esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Fine-Grained Pavement Distress Detection Based on Deep-Object Detection Networks
#### 3.2.3 YOLOv5 Detector  
You only look once (YOLO) [60] is a one-stage object detection system proposed by [PERSON] et al. to directly predict bounding boxes and the confidence and class probabilities of object from input images, and the quantity of bounding boxes per image predicted is much less than Faster R-CNN, achieving end-to-end real-time detection. YOLOv5 is an enhanced model built on the success of previous versions of YOLO [61], and YOLOv5 has an improvement over Faster R-CNN [62], YOLOv3, and YOLOv4 in terms of accuracy, speed, and simplicity [63; 64]. The fundamental principle of YOLOv5 is based on YOLOv4. The biggest feature of YOLOv5 is the use of Focus and a CSP (cross-stage partial connections) [65] backbone network architecture. The focus layer allows for better feature representation while reducing the computation and memory overheads of traditional down sampling methods and achieves better accuracy and faster inference times. The YOLOv5 Focus layer replaces the first three layers of YOLOv3 [66] with a single layer. The CSP layer extends to shallow information in the focus layer to better integrate information across the network's feature maps. Meanwhile, the feature extraction module is iterated to extract more comprehensive and detailed information [67]. YOLOv5 not only uses CSPNet for the backbone but also uses the same for the neck to fuse different levels of the feature maps compared with YOLOv4. In addition, the feature pyramid part uses a path aggregation network (PANNet) [68] for parameter aggregation with the FPN (Feature Pyramid Network) [69]. Furthermore, the bag of freebies (BoF) of YOLOv5 used for training are Mosaic data enhancement, adaptive anchor box calculation and adaptive image scaling. YOLOv5 has four different versions, namely YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, which differ in their memory storage size. However, the underlying principle is the same for all versions, with YOLOv5x having the largest storage size and YOLOv5s having the smallest storage size. We used the most basic YOLOv5s in this experiment. Its structure is shown in Figure 7.
## 7 Conclusions and Future Work
Thirdly, reliance on the system-defined definition of erroneous traces may not always align directly with the underlying issues that rule induction systems aim to address. This mismatch could hinder the effectiveness of the rule induction process by focusing on inaccurately identified erroneous traces. Fourthly, the current sampling strategy is primarily based on trace types and durations, overlooking potentially crucial properties for diverse applications. Lastly, the approach's scalability isconstrained by its lack of full streaming capability, which could pose challenges in efficiently handling large volumes of trace data. In future work, we plan to explore the scalability of our strategy to handle larger datasets or more complex applications. We must investigate how our strategy performs as the volume of data increases, and consider further adjustments to maintain efficiency. Our sampling strategy is based on the statistical properties of application traces. We must delve deeper into the process of refining this by exploring different ML solutions to see how they influence the efficiency of the process. This can involve experimenting with various algorithms and parameters to optimize the sampling approach. It is important to develop specific evaluation metrics to quantify the impact of the sampling strategy on rule induction methods. These could include accuracy, efficiency, the interpretability of the rules generated, or any other relevant performance indicators. Based on those indicators, we should conduct comparative studies to compare the performance of our sampling strategy with existing sampling methods or other rule induction techniques. This can highlight the peculiarities of our approach. It is also important to examine related research areas, such as predictive modeling, anomaly detection, or decision support systems, to identify the impact of the sampling strategies. This will help to broaden the scope of our research and uncover new avenues for exploration.
