esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Several IT companies have developed cloud-based platforms, offering systems that can be exploited for implementing virtual assistants very rapidly and with no specific knowledge of Natural Language Understanding. Moreover, these platforms can be used with only a small amount of examples, with no need for providing a large quantity of training data. The goal of this work is to evaluate the performance of the main cloud-based NLU platforms in a real-world context, using the requests collected through the chat service of _Mega Italia Media_. Except for the anonymization and the removal of personal and sensitive data, the utterances were not pre-processed. Once the service was chosen, we used it to develop the new assistance system, and we compared its performance with the benchmark obtained from the previous evaluations. Several works perform this kind of comparison, mainly from an architectural and implementation perspectives and simply showing the results of the intent recognition task [15; 16; 17], often considering an already existing application [18; 19; 20]; in this paper, we tackle several fundamental aspects that have to be considered in developing a chatbot using a cloud-based platform, such as the number of the training instances for each intention and its relation with the performance. As pointed out in [6], service-oriented chatbots (such as the one we implemented for _Mega_Italia Media_) have their specific lexicon and require a customization process which can be based on a very low number of training instances. Moreover, since in terms of usability the response time has to be considered, we compared the response time of the different platforms in order to identify if there are relevant differences between the cloud-based platforms; in addition, we evaluate the performance also in terms of misspellings and errors. Another important aspect of our work is that it is based on the Italian language, which has a limited diffusion. Given that most studies are conducted with a dataset in English, it can be useful to study in the case that there are major drawbacks in the use of chatbots for less common languages. The structure of this paper is as follows: in Section 2, we overview related works; in Section 3, we describe the considered cloud-based platforms; in Section 4, we describe our case study and the datasets we used for our evaluation; in Section 5, we present the main components of our analysis; in Section 6, we show our results; Section 7 shows the system architecture implemented, and we present the performance in terms of error rate; finally, in Section 8, we put forward our conclusions and future work.
## 5 Routing over the Hierarchical Virtual Topology
### Path Recovery after Node Failures
#### Path Recovery between Two \(N\)-th-Tier Modules  
We suppose that a virtual long-distance link is lost when an end node of the virtual long-distance link fails or the virtual long-distance link cannot be recovered within a certain time. When all physical paths assigned to an \(N\)-th-tier \((2\leq N)\) virtual long-distance link are lost, the \(N\)-th-tier routing tables should be updated to route a data packet in the \(N\)-th tier. Although the principal idea is the same as that of the method shown in Section 5.3.1, flooding in an \(N\)-th-tier virtual network is different from general flooding. We call this modified form \(N\)-th-tier flooding.  
\(N\)-th-tier flooding can be realized by multicasting among only connected nodes, because a connected node list corresponding to an \(N\)-th-tier module list is required for path recovery between two \(N\)-th-tier modules. When the connected node tries to find an alternative route between \(N\)-th-tier modules, it sends an upper route request packet to the other connected nodes belonging to its own first-tier module. Then, that node and each of the connected nodes that received an upper route request packet sends the upper route request packet to all connected nodes that belong to neighboring first-tier modules. When a connected node belonging to the destination module receives an upper route request packet, it sends an upper route reply packet to the sender of the upper route request.
