esa-sceva/satcom-chunk-collection huggingface dataset:
# Computing RF Tree Distance over Succinct Representations
## 4 Implementation
### Parsing Phase  
In the first phase, when parsing the Newick format, the construction of the bit vectors is straightforward since the newick format already has the parentheses in order to represent a balanced parentheses bit vector. The only detail that we need to take into account is that when we encounter a leaf, we need to add two parentheses, the first one open and the second one closed (see Algorithm 1).  
The mapping (CodeMap) exemplified in Equation (1) is also built in the first phase. For this process, we need to have a temporary hash table that associates the labels found in the Newick representation to the indexes of the first tree (HashTable). When a labelled node is found while traversing the first tree, it is added to the HashTable associating the label and the index where it is located in the tree. Then, when a labelled node is found in the second tree, the algorithm looks for that label in the HashTable to find index \(i\) where it is located in the first tree. Then, it stores in position \(i-1\) of the CodeMap the index where that label is located in the second tree.  
To compute the wRF distance, it is also necessary to create two float vectors to save the weights that correspond to a given cluster \(c\) for each tree, i.e., \(w_{\mathsf{T}}(c)\) in Definition 3. These weights are stored in the position that corresponds to the index where they are located in each tree. This process can be seen in Algorithm 1. The first phase takes linear time with respect to the number of nodes if we have a weighed or fully labelled tree, otherwise it takes linear time with respect to labelled nodes.
## 2 Results and Discussion
### Aroma-Active Compounds in Doubanjiang
3-Methylthio) propanal (methional), which imparts cooked potato-like aroma note, is identified as an important aroma-active compound for most paste and sauce products such as soy sauce [14], doubanjiang [3] and gochujang, a Korean fermented red pepper paste [15]. Methional could be formed by Strecker degradation of methionine [21]. \(\beta\)-Damascenone, exhibiting fruity and sweet notes, is an important odorant in modern perfumery and a major contribution to wine aromas [22]. \(\beta\)-Damascenone has a very low odor threshold in water (2 ng/L) (Table 4) and may be formed by acid-catalyzed hydrolysis of certain plant-derived metabolites under heat treatments and low pH during the manufacturing process [23]. Moreover, synergistic phenomena have also been identified, for instance, reducing the orthonasal detection thresholds of linalool and raising the detection threshold of 3-isobutyl-2-methoxyprazine in the presence of \(\beta\)-damascenone [22]. To our knowledge, this is the first time that \(\beta\)-Damascenone is identified in doubanjiang. 3-Isobutyl-2-methoxyprazine has been reported in bell and chili peppers, grapes and wines, and fermented red pepper food such as gochujang, a Korean fermented red pepper paste [15]. The odor threshold of IBMP has an extremely low value of 2 ng/L in water, contributing to a characteristic bell pepper, green pepper and gochujang-like aroma [15]. \(\updownarrow\)-Leucine was speculated to be a precursor in regulating IBMP biosynthesis in grape berry [24]. 3-Isobutyl-2-methoxyprazine was also identified for the first time in doubanjiang to our knowledge. Sotolone, \(\beta\)-damascenone and 3-isobutyl-2-methoxyprazine were identified for the first time in doubanjiang and validated by the mass spectrum, RI (DB-wax and/or DB-5 ms columns), aroma properties and authentic compounds.
## 5 Intelligent Fault Diagnosis of Sensor Faults
### Artificial Intelligence-Based Fault Diagnosis Methods
#### 5.2.3 Machine Learning Methods
[114] performed fault diagnosis for HVAC sensors using a self-associative neural network and compared this technique to principal component analysis. Not only was the diagnostic accuracy of this method significantly enhanced, but it could also diagnose multiple sensors simultaneously. Precision agriculture is characterized by massive normal agricultural data and small sample fault data. The extreme imbalance of these data renders the neural network's fault diagnosis method less stable; The parameters of the model need to be adjusted repeatedly to meet the requirements of the practical application, which requires good model training experience. Although machine learning-based fault diagnosis methods have high time and money costs in network training, they also require relatively large training data samples. However, considering the practical application of sensors in agriculture, the data collected is susceptible to many factors, such as weather, region, season, and changes in the growth stage of animals and plants. Especially for the current large-scale and industrialized farming pattern, it is difficult or even impossible to obtain accurate system models. Small changes in the system can lead to poor failure detection response. Therefore, compared with the fault diagnosis methods based on models and expert systems, the fault diagnosis methodsbased on machine learning have good generalization ability and wider applicability, which are suitable for application in the era of diversified and integrated agriculture 4.0.
