esa-sceva/satcom-chunk-collection huggingface dataset:
# TCNformer Model for Photovoltaic Power Prediction
## 3 Methodology
### Decoder  
In the Transformer model, the Encoder can be calculated in parallel, but the Decoder needs to decode step by step. As with the LSTM model, error accumulation will occur. This study introduced a one-step TCN decoding operation:  
\[X_{0}\ \ =Zeros[O,d] \tag{20}\]  
\[X_{des}=concat(X,X_{0}) \tag{21}\]  
\[X_{de}^{0}\ =CycleEmbed(X_{des}) \tag{22}\]  
In Formula (20), \(X_{0}\) is the result of the zero-filling operation. One-step decoding divides the Decoder's input into two parts through a zero-filling operation. The first \(I\) datum is a known sequence, the last \(O\) datum is a sequence to be predicted, and \(X_{de}^{0}\in\mathbb{R}^{(I+O)\times d_{model}}\) is the Decoder's input data. At this time, part of the time information of the data to be predicted is also transmitted to the Decoder through the period embedding module for  
Figure 6: The structure of the Feedforward layer.  
prediction. The prediction process of the Decoder is similar to that of the Encoder, but it has a more of a self-attention layer than does the Encoder.  
\[S_{de}^{l,1}=ProbSelfAttention\left(X_{de}^{l-1}\right) \tag{23}\]  
\[S_{de}^{l,2}=SelfAttention\left(S_{de}^{l,1},X_{en}^{N}\right) \tag{24}\]  
\[S_{de}^{l,3}=FeedForward\left(S_{de}^{l,2}\right) \tag{25}\]  
\[X_{de}^{l}=S_{de}^{l,3} \tag{26}\]  
In Formulas (23)-(25), \(S_{de}^{l,1}\in\mathbb{R}^{(l+O)\times d_{model}}\) is the calculation result of the sparse attention mechanism in the Layer I Decoder module, \(S_{de}^{l,2}\in\mathbb{R}^{(l+O)\times d_{model}}\) is the result of matching the sparse attention mechanism in the Layer I Decoder module with the feature map obtained in the Encoder, and \(S_{de}^{l,3}\in\mathbb{R}^{(l+O)\times d_{model}}\) is the calculation result of the Feedforward layer in the Layer I Decoder module. The calculation method of \(\text{FeedForward}(\cdot)\) and \(\text{ProbSelfAttention}(\cdot)\) is the same as above. \(\text{SelfAttention}(\cdot)\) is the self-attention mechanism. (See Section 2.3 for the calculation method.)  
\[X_{pred}=TCN\left(X_{de}^{M}\right) \tag{27}\]  
\(X_{pred}\in\mathbb{R}^{O\times d_{model}}\) is the final prediction result of TCNfomer, which uses TCN to make generative predictions. The TCN structure used in this paper is shown in Figure 7.
###### Abstract  
This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thingerio to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l, where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of \(-\)122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.  
Andeen region; intelligent agriculture; LoRa technology; low-cost LoRa node and gateway; wireless sensor networks; IoT system +
Footnote †: journal: Sensors  
doi:10.3390/s22186743  
Footnote †: journal: Sensors
## 2 Case Study  
The considered case study is the Grimsel 2 pumped-storage power plant operated by Kraftwerke Oberhasli AG (KWO) [40] and built between 1974 and 1980 in Switzerland. This power plant is equipped with four 100-MW ternary groups: a horizontal-axis motor/generator, a Francis turbine, and a pump. The number of guide vanes is \(z_{g}=24\), and the number of runner blades is \(z_{b}=17\). The speed, discharge, and power factors along with the specific speed at the BEP are respectively:  
\[n_{ED}= \frac{nD}{E^{1/2}}=0.271 Q_{ED}= \frac{Q}{D^{2}E^{1/2}}=0.183 P_{ED}= \frac{P_{m}}{\rho D^{2}E^{3/2}}=0.171 v= \frac{\omega Q^{1/2}}{\pi^{1/2}(2E)^{3/4}}=0.247 \tag{1}\]  
At the BEP, the Reynolds number is equal to \(21.6\times 10^{6}\) based on the discharge velocity and the diameter at the runner outlet section (these two quantities are used to define the dimensionless variables in the paper. The dimensionless pressure is defined with the head at the BEP).  
