esa-sceva/satcom-chunk-collection huggingface dataset:
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 2 Related Work
### Low-Resource NER  
Addressing the low-resource challenge in NER involves enhancing model generalization and learning from limited samples. In low-resource scenarios, the scarcity of annotated data exacerbates challenges such as the long-tail distribution phenomenon, where certain entity types are underrepresented. To tackle this, researchers have explored strategies that build on fine-grained annotations, semi-supervised learning, and the incorporation of external knowledge.  
E-strPron [18] stands out by integrating a variety of linguistic features, such as pronunciation and radicals, using cross-Transformers and a soft fusion module to enhance the model's grasp of Chinese characters. These methods collectively demonstrate the versatility and continuous evolution of Chinese NER techniques. [PERSON] et al. [19] introduced a semi-supervised pre-training approach that integrates a word library into the language model, enhancing self-attention mechanisms to improve performance on Chinese NER. This method enriches the model's ability to discern entity boundaries and adapt to data-scarce scenarios. Similarly, other approaches have leveraged multilingual training data to improve generalization [6], particularly by explicitly injecting entity labels into the sentence context. Such methods enhance the model's understanding of entity regularities and establish clearer relationships between entities and labels, thus mitigating the scarcity of annotated data.  
MECT [20] introduces a cross-Transformer model that integrates multi-modal data, significantly aiding low-resource Chinese NER tasks. The Multi-Grained [21] method effectively transfers knowledge from larger to smaller models by using an enhanced Viterbi algorithm, while LEAR [8] approaches NER as a question-answering task, proposing label knowledge integration to improve text representation. PCBERT [9] tackles low-sample Chinese NER with a two-stage model. Specifically, P-BERT is a prompt-based model that extracts implicit label extension features from a label extension dataset during the prompt tuning stage and subsequently provides these features to C-BERT during the fine-tuning stage. Meanwhile, C-BERT is a lexicon-based model that incorporates multiple label features for each dictionary entry. Each token is associated with a lexicon set derived from an external dictionary, and each lexicon is mapped to a corresponding label set. This approach can enhance the model's capacity to accurately determine entity boundaries by selecting the lexicon that best aligns with the correct labels. Although validated experimentally, constructing vocabulary trees and label sets for each input sentence is laborious.
## 3 Results and Discussion
### Soil Leaching by Inorganic Acids
#### 3.1.3 Effect of HCl Concentration  
A concentration experiment was carried out to investigate the relationship between the removal efficiency of Pb and HCl concentration; five concentration gradients of 0.1, 0.2, 0.3, 0.5 and 0.7 mol\(\cdot\)L\({}^{-1}\) were set. As can be seen from Figure 2a, when the concentration of HCl increased from 0.1 to 0.7 mol\(\cdot\)L\({}^{-1}\), the removal efficiency of Pb sharply increased from 0.29% to 74.6%, and the content of Pb in the leaching soil decreased from 8021 to 2047 mg\(\cdot\)kg\({}^{-1}\) (Figure 2b). Low concentration of HCl was proved to be inefficient for the removal of lead in the contaminated soil, which was closer to that of deionized water; however, a high concentration of HCl was shown to be more effective. As already mentioned, the pH value of protogenetic soil was 7.89, which caused Pb\({}^{2+}\) to exist in an insoluble form. When the concentration of HCl was low, it was difficult to change the alkalinity of the soil, and the concentration was also easily affected by other heavy metal ions and complexes in the soil, which do not help the desorption of lead ions from soil particles, so the leaching concentration of Pb was lower. [PERSON] [36] reported that the concentration of HCl used for soil leaching should exceed 0.1 mol\(\cdot\)L\({}^{-1}\). Otherwise, it is was found to be difficult to remove the heavy metal elements in the soil, which was consistent with our results. When the concentration of HCl continued to increase, the remediation efficiency increased significantly and tended to be stable when it reached 0.2 mol\(\cdot\)L\({}^{-1}\). There was no significant increase when the leaching concentration continued to increase. It may be that excessive Cl\({}^{-}\) may form a protective layer around the solid particles, thereby inhibiting further dissolution of lead. Moreover, increasing the concentration of HCl will damage soilphysical and chemical properties, resulting in serious soil acidification, and also increase the remediation cost. Therefore, 0.2 mol\(\cdot\)L\({}^{-1}\) HCl solution is the suitable concentration for leaching remediation of heavy Pb-contaminated soil. Herein, the removal efficiency of Pb reached 66.3%, and the lead content in soil after leaching was 2709 mg\(\cdot\)kg\({}^{-1}\) (Figure 2b).
