esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
## 2 Related Work
### Anomaly Detection Research  
Anomaly detection is an unsupervised method of mining abnormal behavior in data and only uses a normal sample to train the model. According to different application fields, these abnormal patterns can be called outliers, inconsistencies, or novelty. In recent years, anomaly detection has been widely used in credit card fraud detection, intrusion detection, and identity recognition. The research on anomaly detection has important theoretical significance and practical value and has been widespread. Due to the imbalance of samples (the number of abnormal samples is far less than the number of normal samples), anomaly detection is very difficult. Therefore, the current research on anomaly detection methods mainly focuses on the unsupervised learning framework and some supervised learning methods using very few labeled abnormal samples, including the following categories.  
The first method is based on a statistical method to deal with abnormal data. This method generally establishes a probability distribution model, calculates the probability of the object according to the model, and regards an object with low probability as an outlier. For example, the RobustScaler method in feature engineering uses the quantile distribution of data features to divide the data into multiple segments according to the quantile when scaling the feature value of data and only takes the middle segment to scale, so as to reduce the impact of abnormal data.  
The second method is anomaly detection based on clustering. Most clustering algorithms are based on the distribution of data features. The data sample size of some clusters is much smaller than that of other clusters after clustering, and the average distribution equivalence of data features in this cluster is also very different from other clusters, as most of the samples in these clusters are abnormal samples.  
The third method is a special anomaly detection algorithm. The purpose of this kind of algorithm is to detect outliers. The representatives of this kind of algorithm include Local Outlier Factor (LOF) [21], Isolation Forest (IForest) [22], One-Class Support Vector Machines (OCSVM) [23], Histogram-based Outlier Score (HBOS) [24], Feature Bagging [25], Principal Component Analysis (PCA) [26], Minimum Covariance Determinant (MCD) [27], k-Nearest Neighbor (KNN) [28], VAE [6; 29], and anomaly detection algorithm [30; 31]. Social media bots in social networks belong to abnormal accounts, and the anomaly detection algorithm is very suitable for the detection of social media bots because of the imbalance of positive and negative samples.
# AAGA: A Priori Adaptive Splitting Algorithm for the Split Delivery Vehicle Routing Problem
## 3 Performance Evaluation
### Benchmarking Solutions  
We benchmark the performance of the AASA algorithm against the following baseline methods:  
* No-splitting (CVRP): The baseline method that treats an SDVRP instance as a CVRP instance and runs the VRPH [32] solver to generate solutions.
* VRPHAS: The method based on a priori splitting that is proposed by [30].
* ILS-MIP: The metaheuristic method proposed by [27].  
We note that when computing the optimality gap corresponding to each method for each experimental instance, we take the best solution known in the literature as the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Number of Instances** & **Number of Customers (N)** & **Vehicle Capacity (Q)** & **Customers’ Demands** \\ \hline SET-1 & 21 & \([8,288]\) & \(100\) & \(\{60,90\}\) \\ \hline SET-2 & 14 & \(\{50,75,100\}\) & 160 & randomly from \([aQ,bQ]\)1  \\ \hline SET-3 & 42 & \([50,199]\) & \([140,200]\) & randomly from \([aQ,bQ]\)1  \\ \hline SET-4 & 11 & \([21,100]\) & \([112,800]\) & No pattern \\ \hline \end{tabular}
\end{table}
Table 1: Evaluation datasets.  
reference solution. Furthermore, we note that in ILS-MIP, the travel cost between each pair of customers is assumed to be their rounded Euclidean distance except for SET-1 instances where the exact Euclidean distance is taken as the cost. Hence, we compare the results of ILS-MIP and AASA only for SET-1. For the rest of the datasets, we compare the performance of AASA against the No-splitting and the VRPHS methods.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.4 Normalized Routing Load (NRL %)  
In this scenario, Figure 9, the UAVs move at an average speed of 20, 40, and 60 m/s. Normalized routing describes the number of control packets generated to the number of packets delivered. We can observe from Figure 9 that the normalized routing load of all benchmark schemes is higher, and AODV in particular offers a very high load value. While other position-based schemes' normalized loads also increase as the mobility of the UAVs increases, because of the rapid movement of the UAVs, the network topology changes dynamically. There are two main reasons for being out of communication range: invalid signal direction and low power signal. However, our proposed schemes have lower normalized load, and PHCC has better results in all mobility scenarios due to utilizing centrality measures: it can choose the best routes and ignore the weak routes. Furthermore, the position-based routing protocols do not have global information of routing paths and only consider local routing paths and directions. Therefore, control packets are forwarded to the whole network. However, in the proposed routing scheme, each UAV node has global information of all routes and connectivity, so it can control the routing packets more easily than the existing routing protocols.