As the electricity market requires more and more flexibility, the number of start-up, stand-by, and shut-down procedures per year has increased strongly during the last decade. Consequently, the turbine spends more and more time at operating points for which the hydraulic conditions are far from the ones at the BEP. After several operation cycles, some cracks have been observed on the runner at the junction between the trailing edge of the blades and the hub. The phenomenon responsible for the development of the cracks is not yet clearly identified. However, experimental measurements on the prototype showed that the maximum amplitude of strain occurs at the SNL operating point during the synchronization of the generator with the electrical network, as well as during the shutdown phase (see Figure 1). The frequency associated with this phenomenon is close to \(49*f_{n}\) (with \(f_{n}\) the runner frequency).
# Simultaneous DC Railway Power System Analysis Method Using Model-Based TPS
## 6 Conclusions  
In this study, a mTPS program was constructed based on the MATLAB Simulink program. Additionally, the speed and traction and breaking power profiles as well as voltage profiles were obtained for the entire route (one-way) for DC 1500 V metro line. The main contribution of this paper is summarized as follows:  
* The mTPS provides fully integrated performance with input vehicle and track data.
* The mTPS results complies with reasonable requirements.
* The mTPS can provide detailed dynamics of the train comparing previous TPS works.
* Simultaneous multi-rates simulation of DC metro with mTPS were shown in the previous section.  
Figure 13: Verification of mTPS with legacy codes. (**a**) Comparative study for mTPS result and code A, B. (**b**) Magnified figure for Figure 13a.  
Further work includes an improvement of the accuracy and temporal resolution of the model produced from this study. So, our final target for train load modeling is to use the mTPS as a single, universal model in OPAL-RT framework for virtual commissioning such as propulsion inverters such as VVVF inverters or DC/DC converters for on-board railway vehicles. At the same time, we plan to integrate it into the hydrogen-supplied train and locomotive model and develop it into a hybrid computation model so that it can be directly used in real-time simulation.  
Conceptualization, H.C. and J.K.; data curation, J.K. and H.K.; formal analysis, H.C.; funding acquisition, H.J.; investigation, H.C.; methodology, H.C.; project administration, H.J.; resources, H.C., H.J. and H.K.; software, H.C.; supervision, H.K.; validation, H.C. and J.K.; visualization, H.C.; writing--original draft, H.C.; writing--review and editing, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from R&D Program of the Korea Railroad Research Institute, Republic of Korea (PK2203E1).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Discussions
### Thermal ExpansionEffects  
Except for the VD induced by the non-tidal loading effects, the observed GNSS height changes are also influenced by thermal expansion. With respect to GNSS solutions, the GRACE mission and GGFC solutions can only capture the signals caused by mass variations, and the effects of thermal expansion of monuments and nearby bedrock did not appear in the GRACE and GGFCVD data [12; 57]. To investigate the thermo-elastic effect on GNSS, the research of [57] extended a theoretical model to estimate the thermal expansions of GNSS monuments and nearby bedrock for 86 globally distributed GNSS stations based upon measurements of surface air temperatures. Their results show that annual temperature variations are the dominant contributors for the thermal expansion of GNSS monuments and nearby bedrock. The contributions of thermal expansion to GNSS height changes display largely spatial variations and can reach to 3.9 mm, which is significantly larger than the prediction from [12]. While the research of [58] indicated that the predicted amplitude of the thermally induced surface deformation in the global scale is at the millimeter level with the largest \(\sim\)2 mm. More recently, the research of [20] also used the annual amplitudes and phases from [57] to correct the effect of the thermal expansion of monuments and nearby bedrock. In their research, only four solutions among eleven solutions showed an improvement after thermal expansion correction, suggesting that the effect of thermal expansion is not significant in the residuals between GNSS and GRACE data, due to the large uncertainty in the GNSS postprocessing methods. Therefore, the thermal effects due to local thermal variations on GNSS observations still need to be carefully taken into account in an analysis of seasonal geophysical signals.
