esa-sceva/satcom-chunk-collection huggingface dataset:
# Named Entity Recognition in the Field of Small Sample Electric Submersible Pump Based on FLAT
###### 1 Introduction  
Knowledge graphs [1], as an important data structure for storing domain knowledge and expert experience, offer numerous advantages such as fast querying, computability, and well-structured organization. They have become the main force in the \"information\" and \"digitalization\" of smart oil and gas fields [2, 3, 4]. In the domain of ESP [5, 6, 7, 8], knowledge primarily originates from textual literature, which suffers from fragmented and highly repetitive information, incomplete coverage, and a scarcity of high-quality corpora. These issues lead to unsatisfactory quality of knowledge graphs, with problems such as insufficient mining of fault entities in ESP wells.  
NER is fundamental to natural language processing and the construction of domain-specific knowledge graphs. Mainstream NER models such as Bidirectional Encoder Representations from Transformers (BERT) [9, 10, 11] and Bidirectional Long Short-Term Memory(BiLSTM) [12; 13; 14] require a large amount of high-quality corpora. The existing ESP corpora only support these models in recognizing common entities, but they fall short when it comes to rare entities like \"tubing scaling blockage\", which highlights their limitations.  
This paper addresses the issue of insufficient entity extraction in the ESP domain, caused by the scarcity of high-quality corpora, by researching a NER model for ESP well working conditions under low-resource conditions. Through this study, this paper aims to provide methods and ideas for NER in other industrial fields that also suffer from limited corpora.  
The main contributions of this paper are as follows:  
(1) Introducing semantic analysis technology into dictionary-based word segmentation methods [15; 16; 17; 18] and combining it with the domain-specific nature of ESP texts to improve the accuracy of word segmentation.  
(2) This paper has constructed an NER algorithm based on FLAT [19] by utilizing character embedding [20] and word embedding [21] methods. Additionally, the domain characteristics of ESP wells were considered to establish a nested entity [22] rule-matching mechanism.  
(3) The experiments validate that the algorithm proposed in this paper can more effectively learn character features and word features, and it has advantages in entity recognition for small samples [23; 24; 25; 26] and rare words [27; 28].
# Decision Support Application for Energy Consumption Forecasting
## 2 Material and Methods
### Forecasting Methods
#### 2.1.3 Frbs  
Fuzzy logic has been proposed by [PERSON] in 1965 [15], and it represents the reasoning of human experts in production rules (a set of IF-THEN rules) to handle real-life problems from domains such as control, prediction, and inference, data mining, bioinformatics data processing, robotics, and speech recognition. FRBSs are the combination of the fuzzy logic and the other data mining techniques which are also known as fuzzy inference systems and fuzzy models [29]. An alternative and effective way to extract the required information in the right format from human experts is to gain the knowledge by generating the fuzzy IF-THEN rules automatically from the numerical training data. While modeling an FRBS, structure identification and parameter estimation must be considered as two essential process where the structure identification is a process to find appropriate fuzzy IF-THEN rules and to determine the overall number of rules, and parameter estimation is applied to tune parameters of membership functions. The FRBSs are the combination of fuzzy logic and other techniques.  
This work takes advantage of using the FRBS package [29] to implement fuzzy rule-based forecasting methods. The package divides these forecasting methods into five groups based on the used techniques in their implementation. These groups are:  
1. FRBS based on space partition
2. FRBS based on neural networks
3. FRBS based on clustering approaches
4. FRBS based on genetic algorithms
5. FRBS based on the gradient descent method  
Three FRBS forecasting methods have been chosen to be used in this work. Following sections explain the structure of these methods.
## 4 Proposed MIMU/GNSS UTC Integration Architecture
### Refined Frequency Processor  
Another reason for the system performance degradation induced by large frequency errors is the loss of consistency between the replica carrier signal and the incoming signal. We introduce refined frequency processors to overcome this problem. The generalized structure of the refined frequency processor is shown in Figure 6.  
The refined frequency processor is the core module of the EUTC integration system. Its main function is to fix coherent integration results. The operation of the refined frequency processor can be divided into three steps.  