# Arabic Gloss WSD Using BERT
## 2 Related Work  
As mentioned previously, knowledge-based WSD is categorized into graph-based approaches and gloss-based approaches [16]. Graph-based approaches depend on language knowledge graphs such as WordNet to represent the sense by its surrounding tokens in that graph. For example, the authors in [17] introduced a _retrofit_ algorithm that utilized Arabic WordNet along with _Glove_[14] and _Word2 Vec_[15] to generate context-dependent word vectors that carry sense information. Finally, they used cosine similarity between the generated word vector and vectors generated for WordNet synsets of that word and selected the sense with the highest similarity score. In [18], the authors utilized Arabic WordNet (AWN) to map words to _concepts_. A concept was defined as a specific word sense. They further utilized the English WordNet to extend the Arabic one by employing Machine Translation for the missing words in AWN. A concept was then selected for a target word by comparing the context of the target word to neighbors of the concept in the extended WordNet. Concepts with high match to the context were selected as the correct sense. In [19], the authors used AWN to generate a list of words that can be potentially ambiguous. For each ambiguous word, they found Wikipedia articles that correspond to the different senses of those words. Those articles were then converted to real vectors by tf-idf [20]. They also generated a context vector by tf-idf of the word in its context. Finally, they used cosine similarity between the two vectors and selected the sense with the highest similarity score.  
The second type of knowledge base approaches is gloss-based approaches. These methods measure the semantic similarity or the overlap between the context of the target word and the dictionary definition of that word (gloss of the word). The authors in [21] used the _Lesk_ algorithm to measure the relatedness of the target word's context to its definition. They used AWN as a source for the word gloss. The authors of [22] combined unsupervised and knowledge-based approaches. They used a string matching algorithm to match the root of salient words that appear in many contexts of a particular sense with the gloss definition. The authors of [23] employed word2 vec [15] to generate a vector representation for the target word's context sentence and the gloss sentence. Then, cosine similarity was used to match the gloss vector and the context vector. The authors in [24] used Rough Set Theory and semantic short text similarity to measure the semantic relatedness between the target word's context and multiple possible concepts (gloss). Recently, The authors in [25] used FLAIR [26], a character-level language model, to generate sentence representation vectors for both context and gloss sentences. The sentence's vector was calculated by taking the mean of its word vectors, causing a loss of information, especially when the sequence is long. Cosine similarity was then used to measure the similarity between the word context sentence and the gloss sentence.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Bespoke Network
#### 3.2.1 Automatic Weather Stations  
A stand-alone AWS RTD has been designed, which is both light (measuring temperature and relative humidity) and made to measure, in collaboration with the enterprise Electronic Engineering iRay [64] (Figure 2). The components, listed below, are mounted on a stainless-steel sheet 316, 2 mm thick, which allows easy placement and removal.  
(a) Sensors  
A high performance sensor is used for temperature (from \(-40\)\({}^{\circ}\)C to 125 \({}^{\circ}\)C, with an accuracy of \(\pm 0.1\)\({}^{\circ}\)C from 20 \({}^{\circ}\)C to 60 \({}^{\circ}\)C and of \(\pm 0.2\)\({}^{\circ}\)C in the rest) and for relative humidity (from 0% to 100%, typical accuracy of \(\pm 1.5\)% from 0% to 80% and of \(\pm 2.0\)% in the rest), with double insulation against water and dust (Sensirion SHT35 [65]). Measurements have a resolution of 0.1 \({}^{\circ}\)C and 0.1%, respectively. Given that the SHT35 sensor is of the integrated type, it does not require signal conditioning. Sensors exhibit an LTD of less than \(\pm 0.03\)\({}^{\circ}\)C/year, which, given their recent deployment, July 2014, results in a maximum accumulated LTD of \(\pm 0.0025\)\({}^{\circ}\)C.  
Figure 1: Diagram of the Bs information system architecture, including AWSs and the centralized processing system for network data.
## 3 DRFM System Model  
The DRFM system was implemented in three parts, as shown in Figure 1, forming the MATLAB testbed and FPGA DRFM implementation: the radar signal sources, the DRFM jammer, and the correlator bank. The system, described in detail in this section, generated radar signals in the testbed, sent them through the DRFM jammer for retransmission and used the correlator to compare the received signals to the transmitted signal in a variety of cases.  
A correlator was used to measure how the jammer waveform and transmitted waveform resembled each other. The output of the correlators was then sent to MATLAB as arrays of samples in the time domain for plotting and analysis; this is discussed in the next section.