# Automatic Era Identification in Classical Arabic Poetry
## 4 Experiments and Results
### Experiment 3: Assessing the Contribution of Different Feature Types  
In Experiment 3, we tested the contribution of each feature type to the classification results of era identification, focusing on the distinction between the two main periods, 'Abbasid and pre-'Abbasid. In this feature ablation study, we trained the best-performing SVM classifier using different subsets of the feature set, where in each experiment we excluded one type of feature from the poem feature descriptions pertaining to each of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Naïve Bayes** & **P** & **R** & **F1** \\ \hline ‘Abbásid & 0.713 & 0.775 & 0.743 \\ Pre-‘Abbásid & 0.756 & 0.692 & 0.723 \\ Weighted AVG & 0.735 & 0.733 & 0.733 \\ \hline
**SVM (SMO)** & P & R & F1 \\ ‘Abbásid & 0.891 & 0.881 & 0.886 \\ Pre-‘Abbásid & 0.883 & 0.893 & 0.888 \\ Weighted AVG & 0.887 & 0.887 & 0.887 \\ \hline
**ADA Boost** & P & R & F1 \\ ‘Abbásid & 0.794 & 0.787 & 0.791 \\ Pre-‘Abbásid & 0.791 & 0.798 & 0.795 \\ Weighted AVG & 0.793 & 0.793 & 0.793 \\ \hline
**Voted Perceptron** & P & R & F1 \\ ‘Abbásid & 0.821 & 0.853 & 0.837 \\ Pre-‘Abbásid & 0.849 & 0.816 & 0.832 \\ Weighted AVG & 0.835 & 0.835 & 0.835 \\ \hline
**PART (DL)** & P & R & F1 \\ ‘Abbásid & 0.813 & 0.814 & 0.813 \\ Pre-‘Abbásid & 0.816 & 0.814 & 0.815 \\ Weighted AVG & 0.814 & 0.814 & 0.814 \\ \hline
**KNN** & P & R & F1 \\ \hline ‘Abbásid & 0.735 & 0.564 & 0.638 \\ Pre-‘Abbásid & 0.649 & 0.799 & 0.717 \\ Weighted AVG & 0.692 & 0.682 & 0.678 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Experiment 1.  
following feature types: statistical, lexical, syntactic, domain-specific, and topical. The results of feature elimination by type appear in Table 6 in terms of precision, recall, and F1. In addition to the results per target period, the table also shows the weighted averaged performances, so as to allow easy comparison. We can see in Table 6 that the removal of the lexical features causes the largest degradation in overall classification performance, even while maintaining a relatively high level of classification resulting in an F1 score of 0.833 versus 0.887, using all features. The removal of any of the other feature types has a relatively minor or no effect on the overall performance. This suggests that the various feature types overlap in terms of discriminative information. To explore this further, we performed another feature ablation experiment, in which each type of feature is modeled as a standalone, without the other features. This analysis allowed us to examine the predictive power of each feature type separately. Table 7 presents the precision, recall, and F1 results of this experiment, assessing the contribution of each feature type. The results show that all of the feature types are informative, i.e., outperform the baseline of a random guess by a large margin. The lexical features provide the highest individual contribution and are the single most important feature type. It is interesting to note that the removal of syntactic features had the lowest effect on the overall results
## 3 Proposed Method
### Disk-Based Greedy Scheduling Algorithm (DGS)  
As mentioned earlier, since sensors have a certain coverage area, and inside the classical routing algorithms, they use all the nodes for data transmission, which is obviously very energy-consuming, and will also bring a large network load. Therefore, we can set a network coverage rate threshold, which is usually given by the actual engineering, and reasonably schedule the nodes that satisfy the coverage rate threshold to work efficiently. In the classical coverage problem study, the coverage model of sensor nodes is considered as disk coverage. The disk coverage model can be expressed as Equation (1).  
\[f(d(n,p))=\begin{cases}1&if\;\;d(n,p)\leq R_{s}\\ 0&otherwise\end{cases} \tag{1}\]  
where \(d(n,p)\) is the distance from the sensor node \(n\) to the spatial point \(p\), and \(R_{s}\) is the sensing radius of the sensor node. It can be seen from the formula when the spatial point \(p\) is located within the sensing radius of the sensor node, the data of the spatial point can be sensed, but beyond this range, it cannot.  
At the very beginning, all the sensor nodes have to send their current geographic locations to the sink, while the sink stores the sensing radius of the sensor nodes and the coverage rate threshold. After the sink receives the nodes' locations, it selects the least sensor nodes required to reach the coverage rate threshold for data collection according to the DGS, and some of the rest of the nodes may have to participate in the forwarding of the data, and the remaining nodes are dormant. The core idea of DGS is to use sensor nodes as little as possible to achieve the corresponding coverage rate requirement. DGS selects a sensor node with the largest coverage each time through multiple iterations until the coverage rate threshold is reached. Whenever a node dies, the sink will re-schedule the nodes to work or sleep.  
