esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods
### Machine Learning-Based Filter Bank (MLFB) Algorithm
#### 2.3.1 Feature Extraction from Sub-Band Pulse Signal  
The 11 features were computed from each decomposed pulse signal, as listed in Table 1. Phase coherence is crucial for assessing the coherence of the rPPG signals. The Hilbert transform computes the coherence of HR signals, in particular facial regions [26]. To extract robust phase coherence, a moving average using a 20 s window was applied to identify the frequency information that was less susceptible to noise. The frequency variability was computed by identifying the frequency peaks within each sub-band and calculating the standard deviation of the intervals between these peaks. The smallest value of this standard deviation was then used as a feature. As the POS amplitude and the mean and standard deviation of the RGB amplitude indicate the strength of the rPPG signal, they were also used as features. Finally, these features formed a 16 \(\times\) 11 \(\times\) T (time) matrix, which was used as the input of the SVM to distinguish between blood flow signals and noise effectively.
## 1 Introduction
This is sufficient for a coarse estimate, but does not satisfy medical standards [23]. Similar results can be obtained by monitoring the body movements with commercial cameras using the spectrum of visible and near infrared light [24; 25]. Since LOS between sensor and PUT cannot always be guaranteed in clinical environments due to clothes and bedding, capacitive concepts were developed and realized as electrode arrays included in the mattress [26; 27]. By this, accurate results can be obtained at the expense of a reduced comfort for the PUT due to the modified bedding. Without requiring any electrodes near the PUT, heart rate and respiration rate can also be measured using microwave radar systems, which are able to accurately detect relative or absolute distances. Displacements of the body surface caused by these vital signs can hence be measured and used for an estimation of the VP. Since clothes and bedding can be penetrated, a direct LOS is not required. Prototypes have been built at different frequencies and modulations, most of them between 2.4 GHz and 60 GHz. To process the data, frequency domain methods like the evaluation of the fast Fourier transform or correlations of the filtered pulse wave are commonly used [28]. However, recent advances in research have shown that heart sounds can be recorded with highly precise six-port based radar systems by filtering the distance signal in a band between 16 Hz and 80 Hz. Hence, these can be used to considerably increase the accuracy of contactless VP measurements [29]. In this publication, a 24 GHz continuous-wave radar system for relative distance measurements is presented. After explaining the physiological properties of the target VP, a prototype system is shown as well as tailored algorithms, both to maximize the signal quality and hence increase the reliability of the system. A clinical study with 30 healthy volunteers compares the proposed radar-based VP assessment with gold standard VP detection by impedance cardiography (ICG) and ECG for heart rate and respiration rate, respectively.
## 2 Materials and Methods
### Satellite Data Pre-Processing  
Because the spectral reflectance value of each pixel is partially influenced by the spectral properties of adjacent pixels [47], pixels at the field border were excluded using a negative buffer of 125 m, as described below. The datasets were then processed with the GEE platform [48] to remove the noise data, such as pixels affected by clouds and cloud shadows (Figure 2). Then, after removing the noise, the whole field NDVI and EVI2 time series were generated. The NDVI and EVI2 values were averaged to provide the whole field value. Product-specific pre-processing steps were also applied, as described in the following sections.
## 4 Algorithm Analysis
### Experimental Results and Comparisons
#### 4.3.2 Algorithm Comparison  
To further validate the prediction performance of the WOA-XGBoost model, comparisons are made with XGBoost without hyperparameter optimization [57], BP [58], LightGBM [59], and SVM [60]. The same training and testing data split, as described in the previous section, is used, and the evaluation metrics for the test set are provided in Table 7. Additionally, the first 30 sets of predictions from the test set are visualized by comparing them with the actual sample data.  
The table above presents the average scores for each output feature, which should be analyzed in conjunction with the comparison chart of predicted values and actual sample values. As shown in Figure 7, it is evident that the BP neural network performs better when handling multiple input variables and complex output relationships. However, when the input-output relationships are simple and only a few input features are required for prediction, the model is prone to overfitting. The overall prediction accuracy of the SVM is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Population Size**} & \multicolumn{4}{c}{**Training Set**} & \multicolumn{4}{c}{**Testing Set**} \\ \cline{2-7}  & **MAE** & **RMSE** & **R2** & **MAE** & **RMSE** & **R2** \\ \hline
25 & 0.0453 & 0.1513 & 0.9525 & 0.0475 & 0.1719 & 0.9201 \\
50 & 0.0431 & 0.1493 & 0.9688 & 0.0466 & 0.1639 & 0.9378 \\
75 & 0.0440 & 0.1503 & 0.9614 & 0.0471 & 0.1685 & 0.9301 \\
100 & 0.0425 & 0.1482 & 0.9814 & 0.0458 & 0.1610 & 0.9451 \\
125 & 0.0443 & 0.1506 & 0.9537 & 0.0473 & 0.1671 & 0.9277 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance Parameters of the WOA-XGBoost Model.  
