esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis and Discussion  
### Construction of the Experimental System  
As shown in Figure 9, we have built a common optical path to obtain shearography-interferometric phase fringe patterns and speckle-interferometric phase fringe patterns based on digital speckle pattern interferometry (DSPI) and digital shearography (DS). The object to be measured is an aluminum plate with a diameter of 250 mm and eight screws around it. There is a spiral structure in the center to load the out-of-plane displacement of the aluminum plate. The laser in the system is a single-mode fiber laser with 532 nm wavelength. The focal length of the imaging lens is 35 mm. The 4 lens comprises two double-glued lenses with a focal length of 75 mm for image transmission. M1, M2, and M3 are the reflecting mirrors with a diameter of 25 mm. Where M1 is the shearing mirror, and M2 is the phase-shifting mirror. BS1, BS2, and BS3 are splitter prisms with 50 mm sides.  
The laser is split into two beams by BS1. One of the beams is expanded to irradiate the surface of the aluminum plate. An imaging lens with a focal length of 35 mm is used  
Figure 8: The axis phase of noise-free speckle pattern and the axis phase of pattern denoised by the sine-cosine transform 5-level wavelet decomposition threshold denoising method: (a) w = 20, I = 0.5 dBW; (b) w = 20, I = 1 dBW; (c) w = 30, I = 0.5 dBW; (d) w = 30, I = 1 dBW.  
Figure 9: Schematic diagram of DSPI-DS common optical path.  
to collect scattered light derived from the surface of an aluminum plate as the object light. When optical switch one is on and optical switch two is off, the DS optical path with a 4f system is formed by M1 and M2. When optical switch two is on and optical switch one is off, the DSPI optical path is formed by M1 and M3.
## 2 Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms
### NSGA-II Multi-Objective Optimization and Decision Algorithm  
#### 2.3.1 The Objective Function  
In the feasible lane changing trajectory cluster, it is necessary to find an optimal lane change trajectory. In this study, the lane changing trajectory is optimized from three aspects: lane changing comfort, lane changing smoothness, and lane changing efficiency, and the influence on traffic flow to obtain the following three objective function formulas:  
1. The weighted root mean square value of acceleration characterizes lane changing comfort: \[WRMS=\alpha_{1}RMS\big{(}\breve{x}(t)\big{)}+\alpha_{2}RMS\big{(}\breve{y}(t) \big{)}\] (9) where \(\alpha_{1}=\alpha_{2}=0.5\).
2. The maximum curvature characterizes lane changing smoothness: \[\rho_{\max}=Max\frac{\left|\frac{d^{2}y}{dx^{2}}\right|}{\left[1+\left(\frac{ dy}{dx}\right)^{2}\right]^{\frac{3}{2}}}\] (10)
3. Lane changing trajectory length represents lane changing efficiency and its impact on traffic flow: \[s=\int_{0}^{\Delta x}\sqrt{1+\left(\frac{dy}{dx}\right)^{2}}dx\] (11) where \(\Delta x\) is the longitudinal displacement.
## 3 Methodology
### K-Means Clustering  
K-means clustering is a popular clustering algorithm that discovers patterns within objects using their similar attributes. The algorithm requires the knowledge of K number of clusters to be predefined and the initial seed points are of importance. K-means clustering aims to partition \(\mathrm{n}\) observation into K clusters in which each observation belongs to the cluster with the nearest means. Given \(\mathrm{X}\) is a set of observations that contain \(\mathrm{n}\) observation (\(x_{1},x_{2},\ \dots\.x_{n}\)) in P dimensional vector, the algorithm follows the following steps:  
* Declare K initial seed points (initial centroids) defined in P-dimensional vectors (\(s_{1},\ s_{2}\ \dots\ s_{p}\)) and the squared Euclidean distance between the \(\mathrm{i}\)th object and the \(\mathrm{k}\)th seed vector is obtained: \[d_{(i,k)}^{2}=\sum_{j=1}^{p}\Bigl{(}x_{ij}-s_{j}^{(k)}\Bigr{)}^{2}\] (8)
* Cluster centroids are assigned and all objects are assigned to the closest centroids.
