esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Vine Shoot Waste for Manufacturing Innovative Reinforced Cement Composites
## 3 Results and Discussion
### X-ray Diffraction (XRD)  
The XRD patterns of raw materials (CEM and VSW), CP and CP10 VSW samples are presented in Figure 4. The XRD pattern of VSW sample presents a broad large peak, characteristic to the amorphous state of the CEM and CP, and the CP10 VSW samples display narrow peaks characteristic to the crystalline phases in the samples. XRD patterns of the CEM sample showed the existence of alite (C\({}_{3}\)S), belite (C\({}_{2}\)S), aluminate (C\({}_{3}\)A) and aluminate-ferrites (C\({}_{4}\)AF) as major crystalline phases, which correspond to the main components of the clinker and gypsum (CaSO\({}_{4}\cdot\)2H\({}_{2}\)O). The XRD patterns of the hydration products of the investigated cement after 28 days of hydration showed the expected hydration products, including calcium hydroxide (CH), calcite (CaCO\({}_{3}\)), ettrinigite, poorly crystallized calcium silicate hydrate (CSH) and unreacted clinker phases (mainly calcium silicates). The occurrence of calcite was attributed to the partial carbonation of portlandite  
Figure 3: TGA curve of the (**a**) CEM, (**b**) CP, (**c**) CP10 VSW and (**d**) VSW samples.  
by the reaction of CH with carbon dioxide from the atmosphere. The CSH products did not display distinct diffraction peaks, owing to their partial amorphous structure. Additionally, an overlapping of the diffraction peaks belonging to the hydrated and anhydrous compounds was observed. Concerning the anhydrous clinker phases, only C\({}_{2}\)S and C\({}_{3}\)S have not entirely reacted. After 28 days of hydration, the same unreacted and hydration products were observed in the CP10 VSW sample. The slightly lower intensity of C\({}_{2}\)S and C\({}_{3}\)S diffraction peaks can be attributed to the charging effects and heterogeneous nucleation due to the addition of VSW content, which results in higher consumption of C\({}_{2}\)S and C\({}_{3}\)S [42]. Similar results were obtained by [PERSON] et al. when using olive kernels and pomace for cement manufacturing [2].
# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 3 Progress of Information Fusion for Multi-Source Material Data  
In this section, we introduce the technical progress in multi-source material data fusion. In order to accelerate the research and development of materials, a number of methods for big data analysis are required. Based on this demand, scientists have built high-throughput computing platforms, such as AFLOW (Automatic Flow) [9] in the US, which is a high-throughput computational software for calculating properties. MatCloud [10] is the first high-throughput material integrated computing and data management platform developed by the Materials Genetics Laboratory of the Chinese Academy of Sciences. This platform integrates material data, and performs high-throughput simulation calculations to generate massive data. After that, it uses material informatics to find the relationship between the structural information contained for prediction. In China, the Institute of Materials and Genomics Engineering of Shanghai University is actively researching the fusion of material data with the cooperation of other schools of computer science and technology, in the process of researching the genome of materials for applications such as simulation computing and machine learning. Figure 1 shows the general process of multi-source heterogeneous data fusion.
# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 2 Materials and Methods
### Classification Models  
#### 2.3.1 Bi-LSTM  
Long short-term memory is a form of recurrent neural network (RNN) [36], which learns long-term dependency information via feedback connections. To acquire short-term memory and abandon long-term memory, the model captures time-series information and sequence data by cyclic connections on their hidden layers. Recurrent neural networks have gradient vanishing characteristics that destabilize the long-term dependency of the model's learning process. However, LSTM resolves this problem by making the hiding layer store the latest information rather than the previous information.  
