esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Method
### Topic Extraction  
In this section, we capture the potential topics of each tweet. Through the literature review, we found that the LDA topic extraction method in short text has very good performance as sentiment classification [40]. Thus, to capture the possible topic of each tweet, we adopt the latent Dirichlet allocation (LDA) model [41]. We extract the best topic from the historical tweets by the topic extraction method. In LDA, we convert each tweet into  
Figure 3: The structure of the commonsense knowledge BiLSTM unit.  
bag-of-words format (so the order of words is not considered). This method mainly uses word probability to represent the topic and obtains the word probability through LDA, and the word with the highest probability in each tweet can be clearly understood. Finally, each tweet is associated with a topic represented as a vector, which is denoted as \(z\).  
Each word in the dataset is obtained through the process of \"selecting a topic with a certain probability, and selecting a word from the topic with a certain probability.\" The specific formula is shown in Equations (16)-(20).  
\[p(\theta\mid\alpha)=\frac{\Gamma\Big{(}\sum_{i=1}^{k}\alpha_{i}\Big{)}}{\prod _{i=1}^{k}\Gamma(\alpha_{i})}\prod_{i=1}^{k}\theta_{i}^{\alpha_{i-1}} \tag{16}\]  
\[p(\theta,z,w\mid\alpha,\beta)=p(\theta\mid\alpha)\prod_{n=1}^{N}p(z_{n}\mid \theta)p(w_{n}\mid z_{n},\beta) \tag{17}\]  
\[p(\mathbf{w}\mid\alpha,\beta)=\int p(\theta\mid\alpha)\Bigg{(}\prod_{n=1}^{N} \sum_{z_{n}}p(z_{n}\mid\theta)p(w_{n}\mid z_{n},\beta)\Bigg{)}d\theta \tag{18}\]  
\[p(D\mid\alpha,\beta)=\prod_{d=1}^{M}\int p(\theta_{d}\mid\alpha)\Bigg{(}\prod _{n=1}^{N_{d}}\sum_{z_{d_{n}}}p(z_{d_{n}}\mid\theta_{d})p(w_{d_{n}}\mid z_{d _{n}},\beta)\Bigg{)}d\theta_{d} \tag{19}\]  
\[p(\theta,z\mid\mathbf{w},\alpha,\beta)=p(\theta,z,\mathbf{w}\mid\alpha,\beta)/p(\mathbf{ w},\mid\alpha,\beta) \tag{20}\]  
Here, \(N\) represents the length of each tweet, parameter \(\theta\) is a random variable of the k-dimensional Direchlet, parameter \(\alpha\) represents a k-dimensional vector with components, and parameter \(\Gamma\) represents the gamma function. \(D\) represents the entire dataset. Equation (16) here represents the probability density function, Equation (17) represents the generation process of the topic. The topic mixture, \(\theta\), the joint distribution of N topics \(z\), and \(N\) words \(w\) can be obtained. Then, Equation (18) represents the likelihood function for each tweet, which is obtained by integrating over \(\theta\) and summing over \(z\); Equation (19) represents the product of the likelihood functions of all tweets in the entire dataset \(D\). Equation (20) represents that the topic distribution is derived by computing the posterior probability of the latent variable \(z\).
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## Appendix B Characteristics of Guided Waves in Rails
### Selection of Guided Modes  
As it can be easily inferred from the dispersion curves shown in Figure 10a, various propagating modes can be excited in rails in the frequency range of interest (25 kHz-45 kHz). However, not all of these modes are suitable for long-range propagation due to the attenuation they experience (e.g., in the foot of the rail) or their high dispersivity [44].  
As already mentioned in Appendix A, a specific mode is highly sensitive to defects at positions of the rail cross section where the energy in the mode shape is most concentrated. The following considerations have to always be kept in mind when selecting the mode best suited for detecting a defect in pulse-echo operation. When a uni-modal wave impinges on a defect (or on another feature), a portion of its energy is reflected, but the reflected wave is no longer uni-modal because of modal conversion. Therefore, it is important to acquire data about reflection coefficients for each combination of incident and reflected modes; such data can be organized in a matrix of reflection coefficients for each defect or feature. A useful way of representing the information contained in such a matrix is through a color or grayscale map of the amplitudes of each element [42]; an example of such a map is shown in Figure A2[44]. Based on this map, it can be shown that, on the one hand, a symmetric mode with energy concentrated in the railhead (e.g., mode 1 of Figure A2) is well suited to distinguish between cracks in the railhead and welds. On the other hand, a mode with energy concentrated in the web (e.g., mode 4 of Figure A2) can be exploited to detect welds and damages in the rail web, but not to differentiate them. Finally, it is worth noting that detecting cracks at thermite welds (through a land-based approach; see Section 6) or in the foot of the rail can be very difficult.