Figure 5 exhibits a schematic of DGS, where 100 sensor nodes are randomly deployed in the monitoring area at a coverage rate threshold of 0.9, and the sink obtains the location of each sensor node and can manage them to work. The yellow area is the area covered by the scheduled nodes, the nodes marked in red are working nodes. Some of the green nodesmay become relay nodes for data forwarding, and the others are in the dormant state, this management makes the network operate efficiently.
## 3 Design and Computation of Orthogonal Simulation Experiment
### Design of Orthogonal Experiment Scheme  
The high-strength mining of a super-large working face means that the mining size is large, and the mining speed is fast. The parameters of the coalface itself were concerned in this study; the mechanical properties and dimensions of the surrounding rocks were fixed. Therefore, the experimental factors (i.e., coalface parameters) were determined as follows:  
1. The length in the dip direction of the coal seam
2. The length in the strike direction of the coal seam
3. The thickness of the coal seam
4. The mining speed  
Since the local coal seam is nearly horizontal, the dip angle of the coalface was not considered and was set to zero in all models.  
For each experimental factor, five levels (i.e., factor values) were designed; their mean values were close to the actual values of the prototype coalface. Specially, the factor of the length in the dip direction (3592 m for the prototype) was designed to be from 600-1000 m to decrease the model size. As the mining depth was around 200 m, the designed dip length values still ensured that the excavations were critical (or super-critical), where \"critical excavation\" is a mining state with respect to the size of the excavation zone. When an excavated zone is sufficiently large, so the induced maximum vertical subsidence just reaches its theoretical extreme value under a given mining condition, the excavation is considered to be critical. When an excavated zone is smaller than this size, the maximum vertical subsidence is lower than the theoretical value, which is the state called sub-critical. Conversely, the super-critical state is where the maximum vertical subsidence does not enlarge while the mining zone is developing, which creates a flat bottom around the center of the excavated zone.  
The specific factors and levels of the orthogonal experiment are shown in Table 1.  
All the designed tests are shown in Table 2 and were based on the orthogonal design principle that considers the selected four factors and five levels. Thus, a total of 25 simulation tests were performed. Compared with the complete experiment scheme, which required a total of 5\({}^{4}\) = 625 models, the orthogonal design method greatly reduced the complexity of the research.
# Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
## 3 Methodology
### Training Details  
The total of training process can be divided into two parts which are described as the following:  
Stage 1: To improve the generation of hierarchical features, the encode part of the fused model was initialized by the pretrained model Swin-S [31], which has been trained on ImageNet-1K dataset [67] for image classification. Subsequently, the fusion model was trained on the training dataset generated above with mean squared loss and the AdamW optimizer [68] for 1000 epochs. It is worth noting that we employed a cosine decay learning rate with an 0.0005 initial learning rate, 0.05 weight decay and 20 epochs of linear warm-up. The batch size during the training period was set to 16.  
Stage 2: Mean square error loss is the basic loss function which is used to regularize the prediction close to the ground-truth output. To encourage the network to produce images with greater texture similarity to the ground-truth fusion images, we incorporated a texture loss into the training model with the same hyperparameters as in the previous stage (1000 epochs, 16 batch size and Adam optimizer). The training loss is expressed as:  
\[F_{loss}=\omega_{1}M_{loss}+\omega_{2}T_{loss} \tag{14}\]  
Here, \(M_{loss}\) and \(T_{loss}\) denote the mean square error and textures loss separately. In this study, \(\omega_{1}\) and \(\omega_{2}\) are both set to 1.