###### Abstract  
Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.  
vehicle mass estimation; model-based approach; artificial neural network; data training; software-in-the-loop simulation (SILS) +
Footnote †: journal: Applied sciences  
1 KATIC, Kunsan National University Advanced Technology Institute for Converence, Gunsan-si 54001, Republic of Korea; [EMAIL_ADDRESS]  
2
# RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation
###### Abstract  
Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.  
Keywords:distributional relation representation; co-occurrence matrix; Neo4j; global vectors; cosine similarity +
Footnote †: journal:
## 6 Conclusions  
The engineering background of this paper is a continuous steel box girder of the large cross-sea bridge, with the goal of developing a construction scheme for the overall lifting of large section steel box girder. The problems encountered in monitoring and the main technical means were studied and analyzed. This paper summarizes the monitoring work and digital monitoring system of the large-section steel box girder hoisting at various stages, including pre-construction preparation, box girder manufacturing, installation, and bridge operation and maintenance after installation. It introduces in detail the importance and difficulties of construction control of large section hoisting. Furthermore, the corresponding alignment, stress, and temperature monitoring schemes are formulated by combining theoretical analysis, the finite element model, and digital application. The construction proceeded smoothly and was completed within the designated timeframe. After the erection of the main beam, the linear accuracy meets the requirements, and the implementation of the project has achieved good results. The bridge has achieved a successful opening to traffic with good operating conditions, serving as a valuable reference for other bridges of its kind. The following conclusions are drawn:  
1. The method of the overall lifting of large-segment girders is increasingly common in use. This paper adopts a comprehensive monitoring approach, starting from the prefabrication stage at the steel box girder factory. Taking the first large segment as an example, specific methods for stress and displacement monitoring are described to ensure the structural safety during the construction process.
2. The influence of material parameters such as weight, elastic modulus, and temperature on the stress and displacement of steel box girders is analyzed. The variations in stress on top and bottom plates and vertical displacement with changes in these parameters are provided as references for the project.
3. Based on this project, a construction monitoring platform has been developed, which mainly realizes on-site data collection to the cloud and real-time remote monitoring of the steel box girder lifting process. This lays the foundation for establishing a digital monitoring system for the entire lifecycle of the bridge.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 2 Preliminary
### BestGMSE Surrogate  
To avoid the problem of choosing inaccurate contributing surrogates, the BestGMSE surrogate, which directly selects a model with the lowest GMSE from the \(n_{M}\) surrogates, is employed and formulated as  
\[\hat{y}_{BestGMSE}=\hat{y}^{*}, \tag{16}\]where \(\hat{y}^{*}\) is the predictor of the model with the lowest error among \(E_{i}\), \(i=1,2,\ldots,n_{\text{M}}\). The BestGMSE surrogate could effectively eliminate the negative effects of the surrogates with lower accuracy, and is easy to implement. However, because the GMSE criterion is based on the training sample points, there is a risk that the BestGMSE model has poor generalization on the test set. To improve the prediction accuracy and robustness of the ensemble model, this paper develops an effective surrogate ensemble modeling method, which is demonstrated in Section 3.
## 1 Introduction
Although most of the constant effects, coming from ferromagnetic material in the vicinity of the sensor,can be compensated by appropriate calibration methods [14, 15], the non-constant effects, e.g., high electrical currents during the operation of rotary-wing UAVs, inhibit sub-degree attitude accuracies in most cases. A third option is the realization of a GNSS multi-antenna system on the UAV to provide a direct yaw measurement. Examples for the use of multi-antenna GPS systems on lightweight UAVs can be found in [3, 8, 16]. Due to the low price and the small weight of some currently available single-frequency GPS receivers, this option is becoming more and more attractive. However, for the attitude determination, GPS carrier phases have to be observed, and a resolution of the carrier phase ambiguities is required. This is a challenging task, especially for low-cost single-frequency GPS observations [17]. During UAV applications in urban areas, frequent losses of the GPS signal lock can occur due to obstacles in the signal path. Since every loss of lock necessitates a re-initialization of the ambiguities, the ambiguity resolution process should be as fast as possible. The contribution of this paper is the presentation and the evaluation of a small and lightweight real-time-capable direct georeferencing multisensor system, which also acts as an attitude and heading reference system (AHRS) for micro- and mini-sized UAVs. The desired attitude accuracy of the system is less than 0.5\({}^{\circ}\) for all attitude angles. To achieve this and to overcome the disadvantages of the different yaw determination options, the following information is used and combined in a suitable way:  
* 3D position measurement: This can be a code-based GPS position with accuracies of several meters or a cm-accurate RTK (real-time kinematic) GPS position. We use an RTK GPS position in this paper. * MEMS IMU readings: The IMU readings include angular rates and accelerations, measured by three-axis gyroscopes and accelerometers. * Single-frequency GPS observations: The GPS observations, measured by two onboard GPS receivers, enable the determination of an onboard GPS baseline, which contributes to the yaw determination. * Magnetic field observations: The magnetic field observations come from a calibrated magnetometer, which is placed as far away as possible from the electric currents on the UAV platform. In the following sections of this paper, we first explain the GPS attitude baseline determination algorithms, including the concept of an instantaneous ambiguity resolution for the single-frequency onboard GPS baseline. Afterwards, the details of the sensor integration and the system design will be presented. The evaluation of different field tests enables the assessment of the attitude accuracy and the ambiguity resolution performance of the system.