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
###### Abstract  
The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine's support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing's fault-related spectral components.  
inter-shaft bearing; fault diagnosis; improved envelope spectrum; noise subtraction; adaptive threshold denoising
## 2 Materials, Experimental Procedures and Methods
### Analytical Methods  
#### 2.3.1 Fluid-Phase Characterization  
Temperature, electric conductivity (EC) and pH were determined with WTW LF/pH 330 m multi-meters connected to TetraCon 325 and SenTix41 probes. The pH electrodes were calibrated against NIST buffer standard solutions at pH 4.01, 7.00 and 10.01 at 25 \({}^{\circ}\)C at an analytical precision of \(\pm\)0.05 pH units [41].  
Copper, K\({}^{+}\) and Na\({}^{+}\) concentration analyses were made on a PerkinElmer Optima (PerkinElmer, Waltham, MA, USA) 8300 DV inductively coupled plasma optical emission spectrometer (ICP-OES) on acidified samples. NIST 1640a, in-house and SPS-SW2 Batch 130 standards were analyzed within repeated sample sequences, yielding an analytical error of \(\pm\)3% and a detection limit of \(<\)0.01 mg/L for each element of interest [42]. From thesechemical data, the removal efficiency (%removal) and the amount of Cu\({}^{2+}\) immobilized by GP (q\({}_{\rm e}\) in mg/g) were calculated following Equations (1) and (2):  
\[\%\rm removal=((c_{0}-c_{e})\cdot c_{0}^{-1})\cdot 100 \tag{1}\]  
\[q_{\rm e}=((c_{0}-c_{e})\cdot m^{-1})\cdot V \tag{2}\]  
where \(c_{0}\) and \(c_{e}\) denote the initial and equilibrium Cu\({}^{2+}\) concentrations in solution (in mg/L), m is the dry mass of GP (in g) and V is the volume of the solution (in L).
## 3 PV Generation Forecaster Models  
This section presents the theoretical fundaments of the models that have been developed to predict PV generation. The first described model is an analytical model developed in the OpenModelica software. The other three models are AI techniques, namely a feedforward neural network (FFNN), a nonlinear autoregressive with exogenous inputs (NARX) neural network, and a support vector regression (SVR).
# Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
###### Abstract  
Recent advancement of satellite synthetic aperture radar (SAR) techniques require more sophisticated system configurations such as the use of bistatic antennas or multi-frequencies. A ground-based experiment is a cost-effective and efficient way to evaluate those new configurations especially in the early stage of the system development. In this paper, a ground-based synthetic aperture radar (GB-SAR) system was constructed and operated in a bistatic mode at Ku-band where a receiving antenna (Rx) follows a transmitting antenna (Tx) separated by a baseline \(B\). A new bistatic GB-SAR focusing algorithm was developed by modifying a conventional range-Doppler algorithm (RDA), and its performance has been evaluated by comparing the results with those from a back-projection algorithm (BPA). The results showed good performance of RDA at far range approaching nominal resolutions of 9.4 cm in range and 4.5 cm in azimuth, but limited quality at near range due to the approximation used in RDA. Signals from three trihedral corner reflectors (CR) reduced with increasing \(B\), showing a typical bidirectional scattering behavior of CR. This GB-SAR system will be a testbed for new SAR imaging configurations with variations in antenna positions and target properties.  
bistatic; GB-SAR; Ku-band; back-projection algorithm; range-Doppler algorithm +
Footnote †: journal:  
Letter Analysis of a Bistatic Ground-Based Synthetic Aperture Radar System and Indoor Experiments
## 2 Materials and Methods
### Optimum Value Computing
#### 2.2.1 Problem Definition  
1. Choosing design variables: Based on the requirements of the approach, the user can choose factors as design variables. These can be varied during the optimization iteration process. Other factors are treated as constants. In this study, the chosen design variables for the obstacle avoidance approach were the number of points on the radius of the scanning sonar (\(P_{i,j=1,2,\cdots,n}\)), the radius of the scanning sonar (\(R_{x},R_{y}\)), and the sector of the scanning sonar (\(\theta\)) (Figure 6).