## 5 Damping of Plasmon Oscillations in Ionic Systems
#### Exact Inclusion of the Lorentz Damping to the Attenuation of Ionic Dipole Surface Plasmons
For spherical electrolyte system one deals with the surface and volume plasmons in analogy to metallic nanosphere. The ionic surface plasmon frequencies are given for multipole \(l\)-th mode by the formula \(\omega_{l}=\omega_{p}\sqrt{\frac{l}{\varepsilon(2l+1)}}\) with the bulk plasmon frequency \(\omega_{p}=\sqrt{\frac{4\pi q^{2}n}{m}}\) (\(n\) is ion concentration, \(q\) and \(m\) are ion charge and mass, respectively, \(\varepsilon\) is the dielectric relative permittivity of the surroundings). For dipole surface plasmons (\(l=1\)), it resolves to the Mie-type formula, [1; 6]\(\omega_{1}=\frac{\omega_{p}}{\sqrt{3}\varepsilon}\). These plasmon oscillations intensively irradiate their own energy and are quickly damped due to Lorentz friction losses (i.e., due to irradiation of EM wave by oscillating charges [8; 22]) which for large systems with large number of ions participating in plasmon oscillations (thus strengthening the Lorentz friction) are much greater than the Joule-heat dissipation caused by Ohmic losses due to carriers scattering (scattering of ions on other ions, solvent and admixture atoms and on boundary of the system), as illustrated in Figure 3, where the crossover in the size dependence of plasmon damping rate is visible (similar as for metal nanospheres [23; 24]). Surprisingly, in the linear chain of spherical ionic systems the irradiation losses are completely reduced to zero, exactly in the same manner as in metallic chains [25; 26; 27]. The irradiation energy losses expressed by the Lorentz friction [8; 22] are ideally compensated by the income of the energy due to radiation of all other spheres in the chain. In the result, the radiative losses are ideally balanced and there remains only relatively small Ohmic irreversible energy dissipation due to ion scattering. The collective surface dipole plasmon-polariton can thus propagate in the chain with strongly reduced damping and if energy is permanently supplemented in a minor scale to balance only the Ohmic losses, this propagation could occur on arbitrarily long distances without any damping.
## 1 Introduction
For global damage detection, we realistically assumed that the IDT sensor and structural system are inherently coupled. We assume that the sensors and the structures are one integral entity and the damage induced in the SAW sensors manifests the initiation of damage in the structural system. In our proposed technique, the IDT sensor has dual advantages: (a) acting as a calibrating benchmark sensor whose health represents the damage in the structure; and (b) acting as a master sensor, which continuously monitors the health of the structure. In this paper, we propose a methodology for quantifying the precursor to damage in the sensors that can provide sufficient warning time for early retrofitting of the structure. The damage precursor is defined as any hidden feature resulting from early degradation of the material, leading to microscale defects or microstructural and morphological changes. Quantification of damage precursors includes one of the aspects such as detection of damage incubators, recognizing initiators such as micro indenters or microstructural phase changes or micro-crack nucleation [36]. In the context of the paper, macroscale damage is defined as an indentation of 20 \(\upmu\)m. A void, cavity or indentation smaller than one hundredth of the detectable crack size (i.e., 20 \(\upmu\)m) is qualified as the precursor to damage. Multiple micro-cracks could coalescence or nucleate to form a macroscale crack. Monitoring the health of the sensors provides value-added information towards a precursor to damage. In this study, a surface defect of depth 0.2 \(\upmu\)m is labelled as microscale damage and is considered to act as a precursor to macro-scale damage. The measurement of surface defects of depth 0.2 \(\upmu\)m is difficult using attenuation and scattering phenomena. The precursor to damage in a piezoelectric crystal could also arise from the cracking and delamination of IDT from the substrate. Precursor damage may arise in an embryonic state in the form of microscale damage such as a surface/subsurface aberration and crack that nucleates together to form macroscale damage. In order to quantify the precursor to damage, non-local field theory is formulated and a damage-sensitive index is derived. The nonlocal parameter provides the state of damage in the material by exploring directional dependent dispersion relationship. The proposed formulation can be adapted for quantification of the precursors arising from fatigue loading, corrosion, and thermal effects. To the best of the authors' knowledge, no studies have been conducted to detect damage induced due to microscale cracks, surface aberration and delamination using non-local field theory. The novelty of this work is it proposes a damage feature that quantifies the precursor to macroscale damage and provides an indication for early warning measures regarding the evolution of damage.