## 2 Materials and Methods
### Surrogate Modeling and Validation
#### 2.2.3 Polynomial Chaos Kriging  
The combination of the universal kriging with the PCE methods allows to obtain a new metamodeling technique that is more efficient than the two methods separately taken. The PC-Kriging in fact uses the regression-type PCE for capturing the global behavior of the computational model, whereas interpolation-type kriging allows to capture local variations as a function of the neighboring points of the experimental design. At the end, a more accurate metamodel is achieved by the use of the two techniques combination. The PC-Kriging method can be defined as a universal kriging model, whose mean value (trend) is formed by a set of orthonormal polynomials as in PCE technique and can be described by the following equation (the apex \"PCK\" stays for \"polynomial chaos kriging\"):  
\[\hat{\gamma}^{PCK}=\hat{M}^{PCK}(\mathbf{x})=\sum_{j=1}^{p}\alpha_{j}\psi_{j}(\bm {X})+Z(\mathbf{x}), \tag{6}\]  
where it can be noticed that the first term \(\sum_{j=1}^{p}\alpha_{j}\psi_{j}(\mathbf{X})\) represents the trend of the model and is equal to the PCE solution (Equation (5)) whereas the second term \(Z(\mathbf{x})\) is a calibration term as in Equation (3), represented by the stationary Gaussian process.  
It is thus evident that the construction of PC-Kriging model requires two steps: (i) the determination of the orthogonal polynomials and their coefficients \(\alpha_{j}\) to estimate the trend and (ii) the calibration of the Kriging model, by evaluating the variance \(\sigma^{2}\) of the Gaussian process and the hyperparameters \(\mathbf{\theta}\) of the autocorrelation function \(R\) as in Equation (4).  
There are various ways to jointly optimize the two parts. Among all the PC-Kriging models available, we chose the optimal PC-Kriging (OPCK) metamodel as the best one that minimizes the leave-one-out error, as detailed in the next sub-section. The OPCK estimates the optimal set of polynomials by least angle regression selection (LARS) algorithm [34], which detects a sparse set of polynomials and their corresponding coefficients in decreasing order according to their correlations to the current residual at each LARS iteration. Then, it estimates the trend of PC-Kriging model by adding each polynomial individually and calibrating for each iteration the new PC-Kriging model. The surrogate models' solutions to PC-Kriging procedure were obtained by the software \"UQLab: The Framework for Uncertainty Quantification\" [35].
## 2 Materials and Methods
### Methods
#### 2.3.1 Neighborhood Spatio-Temporal Feature Extraction Module  
CNN is a kind of deep feedforward neural network with convolution operation as the core, which usually includes input layer, convolution layer, pooling layer, fully connected layer, output layer, and other modules. The feature extraction of data by convolutional neural network mainly depends on the convolution kernel in the convolution layer. The convolution kernel generally performs convolution operations in a step-by-step manner to extract data features (Figure 4).  
Figure 2: Driving factors of LUCC.  
LSTM is a special RNN network, which solves the problem of gradient explosion and disappearance of long time series data in training, and is suitable for long time series analysis and prediction. Compared with the standard RNN network, LSTM adds a memory state unit to the hidden layer neural node to store the past information, and uses three threshold control structures (including input gate layer, forgetting gate layer, and output gate layer) to control the forgetting and updating of historical information, so that the information is effectively filtered. The basic structure of LSTM is shown in Figure 5.  
Figure 4: The process of convolution operation.  
Figure 3: Flowchart of RF-CNN-LSTM-CA model.  
a square neighborhood, a set of units around a given unit. All the Moore neighborhood data provide spatial information for the CNN-LSTM model.  