2. Defining an objective function: The objective function must be defined according to the purpose and requirements of the approach. The objective function in this study was defined as the direction of movement (_DM_) of the ROV: \[\text{minimize}\ DM\Big{(}P_{i},R_{x},R_{y},\theta\Big{)}=C_{Des(i)}+\sum C_{Obs (i)_{j}}\,,\] (8) \[C_{Des(i)}=-\frac{1}{D_{(i)}}=-\frac{1}{\sqrt{\left(X_{Des}-X_{i} \right)^{2}+\left(Y_{Des}-Y_{i}\right)^{2}}}\,,\] (9)  
Figure 5: Heading output surfaces of the FLC. The horizontal axis is the heading error. The vertical axis is the heading error rate. The Z-axis is the output value of the FLC.  
\[C_{Obs(i)_{j}}=\frac{1}{d_{(i,j)}}=\frac{1}{\sqrt{\left(X_{Obs(j)}-X_{i}\right)^{2 }+\left(Y_{Obs(j)}-Y_{i}\right)^{2}}}\,, \tag{10}\]  
where \(D_{(i)}\) represents the distance between point \(P\) and the destination, and \(d_{(i,j)}\) represents the distance between point \(P\) and the obstacle. \(C_{Des(i)}\) indicates the negative inverse value of \(D_{(i)}\), and \(\sum\,C_{Obs(i)_{j}}\) indicate the positive inverse value of \(d_{(i,j)}\) due to the presence of \(n\) obstacles detected. If the minimum value of \(DM\) is located at point \"\(i\)\", the ROV should turn to point \"\(i\)\" and move forward.
3. Identifying constraints: Assuming that \(R\) is the radius of the scanning sonar, \(\theta\) indicates the scanning area for the forward motion. Suggested ranges of the mentioned design variables are summarized as follows: \[-R\ \leq\ R_{x}\ \leq R\,,\] (11) \[0\ \leq\ R_{y}\ \leq R\,,\] (12) and \[-90^{\circ}\ \leq\ \theta\ \leq\ 90^{\circ}\,.\] (13)
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Spectral Data Collection  
For spectral data acquisition, five spectral data campaigns were carried out at different growth stages from June to July 2022 (1 st week: 8 June 2022, 2 nd week: 15 June 2022, 3 rd week: 21 June 2022, 4 th week: 1 July 2022, and 5 th week: 6 July 2022), when most of the plants in the vineyard were still asymptomatic for FD (Table 1). Two portable hyperspectral micro-spectrometers (Hamamatsu C12880 MA series and DLP NIRScan Nano Evaluation Module (EVM), Texas Instruments, Dallas, TX, USA) were used to obtain spectral reflectance measurements of the leaf surfaces. Both spectrometers were handy and practical for field-level scanning due to their small size, portability, and comparatively lower unit cost. The Hamamatsu sensor record data with spectral sampling interval of about 2 nm and the spectral resolution is 15 nm (full width at half maximum), integrating 288 channels ranging from 340 nm to 850 nm. For the spectral data acquisition, the Hamamatsu sensor was mounted on a tripod (Figure 2A) to facilitate the collection from a nadir view. The Hamamatsu spectral data was acquired using an external cylindrical lens from a distance of approx. 0.3 m from the detached healthy and phytoplasma-infected grapevines' leaf surface, resulting in an optical footprint diameter of about 5 cm within the leaf surface. The reflected light enters the device through a cylindrical lens and collimates the light before entering through slit. The Hamamatsu sensor was calibrated using a white reference (95% white reference panel), and we acquired for each date the radiance measurement of the white reference panel. The NIRScan sensor operates in the range of 900-1700 nm at 3.51 nm intervals with 228 spectral bands, and it is equipped with two integrated infrared lamps as a radiation source. The NIRScan acquisition was carried out using a software trigger in the reflectance mode and an average of 30 scans for each spectrum for each leaf of the selectedplant. The NIRScan spectra was acquired using the factory-stored calibration data. The Hamamatsu sensor continuously records the data for approx. 10 s for which it acquired about 30 spectra while the NIRScan sensor recorded a set of 30 scans which means it is repeated 30 times a scan and outputs an average of 30 scans. All spectral measurements were acquitted on a sunny day from 10:00 to 14:00. For each date, three leaves were randomly picked up from each of the selected plants for spectra data acquisition.
## 2 Background and Related Work
### Automotive  
**Scalable Service-oriented Middleware over IP:** SOME/IP is a service-oriented communication protocol standardized by the AUTomotive Open System ARchitecture (AUTOSAR) foundation [34]. SOME/IP defines three communication principles: events, fields, and methods. Thus, any service may offer several endpoints of these principles. In general, communication is initiated by either a _find_ or an _offer_ message of the SOME/IP Service Discovery (SOME/IP-SD) protocol to match the communication partners in the network. All patterns are in a 1-to-N fashion. Accordingly, each client/subscriber binds to a specific service/provider, which is selected for subscription if multiple service providers are available.  