* The centroids changes to move closer to the centre point and the objects are reassigned to the closest centroid.
* Repeat the last 2 steps until no objects can be moved between clusters.
## 3 Results and Discussion
### Relative Density
#### 3.1.2 Image Analysis  
On the basis of Figure 3, the relative densities for the samples with different SLM process parameters were measured using ImageJ software. The results using image analysis are also shown in Figure 2d. In Figure 2d, the Archimedes methods (method 1 and method 4) and image analysis obtained similar results when the relative density was near 100%. However, for the samples with a low relative density, from Figure 2d, it can be observed that the relative density results using the image analysis are obviously lower than those using the Archimedes method (Method 4). That is because some unmelted powder remained inside the sample when measuring the density using the Archimedes method. On the contrary, when measuring the density using image analysis, these unmelted powders in the cavities on the cut side were thrown out from the sample while being cut and cleaned. Figure 2d also indicates that the volume of the unmelted powder decreases with the increase of the energy density and arrived at a nearly constant level. Therefore, the relative density results using different methods are very close for samples 2, 10, 11, 6, and 7, because they are SLMed at a higher energy density(the sample numbers on the top of Figure 2d).  
In addition, comparing the results of measurement using the image analysis and the Archimedes methods in Figure 2d, it can be found that the former is closer to the results using method 1 for samples 2, 6, 11, and 10 with a high density. That is because the Archimedes method considering the effect of the open cavities might cause lower results than the actual density because of the residual liquid on the surface after liquid-impregnation. The residual liquid leads to the higher \(m_{\textit{net}}\) value, which leads to getting the lower density in terms of Equation (3). Therefore, according to Table 2 and Figure 2d, method 1 is more advisable for the sample with a high density of more than 99%, which is measured using method 1.  
In the investigation of the effects of the SLM process parameters on relative density, the method named as method 4* was applied to build the regression model. Method 4* is based on method 4 and method 1. Here, method 4 can also be replaced by method 2-2 and 3-2, because they have close results in Figure 2c. The detailed procedure of method 4*is presented in Figure 4.  
In Figure 4, firstly, the Archimedes method without considering the effect of open cavities according to ASTM Standard B311-13 is applied to measure the relative density of anAlSi10 Mg SLMed sample. If this relative density is more than 99%, this result is adopted. Otherwise this same sample needs to be measured again using the Archimedes method considering the effect of open cavities according to ASTM Standard B962-13 (or method 2-2, method 3-2 in Table 3), and then the result is adopted as the relative density result of the sample.
# Virtual Signal Processing-Based Integrated Multi-User Detection
## 3 Design of VSP-IMUD
### Channel Ambiguity Detection and Elimination
Similarly, we can examine all rows of \(\mathbf{F}\) to determine the ambiguity coefficients for the channels of all \(K\) Txs. We then sort \(c_{k}\) (\(k\in\{0,1,\cdots,K-1\}\)) in descending order. This process establishes the decoding order for the signals that generate ambiguity. Specifically, the signal transmitted via \(\mathbf{h}_{k}\) with the largest \(c_{k}\), denoted as \(c_{max}\), should be detected first. Since Rx has \(N_{R}\) receiving antennas, it can utilize ZF reception to concurrently decode up to \(N_{R}-1\) signal components associated with the channels with the largest ambiguity coefficients in the spatial domain. For the remaining \(K-N_{R}+1\) signals, they are treated as equivalent interference [11]. The Rx then employs SIC to eliminate the detected ambiguous signal components from the original mixed signal and updates the symbol set \(\Omega\) by removing the decoded symbols. Next, the Rx recalculates the ambiguity matrix and the channel ambiguity coefficients \(c_{k}\) for the remaining signals and decodes any signal components that still exhibit structural ambiguity. This processing is repeated iteratively until \(c_{max}=0\), at which point the IMUD method introduced in the previous subsection can be applied to the remaining mixed signals, as there will be no similarities among these components. In this way, the desired data carried in the remaining mixed signal can be uniquely determined using the spatial features of the mixed signal and the decoded referential data symbol. It is important to note that if \(N_{R}\) exceeds the number of signals to be detected, we can directly apply ZF to detect the signals. In other words, the proposed VSP-IMUD is primarily focused on scenarios where the number of signals to be detected is greater than \(N_{R}\), where ZF is infeasible due to the RX's limited number of antennas.