Long short-term memory is operated as follows:  
\[f_{t}=\sigma\Big{(}W_{h}f\cdot h_{t-1}+W_{xf}\cdot x_{t}+b_{f}\Big{)}, \tag{1}\]  
\[i_{t}=\sigma(W_{hi}\cdot h_{t-1}+W_{xi}\cdot x_{t}+b_{i}), \tag{2}\]  
\[\widetilde{\mathcal{C}}_{t}=tanh(W_{hC}\cdot h_{t-1}+W_{xC}\cdot x_{t}+b_{C}), \tag{3}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{\mathcal{C}}_{t}, \tag{4}\]  
\[O_{t}=\sigma(W_{ho}\cdot h_{t-1}+W_{xo}\cdot x_{t}+b_{o}), \tag{5}\]  
\[h_{t}=O_{t}\cdot tanh(C_{t}). \tag{6}\]  
Long short-term memory is composed of four parts: a forget gate \(f_{t}\), input gate \(i_{t}\), output gate \(O_{t}\), and candidate cell value \(\widetilde{\mathcal{C}}_{t}\). Here, \(x_{t}\) is the input; \(O_{t}\) is the output; and \(b_{f},\ b_{i},\ b_{C}\), and \(b_{o}\) are bias terms. Furthermore, \(\sigma\) denotes the activation function, '\(\cdot\)' denotes the matrix multiplication operator, and \(W_{x\times}\) denotes the weight matrix.  
According to LSTM, bidirectional LSTM (Bi-LSTM) integrates the input sequence information in both direct and inverted directions. The output at time \(t\) includes the forward LSTM output and reverse LSTM output [37].  
\[\overset{\rightarrow}{h_{t}}=\varnothing\Big{(}W_{hh}^{d}\cdot h_{t-1}+W_{ xh}^{d}\cdot x_{t}+b_{h}^{d}\Big{)}, \tag{7}\]  
\[\overset{\leftarrow}{h_{t}}=\varnothing\Big{(}W_{hh}^{i}\cdot h_{t-1}+W_{ xh}^{i}\cdot x_{t}+b_{h}^{i}\Big{)}, \tag{8}\]  
where \(\overset{\rightarrow}{h_{t}},\overset{\leftarrow}{h_{t}}\) denote the hidden layer in forward and reverse LSTM directions at time \(t\), respectively. The hidden state \(h_{t}\) is obtained by concatenating \(\overset{\rightarrow}{h_{t}}\) and \(\overset{\leftarrow}{h_{t}}\), which is then fed to the output layer. The output \(O_{t}\) is calculated as follows:  
\[O_{t}=W_{hp}\cdot h_{t}+b_{p}, \tag{9}\]  
where \(W_{hp}\) denotes the weight parameter and \(b_{p}\) denotes the bias parameter.
# Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism
## 3 ESGM Model  
The composition of the ESGM model is shown in Figure 2, which comprises of the BERT encoding module, span extraction module, subject predict extraction module, subject feature-sentence vector fusion module, and object relation extraction module. First, the sentence is encoded using a BERT encoder to obtain a vector embedding for all tokens. These representations are then fed into the span extraction module, which predicts the probability of each word being the start position and end position of a span. In this paper, we propose a one-to-many matching approach to sequentially pair all predicted start and end positions and train a binary classification model to predict whether the paired span is the subject. In order to effectively fuse the global information in the sentence with the subject features, in this paper we design a gate unit to perform the fusion. The fused information is then passed through a relation mapping function to match the corresponding object. In Figure 2, 9 \(\times\) 768 is marked as the vector of the 9 \(\times\) 768 dimension of the instance sentence after BERT, where 9 is the length of the sentence and 768 is the vector dimension of BERT.  
The main functionality of this model is to identify all possible relation triplets in the given dataset while addressing the challenges of relation overlap and nested entities. The model first extracts the subject, then, based on the extracted subject, it simultaneously extracts the relation and object. For example, for the sentence \"New York University is located in the United States\", it first extracts all the head entities in the sentence, as follows: \"New York, New York University, the United States\". Then, under the condition of the head entity \"New York\", the corresponding tail entity \"the United States\" can be found for the relation \"located\", while the corresponding tail entity cannot be found for the relation  
Figure 2: The overall structure of ESGM.  
\"contains\". Finally, all the relations are iterated in turn to find all the relational triples in the sentence, with \"New York\" as the head entity. The same process is followed for the other two head entities. The process of relational triplet extraction in this model can be represented by Formula (1):  
\[P((s,r,o)|x_{j})=p(s|x_{j})\cdot p((r,o),r\in R|x_{j},s) \tag{1}\]  
where \(P((s,r,o)|x_{j})\) represents the probability of extracting the target relation triplet for a given set of sentences, \(p(s|x_{j})\) represents the probability of extracting the subject from the sentences, and \(p((r,o),r\in R|x_{j},s)\) represent the probabilities of extracting the object and relation given the subject and the sentences. In these formulae, \((s,r,o)\) represent a relational triplet, \(R\) indicates the relationship in the dataset, \(x_{j}\) represents the \(j\)th sentence in the dataset, and \((r,o)\) represents the object-relation pair.