First, according to the frequency error output by the large frequency error detector, the retuned replica carrier frequency is obtained as  
\[f_{\alpha,re}=f_{ca,NCO}+\delta f \tag{24}\]  
where \(\delta f\) denotes the output of the large frequency error detector.  
Second, the mixing and correlation operations of the current epoch are performed, again based on the retuned carrier frequency. Since the code frequency is usually much smaller than the carrier frequency, the code Doppler frequency shift is also much smaller than the carrier Doppler frequency shift. If the coherent integration time does not exceed 20 ms, the code phase offset caused by a large frequency error is not significant. Therefore, in most cases, we do not need to correct the code Doppler shift and can continue to use the code NCO command of the current epoch in the refined frequency processor. The correlators output a new set of coherent integration values.  
Thirdly, the code phase error and frequency error are estimated based on new coherent integration values. At this point, the frequency error is nominal. A four-quadrant arctangent frequency discriminator is employed in the refined frequency processor. Moreover, the new coherent integration results are inputted to the C/N\({}_{0}\) estimator to obtain a more accurate C/N\({}_{0}\) estimate. The carrier frequency tracking error of the current epoch can be expressed as  
\[\delta f_{\alpha}=\delta f+\delta f_{re} \tag{25}\]  
where \(\delta f_{re}\) is the frequency discriminator output in the refined frequency processor.  
Figure 6: A block diagram of the refined frequency processor.  
Note that the refined frequency processor does not significantly reduce the operating efficiency of a GNSS receiver. In most operating environments, carrier frequency tracking errors do not exceed the threshold. Therefore, it is not necessary to call the refined frequency processor frequently. In addition, since there is no need to adjust the code frequency, the sample values of the three kinds of replica code sequences (early, prompt, and late) can be saved during the first correlation operation. In this way, in the refined frequency processor, the code NCOs generating replica code sequences can be omitted. For these reasons, the refined frequency processor will not significantly increase the computational burden of the receiver.
# A Noniterative Simultaneous Rigid Registration Method for Serial Sections of Biological Tissues +
## 4 Experiments
### Robustness Test  
As proved in Section 3.6, our algorithm obtains optimal solutions under ideal conditions. Therefore, we investigated whether our algorithm remains robust under nonideal circumstances (i.e., when the original accurate correspondence positions are randomly shifted by nonlinear deformation).  
In this experiment, we used synthetic data to test the robustness. As shown in Figure 1, we sampled approximately 50 points from a fish-shaped point cloud to form 10 point sets. The first and last point sets overlap (at bottom left) to ensure that no relative displacement exists. The points with the same shape and color in adjacent point sets are corresponding points. To further verify generality, we sampled different numbers of points (as illustrated in the middle-right and top-left images). To prove that our algorithm is robust to noise, we added random deviations to each point; the different noise ratios are shown in Figure 1 and range from 0.05 to 0.2. We can see that as the noise ratio grows, the registered point cloud becomes progressively blurrier, but retains the overall fish shape.  
To further demonstrate the generality of our algorithm, we investigated whether our algorithm was able to simultaneously align several point sets with small partial overlaps. As shown in Figure 2, unlike the first experiment, in which a complete fish shape was sampled, in this experiment, we sampled only a part of the fish at one time, resulting in eight point sets that partially overlap the adjacent point sets. Similar to the previous example, the correspondences are denoted by points with the same color and shape. The first and last point sets completely overlap; therefore, their relative positions do not need to be adjusted. We also added random noise proportional to the coordinates of each point to simulate real scenes. The experimental result is shown in the second row of Figure 2.  
These results show that, even when partial overlaps exist, our algorithm still achieves fine registration and is robust to noise.  
To analyze the results quantitatively, we adopted the mean square error (MSE) to measure the accuracy between the ground truth of each example and our registration results. As Figure 3 shows, as the noise ratio increases, the MSE remains at a low level (below 0.06). Hence, we can conclude that our algorithm obtains accurate registration results even when inaccurate one-to-one correspondences are obtained.  
Figure 1: Experimental results. Ten point sets with different degrees of noise ratios from 0.05 to 0.2 are shown from left to right in the first row. The corresponding registration results by our algorithm are shown in the second row.  