Figure 6: Fitness variation curve of the WOA-XGBoost prediction model with different population sizes.  
relatively poor compared to other models. In complex tasks, LightGBM's ability to handle nonlinear relationships is not as strong as XGBoost's. XGBoost demonstrated the best performance in handling complex multi-input, multi-output tasks. After optimization using WOA, its prediction accuracy improved significantly, maintaining high precision in both simple and complex problems. This is particularly evident in predicting results such as maximum hauling speed and cutting depth, where it showed excellent performance. Since XGBoost is an algorithm that uses decision trees as base learners, it combines predictions from multiple three models and averages the results during training. Through several iterations, the model improves its fit to the residuals, leading to better accuracy. Its built-in feature weight evaluation mechanism and regularization effectively filter key features, reduce interference from redundant ones, and suppress overfitting, thereby ensuring the model's ability to generalize.  
Figure 7: _Cont._
## 5 Implementation and Experiment
### HiL Test Environment and Fault Injection  
We constructed the SUT as an OSEK/VDX-based software on the MC9S12X [28] ECU for evaluation [14]. Figure 11 shows the HiL test environment configured for the experiment. The environment consists of the SUT with three ECUs, a test executor, and a monitoring system that collects and stores memory data.  
The SUT has three electronic control units (ECUs) and ten unit functions, and the three ECUs are responsible for the steering and front functions (N1), communication and propulsion systems (N2), and peripheral sensing and rear functions (N3). The software of each unit function is statically assigned to the memory area of each ECU node. Faults injected into the SUT were generated by modifying only one statement in the original source code using the mutation technique [29]. Table 1 summarizes the faults injected into each function.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Node**} & \multirow{2}{*}{**Function**} & **Mutation** & **Fault** \\  & & & **Operator*** & & **Mode** change while accelerating \\ \hline
1 & & Front Turn Signal & OEBA & & \\
2 & N1 & Head Lamp & OCNG & & \\
3 & & Steering & CCSR & & \\ \hline
4 & & AEBS & SSDL & & \\
5 & N2 & AEBS & CSCR & & \\
6 & & Driving Mode & STRI & & \\
7 & & & STRI & & \\ \hline
8 & & Rear Turn Signal & OEBA & & \\
9 & & Brake Lamp & CCSR & & \\
10 & N3 & Emergency Stop Signal & CSCR & & \\
11 & & Rear Alert & OEBA & & \\
12 & & Adv. ESS & OEAA & & \\ \hline \hline \end{tabular}  
* \({}^{*}\)OEBA (OEAA): Operator Replacement (Assignment (=) to Bitwise assignment (=); Assignment to Arithmetic assignment (+ =)) OCNG: Logical context negation CCSR (CSCR): Constant for Scalar (Constant) Replacement SDL: Statement Deletion STRI: Trap on if Condition.  
\end{table}
Table 1: List of fault injection by the selected mutation operators.  
Figure 11: HiL test environment.  
Because the proposed method operates normally in the unit test and the fault occurred in the integration test, the fault to be injected must also pass the unit test. Therefore, we classify the applicable operators for each function among 80 mutation operators in C language.  
Then, the mutation operator that passed the unit test is injected into the fault. At this time, the unit test tests the function on/off in the function initialization state, and in the integration test, the communication between the nodes, the function connection, and the continuous state changes of the single function, are examined. For example, fault #1 modified a statement from the source code of the front turn signal function of Node 1 by using the OEBA operator. This fault has been modified to change an assignment to a bitwise assignment, which works fine in a single command, but the fault occurs when successive commands are received.
