esa-sceva/satcom-chunk-collection huggingface dataset:
# Paraphrase Identification with Lexical, Syntactic and Sentential Encodings
## 4 Experiments
### Main Results
Overall, the curves show that more training data helps the performance, while the curves obtained by LSSE drop slowly compared to the BERT sentential encodings. From the observation, we can conclude that our model works well compared to BERT sentential encodings. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{4}{c}{**MRPC Dataset**} \\ \hline
**\#1 String** & **\#2 String** & **LSSE** & **BERT** & **N** \\ \hline Licensing revenue slid 21 percent, & License sales, a key measure of demand, & & & \\ however, to USD 107.6 million. & fell 21 percent to USD 107.6 million. & TP & FN & 70 \\ \hline For the entire season, the average & & & & \\ five-day forecast track error was & The average track error for the five-day & & & \\
259 miles, [PERSON] said. & & & & \\ \hline By Sunday night, the fires had & & & & \\ blackened 277,000 acres, hundreds of & Major fires had burned 264,000 acres by & & & \\ miles apart. & & early last night. & & \\ \hline Other countries and **private creditors** & & & & \\ are owed at least USD 80 billion in & Other countries are owed at least USD & & & \\ addition. & US80 billion (USD 108.52 billion). & FP & TN & 53 \\ \hline \hline  & **QQP Dataset** & & & \\ \hline
**\#1 String** & **\#2 String** & **LSSE** & **BERT** & **N** \\ \hline What are the most intellectually & What are the most intellectually & & & \\ stimulating movies you have ever seen? & stimulating films you have & TP & FN & 331 \\ ever watched? & & & & \\ \hline How do I get business ideas? & How can I think of a business idea? & FN & TP & 212 \\ \hline How do I remove dry paint from & How do I get acrylic paint out of & & & \\ my clothes? & my clothes? & TN & FP & 201 \\ \hline How do Champcash make money & How do a Champcash customer make & & & \\ from Chrome? & money from Chrome? & FP & TN & 130 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example sentences obtained by our LSSE and BERT model: TP, FP, TN and FN refer to an abbreviation of true positive, false positive, true negative and false negative, respectively.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
###### Abstract
Pruning involves removing redundant parameters and kernels on the neural networks, thereby decreasing model storage and improving model inference speed [10,11]. Parameter quantization reduces the precision of model weights and activations, typically from 32-bit floats to lower-bit such as 8-bit integers. It decreases memory usage and computational costs, enabling efficient deployment on resource-limited devices [12]. As one of the most popular techniques, knowledge distillation constructs a compact student model which achieves comparable performance from a complex teacher model by learning from its soft targets. In the context of bearing fault diagnosis, knowledge distillation has been widely adopted to compress deep learning models for edge deployment [13]. For instance, [PERSON] et al. applied knowledge distillation to compress a 1D convolutional neural network (1 DCNN) for bearing fault diagnosis, achieving a significant reduction in model size without sacrificing accuracy [14]. In [15], a student network is introduced to learn diagnostic features from a larger teacher network with a learnable embedding in the attention mechanism. It is beneficial to new operating conditions via a joint classifier. **Domain Adaptation for Cross-Domain Diagnosis**. In bearing fault diagnosis, operating conditions such as load, speed, and environmental noise can vary significantly [16]. Thus, the domain shift would occur when the distribution of data in the target domain differs from that in the source domain, thereby degrading the model performance. To overcome this, domain adaptation techniques are necessary to align the feature distributions of the source and target domains, enabling models to generalize across different conditions [17]. [PERSON] et al. introduced a domain adaptation method which employs the Joint Maximum Mean Discrepancy and Conditional Adversarial Domain Adaption Network, to align feature distributions across domains [18]. [PERSON] et al. proposed a tailored dynamic conditional adversarial domain adaptation mode for cross-domain fault diagnosis, achieving accuracy and stability for wind turbine gearbox fault diagnosis [19]. These techniques collectively address the challenge of domain shift, enabling robust fault diagnosis across diverse operating conditions.
# 3D Reconstruction of Cultural Heritage Sites as an Educational Approach. The Sanctuary of Delphi
## 4 Materials and Methods
### Educational Content  
Before the final implementation of the software, university students had the ability to test the initial version of the virtual tour and the educational scenario that would unfold. Then, questions were asked in an open discussion which was recorded about their educational needs, and how they could be satisfied with the software.  
The students asked for information on how the buildings were constructed, the time they were constructed, the architectural style of each building, the reason for the location of each building, the orientation, the place of origin of the building materials, and if they had been taken, the archaeometric measurements that had been taken at the monuments. They also asked for some informative videos and the possibility of an audio narration of the information.  
Some examples of this recordings are given bellow:  
1. \"It would be better to have questions at the end as a little evaluation. Many questions during the tour can be tiring\".