# Loss Research and Thermal Analysis of BLDC Hollow-Cup Motor Under Reactor Suppression
## 3 Improvement Design Study
### Core Loss Analysis Considering Inductance Value  
The armature magneto-kinetic potential is generated by the current harmonics, which in turn generate a magnetically dense harmonic in the core, leading to an increase in core loss. Analyzed through the above equations, the armature magneto-kinetic harmonic amplitude is inversely proportional to the armature inductance \(L_{a}\) and positively proportional to its resulting flux density amplitude \(B\). The core loss of the BLDC hollow-cup motor is calculated based on the Bertotti core loss model [20], expressed as follows:  
\[P_{\text{Fe}}=K_{\text{h}}fB^{2}+K_{\text{c}}f^{2}B^{2}+K_{\text{e}}f^{1.5}B^ {1.5}, \tag{9}\]  
where \(K_{\text{h}}\) is the hysteresis loss factor, \(K_{\text{c}}\) is the eddy current loss factor, \(K_{\text{e}}\) is the additional loss factor, \(f\) is the magnetic field frequency, and \(B\) is the flux density amplitude.  
To analyze the effect of reactor inductance on the core loss, the BLDC hollow-cup motor core loss is calculated for each operating condition by increasing the reactor inductance value by 5 uH in the range of 0 uH to 25 uH. The variation of core loss with the value of the inductance of the reactor is presented in Figure 3.  
The analysis in Figure 3 shows that the core loss decreases significantly as the inductance value increases. However, as the inductance value of the reactor gradually increases, the decreasing trend of the core loss slows down. By the time the reactor inductance reaches 15 uH, the loss variation is stabilized. The variation of core loss for reactor inductance values of 0 uH and 15 uH is presented in Figure 4.
# Influence of Altitude on Phytochemical Composition of Hemp Inflorescence: A Metabolomic Approach
###### Abstract  
The phytochemical profiling of hemp infloreserences of clonal plants growing in different conditions related to altitude was investigated. Four strains of industrial hemp (_Cannabis sativa_ L., family Cannabaceae) of [PERSON] variety were selected and cloned to provide genetically uniform material for analyses of secondary metabolites (terpenes, cannabinoids, and flavonoids) at two different elevations: mountain (Alagna Valsesia 1200 m ASL) and plains (Vercelli Province 130 m ASL). Environmental conditions influenced by elevation have proven to be important factors inducing variations in hemp infloreserences' secondary metabolite composition. In fact, all plants grown at altitude exhibited a higher total amount of terpenes when compared with plains counterparts, with \(\beta\)-Myrcene, _trans_-Caryophyllene and \(\alpha\)-Humuliene as the main contributors. A metabolomic, un-targeted approach performed by HPLC-Q-Exactive-Orbitrap(r)-MS platform with subsequent data processing performed by Compound Discoverer(tm) software, was crucial for the appropriate recognition of many metabolites, clearly distinguishing mountain from plains specimens. Cannabidiolic acid CBDA was the most abundant phytocannabinoid, with significantly higher concentrations in the mountain samples. The metabolic pathway of CBGA (considered as the progenitor/precursor of all cannabinoids) was also activated towards the production of CBCA, which occurs in considerably 3 times higher quantities than in the clones grown at high altitude. Isoprenoid flavones (Cannaflavins A, B, and C) were correspondingly upregulated in mountain samples, while apigenin turned out to be more abundant in plains samples. The possibility to use hemp infloreserences in pharmaceutical/nutraceutical applications opens new challenges to understand how hemp crops respond in terms of secondary metabolite production in various environments. In this regard, our results with the applied analytical strategy may constitute an effective way of phytochemical profiling hemp infloreserences.  