During the model training, the normalized data are input into the CNN network for important information extraction, and then the extracted spatial feature data are input into the LSTM model for temporal feature learning. The specific process of CNN-LSTM model used to extract neighborhood spatio-temporal feature data is shown in Figure 6: (1) the land use/cover data of 2000, 2005, and 2010 are clipped with a 3 \(\times\) 3 window size and combined into a data cube according to the time sequence to form a neighborhood dataset; (2) The data of multiple land types in 2010 and 2015 were superimposed for LUCC detection. The change data from 2010 to 2015 were used as the label dataset, and the label categories were expressed by numbers 1-6, that is, converted to different situations of six land types. (3) The above two datasets are integrated as a training set and input into the CNN-LSTM model for full sample learning; (4) Put the land use/cover neighborhood datasets of 2005, 2010, and 2015 into the trained CNN-LSTM model for prediction; (5) finally, the neighborhood probabilities of six different land use/cover types are output through the fully connected layer, which are converted to cropland, grass, forest, shrub, impervious surfaces, and water area, respectively (Figure 7).  
Figure 6: Flowchart of extracting neighborhood probability from CNN-LSTM model.
## 5 Results  
As the first step in the series of experiments, we trained a baseline ASR system using original clean adult speech from the WSJCAM0 database and tested the system with original clean children's speech from the PF-STAR database (i.e., no data augmentation or time-scale modification was used). As expected, this system gave a poor performance (WER = 19.58%) due to the mismatch induced by having adults' speech in training and children's speech in testing. We then also included the other mismatch type discussed in the introduction by testing the same system with noise-corrupted children's speech. The results indicated, as expected, that the system performance deteriorated severely: in the noise condition with SNR = 5 dB, for example, the WER value rose to 82.67%, 87.40%, 92.32%, and 46.12% for babble, white, factory, and volvo noise, respectively. As also reported in previous studies [22; 51], these experiments indicated that there is lots of room for improvement in recognition of children's speech in noisy conditions. In the following sub-sections, we report on the results from the experiments, which were conducted to improve the system performance step by step by first using data augmentation, then time-scale modification based on \(F_{0}\) modification, then time-scale modification based on speaking rate modification, and finally all of these three methods combined.
## 4 The DIK Architecture
### Implementation
#### 4.3.1 Medical Knowledge Extraction Method  
Medical knowledge extraction methods are mainly divided into two ways: manual extraction and automatic extraction. Manual extraction is a sentence of certain rules to collect and organize relevant medical information and extract knowledge, which currently includes ICD-11, clinical medical knowledge base, SNOMED-CT, and so on. Although its accuracy is high, the process of its construction is too time- and energy-consuming. Automatic extraction, on the other hand, uses techniques such as data mining, machine learning, and artificial neural networks to automatically extract essential elements from medical resources. For example, the integrated medical language system UMLS is constructed by the automatic extraction approach.  
Currently, BiLSTM-CRF is the most mainstream deep-learning model for entity extraction in the medical field. [PERSON] et al. (2013) experimentally compared BiLSTM-CRF with other and its learning models for entity extraction of electronic medical records, and the experimental results showed that the BiLSTM-CRF model was effective in improving the accuracy of the results. Therefore, in the medical knowledge extraction phase of our work, we used the deep learning model BiLSTM-CRF model and combined it with a semi-supervised learning method (Bootstrapping) to complete the data extraction task for Chinese electronic medical records, and the extraction results included four types of medical entities and nine types of entity relationships, and on this basis, we constructed a medical knowledge graph based on electronic medical records. In addition, to further improve the accuracy of medical entity recognition, the Attention Mechanism was introduced,  
Figure 5: Comparison of two knowledge graph construction processes.  
and a new model BiLSTM-Attended-CRF model was constructed to assign an attention weight to each word so that the model can assign higher weights to entity words, and experiments were designed to demonstrate the effectiveness of the method [14].  