## 2 Materials and Methods
### Satellite Images  
It was observed that all the sampling locations occurred in three images (path 145 and rows 139, 140, and 141). The three images cover an area of approximately 180 km east-west to 540 km north-south. There could be significant variability in the atmospheric conditions over such an area, which affects the relationship between the top of the atmospheric reflectance retrieved from the satellite data and the insitu water turbidity. This problem is mitigated by using a single image where 13 of the 16 sampling sites were located. The reflected electromagnetic solar radiation is the basis for the spectral examination of satellite imagery, issued to measure turbidity. Unique signatures and curves are generated, depending on the reflection and absorption at different wavelengths [47, 48]. The major errors in the reflected electromagnetic solar radiation remained when retrieving water properties from satellite images. The thirteen samples (RG2-RG14) included in the analysisare located on the image, with path 145 and row 40. Nine samples wereused to retrieve the turbidity model. To validate this model, the measured and predicted turbidity was compared. The four samples that were not included in the model retrieving were used for further validation of the model.
elevation levels of 530 m (RG4) to 259 m (RG5) from the mean sea level. This elevation difference leads to a decrease in potential energy and an increase in the kinetic energy of the river, thereby increasing the sediment-carrying capacity of the river [16].
## 4 Simulation Experiment and Analysis
### Test Results and Analysis  
To verify the effect of the proposed HMSSA to solve the DFJSP, nine test problems were selected to simulate the MHSSA. Each algorithm ran 20 times independently, and a group of [SP, IGD, \(\Omega\)] values was recorded each time. Table 4 lists the mean (Avg) and standard deviation (Std) of each evaluation index of the three algorithms, and the optimal results of each index are marked in bold. It can be seen from the IGD and \(\Omega\) indexes in Table 4 that the diversity, convergence, and dominance of the non-inferior solutions on the Pareto frontier obtained by the MHSSA are the best in the comparison algorithms and that the solution effects of the other two algorithms are not very different. For the SP index, the difference between the three algorithms is not obvious. For all test problems, the MHSSA designed in this paper has the best uniformity of the distribution of non-inferior solutions on the Pareto frontier.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Novel Methods of Applying Other Knowledge  
Faced with the problems of large training data requirements, many parameters, and high consumption of computational resources brought by deep learning methods-based recognition of HCC, some researchers have applied rather innovative methods of other concepts in the face of small samples to bring new ideas to the HCCR task. A handwritten Chinese character recognition system based on image alignment technology was proposed by [PERSON] et al. [71] in 2016. The algorithm builds a nearest neighbor classifier based on template matching and improves the modeling ability of different text types by utilizing the average image transformation as a basic module, and then adopts a fuzzy entropy-based metric function. However, the fuzzy entropy metric necessitates a separate calculation for the similarity relation per pixel stack across all image pixels. The system may not outperform techniques based on feature extraction methods or traditional information entropy, which is a problem that needs to be addressed. Simultaneously, more emphasis should be placed on improving character recognition accuracy for complex structures. Figure 6: The process of recognizing offline HCCs using traditional techniques and deep learning. The larger green rectangle indicates the combination of different features and classifiers in the method. The orange rectangle indicates the traditional technique. The blue rectangle indicates the deep learning method. The smaller green rectangle indicates the combination of machine learning classifier and neural network classification. Figure 7 shows the process of HCC learning with concept learning, first proposed by [PERSON] et al. [72] in 2019. Different from traditional deep learning methods, concept learning only needs one sample to achieve related recognition tasks. The method first builds a meta-stroke library with prior knowledge. Then, combined with the stroke extraction method and Bayesian learning, a conceptual model of Chinese characters is constructed. Finally, a character generation model is constructed through Monte Carlo-Markov chain sampling, to obtain the recognition and classification results of the target characters. However, one limitation of the method is that the number of concept models will increase with the complexity of the characters to achieve better performance, which will take a long time to build and fit.