## 3 Results and Discussion
### Comparisons of COSMIC and IGRA
This indicates that there are some areas with little atmospheric water vapor where radiosonde observations cannot fully capture the real circumstance of the water vapor content but COSMIC can.
## 2 The Proposed LPC-NN Method  
### Linear Predictive Coding  
The main use of the LPC algorithm is to encode speech signals at a low data rate. Figure 1 shows a block diagram of an LPC analysis system.  
The key assumption is that the \(n\)th sample in the time series of a digital signal can be predicted by summing the \(Q\) previous samples after multiplying each sample by a weight, following [34]:  
\[\hat{s}[n]=-\sum_{i=1}^{Q}a_{i}\cdot s[n-i]. \tag{1}\]  
Figure 1: A block diagram of the LPC algorithm.  
Here, \(s\) is the time series of the digital signal, \(Q\) is the prediction order, and \(a_{i}\) are the linear prediction coefficients. Thus, prediction of the sample at any moment is computed as a linear combination of the \(Q\) previous samples of the signal, and hence, the prediction is linear. The LPC model is thus an autoregressive model [35].  
The prediction error is equal to  
\[e[n]=s[n]-\hat{s}[n]. \tag{2}\]  
The optimum predictor is the set of coefficients \(a_{i}\), which produces a residual signal \(e[n]\) with the least energy:  
\[\rho=\sum_{n}e^{2}[n]= \sum_{n}\left(s[n]+\sum_{i=1}^{Q}a_{i}\cdot s[n-i]\right)^{2}. \tag{3}\]  
The linear prediction coefficients for the optimal case are computed by setting the partial derivatives of \(\rho\) with respect to \(a_{i}\) equal to zero, that is,  
\[\frac{\partial\rho}{\partial a_{k}}=2\sum_{n}\left(s[n]+\sum_{i=1}^{Q}a_{i} \cdot s[n-i]\right)s[n-k]=0 \tag{4}\]  
for \(k=1,2,\ldots,Q\).  
Equation (4) can be rewritten as a system of \(Q\) Yule-Walker linear equations involving autocorrelations:  
\[\sum_{n}s[n]s[n-k]+\sum_{i=1}^{Q}a_{i}\sum_{n}s[n-i]s[n-k]=0 \tag{5}\]  
or  
\[\sum_{i=1}^{Q}a_{i}R[i-k]=-R[k] \tag{6}\]  
for \(k=1,2,\ldots,Q\), where \(R[i-k]=\sum_{n}s[n-i]s[n-k]\), \(R[k]=\sum_{n}s[n]s[n-k]\). In matrix form, it can be written as a normal equation:  
\[Ra=-r, \tag{7}\]  
where  
\[R=\left(\begin{array}{cccc}R[0]&R[1]&&\ldots&R[Q-1]\\ R[1]&R[0]&&\ldots&R[Q-2]\\ &\vdots&&\ddots&\vdots\\ R[Q-1]&R[Q-2]&\ldots&R[0]\end{array}\right) \tag{8}\]  
and \(a=\left[a_{1}\;a_{2}\;\ldots\;a_{Q}\right]^{T}\), \(r=\left[R[1]\;R[2]\;\ldots\;R[Q]\right]^{T}\).  
The matrix of the system (7) is a Toeplitz matrix, and linear prediction coefficients can be efficiently computed using the Levinson-Durbin recursion, starting with the first-order predictor and continuing recursively to find the coefficients of the \(Q^{th}\)-order predictor.  
The system of Equation (7) can also be solved using [PERSON]'s method [36]. The difference between the two algorithms is that [PERSON]'s method minimizes the forward prediction error while the [PERSON] method minimizes the forward and backward prediction errors. [PERSON]'s method is often superior to [PERSON]'s in accuracy (see, for example, [37]) but is more computationally expensive [38]. Since the main objective of this paper is to propose a computationally efficient method for bearing diagnostics, we use [PERSON]'s method implemented in the lpc function in Matlab.
# Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space
We would like to outline three specific potential applications that involve the calculation of the tight focus a large number of times: (1) stability/robustness analysis of the outcome of theoretical proposals with respect to spatial laser-pulse imperfections; (2) iterative determination of the input parameters (beam profile and wavefront) from other experimental observables, e.g., electron spatial distribution in a specific experiment, which can even be performed on many single-shot measurements; (3) generation of large sets of simulations for training machine learning methods employed for diagnosis and improvement of tight focusing by means of adaptive optics.
# High-Performance Microwave Photonic Transmission Enabled by an Adapter for Fundamental Mode in MMFs
## 4 Conclusions and Discussions  
We demonstrate a new method to improve the performance of microwave photonics links, that is, using MFAs fabricated by reverse tapering to match the MMF fundamental mode field, confining the signal transmitted only in the fundamental mode of MMFs. This method can effectively reduce the nonlinear effect, and the SBS threshold is 5-dB higher than that of SMFs. We also measure the SFDR of the link, and compare with the link using SMFs, there is a 14.5-dB improvement when the incident optical power is 17 dBm.  
Although we have obtained the improvement on SFDR performance, the fusion of short devices like the MFA and accurate matching between the MFA and the fiber still need to be carefully improved. Besides, the MPI of the link is another subject that needs to be further investigated in the future. Even so, our work is potential to be applied in the field of high-power and long-distance RF signal transmission.  
[PERSON] planned and wrote the paper. [PERSON] participated in the experiments of the second part. [PERSON] contributed to the new idea of this work. [PERSON] revised the whole manuscript and gave some valuable advice. [PERSON] and [PERSON] monitored the work and gave technical guidance. All co-authors participated in the review and editing job. All authors have read and agreed to the published version of the manuscript.  
The work was supported by National Key R&D Program of China under grant 2019 YFB2203902, and National Natural Science Foundation of China (NSFC) under grant 62105241.  
Not applicable.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this manuscript.  
The authors declare no conflict of interest.
## 3 Control Design Methodology
### Design of the Low Level Control  
The setup of the EPOS boards' controllers is carried out with the following steps: (i) communication of the motor specifications (torque gain, resistance, etc), (ii) communication of the PID gains for the position, velocity and current loops (that operates at \(f=1\) kHz), (iii) communication of the desired operational limits. The communication protocol used to work with EPOS4 is the CANopen protocol. Inside the CANopen network the EPOS4 controllers are controlled as child nodes and coordinated by the Simulink model running on Speedgoat real time target machine. CAN protocol has been setup by employing IO614 reading and writing modules on Speedgoat Baseline Real-Time Target Machine [18]. With reference to the top layer of the model (Figure 5), CAN SETTINGS module is built to set up CAN connection exploiting CANopen protocol; INPUT RECEIVED SIGNALS module manages the input signals from SEA sensors and EPOS boards (acquired at \(f=1\) kHz); CAN SETTINGS TO OUTPUT COMMANDS module sets up all the output control signals (computed and provided at \(f=1\) kHz) to communicate via CAN with the sensors and EPOS motor driver boards.
## 10 Research Challenges and Future Directions
### Evaluation and Governance  
As agent-driven annotation becomes increasingly prevalent, comprehensive governance frameworks are essential to ensure quality, accountability, and responsible deployment. In line with established quality assurance practices in software development, machine learning data pipelines that incorporate AI annotators must implement systematic auditing and validation protocols. Industry standards are needed to certify annotation agents for specific use cases, following models similar to those used for medical devices and laboratory testing procedures. For example, the deployment of AI agents in clinical trial data annotation requires compliance with defined accuracy benchmarks and the integration of continuous monitoring mechanisms. Within organizations, dedicated dashboards should track annotation agent performance using metrics such as human override frequency and error pattern trends. To maintain reliability over time, systems must include robust mechanisms for identifying and correcting performance drift, especially in dynamic environments such as social media, where evolving language can cause annotation errors. These challenges call for safeguards such as continuous learning systems with HITL oversight to prevent the compounding of model errors.  