hemp; cannabinoids; terpenes; altitude; high-resolution mass spectrometry +
Footnote †: journal: molecules
# Experimental Research on Bond Behavior between GFR Bars and Stirrups-Confined Concrete
## 1 Introduction
[34] showed that configuring stirrups in concrete is able to decrease the amount of required development length in order to obtain the necessary bond strength between the steel bars and concrete. In effect, the ACI 408R-03 [35] standard points out that stirrups are required to be configured in concrete in beam or pullout tests, and it provides detailed regulations for stirrup configuration. However, in the vast majority of research on the bond behavior of GFRP bars, there is little mention given to the influence of the confined effect of stirrups on the bond behavior of the GFRP bars. [PERSON] has studied the influence of different confining pressures on the bond behavior of GFRP bars through devising a confining torus on the concrete surface of the specimens and imposing load onto the torus [18]. This study could not reflect the influence of stirrups on the bond properties of GFRP bars, because the confined effect of confining torus on concrete is obviously different from that of stirrups. [PERSON] and [PERSON] [36] have pointed out that, unlike steel bars, there is no relative higher rib area on the surface of an FRP bar and, therefore, the presence of confinement that is provided by stirrups may not demonstrate an apparent effect upon the bond behavior of an FRP bar. Yet, this conclusion relies on very few test samples; certain research that was conducted on the GFRP-RC structure indicates that the various configuration of stirrups determines the failure modes and usability of a GFRP-RC structure to a great extent [37; 38; 39]. [PERSON] et al. [40] have explicated the methodology of FRP bar pullout testing, requiring stirrup configuration in concrete cubes. However, they have not delineated the relative conclusions of such tests. The Canadian design standard (CAN/CSA 8086-02 [29], CSA S6-06 [30]) and Japanese design standard (JSCE) [31] established an explicit, predictor formula for the determination of the bond strength of FRP bars. This considers the confined effect of stirrups with only one adjustment coefficient in the formulas. The formula is weakly targeted and it lacks verification. Therefore, it is indispensable that the influence of the confined effect of stirrups on the bond behavior between GFRP bars and concrete is studied. In this paper, 100 pullout tests for GFRP bars and 20 pullout tests for steel bars have been carried out according to CSA S806-02 [29] and ACI 440.3R-04 [41] standards. These tests investigated the effects of various parameters on bond behavior (e.g., bond strength, slip capacity, and failure mode) of GFRP bars that are embedded in concrete. Furthermore, the analytical model was also used in order to predict the \(\tau\)-\(s\) relationship of GFRP bars in stirrups-confined concrete, and the appropriate parameters were derived from the present experimental data.
## 2 Overview
### Blockchain
#### 2.2.2 Consensus Algorithms  
Consensus algorithms are essential to specify a set of rules and perform procedures when there is no mutual trust between network participants. By their very nature, they incentivize participating nodes to be trustworthy and produce or add new blocks to the blockchain. The use of consensus algorithm started and was utilized in cryptocurrency-based systems. Then, it was further extended to incorporate various applications, since each domain has its own set of requirements. Consensus algorithms represent the key function, demonstrating the methodology required to achieve absolute agreement between participants when verifying a new block. There has been increasing interest in existing consensus and replication processes, which can be used in blockchain systems [58].  
Currently, consensus algorithms are employed in a variety of applications, such as banking and finance, supply chain management, healthcare, real estate, media, record management, and cybersecurity. Examples of consensus algorithms include: Proof of Work (PoW) [59], Proof of Stake (PoS) [60], Delegated Proof of Stake (DPoS) [61; 62], Transactions as Proof of Stake (TaPoS) [60], Proof of Activity [63], Proof of Capacity, Byzantine Fault Tolerance (BFT), Replication [64], Practical Byzantine Fault Tolerance (PBFT) [65; 66], Delegated BFT (DBFT), BFTRaft [67], Proof of Authority (PoA), Proof-of-Stake-Velocity (PoSV) [68], Proof of Burn [69], Proof-of-Personhood (PoP) [70], Proof of Bandwidth (PoB) [71], Proof of Elapsed Time (PoET) [72], Stellar Consensus Protocol (SCP) [73], Bitcoin-NG [74], Sieve [75], Ripple [76], and Tendermint [77]. Further details on different consensus algorithms, characteristics, advantages, and disadvantages can be found in [78; 79; 80].