Figure 2: Experimental results. Eight point sets with different degrees of noise ratios from 0.05 to 0.2 are shown from left to right in the first row. The corresponding registration results by our algorithm are shown in the second row.
## 3 Methods
### Settlement Point Identification Model  
The deformation of the ground elevation is the basis for the selection of settlement points. InSAR is a space-to-earth observation technology, which can calculate the topography and surface changes in the target area by using the complex image pairs observed by the radar at different times or different angles in the same ground area [23]. Coherence and atmospheric inhomogeneity limit the use of InSAR [24; 25]. The PS-InSAR algorithm, by placing the core of the calculation on a part of the target with high coherence, can ensure that the target maintains stable scattering characteristics over a long time sequence [30], which is highly spatially and temporally independent. In addition, it can acquire data over long-time spans with sub-millimeter accuracy and is capable of round-the-clock, all-day monitoring. These PS points are generally distributed on some hard ground objects without vegetation coverage, so this technology is suitable for urban areas.  
After inputting the prepared 15 scene images data into the SARscape software (v.5.6), a connection map can be generated. The central scene image is selected as the basis of the other 14 scene images for registration to eliminate the relative offset between the primary and auxiliary images caused by orbital errors.  
Subsequent differential interference processing is performed on the enslaver and enslaver person images to obtain the interference image set based on the familiar master image. The differential interference atlas of the region is obtained by subtracting the DEM information and the interference atlas. Then, the amplitude dispersion index (ADI) threshold method is used to identify the point target with stable radar scattering. After the above processing, the remaining phase includes the deformation phase, the noise phase, the elevation error and the atmospheric delay phase. The SARscape software can eliminate factors affecting monitoring accuracies, such as terrain residuals, deformation, elevation, and climate errors. In order to obtain more accurate deformation information, points with an error greater than 1 mm were deleted in ArcGIS, and 9,998,423 PS points were obtained by processing satellite data. The settling velocity ranges from \(-\)41.06 mm/y to 7.93 mm/y. Figure 2 shows the PS-InSAR technology processing flow.
## 1 Introduction
proposed an average data augmentation method based on dynamic time warping (DTW) distance [33] called DTW barycentric averaging (DBA) and obtained at least 60% of the two training sets in the UCR archive (containing 16 sets of data and 57 sets of data, respectively). The improved prediction accuracy demonstrates the effectiveness of the method for small datasets [34]. However, this method also has problems, such as the amplification result being easily affected by abnormal sequences and the amplification process being cumbersome. Therefore, this paper proposes a group calculation and random weight dynamic time warping barycentric averaging (GRW-DBA) data augmentation algorithm and ANN model for concrete fatigue life prediction with small datasets through the following innovations. 1. This paper proposes an optimized GRW-DBA data augmentation algorithm based on group computing and random weight mechanism. Compared with other algorithms such as classic DBA, the GRW-DBA algorithm has a simpler operation process, is not easily affected by outliers, and can obtain generated data that are more in line with the distribution of source data. 2. We construct a prediction model based on a GRW-DBA data augmentation algorithm and ANN and develop a graphical user interface. Compared with classical mechanical methods, the model can significantly improve the prediction accuracy and at the same time facilitate engineering applications. The paper is organized as follows. Section 2 shows the innovative data augmentation algorithm and the predictive model used. Section 3 describes the extended data-based modeling process and the experimental process of model hyperparameter selection. Section 4 describes the experimental validation. Section 5 provides a discussion of the results. Section 6 draws conclusions.
# High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
## 4 Results and Discussion  
### Data Processing and Analysis  
#### 4.1.1 The Temperature Stable Period  
Each section of the temperature stable periods lasted for 12-15 h except for the third day with only approximately 2 h. We extracted data for each day with about half an hour to calculate the typical propagation delay in specific temperatures, and got a fitting equation as  
\[d=20.8T+32651.2 \tag{3}\]  
If we want to compensate all the data to 25 \({}^{\circ}\)C, the compensated ideal propagation delay \(d_{c0}\) can be calculated as 33170.7 ps.  