# An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
## 2 APIT Algorithm  
The principle of APIT is to judge whether the unknown node is inside the triangle using the surrounding nodes. As shown in Figure 1, the nodes surrounding an unknown node D are nodes 1, 2, 3 and 4. If there is a neighbor node that is simultaneously far away from or close to the anchor nodes A, B, C, it is considered that the unknown node is outside the triangle composed of A, B and C. Conversely, it is also considered to be inside triangle ABC.  
In the APIT algorithm, the unknown node cannot be positioned when the number of surrounding anchor nodes is less than 3 or a valid triangle cannot be formed.  
Meanwhile, the APIT positioning method calculates the coordinates of unknown nodes as the geometric center of the intersection, which is complicated and suboptimal [21]. Assume that there are N neighboring anchor nodes around the unknown node, and M valid triangles through the  
Figure 1: Schematic diagram of Approximate Point-In-Triangulation (APIT). (**a**) Outside the triangle. (**b**) Inside the triangle.  
APIT judgment on \(C_{N}^{3}\) triangles. The computation of the intersection of M valid triangles is time consuming [22]. Moreover, different valid triangles should have different roles in the evaluation which is not included in the traditional APIT.  
Another significant problem of APIT is the In-to-Out misjudgment [23]. As shown in Figure 2, when the unknown node D is inside triangle ABC, and if node D refers to node 2, node D will be judged to be outside triangle ABC--this is In-to-Out misjudgment.  
From the above discussion, the limitation of the APIT algorithm is the requirement of a high density of anchor nodes and a large communication radius of per anchor node. It is not suitable for a small to medium localization area. If it is the case that the above requirement cannot be met, a considerable number of nodes cannot be positioned and the probability of positioning error will be high. Moreover, with an increasing number of neighboring nodes, the positioning accuracy may decrease.
## 4 Comparative Evaluation of User Interaction in Wearable AR
### Experimental Design  
For the comparative evaluation of visual contexts and interaction gestures in the wearable AR environment, we designed two user studies. Three experimental tasks, such as (1) selection, (2) dragging and dropping, and (3) 3D matching, were designed to compare and evaluate the usability and task performance concerning the visual context and interaction gesture in wearable AR. Among the three tasks, two tasks, such as dragging and dropping and 3D matching, were conducted to evaluate the visual context. Two tasks, such as selecting and 3D matching, were conducted to assess the interaction gesture. When a subject looked at a specific area with a generic feature-based AR marker(s) through the camera of AR smart glasses, virtual objects were augmented on or above the AR marker, and then the participant was required to perform the given task.  
Twenty university students (fourteen males and six females) were recruited with an average age of 25.9 (SD = 1.25) in a controlled laboratory environment [44]. Four of them had experience in AR applications, two had experience in using Leap Motion, and the others had no such experience. Before the experiment, subjects were instructed on how to perform the three tasks, along with a description of the visualization and interaction methods, and then practiced until they were almost familiar with each method. After practice, subjects performed full-scale tasks in a random order to avoid the learning effect. In other words, the order of conducting three tasks, and the order of user studies were randomly assigned to each subject.  
Through the experiment, quantitative and qualitative assessments of visual contexts and interaction gestures were conducted. For the quantitative evaluation, the completion time of each task was measured. After the completion of all the tasks, surveys and interviews were also conducted for qualitative evaluation. The questionnaire consisted of questions that were evaluated based on a 7-point Likert scale (1: strongly disagree, 7: strongly agree). In particular, we used all the questions from the System Usability Scale (SUS) [45] without any modification. SUS is well-known as an objective questionnaire for usability evaluation. We have not listed them in this paper. Figure 11 shows a snapshot of the worker who performed a matching task with the Universal Robot-3 (UR3) robot. UR3 is a famous intelligent and collaborative robot widely used in manufacturing environments.  
Figure 10: Actual hand detection and calibration with the virtual hand in the wearable AR.