Future research should prioritize the development of standardized evaluation frameworks tailored to annotation agents, along with governance models that balance technological innovation with ethical accountability. In addition, adaptive monitoring systems capable of detecting subtle forms of performance degradation must be designed to ensure long-term reliability. Progress in these areas will depend on close interdisciplinary collaboration among technical developers, domain experts, and policy researchers. Such cooperation is necessary to build governance infrastructures that address the specific demands of agent-based annotation systems while supporting their responsible evolution across domains.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 4 The Feature-Selection Process
### Random Forest Algorithm  
Understanding which features have the most significant impact on the predictions of a random forest model is crucial. We calculate the feature importance scores based on how much each feature contributes to the model's accuracy. Features with higher importance scores are more influential. These scores help guide feature selection, where we choose to keep only the most important features for the final model. We then remove each feature with less importance and verify the accuracy. If the accuracy decreases, we stop the process. If not, we repeat the process to simplify the model without sacrificing predictive performance.  
To determine the accuracy of a model, we use a cross-validation algorithm, which helps us understand the significance of the features used. In a random forest (RF) model, the sum of all feature importance scores equals one, or 100%, normalizing each feature's importance within the model's context. We applied this algorithm using the scikit-learn library in Python [21]. In the initial stage, all eleven features produced cluster outputs with the same 70% accuracy. The importance score of the first feature ranking is presented in Figure 9. The analysis reveals that the feature 'ARE' has a relatively small importance score of less than 0.01, representing only 1% of the importance in prediction. On the other hand, the feature 'HAC' has the highest importance score, accounting for almost 25% of the prediction importance.  
Figure 8: Model accuracy vs. number of features with SOM. All features, process ends.  
The RF algorithm first removes the feature 'ARE' and checks for any change in the model accuracy. As there is no change, the process is repeated. The features are dropped individually, based on their importance score in ascending order presented in Figure 9. The RF process stops after dropping ARE, WPU, COE, PET and TYP features, which are not necessarily in this order (Figure 10).  
According to the importance ranking, the feature 'TYP' was removed, and the next feature, 'U18', has an importance score of approximately 10% (as shown in Figure 10). If 'U18' is removed, the model accuracy will decrease to 68% in k-means and AHC, and 69% in SOM. Removing any other features will also negatively impact the model's accuracy. Therefore, the RF process stops here, and the selected features are U18, INC, BED, DHW, NPE and HAC.
## 4 Experimental Results
### Performance Measure  
In general, a confusion matrix is used to measure the performance of binary classification models. It consists of four parameters, including TP (True Positive), FP (False Positive), FN (False Negative), and TN (True Negative). TP is the number of records whose labels are predicted as positive and are actually positive. FP is the number of records whose labels are predicted as positive but are actually negative. TN and FN can be interpreted in the same manner.  
A traditional measure to evaluate classification performance is accuracy, which is defined as the number of classifications that a model correctly predicts, divided by the total number of predictions.  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
Accuracy may not be used properly when the data are skewed to one class. For example, suppose that the ratio of the positive class is 5%. One way to achieve high accuracy in this case is simply to predict all records as negative, which will result in 95% accuracy. On the other hand, if the opposite prediction is made, the accuracy drops to 5%. This example indicates that FP and FN should be considered together when measuring  
Figure 1: Combination of data imbalance processing algorithms and machine learning algorithms that can be used to implement a model for the detection of irrelevant reviews.  
classification performance. To measure the performance from the perspective of FP, the following precision can be used.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
To measure the performance from the perspective of FN, recall can be used.  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
A simple way to measure performance considering both FP and FN is to use the average of precision and recall. The F1-score is defined as the harmonic mean of precision and recall, as follows:  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
The F1 score can effectively express the performance of models when the positive class is a minority with a small ratio; however, it may not be effective in the opposite case. To complement this, balanced accuracy can be used, which is defined as the arithmetic mean of the true positive rate (also called sensitivity or recall) and the true negative rate (also called specificity). Balanced accuracy can express the performance of models appropriately when the positive class is a majority.  
\[Balanced\ accuracy=\frac{1}{2}\times\left(\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\right) \tag{5}\]  
In this paper, precision, recall, the F1 score, and balanced accuracy were used together to validate the performance of each model.