esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Model Kinematic and Dynamic Definition  
The model introduced at the beginning of the chapter was imported into the MSC Adams environment, and its kinematics has been defined. Referring to Figure 2, the rotation of the ATP Body around the \(Z\)-axis (azimuth DoF) has been inserted as a rotational joint, which was also employed to specify the ATP Pointer's rotation (elevation DoF) around the \(Y\)-axis. The respective torques have been added to these two joints and defined as system variables. The ATP system displacements caused by ship motions have been introduced through a spherical joint placed at the base of the ship deck segment and angular displacements have been defined as system variables.  
At the output, the displacement in terms of Euler angles of the Pointer was measured and it was used to measure the accuracy during the pointing operation phase. The ADAMS model with input and output quantities is presented in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Angle** & **Accuracy** \\ \hline Azimuth & \(\leq\)0.18 mrad (RMS) \\ Elevation & \(\leq\)0.09 mrad (RMS) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Azimuth and elevation error quantities.  
Figure 5: Ship motions expressed by roll, pitch and yaw.
## 3 Time Series Prediction of Aerodynamic Noise  
### Data Preparation  
Aerodynamic noise induced by flow around a cylinder is a critical factor in engineering design and serves as the foundation for techniques of noise control. Here, the sound pressure time series of aerodynamic noise induced by flow around a cylinder is taken as the research object.  
Referring to the experimental model conducted by [PERSON] et al. [33], we employ ANSYS Fluent computational fluid dynamics (CFD) software with launcher version of 18.0.0 to perform numerical simulations of aerodynamic noise induced by flow around a circular cylinder, generating the dataset for model training and prediction. As is illustrated in Figure 2, a two-dimensional physical model of aerodynamic noise induced by flow around a cylinder is established. The cylinder has a diameter of 19 mm. On the left side, the velocity inlet is defined, while the right side is specified as the pressure outlet. The top and bottom sides are set as symmetry boundaries. The Smagorinsky-Lilly model of large eddy simulation is used, and the fluid medium is an incompressible ideal gas at a temperature of 300 K with a viscosity of 1.7894 \(\times\) 10\({}^{-5}\) kg/(m-s). The time series of aerodynamic noise is captured by a receiver positioned at (0, \(-\)0.665 m). Figure 3 shows sound pressure values collected in 3988 steps with inlet velocities of 60 m/s, 69.2 m/s, and 80 m/s. Among these cases, the one with an inlet velocity of 69.2 m/s and a Reynolds number of 90,000 is a classic case and has been widely used to validate and compare the performance of different methods and turbulence models. The high Reynolds number means it is at a turbulent state where the inertial forces significantly outweigh the viscous forces and result in a complex and unstable flow behavior, which poses significant challenges and provides an ideal benchmark for testing the accuracy and reliability of turbulence prediction models.
# An Approach for Modelling Harnesses in the Extreme near Field for Low Frequencies
## 2 Near-Field Approximations Very Close to the Dipole Source
### Near Field Representation of Harnesses in the Low-Frequency Domain
In order to overcome this, we need to consider the general problem of multiple infinitesimal sources carrying electric currents and individually satisfying each of the above three necessary conditions. The vector potential \(\mathbf{A}(x,\;y,\;z)\) is calculated as the sum of the individual vector potentials of all sources:  
\[\mathbf{A}(x,y,z)=\sum\mathbf{A}_{i}(x,y,z)=\sum\left(\frac{\mu}{4\pi}\int\;I_{e,\;i} \left(x^{\prime}{}_{i},y^{\prime}{}_{i},z^{\prime}{}_{i}\right)\frac{e^{-jkr_ {i}}}{r_{i}}dl^{\prime}_{i}\right) \tag{8}\]  
where \(I_{e,\;i}\left(x^{\prime}{}_{i},y^{\prime}{}_{i},z^{\prime}{}_{i}\right)\) is the current of the source \(i\), \(r_{i}\) is the distance between any point of the source \(i\) and the observation point \((x,y,z)\), and \(dl^{\prime}_{i}\) is the length of the source \(i\). It should be noted that in the case of a single harness topology with the same current, the current is constant for every \(i\) (\(I_{e,\;i}\left(x^{\prime}{}_{i},y^{\prime}{}_{i},z^{\prime}{}_{i}\right)=I_{0}\)). Consequently, the total field at the observation point is the sum of the fields of all individual sources according to (1)-(3). This is valid for an infinite number of segments, but taking into account a specific required level of accuracy, it can be finally approximated with a finite number of segments. Thus, by carefully choosing the proper segmentation of the source--in order for its geometrical characteristics to fulfill the three necessary conditions--the radiated field can be estimated with satisfying accuracy. In the next section, we showcase the aforementioned segmentation technique and discuss its applicability.