## 4 Optimization Design Algorithm
### Ordinary Multi-Objective Particle Swarm Optimization Algorithm
#### 4.2.1 Basic Particle Swarm Optimization Algorithm  
In the particle swarm optimization algorithm, each particle is denoted by \(x_{i}=[x_{i1},x_{i2},\ldotsldots,x_{id}]\), where \(i=1,2,\ldots\ldots,N\), \(N\) is the number of particles, and \(d\) is the dimension of the particle. Each particle updates the position using Equations (14) and (15) according to its optimal position and the optimal global position during the iteration process [30]:  
\[v_{i}^{t+1}=w^{t}v_{i}^{t}+c_{1}r_{1}^{t}(pb_{i}^{t}-x_{i}^{t})+c_{1}r_{2}^{t} (gb^{t}-x_{i}^{t}) \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Structural Parameters** & \(d\) **(mm)** & \(I\) **(mm)** & \(h_{av}\) **(mm)** & \(n\) \\ \hline Initial value & 0.2 & 15 & 0.02 & 8 \\ Optimization range & 0.2–0.3 & 10–20 & 0.015–0.025 & 6–10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization range of structural parameters.  
Figure 9: Influence of the number of orifices on (**a**) bearing capacity and (**b**) gas consumption.  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{15}\]  
where \(t\) is the number of iterations, \(v_{i}^{t},x_{i}^{t}\), and \(pb_{i}^{t}\) are the speed, position, and self-optimal position of the \(i\)th particle at the \(t\)th iteration, \(gb^{t}\) is the optimal global position at the \(t\)th iteration, \(w^{t}\) is the inertia factor, \(w^{t}\) decreases with the increase in the number of iterations, \(c_{1}\) and \(c_{2}\) are learning factors, and \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1.
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 2 Materials and Methods
### Phase Pattern Calculation
#### 2.1.1 Linear Array  
We limited the choice to OAM values in the set from \(\ell=-3\) to \(\ell=+3\) for a total of 7 OAM values (\(n=7\)). The spatial frequencies have been fixed in such a way that the far-field peaks were arranged along a line at equally spaced \(x\)-positions (see Figure 2b):  
\[\begin{array}{l}x_{j}^{(+)}=\alpha\Bigl{(}\frac{n+1}{2}-j\Bigr{)}\frac{f}{k }=\alpha(4-j)\frac{f}{k}\\ y_{j}^{(+)}=\beta\frac{f}{k}\end{array}\, \tag{8}\]  
where \(j=1,\dots\) 7. Considering Equation (7) and the symmetry of the far-field channels constellation, it results that:  
\[\begin{array}{l}x_{j}^{(-)}=-\alpha(-4+j)\frac{f}{k}=x_{j}^{(+)}\\ y_{j}^{(-)}=-\beta\frac{f}{k}=-y_{j}^{(+)}\end{array}\, \tag{9}\]  
The two orthogonal polarizations are therefore sorted over two distinct linear arrays without overlapping, as depicted in Figure 2b.
## 3 Results
### Analysis of NEP and Temperature and Precipitation  
The carbon sink capacity of natural terrestrial vegetation ecosystems in this region is not only related to the autotrophic respiration and heterotrophic respiration of terrestrial plants, but it also affected by the external environments, such as climate, soil, seaside, and other conditions [39]. In addition, it is also affected by human activities. The overall annual mean _NEP_ in the Yangtze River Delta region from 2000 to 2019 was recorded, and the interannual variation curves between _NEP_ and the annual mean temperature and annual precipitation were drawn (Figure 10) to analyze the relations between the _NEP_ and the annual mean temperature and annual precipitation. It is found that there is no significant correlation between the _NEP_ and the annual mean temperature and annual precipitation factors in this region. This is due to the fact that the terrestrial vegetation ecosystem in the Yangtze River Delta region is seriously affected by the external environment and human activities. In addition, the region has a wide range, large area, complex and diverse underlying surface, and the meteorological stations are relatively sparse. The meteorological data of all stations are simple and average throughout the year, which cannot represent the actual situation. Therefore, according to subregional statistics, it is found that the _NEP_ values of Shanghai and Zhejiang have a negative correlation with the annual average temperature (r = \(-\)0.43), and Anhui has a positive correlation with the annual rainfall (r = 0.36).  