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 2 Related Works  
ML has also been used in NOMA communication in the existing literature. The authors of [12] proposed a pilot-assisted learnable SIC model for enhancing bit-error rate performance. Single-input multiple outputs are considered to train the deep-learning network to learn SIC detection parameters. In [13], the authors proposed a DNN based receiver which solves CE error, time delay, and flexible detection of different UE data. The proposed DNN network consists of fully connected layers, a sigmoid function, and a Relu activation function. A CNN-based SIC scheme was proposed in [14]. The results showed that sum rate loss caused by imperfect SIC can be mitigated by the proposed CNN model. The authors of [15] proposed a deep-learning approach for analyzing CSI and detecting the original transmit sequence. The proposed model was built using seven layers of one input layer, one output layer, and five hidden layers. The complex received signal is used as input data and it is mapped to an output group of four alternatives. The study [16] proposed a deep-learning-based receiver for uplink multiple input single output using NOMA. In [17], the authors proposed deep-learning-based method for signal detection and power allocation in NOMA-based futuristic cognitive radio networks. The proposed system was optimized and determined the desired solution in one step without CE via the DNN model. For detection of the modulation order of interference signal in the NOMA system, a ML-based blind detection method was proposed in [18]. In [19], the authorsproposed a deep-learning-based UE selection method for a cooperative NOMA system with imperfect CSI. To maximize the sum rate and efficiency of energy, a deep-learning-based multiple input multiple output (MIMO)-NOMA system was proposed in [20]. The proposed model was built with effective communication DNN, which consisted of several convolutional layers and multiple hidden layers. In [21], the authors proposed a DNN based MIMO-NOMA which optimized both precoding and SIC decoding in the sense of minimizing the entire mean square loss of the UEs signal. The SIC method has limitations in the complex multipath channel environment, as well as imperfect interference cancelation and propagation errors. To overcome these problems, a DNN which has a Bi-LSTM for joint CE and signal detection of multiuser NOMA-OFDM is proposed.
## 2 Materials and Methods
### Low-Complexity Correlation Approach
Instead of conducting intensive correlation calculations with signal replicas generated by the candidate's PVT and the received signal, the correlation process is replaced by the autocorrelation function by comparing the time delay for each candidate with that of the received signal.
## 4 Implementation of Security Mechanisms into SDNFV-Based Networks
### SDN Network  
As highlighted in the preceding section, the SDN network incorporates two distinct switch types. OVS switches, functioning as nonintelligent switches, primarily operate by following packet switching rules established by the controller. They are also integrated into the Docker cluster to facilitate the deployment of Dockers that implement the NFV architecture.  
Figure 3: Proposed SDNFV network.  
The CPqD switches, designed with BEBA support, embody the proposal known as OpenState [31; 32]. OpenState extends the core functionalities of OpenFlow, enabling the application of diverse match-action rules based on various states detected within the SDN flow tables of the switch.  
This enhanced functionality empowers the switch to respond to packet-level events by analyzing the flows being switched. If the analysis aligns with the predefined rules in the flow tables, the switch can act following those rules.  
OpenState operates as an extension of OpenFlow, integrating a traditional match/action flow table with an additional state table containing flow states. BEBA-enabled switches initially match packets with states from the state table before executing the flow table to determine the subsequent actions to take. Figure 4 illustrates the architecture of OpenState.
# Increasing the Insertion Loss of Sonic Crystal Noise Barriers with Helmholtz Resonators
###### Abstract  
Helmholtz resonators (HRs) have the advantage of extending and improving their insulating capacity when used as scatterers in noise barriers made of periodic media, such as sonic crystals (SCs). However, the interaction between multiple Bragg scattering and local resonance phenomena can increase or decrease the insulation of the barrier depending on its design. In the present work, we numerically investigate the factors that determine how such interferences occur and the specific conditions to increase the insertion loss of sonic crystal noise barriers (SCNBs) made of cylindrical scatterers with HRs. Two factors are crucial for the variation of the isolation of the barrier in the Bragg-bandgap (Bragg-BG): the orientation of the resonator mouth with respect to the incident wave, and the resonance frequency of the resonator with respect to the central frequency of the Bragg-BG. Based on this phenomenon, we propose a sonic crystal noise barrier consisting of scatterers with two Helmholtz resonators. The insertion loss of the structure is determined numerically and shows an increase of 20 dB at the BG compared to a conventional barrier with cylindrical scatterers.  