## 3 Results and Discussion
### Establishment of Identification Model  
The flow chart of the overall system of the intelligent sound-aided water leakage identification model is shown in Figure 12. The system in this project mainly adopts \"rolling monitoring\", sets up a monitoring window to analyze the data in the window, and completes \"detection\" and \"identification\" in sequence. Then, the latest sampled data are included in the monitoring window, the oldest data in the monitoring window are removed, and the above process is repeated. The feature extraction part in Figure 12 is the process developed in this study. The key steps are as follows:  
1. Establish the training database;
2. Develop the intelligent sound-aided water leakage identification model;
3. Establish the automatic encoder based on the convolutional layer;
4. Establish the automatic encoder based on the full connection layer;
5. Compare and verify the detection performance of the above algorithms;
6. Extract features based on deep learning technology.  
According to the field signal database collected from a bamboo seedling field, the \"deep automatic coder\" and \"convolutional neural network\" were used as the working condition detection and feature extraction methods to learn the steady-state data (leakage data) in the training field signal database, and the test data were used to test whether the automatic coder can successfully detect transient occurrences, as shown in Figure 13. The criterion for transient determination is the error between the restored signal of the automatic encoder and the original signal. If the error exceeds the threshold value (the threshold value is a signal-to-noise ratio of 500) set in this study, it is determined to be transient, as shown in Figure 14.  
Pre-labeled data are imported into the convolutional neural network for fitting, and the most representative characteristics of the data are obtained. Whether the prediction index of the convolutional neural network conforms to the label characteristics is observed. If the reduction is better, it means that the extracted features are more representative. Figure 15 is a graph of model training results and errors. In the displayed training curve, the accuracy of the validation data set (orange line) reaches 92%, indicating that the model has the ability to reliably test the characteristics of water leakage sound.  
Figure 12: Flow chart of the whole system of intelligent sound-aided water leakage identification model.  
Figure 14: Schematic diagram of automatic encoder restoration error value and threshold value.  
Figure 13: Flow chart of automatic encoder system.  
Figure 15: Training results and error values of convolutional neural networks.
###### Abstract  
Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is appealing for the provision of high spectral efficiency in digital terrestrial broadcast systems. To fully obtain its advantageous features, it is very important to remove the frequency mismatch between the transmitter and the receiver. In this paper, we present the performance analysis of joint estimation of carrier and sampling frequency offsets in the MIMO-OFDM-based advanced television systems committee (ATSC) 3.0 system. In the MIMO-OFDM ATSC system, the continual pilot (CP) is primarily utilized to perform frequency synchronization. To efficiently suppress an unwanted bias introduced by the presence of random-likely located CPs, an optimal pilot subset is selected to form the basis of least squares frequency-offset estimation. A closed-form mean squared error is derived in the context of MIMO-OFDM, considering the multipath fading channel. We show via computer simulations and numerical analysis that the proposed estimation method achieves higher estimation accuracy than the existing estimation method.  
Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM); advanced television systems committee (ATSC) 3.0; frequency offset; pilot subset +
Footnote †: journal:
# Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification
## 3 Materials and Methods
### Prompt Tuning Construction  
Prompt tuning formalizes the classification task into a masked language modeling problem. Specifically, prompt-tuning wraps the input sentence with a natural language template, where several words are obscured that imply a relation between two entities contained in the sentence. For example, as show in Figure 2, an input query is \"_London is the biggest city in the UK_\". The entities contained in the query are kept. The converted input to the model is \"_[CLS] + original sentence + In this sentence, the London is the [MASK] of the UK.[SEP]_\". Similarly, a support sample for the relation class of the above query, \"_Washington is located in the United States._\", is converted to the sentence, \"_[CLS] +\(x_{support}\)+ In this sentence, the Washington is the [MASK] of the United States.[SEP]_\". Let \(\mathcal{M}\) be a language model pre-trained on large scale corpora (in this paper, we use BERT). Let \(q=\left(x_{1}^{q},h_{q},x_{3}^{q},\ldots,t_{q},\ldots,x_{n}^{q}\right)\) be a query sentence, where \(h_{q}\) and \(t_{q}\) are two entities, and \(n\) is the length of the query. After preprocessing, the converted sentence of \(q\) is input to the \(\mathcal{M}\). Its contextualized representation is produced, such as \(\left\{h_{[CLS]}^{q},h_{1}^{q},\ldots,h_{[MASK]}^{q},\ldots,h_{[SEP]}^{q}\right\}\). Finally, the representation of [MASK] is fed into an output layer of \(\mathcal{M}\) to predict the probability distribution over the vocabulary space:  
\[P_{q}(h_{[MASK]}^{q}=v,v\in V|q)=softmax(\mathbf{W}h_{[MASK]}^{q}+\mathbf{ b}), \tag{1}\]  
where \(\mathbf{W}\) and \(\mathbf{b}\) are trainable model parameters, \(V\) is the vocabulary of the model, and \(v\) is a word in the vocabulary \(V\). The above formula gets the probability that the relation contained in the question is \(v\). Similarly, input the support samples of each class separately, and the similarity between each class and each word in the vocabulary is obtained. Let \(s_{ij}=\left(x_{1}^{s_{ij}},h_{ij},x_{3}^{s_{ij}},\ldots,t_{ij},\ldots,x_{m}^{ s_{ij}}\right)\) be the \(j\)-th support sample for relation class \(r_{i}\). Wrap \(s_{ij}\), input to the pre-trained language model \(\mathcal{M}\), and obtain the representation of [MASK], \(h_{[MASK]}^{s_{ij}}\). The similarity between the relation represented by this support sample, and each word in the vocabulary is  
\[P_{ij}(h_{[MASK]}^{s_{ij}}=v,v\in V|s_{ij})=softmax(\mathbf{W}h_{[MASK]} ^{s_{ij}}+\mathbf{b}). \tag{2}\]  
For the \(K\)-shot task, there are \(K\) support samples for each relation class. Thus, input \(K\) support samples separately and obtain \(K\) probability distributions. Finally, the mean-pooling operation is performed to integrate the probability distributions over the vocabulary space obtained by \(K\) support samples of a relation label:  
\[P_{r_{i}}(r_{i}=v,v\in V|S_{i})=POOL(P_{l1}(h_{[MASK]}^{s_{1}}=v,v\in V|s_{i1}),\ldots, \tag{3}\] \[P_{iK}(h_{[MASK]}^{s_{iK}}=v,v\in V|s_{iK})) \tag{4}\]  
where \(POOL\) represents a mean-pooling operation.
# Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing
## 4 Experiments
### Evaluation Metric of Hypothesis  
The character error rate (CER), word error rate (WER), and sentence error rate (SER) are the three main evaluation methods used to measure the performance of the speech recognition hypothesis. These methods can objectively reflect the corresponding relationship between the recognition result and ground truth involved in the deletion (D), insertion (I), and substitute (S) errors. As is well-known, we can guess the real meaning of the hypothesis even if some recognition errors may exist. However, there is another possibility that some sentences are difficult to understand even if there is only one recognition error in it. To some degree, the common evaluation criteria, such as CER, WER, and SER, cannot reflect the legibility of the final recognition result in a more direct way. In some scenarios, the WER or CER improvement may no longer correlate with a practical value. It is far from being enough to take the above evaluation criteria as the only measure of the quality of an ASR system.  
