc}}\leq f_{tk} \tag{13}\]  
where \(f_{\text{tk}}\) is standard value of axial tensile strength of pile material.  
(3) Level 3 Crack Control:  
Level 3 crack control design allows for the occurrence of cracks. It is required that, under the prescribed combination of load effects, the maximum crack width of the pile shaft meets the specified criteria:  
\[\omega_{\text{max}}\leq\omega_{\text{lim}} \tag{14}\]  
where \(\omega_{\text{max}}\) is maximum crack width and \(\omega_{\text{lim}}\) is the limitation of crack width corresponding to the crack control level, which shall be selected based on relevant specifications.
# Biotechnological Fungal Platforms for the Production of Biosynthetic Cannabinoids
## 1 Introduction  
_Cannabis sativa_ produces about 100 (alkyl-)phytocannabinoids (meroterpenoids and their analogs derived from cannabis), generically known as cannabinoids [1; 2; 3]. Cannabis is supposedly of Asian origin and is one of humanity's oldest cultivated crops [4; 5; 6]. It is an annual, usually dioecious and sporadically monoecious, wind-pollinated species that is extremely allogamous (cross-fertilized) in nature [2]. The taxonomy of the _Cannabis_ genus is complex due to the existence of cultivated and wild varieties, as well as its economic importance and uses (hemp, recreational or medical). The current botanical nomenclature [7] includes just one species in the genus, _C. sativa_, and two subspecies (subsp. _sativa_ (fibers and oil cultivars) and subsp. _india_ (\"narcotic\" cultivars)) based on the THC (tetrahydrocannabinol) content (hemp varieties should not contain more than 0.3% THC in dried female flowering tops). The domestication status is also used as a characteristic to distinguish the two varieties within each subspecies [4; 8; 9].  
The fiber varieties (\"hemp\": fiber extracted from the stems of _C. sativa_) present a high carbon-sequestering potential due to their rapid growth, and so can be used as building materials or biofuel. However, the traditional use of fiber includes the manufacture of rope, shoes, canvas, paper, clothing, and sails [4; 9; 10]. On the other hand, the cannabinoids present in the sticky resin produced by the female plant, including psychoactive compoundssuch as THC and \((-)\)-trans-\(\Delta^{9}\)-tetrahydrocannabinol (\(\Delta^{9}\)-THC) and non-psychoactive components with potential therapeutic effects (e.g., reducing inflammation, chronic pain, and nausea) such as cannabidiol (CBD) [11; 12; 13], are other relevant products of _C. sativa_.
# Experimental Study of Entrainment and Mixing of Renewable Active Particles in Fluidized Beds
## 4 Conclusions  
In this study, particle entrainment and mixing of industrially relevant fuels such as solid refused fuel/refuse derived fuel (SRF/RDF), bark, sunflower shell, and wheat shell were investigated. A lab-scale fluidized bed experimental facility was used for the measurements. The superficial velocity range was 1-3 m/s in the entrainment measurements, while in the mixing measurements, it was 1.2-2.0 times the minimum fluidization velocity. In the mixing measurements, the mass ratio of the active particles was within the industrial relevance range (1.96-3.2%). The findings obtained from this study can be summarized as follows:  
Figure 10: Comparison among the vertical concentrations of the investigated active particles and the spherical tracer [31] in the bubbling fluidized bed. (\(u_{g}/u_{mf}\) = 1.6 \"present measurements\", \(u_{g}/u_{mf}\) = 1.52 “Wirsum [31]”. The percentage errors of the present concentration results are \(\pm\)12.5% at the bottom, \(\pm\)8.3% in the bed body, and \(\pm\)50% on the top region.)  
Entrainment:  
* The sunflower shell entrained at the highest degree.
* At very low velocity, the entrainment of the wheat shells is the most intensive.
* SRF and bark has similar entrainment behavior, but with a less steep gradient. Mixing:
* SRF has relatively high _mass fraction_ in the bottom and center regions of the fluidized bed at low superficial velocities.
* Unlike all the biomass fuels, SRF tends to shift upwards at elevated velocities.
* The nonspherical active particles have higher concentrations at the bottom region than spherical ones.  
Finally, we can conclude that the sunflower shell and wheat shell are not suitable for fluidized bed combustion because they entrain from the bed rapidly.  
All the authors of the present research contributed equally to conducting and writing this paper. All authors have read and agreed to the published version of the manuscript.  
The research reported in this paper has been supported by the National Research, Development, and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program and the National Research, Development, Innovation Fund of Hungary in the frame of FIEK_16-1-2016-0007 (Higher Education and Industrial Cooperation Center), and the UNKP-19-3-I-BME-270 New National Excellence Program of the Ministry for Innovation and Technology.  
The authors thank [PERSON] for permitting the use of her lab instruments for the present density experiments. We also extend our thanks to her lab member [PERSON] for his support.  