## 2 Background and Literature Review  
### Vanet  
A VANET depends on the operation of the automobiles themselves and lacks a dedicated communication infrastructure [1]. A VANET is a subclass of a Mobile Ad Hoc Network (MANET), which is an autonomous group of mobile devices working together to offer network features in the absence of a permanent infrastructure. MANETs interact with one another using wireless networks and collaborate with one another in a dispersed fashion [2]. These networks tend to be defined by self-organizing, uncertain, and dynamic linkages between the wireless nodes, as well as variable topologies for the network [3]. The immense mobility of the nodes with changing topologies of the network and the VANETs' vast number of nodes (vehicles) with tremendous processing capacity are some of the characteristics that clearly separate them from MANETs [4]. Dedicated Short-Range Communication (DSRC), mobility patterns restricted by roadways, and a lack of substantial power limitations are other features of VANETs [5]. A VANET is made up of automobiles that include an On-Board Unit (OBU) that may communicate with other automobiles or Road Side Units (RSUs) [6]. In a VANET, RSUs are immobile nodes for communications that are placed close to the pathways to improve connection [7]. Vehicle-to-Vehicle (V2V) communication refers to communication between motor vehicles, whereas Vehicle-to-Infrastructure (V2I) communication refers to information dissemination between vehicles and other infrastructure, such as RSUs, tariffs, and web access points [8]. V2X, broadly, stands for Vehicle-to-Everything communication. A Vehicular Sensor Network (VSN) is made up of detectors in automobiles and RSUs that can perceive information about traffic or environmental factors, including warmth, motion, stress, shock, etc. These data are  
Figure 3: Approach to adaptively compute routes, update flow table, and create a FlowMod packet upon receiving of a packet_in message.  
analyzed by vehicle applications to produce messages that can be transmitted via the VANET [9]. Applications for VANETs include remote wireless diagnostics, entertainment, driving assistance frameworks, ideal speed recommendation frameworks, and accident alert systems [10]. However, VANETs have their own unique set of difficulties, including high automobile movement, security issues, and routing problems [11].
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### HMM-Based KPI Anomaly Detection
#### 3.5.2 HMM Training  
This section will train HMM \(\lambda=\{A,B,\pi\}\). HMM training first estimates the maximum likelihood of model parameters based on the observed state sequence, but the time complexity of this direct calculation dependent on the probability formula is very high. The Baum-Welch algorithm [30] is usually used to update transition probability \(a_{ij}\), observation probability \(b_{i}(v_{k})\), and initial observation probability \(\pi_{i}\), from which the updated hidden state probability matrix \(\widetilde{A}\), observation state probability matrix \(\widetilde{B}\), and initial observation probability \(\widetilde{\pi}\) are obtained. After obtaining the updated parameters, the Viterbi algorithm [31] is used to predict the state, and the final anomaly prediction result is obtained; that is, the abnormal KPIs that are most likely to affect this KPI. The Viterbi algorithm uses dynamic programming to find the maximum probability path and finally realize a prediction of the HMM, using a class of KPIs that cause anomalies related to a certain fault. Finally, other KPIs that cause a KPI exception are obtained.
# Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario +
## 3 The OneM2M IoT Platform and Proposed Implementation for Optical Network Control
### The OneM2M Platform for an Optical Network
However, the considered data model fully supports also the IPv6 network addressing scheme. Each Docker container emulates a network device by leveraging two Python scripts. The first Python script implements the monitoring functionalities. In particular, the script pushes towards the OneM2M platform periodic updates related to all the main components of the node (each update message includes around 90 ports, with values mapping the real equipment in the optical laboratory). The update interval can be configured, regulating the rate of the update messages' generation. The second Python script has been adopted to test the configuration functionality of the system, meaning the ability to receive and log the configuration messages received from the OneM2M platform. Additional details regarding the considered scenarios will be provided in the following sections. The work focuses on two main experiments. The goal of the first experiment is twofold: (i) assess the capability of the network devices to efficiently provide parameter status updates to the controller in real-time; and (ii) assess the scalability performance of the Mobius OneM2M platform to efficiently handle a large number of network devices and an increasing number of update messages. In the second experiment, the goal is to assess the capability of the Mobius OneM2M platform to reconfigure network elements, which means: (i) to assess the capability of the platform to configure network elements for provisioning purposes; and (ii) to assess the reactivity of the network when it has to change its working parameters after receiving updates (e.g., a connection link that cannot be reached anymore). As explained in Section 3.2, the performance metrics \(\text{R}_{\text{POST}}\) and \(\text{T}_{\text{C}}\) have been considered to map the two basic functionalities of an SDN controller (i.e., configuration and monitoring). More specifically, considering optical-network control, it is common to evaluate the performances of the proposed controller procedure in terms of control-plane execution time (i.e., \(\text{T}_{\text{C}}\)), as shown in [2; 5]. It is also common to evaluate the load due to the update messages received from the network devices (i.e., \(\text{R}_{\text{POST}}\)), mapping the monitoring aspects of the SDN methodology. In general, optical networks are employed in urban scenarios. An optical metro network is designed to aggregate metro-access rings and provide local traffic to selected metro-core nodes. For this reason, for the experimental assessment, the typical dimension of such networks has been considered, identified in a full/partial mesh of up to 30 nodes [35] or at the varying of the regional population density [36].
## 4 Conclusions  
The surface electromyography (SEMG) is one of the bioelectrical signals for identifying muscular and nervous diseases. Studies show that several cases of nerve diseases lead to muscular dystrophy which, in turn, requires physiotherapy or even external electrical signals.  
The SEMG signal is becoming more important in various applications, in this research; the SEMG signal is acquired from healthy subjects with three loads during dynamic contraction to model this signal that can be used to stimulate the muscle by electrodes to improve the possibility of movement in the human arm muscles. This was achieved using equipment developed to monitor and recode the SEMG signal. The signal was digitally filtered and used to estimate the average bicep muscle force by extracting five-time domain features. The model SEMG signal was achieved by using elbow joint torque for training the artificial neural network (ANN) with a different number of hidden neurons in order to optimize the network performance.  
Based on the results, it can be concluded that the ANN model with two nodes at the input layer and 60 hidden neurons for three loads 3 kg, 5 kg and 7 kg produce MSE of 1.145, 1.3659 and 1.4238 and average regression of 0.99993, 0.99999 and 0.99999 for the SEMG signal with 5 kg and 7 kg. It is a good performance result as it shows that this neural network model can clearly represent the relationship between elbow joint torque and SEMG signals. The R-squared value determined the correlation between the SEMG amplitude with the loads. MAV was chosen as the reference feature for estimation of the bicep muscle force because of its R-squared value equal to 0.9674. By referring to the MAV, the linear equation obtained from the correlation was used for estimation of the bicep muscle force in the elbow joint torque.  
Figure 21: Regression of the trained model for 7 kg load.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] conducted validation; [PERSON] and [PERSON] conducted the review & editing.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Measurement Devices and Setups  
The OMICRON CPC-100/CPTD1 (Vorarlberg, Austria) device was used for dielectric parameter measurements, and the OMICRON MPD600 kit (Vorarlberg, Austria) was used for PDIV measurements. The measurement devices are shown in Figure 5.  
In partial discharge measurements, the MPD600 kit was first calibrated using a calibrator unit. PDIV measurements were recorded by the software when partial discharge events exceeding the predefined threshold of 5 pC were detected as the voltage gradually increased.  
Figure 4: Equivalent circuit and phasor diagram of insulating material.  
The experimental setup for the breakdown measurements is presented in Figure 5c. As an example, a photograph of the test conducted with the PETG capacitor is provided. A 220 V/100 kV test transformer was employed as a voltage source. In addition, a 50 k\(\Omega\) current-limiting resistor was used for protection purposes, while a 1000:1 capacitive voltage divider was employed for voltage measurements.