## 3 AI-Based Early Fault Detection
### One-Class Support Vector Machine (ML)
The new function of the separating plane \(f(x)=w\cdot\phi(x)-\rho=0\) is obtained, where \(\phi(x)\) is a mapping into a higher-dimensional space, and \(\rho\) is a threshold value. This results in an optimization problem that is expressed as  
\[min_{w,\rho}\frac{1}{2}\|w\|^{2} \tag{7}\]  
with the constraint  
\[(w\cdot\phi(x_{i}))\geq\rho,\ \forall_{i} \tag{8}\]  
By solving this problem, the OSVM can recognize data outside the trained space as an anomaly. This approach yields favorable results for instances where the nature of the error is not known in advance and may affect the data in various ways. For implementation in MATLAB, the seven features from the torque signal at a constant operating point are extracted as described above using the Diagnostics Feature Designer App in MATLAB. The feature values obtained in this way are used to train an SVM model using the fitcsvm function with the input parameter from Table 2. The measured values are standardized before being transferred and assigned the label of their class. It is assumed that the measurement data do not contain any outliers. The result is a trained SVM model that can recognize anomalies in the measurement data. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Table—Sample Data & Extracted feature data \\ Label & “normal” \\ Outlier Fraction & 0.00 \\ Standardize & “true” \\ Kernel Scale & “auto” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input parameter values of the fitcsvm function to train the SVM model. There are several graphical tools for displaying the quality of the trained model, including the frequency distribution as a confusion matrix. Figure 4 shows the confusion matrix of the anomaly detection of the OSVM, which was trained using data from the first test and then applied to run 30, which contains measurement data from the normal state, and run 460, in which the differential is barely operational and exhibits abnormal behavior, of the same test. It can be seen that normal data were recognized as normal, with a probability of 100%. Anomalies were recognized as abnormal, with 100% probability. This means that the algorithm can differentiate normal from abnormal data with absolute certainty. Misclassifications, which indicate uncertainties in the model, are not present for these two runs.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 2 Background
### Concept of O-RAN Technology
The interfaces are designed to be open and interoperable to facilitate the seamless cooperation of different vendors. Some of the interfaces that are defined and standardized by the O-RAN Alliance include the following: The **O1** interface, which connects the SMO with the remaining components (CU, DU, RU, and near-RT RIC) of the RAN stack to manage and orchestrate the RAN as well as enable the integration of the third-party apps. The **A1** interface, which provides a connection between the non-RT-RIC and the N-RT-RIC to send intelligent policies and telemetries to the N-RT-RIC and allow the N-RT-RIC to control the RAN intelligently. The **E2** interface, which connects the N-RT-RIC with the E2 nodes, enabling different RAN optimization and automation services like RAN control, monitoring, and configuration. Other interfaces include the **fronthaul**, which connects the RU with the DU. **Midhaul** enables connections between the DU and the CU, and backhaul connects the RAN with the core network. Figure 2 describes the architecture of O-RAN as defined by O-RAN Alliance [32]. The alliance is a global industry consortium that promotes developing and adopting open and intelligent RAN technologies. It was initiated to transform traditional RAN infrastructure by enabling greater interoperability, flexibility, and innovation in the mobile telecommunications industry. The alliance plays a pivotal role in driving innovation and openness in RAN technology, which is essential for the evolution of 5G networks and the deployment of future wireless communication technologies. Figure 1: Building block of IoT systems. Figure 2: O-RAN architecture as defined by the O-RAN Alliance [32].