2. \"Badges and rewards I think are for younger ages. It is better to have more information about the buildings and archaeometric analysis if it has been done\".  
Figure 4: Import of 3D reconstructed buildings from the museum archive to Unity 3D (© Authors).  
3. \"Two or three informative videos would be useful for better understanding the history of the archaeological site during the exploration\".  
The educational content was implemented with the contribution of the students and included beside the visual contact with the Sanctuary of Delphi, 3D reconstructed buildings and monuments along the route, information with texts presented in tables, recorded texts and compiled video clips, a menu of options for additional information, while students can ask any questions through a contact form (Figure 5).  
During the tour, students could learn about the history of the archaeological site and elements of architectural construction of the main buildings. In more detail, the educational content of the software contained the following elements:  
1. Text: Hovering annotations with information at each educational point.
2. Audio narration: Students could activate audio narration for each information text.
3. Short video clips: Six video clips with historic information about respective interest points.
4. Compass: To observe the orientation of buildings.
5. Query form: Students could pose their questions anytime during their navigation. The answers were sent to students by e-mail.
6. Information document. A document with generic information about the history of the archaeological site and architectural constructions of the main buildings. This information file is updated and enriched to cover the student's needs, as a feedback to their questions during their navigation.
7. Assessment questionnaire: Twenty multiple-choice questions created in collaboration with [PERSON], Professor of Classical Archeology at the Department of Mediterranean Studies of the University of the Aegean.
# Hexadecimal Aggregate Approximation Representation and Classification of Time Series Data
## 1 Introduction  
Time provides a basic cognitive variable for the continuity and sequential description of object movements and changes in the world [1; 2; 3; 4; 5; 6]. Human society is facing many challenges, such as environmental pollution, population growth, urban expansion, the transmission of infectious diseases and various natural disaster monitoring and prevention issues, etc. These are all closely related to the concept of time and produce massive data containing information regarding time. Especially in recent years, a large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices [2; 3; 4; 5; 6]. Time series data can help us understand history, master the present and predict the future, as well as improve our ability to gain insight, perception and prediction of the evolution of various existences and states in the real world. Many applications in the fields of scientific research, industry and business produce large amounts of time-series data that need effective analysis, requiring rational representation and efficient similarity computing and search. These applications cover the domains of images, audio, finance, environmental monitoring and other scientific disciplines [7; 8; 9]. Many creative representation methods for time series data have been proposed for similarity computing, clustering [10], classification [11], indexing and query [3; 8; 9; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. The taxonomy of time series representations includes four types [10]: data-adaptive, non-dataadaptive, model-based and data dictated. The main representation methods for time series are shown in Table 1.  
The most commonly used approximation representations are the PAA [42] and SAX [19; 22; 64] methods. In the last decades, most of time series data indexing methods [23; 25; 31; 32; 65; 66] have been based on SAX [19; 22]. The SAX method reduces an \(n\)-length time series to a \(w\)-length (\(w<n\)) string with an alphabet parameter named \(\alpha\). Though highly simple and straightforward, it is a major limitation because it may lead to some important features being lost [67]. To avoid this problem, the ESAX [68] and SAX-TD [67] methods improve the accuracy of SAX. The ESAX method adds two additional values, a maximum value and a minimum value, to each time series segment as the new
# Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method
With a high degree of precision, crop growth models replicate the growth of crops and the reactions to their surroundings. However, a variety of characteristics, such as environmental factors and crop physiology, are needed for crop growth models. It is challenging to implement on a broad scale because of the complexity and ambiguity of these parameters [23; 24]. The LUE model was developed to estimate GPP [25], based on the hypothesis of plant photosynthesis, which contends that the dry matter collected by vegetation is obtained from photosynthetically active radiation absorbed by LUE. The LUE model has been shown to be capable of estimating GPP in terrestrial ecosystems at both the regional and global levels. Several trustworthy and practical products, such as MODIS GPP, VPM GPP, and EC-LUE GPP, have been released and have been crucial in tracking ecosystem change. Because crops are not finely separated (e.g., C3 or C4), these products might not be accurate enough for crop-specific GPP [26; 27]. Further, through subdividing crop types, several studies have successfully simulated various crop yields and their spatiotemporal changes using LUE models from local to regional scales, by distributing the GPP to crop harvesting organs. An effective and reliable way to measure agricultural output on a large scale has been made available by the satellite-based LUE model, which has an ecophysical basis and simplifies the mechanism model's complexity. Crop yield estimates across substantial areas are still challenging. It is well known that the interaction of the genotype (G), environment (E), and management (M), all of which exhibit significant variation, determines crop yields [28]. Fortunately, yield may be calculated by multiplying the GPP by the empirical harvest index (HI) for a certain crop [29; 30]. The HI measures the percentage of above-ground biomass that is converted to economic yield [31]. Nevertheless, obtaining a HI for a region with significant spatial variation is still difficult. Some studies have employed biomass or evapotranspiration before and after anthesis of crops to estimate the HI [32; 33]; however, there are significant uncertainties in crop anthesis judgment and transpiration, which further contributes to the estimation error of the HI. Utilizing the ratio of estimated biomass to statistical yield is another approach for determining the HI that lessens acquisition challenges [34]. Thiswork intends to investigate the capacity of the EC-LUE model combined with a HI to measure summer maize yields in the NCP area (North China Plain), taking into account that previous studies have primarily focused on winter wheat [14; 35]. The goal of this study is to: (1) obtain a region-specific HI for summer maize and (2) assess how well the EC-LUE model performs for maize yield estimation.