In order to give a more objective and comprehensive evaluation of the speech recognition hypothesis, another evaluation method involved in the grammatical knowledge is defined in this work as follows:  
Grammatical deviation distance (GDD): The study of its characteristics is to objectively and accurately redefine a new evaluation metric for the hypothesis, show concern for grammar knowledge, and pay attention to the grammatical deviation distance between the hypothesis and ground truth. GDD is proposed as a complementary method of the speech recognition evaluation criteria. Specifically, we first use the LTP toolkit to obtain the linguistic tags for the hypothesis and ground truth, such as part-of-speech (POS) and dependency parsing (DP), etc. Then, we align each of them on the linguistic tag level. Finally, we calculate the GDD of each utterance according to the weights corresponding to the linguistic tags. We believe that different linguistic tag errors have different effects on the GDD. Moreover, we also prove that the design of linguistic tag weights (LTW) is almostconsistent with the manual review results. The following formula shows the details of the calculation for the grammatical deviation distance.  
\[U_{GDD}=\frac{LTW_{error}}{LTW_{total}} \tag{10}\]  
\[\text{GDD}=\frac{\sum_{i=1}^{M}U_{GDD}}{M}\times 100\% \tag{11}\]  
where \(U_{GDD}\) is the grammatical deviation distance of each utterance, \(LTW_{error}\) refers to the sum of the linguistic label weights corresponding to the error linguistic label position, and \(LTW_{total}\) is the sum of all linguistic tag weights. In addition, \(M\) is the number of utterances in the data set.
## 2 Experimental Section
### On-fiber Derivatization HS-SPME  
The derivatizing agent was selected based on findings in the literature. PFBHA was found to be the optimal derivatization reagent for SPME analysis [22]. Other derivatizing agents, e.g., 2,4-dinitrophenylhydrazine (DNPH) and hydroxymethyl piperidine (HMP) were found to generate a number of byproducts and potentially interfering chromatographic peaks in application with SPME. Several researchers reported that at least 30 min of derivatization reaction time is required in a diluted solution of derivatization reagent and carbonyl compounds for direct (_i.e._, from the liquid phase) derivatization [13, 26, 27]. An on-fiber derivatization method was used in the present study similarly to the approach taken by others [22, 23, 24, 26]. The derivatization reagent is first doped onto the SPME fiber in this process, and then the fiber coated with PFBHA is exposed to the headspace of the sample solution to react with carbonyl compounds. A 65 \(\upmu\)m PDMS/DVB (poly-dimethylsiloxane/divinylbenzene) fiber was selected according to a previous study by [PERSON] and [PERSON] (2001) [22].  
The effects of dopant concentration, derivatization temperature and derivatization time were studied in this research. PFBHA doping concentrations ranged from 0.5, 3.75 and 17 mg/mL. The latter being consistent with [22, 24]. Lower dopant concentrations were tested in order to further economize on the use of dopant and derivatization time in this method. The concentration of each carbonyl compound in the standard solution was about 5 \(\upmu\)g/L A 1 cm PTFE-coated stir bar was placed in the sample, to stir at 800 rpm, and the vial was placed in a water bath set to a specific temperature (40, 60 or 80 \({}^{\circ}\)C, depending on the experiment) during both the doping and derivatization procedures. All experiments were conducted using aqueous solutions of 1 mL that were placed in a 4 mL polytetrafluoroethene (PTFE)-capped vial to perform HS-SPME. For PFBHA doping, a PDMS/DVB fiber was exposed to the headspace of 1 mL PFBHA aqueous solution for 15 min. The SPME fiber was introduced into the headspace of a sample solution immediately after doping with PFBHA. Direct extensions by immersing a PFBHA coated PDMS/DVB fiber into the sample solution was found to dissolve the PFBHA coating [26]. Headspace extraction was performed for a specific time ranging from 2 s to 60 min. The doping procedure and on-fiber derivatization were both performed in the same thermal water bath at the same temperature. Derivatization efficiencies were evaluated by comparing the MS detector response to carbonyl derivatives for determination of optimum SPME conditions.
# Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques
## 1 Introduction
They presented the work of many authors who approached different challenges in this mechanism using different models that have been developed and adapted for a specific flotation problem. They identified different categories of models, such as \"Predictive Models for Evaluation and Recovery\", \"Models Developed to Evaluate the Importance of Flotation Parameters\" and \"Analysis of Flotation Foam Images with Machine Learning\". The last category was divided into \"Image Extraction of Foam\", \"Size Bubbles and Distribution Analysis\", \"Flotation Performance Predictions and Feature Importance Analysis Based on Ash Images\" and \"Ash Image Analysis and Predictions for Ash Content in the Coal Flotation Process\". Generally, researchers have presented potential solutions for the mineral beneficiation process using machine learning and artificial intelligence techniques, but limited efforts were made for the prediction of grade and recovery in the flotation of other materials. In this study, an attempt has been made to estimate Fe grade and cellulose recovery in the froth flotation products in laboratory conditions. This study performs a comparative analysis of different machine learning methods for the prediction and modeling of deinking flotation performance. To the best of our knowledge, there are no previously reported studies that use Gaussian Process Regression (GPR) and Regression Tree Ensembles for this purpose. The remainder of this paper is organized as follows. In Section 2, a brief overview of machine learning techniques used in this paper is given. The dataset developed for model training and testing is also presented in Section 2, while experimental results are discussed in Section 3. Concluding remarks are given in Section 4.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Deployment Related Problems in WSNs
#### 3.3.2 Energy Conservation  
Owing to the peculiarities of wireless sensor networks, energy conservation or energy efficiency is one of the most critical objectives and has been tackled at various levels namely, design, operation and deployment. Various deployment strategies have been proposed in the literature focusing on the maximization of energy conservation. For example in [9], the authors have proposed an optimal deployment scheme which considered minimization of net energy consumption along with other objectives including maximizing the area of coverage, maximizing the network life time, and minimizing the number of deployed sensor nodes. Energy conservation has been tackled in [109] through optimal deployment of body sensor network for the objectives of minimizing energy consumption, minimizing bandwidth and maximizing data yield. Deployment problem of relay node has been addressed in [110], where authors have proposed a multi-objective optimization formulation to minimize energy consumption and to maximize the coverage area.
# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 4 LSTM Prediction Model
### Wheelset Key Parameters and Data Preprocessing  
The wheel diameter \(D\), flange thickness \(Sw\), and flange height \(Sh\) of the left and right wheels are the focus of this study on wheelset key parameters. The data are sourced from the wheelset measurements obtained by a monitoring system alongside a certain metro line in the Shanghai Metro. The time span of the measured wheelset data ranges from December 2017 to October 2022. Due to the susceptibility of the trackside monitoring system to factors such as the environment at the train base, the wear of the wheelset is influenced by various factors. Therefore, the original data are processed first, followed by handling of outliers  
Figure 10: Flowchart of wheelset key parameter prediction model.  
Figure 9: Changes in the flange height of a certain wheelset.  
and missing values. Since the measurement intervals are relatively short, the method used for handling outliers involves taking the average of the changing values. The calculation is given in Equation (12):  
\[X_{t}=\frac{\Delta\overline{X_{1}}+\Delta\overline{X_{2}}}{2}+X_{t-1} \tag{12}\]  
In Equation (12), \(X_{t}\) represents the corrected value for an outlier, \(\Delta\overline{X_{1}}\) represents the average of the changing values measured each time in the month before the outlier point, \(\Delta\overline{X_{2}}\) represents the average of the changing values measured each time in the month after the outlier point, and \(X_{t-1}\) represents the value measured at the previous point before the outlier.
# Analysis of New Orthogonal Transforms for Digital Watermarking
## 4 Experimental Results  
### Image Dataset  
For test purposes, 32 images from the 'Images 4k' dataset [19] have been selected. The dataset consists of 2057 files. The selected images represent various visual characteristics in order to generalize the results and make them more reliable. These characteristics include low contrast, high contrast, low brightness, high brightness, and different color distributions (dominant colors and color variety). The width and height of test images were reduced by half, resulting in a 1920 \(\times\) 1080 size to speed up the calculations and watermark embedding process.