## 2 Rates of Surface and Subsurface Soil \(\mathbf{C_{\text{ORG}}}\) Mineralization
### Comparing Surface and Subsurface CO\({}_{2}\) Fluxes  
Ten studies to date have simultaneously measured CO\({}_{2}\) fluxes across the soil surface and in subsurface deposits in mangrove forests in Australia, Southeast Asia, China, and East Africa [54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. Rates of CO\({}_{2}\) production across the soil-water/air interface averaged 1878.3 \(\pm\) 230.0 mg C m\({}^{-2}\) d\({}^{-1}\) and rates of subsurface DIC production were nearly three times greater, averaging 5188.0 \(\pm\) 630.3 mg C m\({}^{-2}\) d\({}^{-1}\). Even assuming inaccuracies from methodological shortcomings, subsurface DIC production is clearly greater than CO\({}_{2}\) production at the soil surface. Nevertheless, there was a positive correlation between both sets of measurements (Figure 3), indicating that mineralization rates in surface and subsurface soils respond to identical factors, such as temperature, salinity, pH, frequency of tidal inundation, forest age and composition, root activities, microbial community composition, and soil nutrient and CORG content.  
The extent of the discrepancy between both sets of measurements increases with increasing topographic height (m above mean sea level), indicating that there is a 'DIC reservoir-pump system' [2] beneath the forest floor (Figure 4). The greater the tidal elevation of a forest, the greater the discrepancy between carbon respiration at the soil surface and in deep soils. That is, the volume of soil susceptible to drainage increases with forest height above mean sea level. The cause of this phenomenon is that the deeper soils act like a sponge through which porewater is pumped by tidal advection and/or seeps from the forest floor laterally to adjacent waters. Such drainage has also been observed in tidal marshes [64; 65].  
Figure 2: Vertical profiles of mean rates of DIC production at intervals to a soil depth of 1 m. Core samples and sample processing details were identical to the methods described in [54; 55; 56; 57; 58; 59; 60; 61; 62; 63]. The mangroves at Hinchinbrook Channel, north Queensland, Australia, were sampled in June 2014, and the Darwin Harbour mangroves in the Northern Territory, Australia, were sampled in October 2014.
# A 2D-View Depth Image- and CNN-Based 3D Model Identification Method
## 3 Experimental Results
### Deformable 3D Model Retrieval with Missing Parts
From Table 4, it can be observed that 2 VDI-CNN achieves top scores in terms of NN, 2-tier and DCG, except the 1-tier metric take the second highest score. In summary, the retrieval performance of our method is ranked third in the irregular holes challenge among 11 competing methods and ranked first in the range data challenge among six competing methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **NN** & **1-Tier** & **2-Tier** & **DCG** \\ \hline
2 VDI-CNN (ours) & 0.969 & 0.906 & 0.977 & 0.980 \\ SBoF & 0.811 & 0.317 & 0.510 & 0.769 \\ BoW+RoS-2 & 0.643 & 0.910 & 0.962 & 0.962 \\ BoW+RoPS-3 & 0.639 & 0.908 & 0.964 & 0.965 \\ BoW-HKS & 0.519 & 0.326 & 0.537 & 0.736 \\ BoW+RoPS-1 & 0.515 & 0.915 & 0.959 & 0.960 \\ BoW-siHKS & 0.377 & 0.268 & 0.485 & 0.699 \\ GMR & 0.178 & 0.184 & 0.371 & 0.640 \\ DNA & 0.130 & 0.183 & 0.366 & 0.640 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Retrieval performance for the range data challenge, sorted decreasingly by NN score. Best results are reported in bold. Figure 8: Precision-recall curves for all methods in the two challenges. Left graph is for irregular holes, right graph is for range data. 2 VDI-CNN: 2D-view depth image- and CNN-based method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **NN** & **1-Tier** & **2-Tier** & **DCG** \\ \hline DLSF & 1.000 & 0.971 & 0.999 & 0.998 \\
2 VDI-CNN (ours) & 0.906 & 0.818 & 0.937 & 0.954 \\ SBoF & 0.815 & 0.326 & 0.494 & 0.780 \\ BoW-siHKS & 0.710 & 0.370 & 0.566 & 0.790 \\ BoW+RoPS-3 & 0.607 & 0.918 & 0.970 & 0.968 \\ BoW+RoPS-1 & 0.597 & 0.877 & 0.963 & 0.956 \\ BoW-HKS & 0.578 & 0.261 & 0.436 & 0.725 \\ RMVM-Euc & 0.392 & 0.226 & 0.402 & 0.679 \\ BoW+RoPS-2 & 0.380 & 0.894 & 0.965 & 0.955 \\ NPSM & 0.347 & 0.222 & 0.395 & 0.676 \\ RMVM-Spe & 0.251 & 0.228 & 0.410 & 0.676 \\ SR & 0.241 & 0.225 & 0.395 & 0.676 \\ GMR & 0.186 & 0.172 & 0.343 & 0.642 \\ SnapNet & 0.117 & 0.172 & 0.349 & 0.641 \\ DNA & 0.078 & 0.163 & 0.348 & 0.632 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Retrieval performance for the _Irregular holes_ challenge, sorted decreasingly by the neural network (NN) score. Best results are reported in bold. DCG: discounted cumulative gain.
# Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
## 4 Conclusions  
1. The shear stress of sandy soil and steel plate is influenced by the particle size, gradation and compactness. It increases with the decrease of the average particle size of the sandy soil. The main reason is that the effective contact area between the sandy soil with small average particle size and the steel plate is large, and the interaction force between the soil particles and the steel plate is also larger.
2. The magnitude of normal stress has no significant effect on the shear stress-displacement curve between the sand and the steel plate, but it has an effect on the peak shear stress, the stability value and the shear displacement corresponding to the peak on the shear stress-shear displacement curve at the interface. Among them, the peak shear stress and the stability value are positively correlated with the normal stress, while the shear displacement corresponding to the peak shear stress increases roughly with the increase of the normal stress.
3. With or without slurry lubrication, the shear stress curves between the sand and steel plate are a strain-softening type, and the use of slurry lubrication can effectively  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sandy Soil Type**} & **Angle of Internal** & **Cohesion** & **Coefficient of Sliding Friction** \\  & **\#/(\text{c})** & **\#** \\ \hline Gravelly sand & 26.579 & 5.560 & 0.456 \\ Coarse sand & 26.807 & 5.418 & 0.472 \\ Medium sand & 26.026 & 6.717 & 0.466 \\ Fine sand & 27.714 & 5.677 & 0.518 \\ Powdered sand & 28.295 & 5.917 & 0.538 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical parameters of interface between sand with different particle sizes and steel plate.  
reduce the shear stress between the sand and steel plate by 20-30%. In practical engineering, the lubrication effect will be better due to the continuous grouting and the different permeability of the formation.
* (4) When there is no lubricated slurry, the mechanical parameters of the interface between the sand and steel plate are related to the sand particle size, particle gradation and roughness of the pipe itself. The friction coefficient and internal friction angle of the interface of the sandy soil and steel plate decreased with increasing particle size. The friction coefficient ranged from 0.45-0.54, the internal friction angle ranged from 26-29\({}^{\circ}\), and the cohesive force of the interface ranged from 5.41-6.72 kPa.  
Conceptualization, Z.M.; Methodology, J.H.; Software, A.X.; Validation, J.M.; Formal analysis, A.X.; Investigation, J.H.; Data curation, Z.M. and A.X.; Writing--original draft, Z.M.; Writing--review & editing, P.Z.; Visualization, J.M.; Project administration, P.Z.; Funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Nature Science Founds of China (52008383).  
The authors declare no conflict of interest.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems
### Model-Based FDD Methods for Railway S&C Systems
#### 3.1.2 FDD Methods  
A model-based FDD algorithm was applied to S&C system fault diagnosis by [PERSON] and [PERSON] [12]. In this work, the model-based FDD method developed was applied to an S&C system with a speed of up to 160 km/h. The FDD method introduced was based on a fuzzy neural network algorithm to build a reference model of a railway S&C system. Different main faults of S&C systems, which account for more than 90% of total failures under high-speed conditions [77; 78; 79], were considered, including; switch machine idle, switch operation has resistance, switch moving parts lacking oil, switch hard to release, and switch cannot lock, acquiring only the current signal. They achieved good detection and diagnosis results: accuracy of 96.7% with a 3.3% false alarm rate, and a 0% missing alarm rate.  