## 4 DLRSD-Based Surface Defect Segmentation
### Enhancement  
As shown in Figure 2, the original foreground image \(F\) can be enhanced in consistency, completeness of defect objects and suppression of background noise. In the paper, the regression optimization method is adopted by combining foreground image \(F\) and background image \(B\). The optimization problem can be formulated as follows:  
\[\min_{s_{i}}\left(\sum_{i=1}^{K}w_{i}^{f}(s_{i}-1)^{2}+\sum_{i=1}^{K}w_{i}^{b} s_{i}^{2}+\sum_{i,j=1}^{K}w_{ij}(s_{i}-s_{j})^{2}\right) \tag{23}\]  
where \(w_{i}^{f}\) denotes saliency value of sub-region in foreground image \(F\), \(w_{i}^{f}=Val(P_{j})\); \(w_{i}^{b}\) denotes saliency value of sub-region in background image \(B\), \(w_{i}^{b}=Val(Q_{j})\); \(s_{i}\) denotes the optimized saliency value of sub-region in foreground image \(F\).  
According to \(\mathbf{s}=(s_{1},\,s_{2},\,\cdots,\,s_{K})^{T}\in\mathbb{R}^{K\times 1}\), \(W^{b}=\text{diag}\bigg{[}\left(w_{1}^{b},\,w_{2}^{b},\,\cdots,\,w_{K}^{b} \right)^{T}\bigg{]}\in\mathbb{R}^{K\times K}\), and \(W^{f}=\text{diag}\bigg{[}\left(w_{1}^{f},\,w_{2}^{f},\,\cdots,\,w_{K}^{f} \right)^{T}\bigg{]}\in\mathbb{R}^{K\times K}\), the Equation (23) can be reformulated as follows:  
\[\min_{\mathbf{s}}\left(\mathbf{s}^{T}W^{b}\mathbf{s}+\mathbf{s}^{T}W^{f}\mathbf{s}-2W^{f}\mathbf{s}+W^ {f}\mathbf{1}+2\mathbf{s}^{T}M\mathbf{s}\right) \tag{24}\]  
where \(\mathbf{1}\in\mathbb{R}^{K\times 1}\) denotes a one vector, \(M\in\mathbb{R}^{K\times K}\) denotes the same Laplacian matrix in Equation (5).  
Differentiating it with respect to \(\mathbf{s}\), and let it to be zero, therefore  
\[2W^{b}\mathbf{s}+2W^{f}\mathbf{s}-2W^{f}\mathbf{1}+4M\mathbf{s}=0 \tag{25}\]  
The solution is  
\[\mathbf{s}=\left(W^{f}+W^{b}+2M\right)^{-1}\!\!W^{f}\mathbf{1} \tag{26}\]  
Through Equation (26), the sub-regions within the same class (foreground or background) have more similar saliency values while the sub-regions from different classes (foreground and background) have different saliency values. The saliency value of defect sub-region in foreground image is bigger, while the saliency value of background sub-region is smaller, so that the surface defect object can be highlighted further.
## Appendix B
### The \"Orbit\"-the Approach to Repeat-Pass Imaging  
The purpose of the Orbit is to enable repeat-pass observations of the scene in order to observe how atmospheric features are translated (the process as \"advection\") by the wind between observations. The orbit straight and level sections are flown in approximately 6 min and were chosen to allow two or three hyper-cube collections, referred to as \"sweeps\" while the ER2 was in level flight. The individual orbit period was approximately 1000 s, and was selected to balance the needs for infrared radiance data SNR (the longer the viewing, the better), and to reduce the likely-hood of the clouds and moisture patterns from either moving out of the field of repeat-observation, or undergoing excess shape or feature change between observations (a motion-vector wind observation employs multiple observations of the scene and observes where features (clouds or distinctive features in the moisture field) have moved between observations to compute the wind velocity).