The compensated delay \(d_{ci}\) can be calculated by  
\[d_{ci}=d_{i}-20.8T+519.5 \tag{4}\]  
All of the compensated data are calculated and shown in Figure 4, and the slope of the delay changed to almost flat as \(-0.034\) ps\({}^{\circ}\)C. With the raw and compensated data, we got the TDEV before and after compensation. Figure 5 shows the stability described above, where the black dots and lines represent the raw data results, and the red dots and lines represent the compensated data results. For approximately a half-day averaging time, TDEV achieves 1.16 ps from the original value of 16.2 ps (over 40,000 s). The improvement by temperature effect compensation is more than one order of magnitude.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### End-to-End Speech Synthesis
#### 4.6.2 Speech Synthesis Based on Tacotron  
Tacotron [63; 64] is a fully end-to-end speech synthesis model. It is capable of training a speech synthesis model given <text, audio> pairs, thus alleviating the need for laborious feature engineering. In addition, since it is based on character level, it can be applied in almost all kinds of languages including Chinese Mandarin.  
Like WaveNet, the Tacotron model is also a generative model. Different from WaveNet, Tacotron uses a seq2 seq model with an attention mechanism to map text to a spectrogram, which is a good representation of speech. Since a spectrogram doesn't contain phase information, the system uses the Griffin-Lim algorithm [65] to reconstruct the audio by estimating the phase information from the spectrogram iteratively. The overall framework of the Tacotron speech synthesis model can be seen in [63].  
Since Tacotron is a fully end-to-end model that directly maps the input text to mel-spectrogram, it has received a wide amount of attention of researchers and various improved versions have been proposed. For example, some researchers implemented open clones of Tacotron [66; 67; 68] to reproduce the speech of satisfactory quality as clear as the original work [69]. The authors in [70] introduced deep generative models, such as Variational Auto-encoder (VAE) [71], to Tacotron to explicitly model the latent representation of a speaker state in a continuous space, and additionally to control the speaking style in speech synthesis [70].  
There are also some works that combine Tacotron and WaveNet for speech synthesis, such as Deep Voice 2 [72]. In this system, Tacotron is used to transform the input text to the linear scale spectrogram, while WaveNet is used to generate speech from the linear scale spectrogram output of Tacotron. In addition, the authors in [73] also proposed the Tacotron2 system to generate audio signals that resulted in a very high mean opinion score (MOS) comparable to human speech [74]. The authors in [73] described a unified neural approach that combines a seq2 seq Tacotron-style model to generate mel-spectrogram and a WaveNet vocoder to synthesize speech from the generated mel-spectrogram.
## 4 Transmission Through the Switch  
Transmission from the switch to the destination occurs either using SWIPT or ABCS, depending on the energy requirements of the switch and the algorithm proposed in Section 5. We assume that the system operates under a normal transmission network with packet transmission from the \(S\) to \(R\) node and from the \(R\) to \(D\) node. It adapts BPSK modulation for SWIPT-based operation.  
The transmission power requirements and energy harvesting capabilities differ significantly between SWIPT and ABCS modes. SWIPT requires higher transmission power but offers simultaneous energy harvesting capability, while ABCS provides energy-efficient transmission through signal reflection but without energy harvesting capability. This fundamental difference in power characteristics plays a crucial role in the switch's operation and mode selection.  
Figure 2: Transmission path with S to RN (in red), and RN to DN (in blue) with the positions of each node. S node changes location with time.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 3 Proposed Scheme Based on Homomorphic Encryption and Classic McEliece
### Generation and Matching of Protected Biometric Features
#### 3.2.1 Generation of Protected Biometric Features  
The _ENCODE_ algorithm is applied at enrollment and verification to obtain the vectors \(cb^{R}\) and \(cb^{V}\). The pseudocode to generate protected biometric features is shown in Algorithm 1 and works as follows:  
1. If biometric features \(bf\) are not binary, a binarization process, which preserves distances, is applied, such as Linearly Separable Subcode (LSSC) [35], thus, obtaining a binary biometric feature string \(b\).
2. The binary string \(b\) is divided into \(N\) substrings, \(b=(b_{1},\dots,b_{N})\), with, respectively, \(z_{1},\dots,z_{N}\) bits, where \(z_{i}\) is greater than or equal to \(t\), with \(i=1,\dots,\ N\).