A more recent work, published in 2018, used a model-based FDD method based on an auto-associative residual (AAR) approach for railway S&C system FDD [80]. Auto-associative multivariate state estimation (AAMSET) was chosen to build the AA models as it has a high reconstruction accuracy and robustness [81] with the Euclidean norm. The AA model used raw input/output data for training and testing. The AAMSET-based FDD scheme developed can be seen in Figure 12. A data set collected from an in-lab test bench with induced faults--within a climatic chamber with controlled temperature and humidity--was considered to validate the proposed approach. Three faults were induced: misalignment, obstacle, and excessive friction. Considering different scenarios for each fault, 17 health condition modes were tested (e.g., different size of the obstacle considered: 1, 2, 3, and 4 mm) including the normal case. The signals acquired for analysis were the speed and torque signals. Even though the results showed good accuracy of the proposed AAR model-based FDD method, some limitations are still to be dealt with, including defining the level of correlation among the input variables and determining the optimal number (or subset) of input variables, especially when the number of variables is large.
# Finite Element Analysis of a Novel Approach for Knee and Ankle Protection during Landing
## 5 Conclusions  
Existing prophylactic knee and ankle braces, designed to reduce lower-limb injuries during landing due to the high GRF, work primarily by restraining the joint ROM; however, this protective approach is still controversial in consideration of the negative effects on joint function. The purpose of this study was, therefore, to provide a novel double-joint protection for the knee and ankle during landing and to evaluate its protective performance by using FE analysis.  
Our results can be summarized as follows:  
1. By absorbing some of the impact energy that would otherwise be experienced by the human body, the semi-rigid support at the ankle joint could reduce the peak stress on the inferior articular surface of the tibia and the horizontal force on the talus, thus, reducing the risk of ankle injury.
2. Additionally, the decreasing impact force on the lower extremity due to the semi-rigid support at the ankle joint could also reduce the stress peak of the menisci and articular cartilages in the knee joint.
3. Notably, as a function of the retractive force generated by the spring component at the knee joint, it could distribute the load in a better way such that the stress distribution and contact area between the menisci and articular cartilages were more equally distributed.  
In contrast to previous work in this area, the brace we designed can provide double-joint protection for both knees and ankles, without limiting the ROM as much as prevailing braces. According to the protective principle of this brace, with the exception of those who suffered a musculoskeletal injury to the lower limbs, the target population can obviously be extended to anyone with impact resistance demands during jump landing, such as paratroopers and skydivers undertaking landing maneuvers. Future work could aim at improving comfort when wearing this brace.  
Conceptualization, B.P. and X.W.; methodology, X.W.; software, W.W.; validation, D.L., L.G. and P.H.; formal analysis, X.W.; investigation, X.W.; resources, X.W.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, B.P.; visualization, B.P.; supervision, B.P.; project administration, B.P.; funding acquisition, B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Defense Industrial Technology Development Program, grant number JCKY2018601B106.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of School of Biological Science and Medical Engineering in Beihang University (protocol code: BM201900125; date of approval: 10 February 2019).  
An informed consent was obtained from the subject involved in the study.  
We are indebted to [PERSON] and [PERSON] for providing the helpful advice and consultation.  
The authors declare no conflict of interest.
# Image Completion with Large or Edge-Missing Areas
###### Abstract  
Existing image completion methods are mostly based on missing regions that are small or located in the middle of the images. When regions to be completed are large or near the edge of the images, due to the lack of context information, the completion results tend to be blurred or distorted, and there will be a large blank area in the final results. In addition, the unstable training of the generative adversarial network is also prone to cause pseudo-color in the completion results. Aiming at the two above-mentioned problems, a method of image completion with large or edge-missing areas is proposed; also, the network structures have been improved. On the one hand, it overcomes the problem of lacking context information, which thereby ensures the reality of generated texture details; on the other hand, it suppresses the generation of pseudo-color, which guarantees the consistency of the whole image both in vision and content. The experimental results show that the proposed method achieves better completion results in completing large or edge-missing areas.  
image completion; generative adversarial networks; large missing area; edge-missing area +
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