# Part II--Aroma Profile of Twenty White Grapevine Varieties: A Chemotaxonomic Marker Approach
## 2 Materials and Methods  
### Vegetal Material  
Grapes from 20 white genotypes of _Vitis vinifera_ L. from the EVEGA germplasm bank were analyzed in three consecutive vintages, 2015, 2016 and 2017: 'Agudelo'--VIVC code 2527 [41] 'Albilla do Avia'--VIVC code 24392, 'Batoca'--VIVC code 1037, 'Blanca de Monterrei'--VIVC code 2124, 'Branco Lexitimo'--VIVC code 22838, 'Dona Branca'--VIVC code 2742, 'Fernao Pires'--VIVC code 4100, 'Garrido Fino'--VIVC code 4470, 'Godello'--VIVC code 12953, 'Italia'--VIVC code 5582, 'Jarrosuelto'--VIVC code 24138, 'Lado'--VIVC code 23156, 'Moscatel de Bago Miudo'--VIVC code 8193, 'Palomino'--VIVC code 8888, 'Prixielira'--VIVC code 2476, 'Planta Fina'--VIVC code 9542, 'Ratino'--VIVC code 24127, 'Silveirina'--VIVC code 4178, 'Torrontes'--VIVC code 715 and 'Treixadura'--VIVC code 12629. All varieties are wine grape varieties, except for 'Italia', which is a table grape [42] and therefore not suitable for wine making; it is included in the study because it belongs to the EVEGA germplasm bank, and it could be important to complete the characterization to check how the aromatic grouping behaves with respect to the genetic-geographic one.  
From all the varieties analyzed, 'Agudelo', 'Blanca de Monterrei', 'Branco Lexitimo', 'Dona Branca', 'Godello', 'Lado', 'Palomino', 'Torrontes' and 'Treixadura' are included within the different Protected Designations of Origin for wine in the community. 'Fernao Pires' is a white variety broadly used in Portugal, which was firstly recovered in the germplasm bank known as 'Torrontes II' and lately identified through molecular techniques. 'Albilla do Avia' and 'Ratino' have been recently included in the commercial grapevine register (10/2021). The experimental field was in Ourense (42\({}^{\circ}\)21'34.5'' N, 8\({}^{\circ}\)07\({}^{\prime}\)08.2'' W, elevation 87 m above sea level (a.s.l.)), Galicia (Spain).  
Vines are around 30 years-old, grafted on 196-17C rootstock and trained into a vertical trellis, with a planting frame of 1.8 m between rows and 1.2 m within the row and facing east-west. Varieties are in duplicate plots, from 6 to 11 plants [36; 37].  
Grape maturity was monitored weekly from veraison to harvest. At the optimum industrial ripening stage (19-23 \({}^{\circ}\)Brix), 500 berries were hand-picked from each variety from the top, bottom, and center of the bunch to obtain a representative sample. Subsequently, from the total of each sample collected, 100 berries, in duplicate, were frozen at \(-20\)\({}^{\circ}\)C until the evaluation of aromatic compounds. Another two aliquots of 100 berries were saved to obtain the corresponding must to evaluate the physicochemical parameters.
## 3 Results and Discussion
### Durability against Alkali-Silica Reactivity  
Figure 11 shows the expansion of mortar-based composite containing different ratios of RWG with different colors. At first glance, it can be clearly observed that the expansions relating to the ASR increased with the increasing percentage of RWG in mortar. This behavior was reported by [PERSON] et al. [53] and [PERSON] et al. [54] who studied the effect of color, size, and mass content of recycled waste glass incorporated in concrete. They showed respectively that green glass aggregate presents the best performance for 25% glass content  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Spots/ASR gel**} & \multicolumn{2}{c}{**Chemical Components (Weight \%)**} & \multirow{2}{*}{**Ca/Si (W \%)**} & \multirow{2}{*}{**Ca/Si (W \%)**} & \multirow{2}{*}{**Ca/Si (W \%)**} & \multirow{2}{*}{**Ca/SiO\({}_{2}\)**} \\ \cline{2-2} \cline{5-6}  & **Ca** & & & & & **(W \%)**[51] & & **(W \%)**[50] & **(W \%)**[35] \\ \hline
**spot 1-RWG** & 6.85 & 29.91 & 12.15 & 0.21 & & & \\
**spot 2-Cement paste** & 81 & 15 & 0 & 5.4 & & & \\
**spot 3-Glass-paste interface** & 18.99 & 12.55 & 4.03 & 1.51 & & & \\
**spot 4-Glass-paste interface** & 44.74 & 23.84 & 2.39 & 1.87 & & & \\
**ASR gel** & & & & & 0.58 & 0.42 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: SEM-EDS analysis of mortar incorporated with 50% volume content of RWG (MRWG50).  