The authors declare no conflict of interest.
## 1 Introduction
Nevertheless, the level of agreement at which height of, extent of, and density of aquatic reed beds can be mapped with LiDAR data remains unknown. The new LiDAR system configured to function in the green wavelength of the electromagnetic spectrum offers new possibilities in the research of aquatic reed beds. Green light, the opposite of infrared, propagates in water by reflecting off the bottom surface, or in medium content materials [28]. In addition, green light not only propagates in water but can also reflect off land surfaces. These characteristics make the Green-LiDAR scanner suitable in mapping bathymetry [29], bottom structures [30; 31], or even for topo-bathymetric applications [32; 33]. Extents are important in the monitoring of aquatic reed beds, in which the mapping of the shoreline and the frontline are crucial. The technological benefits of a Green-LiDAR scanner could contribute to the accurate mapping of reed bed boundaries. The frontline is scanned from reed stems and leaves above the water surface and thanks to green light propagation, the shoreline can be obtained independent of the water surface level during surveying. Aquatic reed is an important indicator for the ecological assessment of freshwater lakes listed by the European Water Framework Directive (EU-WFD). As in other European countries, German federal state environmental agencies have to regularly report on the ecological status of freshwater lakes which involves a substantial monitoring effort. The core objective of the present study was to evaluate the applicability of Green-LiDAR technology in aquatic reed monitoring. We addressed the following research questions by focusing on the accuracy of diagnostic determination:* How accurate is the estimation of aquatic reed extent in LiDAR point clouds? * How accurate is the estimation of aquatic reed density in LiDAR point clouds? * How accurately can aquatic reed bed DSMs be calculated from LiDAR point clouds?
# Sustainable Parking Space Management Using Machine Learning and Swarm Theory--The SPARK System
## 3 Methodology Concept
### Applied Algorithms and Technologies in This Study
Unlike object detection, which merely localizes objects and defines their general position through bounding boxes, segmentation provides detailed information about each pixel belonging to a given object, which is crucial for the precise management of parking spaces. The result of segmentation is a set of polygons describing the location of objects of various classes within the pixel coordinate system, precisely defining the shape and boundaries of each object, allowing for detailed spatial analysis and object classification within the image. For the efficient segmentation of parking images, YOLO models dedicated to segmentation, such as YOLOv5 and its extensions with segmentation capabilities, as well as more advanced versions such as YOLOv9 Seg, were employed. These models combine the advantages of rapid detection, characteristic of the YOLO algorithm family, with the precision of semantic segmentation [77]. YOLOv9 Seg offers even greater segmentation accuracy due to its improved neural network architecture and optimized image processing algorithms, making it ideal for analyzing complex parking scenes where object recognition precision is paramount. Compared to other segmentation methods, such as Mask R-CNNs or DeepLab, YOLO models offer significantly higher computational efficiency with comparable segmentation accuracy, which is essential in the context of parking monitoring systems [78; 79]. The use of YOLOv9 Seg further enhances object recognition accuracy and operational stability, making it one of the most effective solutions available on the market today. Unlike previous studies that focused primarily on object detection through bounding boxes or static image analysis, our approach leverages segmentation to obtain more detailed scene information. Examples of such studies can be found in works that employed BBox (Bounding Box) detection for monitoring [61; 80]. Segmentation provides the necessary data for dynamic and flexible parking space management, representing a significant advancement in the field of intelligent parking systems. The use of YOLO models dedicated to segmentation enables the integration of precise image analysis with efficient resource management, which distinguishes this research from previous studies in this domain.
# Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
## 6 Conclusions  
This paper has developed point-traversability classifiers for the 3D laser scans acquired by the mobile robot Andabata on natural environments. For this purpose, seven potent supervised learning methods from the Python library _Scikit-learn_ have been employed. Apart from being very complete and using free software, this library has also facilitated the work flow to a great extent.  
Furthermore, to perform training and validation, it has been necessary to use binary-tagged 3D point clouds obtained automatically with the robotic simulator Gazebo. This gravity-levelled data resembles closely that obtained by the 3D sensor of Andabata on non-slippery solid terrain. The main difference is that each synthetic point has associated a traversability label, that depends mainly on its intensity measurement.  
For traversability assessment, three simple spatial features have been computed for every Cartesian point. However, feature extraction is a time-demanding process in Python that has been necessary to accelerate via compilation. On the contrary, prediction times, once obtained the features, are generally negligible. All in all, Andabata would be able to determine the traversability of a whole 3D laser scan well before the following 3D scan is available.  
High accuracy indices for unbalanced validation data have been obtained for most estimators, outstanding the Random Forest method for both synthetic and real 3D point clouds. It has also been confirmed that the traversability classifiers, trained only with simulated data, can perform very well with real data.  