Web crawlers as a widely used and automatic way to collect data can obtain the needed data by fetching the specified web pages and parsing them. [PERSON] et al. [15] designed and implemented a distributed crawler-based Web spatial data acquisition system and tested the effectiveness of the system. [PERSON] [16] designed a Python-based distributed crawler system, which mainly includes three modules: a data storage model, web capture module, and web analysis module, and the three modules cooperate to complete the data collection task. We used the existing Scrapy crawler framework to implement the data collection of online medical communities.
## 2 Metallic Transmission Medium Testing
### Metallic Ended Line Testing MELT  
Testing of a metallic transmission medium, or in other words, evaluating of the parameters acquired from a given homogeneous symmetric line, begins with the identification of the line on which the DSL service interruption has been reported. This can be achieved via multiple identifiers, such as a phone number, an address or a unique operator identifier.  
The MELT test is performed only when the customer reports a fault with the DSL connection, i.e., it is not performed automatically. It often serves as the first fault diagnosis, upon a request from the network operator. If MELT tests were used automatically and for continuous fault diagnosis, it would represent a large and often unnecessary traffic load on lines. This increased traffic load can limit customer service provision, and therefore the number of MELT tests is limited to a maximum of one hundred per day. The overall MELT test procedure takes up to 20 s (as specified) from the test start, and the results are processed almost immediately after completion. The basic parameters of the MELT test that can be used for evaluation are [32]:  
* foreign voltages (DC and AC),
* capacity.  
Since the electrical characteristics of the conductors are measured individually in relation to the reference points, they are more suitable for better analysis of failures. In comparison, DELT and SELT tests can only acquire information about value changes in transmitted signals.
# Design and Implementation of a Semantic Information Expression Device Based on Vibrotactile Coding
## 1 Introduction
The first time the vibration function of the phone was utilized for Braille tactile reading was realized in 2009 [22], where the user crossed his finger over the screen of the phone to feel the concave and convex vibration information of the six-dot Braille for reading. However, it was not widely used because it required another special piezoelectric sensor device on the phone. Later, [PERSON] et al. improved the device on this basis, using the law of vibration of the cell phone and the Morse code to encode Braille, which made the application of Braille reading by phone wider [23], but the recognition accuracy of Braille is low, only 71%. In addition, in the category of direct-contact devices, there has been a lot of research on vibrotactile keyboards in recent years [24; 25; 26], and most of the keyboard devices are used in conjunction with Braille displays to realize human-to-human communication. For example, [PERSON] [27] designed an electronic Braille reading and writing device that uses six vibrotactile actuators (a.k.a. keyboards) to represent a Braille cell, which is used for both sensing vibrations and inputting Braille characters. However, it still contains errors in reading and takes a relatively long time to read. The above are some studies of the two types of devices, and the device discussed in this work belongs to the direct-contact device of the two types of devices. The device uses the index and middle fingers for touch-reading, as the fingers have a higher tactile sensitivity from a psychophysical point of view [28]. In addition, this device can touch-read one Braille cell at a time, which improves reading efficiency. This coding scheme is designed with reference to the \"Chinese Common Braille Scheme\", which is the most suitable Braille scheme for Chinese characters after a long period of research and exploration, modification and improvement by experts. Vibrating motors are arranged according to the distribution of Braille cells in order to achieve the maximum similarity with paper Braille so as to match the user's reading habits to the greatest extent. Moreover, research at home and abroad on using vibrotactile coding to express Chinese Braille is relatively scarce, and most of it focuses on English Braille. Therefore, the main research goal of this paper is to use vibrotactile to encode Chinese Braille, and to design a device that allows users easy and convenient interpretation of the semantic information contained in the encoding, so that visually impaired people or people in complex environments (e.g., darkness or noisy environments) can use the device to read the Chinese text and obtain the information contained in the text without any barriers. The main work of this paper is as follows:  
* Testing out the appropriate vibration intensity and designing a suitable vibration coding scheme. * Several days were spent for subjects to learn Braille and device usage [29]. * Evaluate the usability of the device, mainly in terms of Braille recognition accuracy.