sonic crystals; Helmholtz resonator; noise barrier; numerical methods; FDTD +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.4 Improved Location Visualization  
Appropriate visualization can support the acquisition of spatial knowledge [68]. Previous research has highlighted the benefits of using 2D, 3D and augmented reality (AR) technology for visualization in indoor and outdoor navigation apps. The results from the research by [PERSON] et al. [10] indicated the usefulness of visualizations (AR viewer and intuitive map-based view) in locating university facilities and improving spatial orientation. They developed and evaluated two mobile-based indoor-outdoor navigation applications: SmartUJI APP, which uses an intuitive map-based view to visualize information, and SmartUJI AR, which uses an AR interface with interactive icons placed over campus facilities allowing users to interact with the campus and visualize the location of the nearby facilities and points of interest with an AR viewer through the mobile device camera. [PERSON] et al. [84] used 3D-Enhanced Facility Management System to assist visually impaired users in indoor navigation and achieved satisfactory preliminary results regarding user experience.  
In the present study, participants also suggested improving the visualization for indoor-outdoor campus navigation systems. Some of the comments from participants interviews include: \"Visualising buildings by their photos\", \"Street view/corridor view inside the building\", \"Show picture of target room (sometimes no room number is given at the door, or it is hard to find)\", \"Different angles to see where I am waiting\", \"It should be more visualised with the 3D view and updated always\".
# Multi-Granularity User Anomalous Behavior Detection
## 4 Methodology
### Model Overview  
Our proposed model MG-UABD for insider threat detection is illustrated in Figure 2. MG-UABD consists of three modules: data fusion and partition, coarse-grained anomaly detection, and fine-grained anomaly detection.  
Firstly, the data fusion and partition module integrates different types of user behavior data, including network traffic, login and logout, file operation, email transmission, and the use of USB devices. This module firstly designs features for the fused data and construct representation features for them. Then, this module split the behavior data after feature representation into training and testing sets based on the number of weeks, and performed coarse-grained anomaly detection, i.e., anomalous behavior. In addition, the behavior data of all users need to be divided according to the user to which the behavior belongs. Each user's behavior data need to be stored separately to support the fast reading of the fine-grained anomaly detection module.  
Secondly, in the coarse-grained anomaly detection module, a random forest model is constructed based on the historical behavior data of all users. This module can detect anomalies in user behavior, and output classification labels for user behavior and its corresponding users. This module extracts all the behavior data of anomalous users and inputs them into the fine-grained anomaly detection module.  
Finally, the fine-grained anomaly detection module extracts all the corresponding behavior data for each anomalous user, which are output by the coarse-grained anomaly detection module. This module establishes individual random forest classification model for each anomalous user. Each single model learns the historical behavior data of the anomalous user, and output the label of their behaviors. In the process of learning behavior for specific users, to address the problem of sparse anomalous behavior samples, we adopt random oversampling algorithms or SMOTE algorithms to oversample the training set for each anomalous user.  
The reason for using the random forest algorithm is that in the random forest model, each decision tree is obtained based on random sampling, which can reduce the risk of oversampling and improve the generalization ability of the model. In addition, the random forest model can evaluate the importance of features and automatically identify features that have a significant impact on anomalous user behavior representation. After trying various machine learning algorithms, we found that random forest outperforms  
Figure 2: Overview of MG-UABD. In the data fusion and partition module, different user colors denote the user identities. In following modules, the anomalous users are marked in red, and the normal users are marked in green. The node color in each tree of the random forest model represent the decision process for the user.  
other classifications and achieves the best performance in user anomaly detection tasks. Therefore, in this study, random forest was adopted as the classification algorithm for anomaly detection.