## 6 Conclusions and Future Work  
We investigated the capacity problem of IWSSN. The motivation was to provide insights and design guidelines for optimizing IWSSN in emergency scenarios. Firstly, we formulated a general model to evaluate the remote sensor and satellite network capacity. Four major constraints were introduced, including availability, transfer rate, establishment of the path and path efficiency. Then, we combined the geometric satellite orbit model and STK engineering software to quantify the trends of the capacity constraints in the simulation section. We discussed each major capacity constraint, exemplify their impacts with representative networks and showed the opportunities to optimize the IWSSN network schedules through intelligent deployment and flexible scheduling.  
However, there are important issues that are still open and should be further investigated in the future. Specifically, although the arbitrary sensor and satellite network capacity model is provide with major capacity-impacting constraints, it is noted that more high fidelity constraints can be considered, such as energy limits, satellite attitude control and data processing. It is also seen that we only show the optimizing opportunities of IWSSN network schedules in this paper. Optimal scheduling algorithms that seek to maximize a capacity-related objective function will be developed in our future work. Furthermore, we are developing models and algorithms to calculate the exact network capacity value for IWSSN based on our current network capacity model.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 3 System Overview  
Our proposed system consists of region estimation and coordinate estimation. In the region estimation phase, a sequence of binary classification algorithms gradually locates the relevant region. In the position estimation phase, the specific coordinates are determined by the proposed fingerprint-based deep neural network architecture. Figure 1 illustrates the overall framework of our method, providing a visual overview of both phases. Next, we present more details of each phase.
# System Design and Monitoring Method of Robot Grinding for Friction Stir Weld Seam
## 2 Method
### Extraction Method of Weld Seam
#### 2.2.1 Weld Seam Detection
After the registration, the distance between the points in the \(i^{th}\)voxel and the registrated surface are obtained as \(d_{i}\), and \(d_{i}\) is the average value of all the distances between all the points in the voxel and the surface. The normal distribution is used to estimate the probability that each voxel belongs to the weld seam. \(p(d_{i})=\frac{1}{\sqrt{2\pi}\sigma_{d}}\exp\left(-\frac{\left(d_{i}\right)^{2 }}{2\sigma_{d}^{2}}\right)\). In the estimation, it is considered that the point farther away from the registration surface is more likely to belong to the weld seam. For example, when \(d_{i}=0.8\) mm, the probability that it belongs to the weld points is set to 0.5. Then, the \(\sigma_{d}\) can be obtained. Those voxels whose probabilities are larger than a threshold are selected as initial voxels to help optimize the \(E_{consist}\) term. (3) Consistency check. To optimize the \(E_{consist}\) term, the authors analyze the initial voxels and link them up to form a consistent result. The Dijkstra algorithm is adopted to calculate the shortest paths between each group of voxels to all other groups. The authors simply define the weights between two neighboring voxels as \(W(vi,vj)=(Pr(vi)+Pr(vj))/2\). The authors then selected the shortest paths to merge two groups until the detection result forms one single group or exceeds the distance threshold. (4) Voxel-level Graph-Cut. Optimize the \(E_{data}+E_{pairwise}\) term with the well-known graph cut algorithm. Note that for voxels selected by consistency check, the authors double their probability of being weld seams. (5) Repeat (3) and (4) until the detected weld seams meet the consistency requirements, or the algorithm reaches the maximum iteration threshold. (6) Point-level Graph-Cut. For each voxel, the authors optimize \(E_{data}+E_{pairwise}\) at point level by graph cut. The neighbors of each point are initialized as its four nearest points in 3D space. The result of the feature extraction is a point cloud representing one weld seam. There are two forms of the weld seam. One is a uniform line, which is higher than the surrounding plane, and the other is the uneven part. The differences between the two types and the monitoring methods will be discussed in the following chapters.