Figure 9: Change rate of _NEP_ in Yangtze River Delta (2000–2019).  
However, it can be seen from Figure 10 that the temperature increases in the Yangtze River Delta region from 2000 to 2019 were very small, with a slope of only 0.04; however, the precipitation increased significantly, with a slope of 11.0. The climate in the entire region is dominated by humidification. Through the identification of mutation points, it is further confirmed that there is no mutation point in the annual average temperature of the five regions, and the change trend is not obvious. Only the Yangtze River Delta and Shanghai have abrupt changes in annual precipitation; however, the increase is not significant.
###### Abstract  
The automatic identification of seedling defects is an important technology of an intelligent automatic transplanting machine, which effectively improves the quality of the transplanting machine's operation. The accurate segmentation of seedling substrate and seedling region is the key to the success of the seedling defect recognition algorithm. This paper proposes the maxIOU algorithm to calculate the image segmentation threshold: The image G channel and excess green color space were selected as the color space for the segmentation of the substrate region and seedling region by analyzing the color histogram. Several images were randomly selected from the dataset to generate a training set and were labeled manually as the ground truth. The training set images were segmented using a threshold of zero to 255, and the intersection over union (IOU) were calculated using the algorithm segmented result and the ground truth. The threshold corresponding to the average IOU maximum was used as the segmentation threshold. After image segmentation, three features (area of the substrate, area of the seedling, and filling ratio of the lower part of the substrate) were obtained by the algorithm, and the image was identified for whether there was an empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate. The algorithm was tested on the automatic transplanter test platform. The experiment results were as follows: Firstly, the image segmentation threshold was calculated by the maxIOU method. The color component interval corresponding to the segmented substrate region was [0, 24] in the G channel, and the color component interval corresponding to the segmented seedling region was [21, 255] in the excess green channel. The average IOU of the substrate area was 0.854, and the average IOU of the seedling area was 0.820 in the verification experiment. Secondly, a dataset including 431 normal seedling images and 69 defective seedling images (empty conveyor belt, seedling deficiency, multiple seedlings, skew, and damaged substrate) was identified for defects. The accuracy, precision, and recall were 97.6%, 97.4%, and 99.8%. The processing time was 71.4 ms. The conclusion of the experiment was as follows: the maxIOU algorithm had high accuracy in the segmentation of the substrate and seedling region. The defect identification algorithm had high accuracy for defect identification of cabbage seedlings, and the algorithm had good real-time performance, which can be applied to high speed field transplanters.  
machine vision; image segmentation; fast cabbage transplanter; defect identification
## 2 Related Works
### Airport Scene Surveillance  
As the demand for security technology at airport surfaces continues to increase, Surface monitoring and control systems (SMGCS) have emerged and become the core equipment for safe and efficient operations at major airports worldwide. Currently, the mainstream surface monitoring system installed in most large airports at home and abroad is the Advanced Surface Movement Guidance and Control System (A-SMGCS), which combines ADS-B, MLAT, and SMR. This system combines surveillance techniques. Still, it relies on sending information such as the velocity and position of the acquired surveillance target to a surveillance center. This system could be more efficient as the field structure becomes more complex and digitized information increases.  
In recent years, a low-cost, video-based field surveillance technology has emerged and gradually replaced expensive radar-based surveillance systems. It is mainly implemented through the arrangement of multiple omnidirectional cameras to collect the visible video in the field. Using machine vision for real-time detection of the captured video, we can obtain the real-time trajectory of the target, enabling more efficient monitoring of the field. The INTERFACE and AVITRACK, developed by the European Union at the beginning of the 21 st century, are the first generation of airport surface surveillance systems [11]. These methods enable the detection, identification, and tracking of aircrafts, vehicles, and people, as well as their movements on the ramp, and they output the corresponding bounding boxes. However, they are not capable of human behavior recognition and anomaly localization. For small passenger airports located in remote areas, due to the visual blindness of traditional towers nearby, Sweden's SAAB Group put the first remote tower system into use at airports in 2015, which puts video images of all critical areas on a panoramic high-definition large screen in the remote control center through cameras in real-time. It combines visual image technology and radar data to display a variety of information synchronously. This field surveillance system improves the efficiency of the monitoring work. Still, it cannot make more intelligent judgments on the behaviors of non-cooperative targets.