Work in progress includes autonomous navigation of Andabata on natural environments based on the continuous traversability classification of successive 3D laser scans. It is also of interest to perform the tuning of hyper-parameters of classifiers to improve traversability estimations.  
Figure 11: A photograph of an underpass (**a**) and a real 3D point cloud from Andabata manually-tagged (**b**) and classified with RF (**c**).  
Author Contributions:[PERSON] and [PERSON] conceived the research. [PERSON], [PERSON] and [PERSON] developed and implemented the software. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the results. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Andalusian project UMA18-FEDERJA-090 and by the Spanish project RTI2018-093421-B-I00.  
The authors declare no conflict of interest.
## 4 Discussion
### Pathologies Caused by Defects of the Vascular Wall
#### 4.4.1 Ehlers-Danlos Syndrome  
Ehlers-Danlos syndrome (EDS) comprises a group of clinically and genetically heterogeneous inherited diseases of the connective tissue [22]. Various defects in collagen synthesis led to greater elasticity within different types of connective tissue (joints, muscles, tendons, blood vessels, skin, and visceral organs). The current Villefranche classification recognizes six main genetic subtypes: classical (type I and II according to the old \"Berlin classification\"), hypermobility (type III), vascular (type IV), kyphoscoliotic (type VI A), arthrochalase (type VII A and B), and dermatosparaxis (type VII C). Most are linked to mutations in one of the genes that code for fibrillar collagen proteins or enzymes involved in the post-translational modification of these proteins). Depending on the specific subtype and individual aspects, the defects are mild to life-threatening. Each subtype has typical symptoms, although individual symptoms and severity depend on each specific patient. The classic type (type I and II) presents with severe hyperextensibility and fragility of the skin, delayed wound healing, easy bruising, and generalized hypermobility of the joints, while the type of hypermobility (Type III) presents with generalized joint hypermobility, recurrent dislocations, and chronic musculoskeletal pain, but less severe skin fragility [22, 23, 24, 25].  
The most severe subtype is the vascular subtype (type IV), characterized by extremely fragile blood vessels and internal organs, such as the gastrointestinal tract, spleen, and liver, which are subject to laceration, but also the fragility of the pregnant uterus [24, 25, 26].  
From an operative, anaesthetic, and surgical standpoint, most of the subtypes share the same problems and complications.  
Therefore, accurate patient classification should be planned before any invasive procedure.  
Regarding anaesthetic procedures, local anaesthetics may have little or no effect in some patients [24, 25], due to tissue scarring that determines a reduced diffusion of local anaesthetics and the failure of nerve conduction block. This includes the use of EMLA cream as a topical anaesthetic. In dental surgery, reports of analgesia failure are mainly related to local anaesthesia and peripheral nerve blocks [25].  
The risk of postoperative bleeding in these patients must be carefully evaluated through a detailed medical history. Tests of coagulation usually give results in the normal range, but the clotting time may be prolonged, particularly in patients who have already had bleeding episodes in their medical history [25].  
Patients with EDS require special care during mobilisation because they are prone to easy skin lesions and hematoma formation. Moreover, during oral surgery manoeuvres, the ease of dislocation of the joints should also be remembered, with specific regard to the temporomandibular joint. Additionally, surgical cutting procedures should be reduced as much as possible, as wound healing and bone remodelling are impaired in severe EDS subtypes [24, 25, 26].
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 2 Related Work
Although the authors stated that a single reflection was considered to simulate its impact on the network performance, there is a lack of results in this regard. The authors did not present the resulting impulse responses in the scenario, despite their modeling effort. Therefore, their results are hardly reproducible. Nonetheless, [PERSON] and [PERSON] demonstrated that considering the optical channel may have a dramatic impact on parameters such as latency, throughput, and reliability. [PERSON] et al. developed a VLC simulation block for NS3 in ref. [22] and performed validation experiments. Their experimental results were accurate in respect to the simulation results, but the only tested scenario was point-to-point. Therefore, the impact of contention-based access using CSMA/CA, which is the most important part of the MAC-layer, was not properly validated. A few years later, [PERSON] et al. developed real implementation of IEEE 802.15.7 and tested a four-node star topology [23]. Nonetheless, due to the link geometry and optical receiver sensitivity,CSMA/CA was not actually working, obtaining a significant number of collisions and hence impairing network performance. Although it falls outside IEEE 802.15.7 standard, Orthogonal Frequency Division Multiplexing (OFDM) has been amply studied and experimentally evaluated in VLC [24], and the currently under-development IEEE 802.15.13 and IEEE 802.11 bb standards are focused on using this modulation scheme due to its flexibility. There has been a significant effort on developing multiple access techniques based on OFDM in VLC [25; 26]. Nonetheless, there is no available literature (up to the knowledge of the authors) involving MAC and PHY simulations considering specifically OFDM.