Figure 9: Optical microscopic view of MRGW50 after autoclaving test of durability against ASR: (a) at 50\(\times\) magnification; (b) at 100\(\times\) magnification.  
regarding ASR resistance, and the expansion falls below 0.1% when the maximum dosage of glass particles with maximum size of 2.36 mm is 10%.  
Therefore, the expansion of mortar reinforced with different volume contents of RWG is considerably lower than the maximum value of 0.15% prescribed in the standard specifications [44]. Indeed, the expansions of RM, MRWG10, MRWG20, MRWG30, and  
Figure 11: Expansion of different mortar-based composites reinforced with different volume contents of RWG, compared to limit expansion value of 0.15%.  
Figure 10: Optical macroscopic view of CRWG50 after 90 days of curing: **(a,b)** at magnification 100\(\times\); (c,d) at 200\(\times\) magnification; (e) at 500\(\times\) magnification.  
MRWG50 are lower to limit expansion value respectively by 71.46%, 72.76%, 67.50%, 65.14%, and 62.87%. The result of MRWG50 indicates that ASR increased with the RGW amount increase in concrete, but it remained below the limit expansion value. This finding is in line with the small amount of cracked particles noticed in Figure 9.  
The relationship between the expansion and the volume percentage of RWG, for the studied composite materials, can be expressed as a linear line, with a correlation coefficient of R\({}^{2}\) = 0.88.  
\[\epsilon=0.0003\theta+0.0414 \tag{8}\]
## 2 Uncertainty Processing Based on Clustering Technique
### Selection Strategy for Power System Planning Scenarios from Clustering Result  
In order to ensure that the selected scenarios can contain sufficient information on the whole power dataset, as well as control the scenarios' number to an acceptable range, this paper proposes an effective data sampling strategy as follows:  
Figure 3: Clustering result of wind power dataset.  
Figure 2: Stability degree boxplot for cluster number analysis.  
On the basis of clustering results, calculate the radius of each cluster as follows: \[r_{i}=\frac{\sum\limits_{k=1}^{n_{i}}d(x_{k},\lambda_{i})}{n_{i}}\] (8) where \(r_{i}\) is the radius of the \(i\)th cluster; \(n_{i}\) is the data sample number of cluster \(i\); and \(x_{k}\) is the data sample belonging to cluster \(i\). (b) Set \(t_{\text{min}}\) as the minimum sampling number of a single cluster, then determine the sampling number of each cluster by: \[t_{i}=\left\lfloor t_{\text{min}}\frac{r_{i}}{r_{\text{min}}}\right\rfloor\] (9) where \(t_{i}\) is the sampling number of cluster \(i\) and symbol \"\(\left\lfloor\right\rfloor\)\" is the rounded-down value of a quantity. It can be inferred that a large sampling number will be applied to the cluster with a large radius. This means that the cluster having the larger distribution range will be sampled more times, thus fully extracting the corresponding data information. (c) According to the calculated sampling number, a total of \(\Sigma t_{i}\) data samples are sampled at random to generate the typical scenario set. For each scenario, the corresponding occurrence probability is given by: \[\alpha_{k}=\frac{n_{i}}{t_{i}\sum_{j=1}^{h}n_{j}}\;\;if\; scenario\;k\in cluster\;i\] (10) where \(\alpha_{k}\) is the occurrence probability of scenario \(k\); \(h\) is the cluster number. (d) Examine the accuracy of the generated typical scenario set by calculating the correlation coefficient between the data distribution represented by the typical scenario set and the whole power dataset. (e) Repeat steps (c)-(d) \(s_{m}\) times. Based on \(s_{m}\) times calculation, the scenario set with the largest correlation coefficient is determined to be the optimal scenario set and is employed in the subsequent TNEP model.