3. Each substring \(eb_{i}\), with length \(n\), is composed of \((n-k)\) random bits from the position \(1\) to \((n-k)\), concatenated by the \(z_{i}\) bits of \(b_{i}\) and by \((k-z_{i})\) random bits from the position \((n-k+z_{i}+1)\) to \(n\). Figure 3 shows the transformation of the binary biometric features \(b_{i}^{R}\) (Figure 3a) and \(b_{i}^{V}\) (Figure 3b). 1. The \((n-z_{i})\) random bits that perform as a padding come from a secret substring \(p_{i}\). The secret string \(p=(p_{1},\dots,p_{N})\) is chosen randomly at enrollment to ensure that the Hamming weights of \(eb_{1},\dots,eb_{N}\) are greater than \(t\). 2. Depending on the realization, string \(p\) can be stored in the client device or reconstructed in some way whenever required (from what the user knows or from a seed the device reconstructs with a Physical Unclonable Function [23]).  
The bits from position \((n-k+1)\) to \(n\) can be randomly permuted, as illustrated in Figure 3a. The permutation is the same at enrollment and verification, as illustrated in Figure 3b.
3.3. The client device checks that the Hamming weights of the substrings \(b_{i}\) are neither zero nor have a low value.
4. _ENCODE_ algorithm is applied to the enrollment and verification substrings \(eb_{i}^{R}\) and \(eb_{i}^{V}\), respectively, obtaining the ciphertexts \(cb^{R}=(cb_{1}^{R},\ldots,cb_{N}^{R})\) and \(cb^{V}~{}=~{}(cb_{1}^{V},\ldots,cb_{N}^{V})\), with \(cb_{i}^{R}=H\cdot eb_{i}^{R}\) and \(cb_{i}^{V}~{}=~{}H\cdot eb_{i}^{V}\).
## 2 Methods
### Study of RFPA with Combiner MRI Experiments  
#### 2.4.1 High Power and Linearity Analysis of RFPA  
The high-power, small-size MRI combiner studied in this work adopted a planar circuit and capacitor loading structure. This design enabled integration with the RF amplifier section of the MRI RFPA on the same PCB, as illustrated in Figure 7, contributing to the miniaturization of the entire MRI RFPA device. By reducing losses in the key combiner of the high-power output section, the overall efficiency of the MRI RFPA could be enhanced. Simultaneously, the resultant heat reduction from decreased losses improved the RFPA's nonlinearity characteristics.  
To validate the linearity characteristics of high-power combiners in RFPA systems, a magnetic-core combiner from Xingaoyi medical equipment company (Ningbo City, China) was implemented. This device demonstrated an insertion loss of 0.6 dB at 128 MHz, and comparative testing under identical conditions revealed nonlinear behavior. When the RFPA input power reached 0 dBm (achieving maximum output), the combiners' power responses were measured at 10, 20, 30, and 60 min operational intervals as shown in Table 8.  
As demonstrated in Table 8, the proposed combiner achieved approximately 0.2 dB higher output power than the magnetic-core counterpart, attributed to its lower insertion loss. Regarding linearity performance, the proposed design maintained output variations below 0.05 dB during extended operation, whereas the magnetic-core structure showed a 0.18 dB drift. This confirms that ferrite components develop nonlinear characteristics under thermal variations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Time (Minutes)**} & \multirow{2}{*}{**RF In(dBm)**} & \multicolumn{2}{c}{**RF Out**} & \multirow{2}{*}{**RF Out**} \\  & & **(dBm)-VSWR** & & **(dBm)-VSWR (1:2)** \\ \hline
0 & 0 & 69.2 & 68.69 \\
10 & 0 & 69.15 & 68.64 \\
20 & 0 & 69.17 & 68.66 \\
30 & 0 & 69.14 & 68.63 \\
60 & 0 & 69.11 & 68.60 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Power variation test results @128 MHz, with a 5% duty cycle.  
Figure 9: Temperature tests in different locations and with different VSWRs.  
The results in Table 9 indicate similar SNR values between the two devices; however, the RFPA with the proposed combiner demonstrates superior excitation efficiency due to its lower insertion loss (IL).