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
###### Abstract  
Natural disasters and terrorist attacks pose a significant threat to human society, and have stressed an urgent need for the development of comprehensive and efficient evacuation strategies. In this paper, a novel evacuation-planning mechanism is introduced to support the distributed and autonomous evacuation process within the operation of a public safety system, where the evacuees exploit the capabilities of the proposed ESCAPE service, towards making the most beneficial actions for themselves. The ESCAPE service was developed based on the principles of reinforcement learning and game theory, and is executed at two decision-making layers. Initially, evacuees are modeled as stochastic learning automata that select an evacuation route that they want to go based on its physical characteristics and past decisions during the current evacuation. Consequently, a cluster of evacuees is created per evacuation route, and the evacuees decide if they will finally evacuate through the specific evacuation route at the current time slot or not. The evacuees' competitive behavior is modeled as a non-co-operative minority game per each specific evacuation route. A distributed and low-complexity evacuation-planning algorithm (i.e., ESCAPE) is introduced to implement both the aforementioned evacuee decision-making layers. Finally, the proposed framework is evaluated through modeling and simulation under several scenarios, and its superiority and benefits are revealed and demonstrated.  
evacuation planning; public safety systems; rescue management; adversarial environments; terrorist attacks; natural disasters; reinforcement learning; minority games +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 20; doi:10.3390/f11010020
## 1 Introduction  
Reinforced concrete pipe is a kind of reinforced concrete structure, which is a fully enclosed underground structure with the functions of accommodation, transportation and water-crowning. It is also one of the common forms of urban underground space utilization [1]. Reinforced concrete pipes have the advantages of high construction accuracy, high structural strength, good safety and durability. They are widely used in underground crossings, utility pipes, power pipes, and water supply and drainage pipes. They is an important part of municipal infrastructure [2].  
Reinforced concrete pipes are often located in weak strata in urban areas with complex geological conditions. Their own structures often have defects such as corrosion defects and cracks. As time goes by, their materials have the risk of deterioration and have certain safety hazards [3]. Studying the mechanical properties of in-service reinforced concrete pipe structures can provide a scientific basis for quantitative evaluation of structural health status, and provide a theoretical basis for subsequent maintenance and repair [4].  
Compared with circular reinforced concrete pipes, rectangular reinforced concrete pipes have the advantages of high space utilization, good stability and good foundation applicability [5]. However, for rectangular reinforced concrete pipes, the difference in cross-section shape leads to differences in the force form, failure mode and bearing capacity analysis method of the two in the stratum [6]. At present, research on the mechanical behaviors of rectangular reinforced concrete pipes with corrosion defects, at home and abroad, mostly considers the influence of the concentration and diffusion law of microscopic particles such as chloride ions on the compressive strength of concrete and the tensile and compressive strength of steel bars. Although the relevant research accurately explains the corrosion mechanism of corrosive media on reinforced concrete materials, it lacks focus on the overall mechanical properties of reinforced concrete structures under corrosion conditions [7; 8].  
In summary, there are few studies on the overall mechanical properties of rectangular reinforced concrete pipe structures with corrosion defects [9]. In this paper, an external pressure load test is carried out on rectangular reinforced concrete pipe with corrosion defects, and the influence law of bearing capacity of pipe with different corrosion depths is determined by numerical simulation.
# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 3 Model and Problem Formulation
### Network Model
#### 3.1.2 User Layer  
In the user layer, we use \(U_{v_{j}}=\{u_{i}\}\) to denote the set of users that connected with edge node \(j\), and \(u_{i}\) denotes the \(i\)th user. The task of device \(u_{i}\) to be processed was defined by a triplet \((T_{i},M_{i},D_{i}^{d})\), where \(T_{i}\) denotes the size of the task in bits, and \(M_{i}\) denotes the Million Instructions Per Second (MIPS) of the task. Let \(D_{i}^{d}\) denotes the maximum tolerant delay of task \(i\).  
In order to ensure the feasibility of our model in a real network, we assume that a set of tasks that arrive at the same time are independent and in line with the normal distribution. Each task evaluates the size of the task by the number of clock cycles it needs to process. Since the scheduling algorithm described in this paper is independent with the amount of the computation of tasks, so we assume that the numbers of clock cycles required to process the tasks are the same. In order to ensure the stability of the experimental results, we use the normal distribution to control the frequency at which tasks are generated. Figure 2 shows the probability of the task we configured. The four lines are the mean parameters of the four normal distributions we set. The horizontal axis is the calculated delay of the next task, and the vertical axis is the possibility of the delay. So, the smaller the mean is, the larger the number of tasks handled. For example, when the value of the mean is 5, the most likely scenario is to wait for 5 clock cycles to generate the next task. With the constraint of this normal distribution probability, both the generation of dynamic tasks and the large deviation of the results will not occur in the process of executing the benchmark multiple times.