The publish/subscribe pattern is mainly implemented by _events_. Clients subscribe to required information from a matching provider by using the discovery mechanism of SOME/IP-SD. The subscriber gets notified when the information changes or cyclically. The Remote Procedure Call (RPC) pattern is implemented with _methods_. Depending on whether the request should return a response or not, SOME/IP distinguishes between request/response methods and fire-forget methods. In addition, SOME/IP specifies _fields_ as a special case, which is a combination of events and methods. Fields include a get and set method but at the same time a notification when the value changes.  
**Data Distribution Service:** The DDS standard [35] was developed by the Object Management Group (OMG) and is used in various application domains, e.g., automotive applications and healthcare [36]. At its core, DDS provides the stream pattern using its Data-Centric Publish-Subscribe (DCPS) mechanism. Unlike the SOME/IP approach, this is inherently not a pattern for searching and binding to a specific service instance but rather for searching and binding to a data stream. Data are transferred via topics with specific names and data types. Subscribers and publishers identify the topic they want to link to by its name and matching data type. A discovery mechanism for participants and available topics using four predefined topics (topic names: \"DCPSParticipant,\" \"DCPSSubscription\", \"DCPSPublication,\" and \"DCPSTopic.\") is part of DDS. OMG also specified a RPC standard [37] via DDS. The DDS-RPC standard provides a request-reply pattern to the network participants, on top of the substantial parts of DDS. For every request-reply connection, two topics are used: a request topic and a reply topic. A special feature of DDS is that it ensures flexible and comprehensive Quality of Service (QoS) settings. In addition, virtual subnetworks can be set up using different _Domain_ identifiers.
## 2 Methodology
### Point Cloud Preprocessing
#### 2.2.2 Point Cloud Image Segmentation  
As shown in Figure 9a, a large number of points--including the ground and background--are captured by the depth camera during image acquisition. These irrelevant points have little correlation with the target object and may interfere with the point cloud registration process, negatively affecting the accuracy of subsequent processing. To address this issue, sample consensus segmentation (SAC) is employed to segment the target point cloud from the background. As illustrated in Figure 9b, after segmentation, the cluttered background points in the original scene are successfully removed, leaving only the point cloud of the target object. This significantly improves the quality of the input data and facilitates accurate and efficient point cloud registration and pose estimation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison Parameter** & **Desk Point Cloud** & **Workpiece Point Cloud** \\ \hline Number of Original Points & 460,400 & 7197 \\ Number of Filtered Points & 41,049 & 2126 \\ Original Point Cloud Load Time (s) & 0.562537 s & 0.0231155 \\ Filtered Point Cloud Load Time (s) & 0.052796 & 0.0105605 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of point cloud size and loading time before and after filtering.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Original** \\ **Points** \\ \end{tabular} & \begin{tabular}{c} **Points After** \\ **Filtering** \\ \end{tabular} & \
\begin{tabular}{c} **Filtering Time** \\ \end{tabular} &
\begin{tabular}{c} **Total Time** \\ \end{tabular} \\ \hline No Filtering & 626,910 & - & - & 6152 ms \\ After Filtering & 626,910 & 233,783 & 1681 ms & 3677 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Runtime comparison before and after voxel filtering.  
Figure 9: Comparison of scene point cloud before and after segmentation: (**a**) original point cloud; (**b**) point cloud after SAC segmentation.
## 3 Results
### Feature Inference and Extraction from Non-Etched Metallographic Microstructures
#### 3.2.2 Classification of Nodules  
The classification of graphite nodules is essential for evaluating the mechanical properties and overall quality of nodular cast iron. Solid classifications facilitate process control and performance prediction. Specifically, this classification traditionally relies on manual assessment by metallography experts, which can be subjective and time-consuming. In contrast, trying to make this process easier, [PERSON] et al. [7] proposed an automated classification framework based on CV and ML, which standardizes and accelerates the process. The method extracts key morphological features such as roundness, circularity, and AR from segmented nodules, which are used to distinguish between types (e.g., Type III, V, VI). Specifically, we included in our research the same calculation method that was explained in that paper.  
Table 5 summarizes the classification performance on a dataset of 4608 nodules, showing the counts and error percentages for both CV and ML approaches.  
The CV method achieves an overall error rate of approximately 1%, demonstrating strong reliability in morphological classification. The ML method, while slightly less accurate (2.5% error), shows a promising way of working for handling larger datasets and learning more complex classification boundaries.