## 2 Review of Synchrosqueezing Wavelet Transform
\[s(b)\approx\Re e\left[C_{\psi}^{-1}\sum_{k}W_{s}(a_{k},b)a_{k}{}^{-3/2}(\Delta a _{k})\right]=\Re e\left[C_{\psi}^{-1}\sum_{l}T_{s}(\omega_{l},t)(\Delta\omega)\right] \tag{6}\]  
where \(C_{\psi}=\frac{1}{2\pi}\int\limits_{-\infty}^{\infty}\hat{\psi}^{*}(a\varsigma )d\varsigma\) and \(C_{\psi}\) is a constant dependent on the selected wavelet. The \(k\)-th component of the multi-component signal can be reconstructed by  
\[s_{k}(b)=\lim\limits_{a\to 0}\frac{1}{C_{\psi^{\prime}}}\int_{[\delta-\delta_{ \psi}^{\prime}(b)]<\rho}T_{s}(\varsigma,b)d\varsigma \tag{7}\]where \(\rho\) is the width of the zone to be summed up around the ridge corresponding to the instantaneous frequency of the \(k\)-th component. In the discrete format, the \(k\)-th component is  
\[s_{k}(t_{m})=\frac{2}{C_{\psi}}\Re e\left[\sum_{l\in L_{k}(t_{m})}\widetilde{T}_ {\widetilde{s}}(\omega_{l},t_{m})\right] \tag{8}\]  
where \(\widetilde{T}_{\widetilde{s}}(\omega_{l},t_{m})\) is the discretized estimate of \(T_{s}(\omega_{l},t_{b})\), and where \(t_{\text{m}}\) is the discrete time \(t_{\text{m}}=t_{0}+m\Delta t\) for \(m=0\), \(1\),..., \(n-1\) and \(n\) is the total number of samples in the discrete waveform, \(\widetilde{s}\). The reconstruction of the \(k\)-th component from \(\widetilde{T}_{\widetilde{s}}(\omega_{l},t_{m})\) is the inverse CWT over a small frequency band \(l\in L_{k}(t_{m})\) around the \(k\)-th component. Figure 1 illustrates the application of SSWT on extracting the time-frequency ridges and reconstruction of each extracted frequency component of the input time-domain signal. In case the frequencies of harmonics and interharmonics in a time-domain signal are too close to each other, it is likely to cause blurry ridges or even ridges to be mixed up. SSWT can further improve this effect by setting parameters near a central frequency \(\omega_{l}\) of (5) or combining with the clustering method for an accurate frequency detection. It is helpful to analyze TF ridges since it can keep the ridges more concentrated and thus reduce the detection errors.
## 4 Test Setup, Instrumentation, and Crack Formation
### The Effects of Investigated Parameters on the Behavior of Anchors
#### 5.3.4 The Effect of Repeated Loading  
The effect of repeated loading on the behavior of anchors in tension was investigated for anchors installed in wallettes made with weak mortar, in two locations: (i) L4T, i.e., cracked mortar T-joint (crack width = 0.50 mm); (ii) L1, anchor installed in uncracked brick. As aforementioned, T-joint, location, was selected as the most adverse in terms of expected resistance, while L1 is the most favorable location. For the displacement-controlled tests of this series, the displacement steps were defined based on the monotonic curves obtained for the same location. Two basic rules are applied for the definition of displacement steps, namely, the interval between consecutive steps should allow for a smooth hysteresis-loop envelope to be recorded, while at least three cycles should be performed after the attainment of the maximum resistance and before failure occurs. On the other hand, it is desirable to limit the displacement steps to an adequate minimum number, to avoid unnecessary testing effort. A summary of relevant experimental results is presented in Table 7.  
After the completion of each test, as shown in Figure 4b, hysteresis-loop envelopes are drawn for the three cycles per selected imposed displacement value (Figures 24a and 25a). For the envelope curve of each cycle, the value of tension resistance, Fu,n, n being the number of the cycle (1, 2, or 3), is determined and the effect of repeated loading is calculated as the ratio (reduction factor) between the maximum resistance obtained during the third and that activated during the first cycle (Table 7, Figures 24b and 25b).  
Figure 24: Cont.  
Figure 24: (**a**) Tests (1, 2, 3), experimental curves in repeated tension tests; (**b**) summary of envelope curves of 1 st and 3 rd cycle in the case of repeated tension tests in L\({}_{\text{4T}}\).  
Figure 25: (**a**) Tests (1, 2, 3), experimental curves in repeated tension tests; (**b**) summary of envelope curves of 1 st and 3 rd cycle in the case of repeated tension tests in L\({}_{\text{1}}\).  
The experimental results show that the ratio between the tension resistance at the third loading cycle and that of the first cycle is practically the same for both examined locations, L\({}_{4\text{T}}\) and L\({}_{1}\) (0.77 and 0.78, respectively).
# Kernel Point Convolution LSTM Networks for Radar Point Cloud Segmentation
## 2 Related Work
### Lidar Object Detection  
3D Object detection networks estimate the position of 3D bounding boxes and associated classes in the 3D space. As they process the same input data as the segmentation networks, they can make use of the same backbone structure. Similar to point cloud segmentation, it is possible to project the point cloud data to a grid or voxel structure, or directly process the points of the point cloud to perform object detection from lidar data.  
[PERSON] [9] builds upon a spherical projected point cloud representation to extract features. In a second processing step, they project the input point cloud to a bird's-eye view (BEV) representation to estimate 3D bounding boxes.  
[PERSON] [10] presents the current state of the art in object detection according to the KITTI 3D Car leader board. They use both a projection to voxels followed by point-wise processing, to perform object detection.  
VoxelNet [11] first generates point-wise features and then uses a max pooling operation to project the point features to a voxel grid structure. The object predictions are then generated by a convolutional detection head. Similarly, [12] combines pointnet and voxel-based processing to generate the object proposals.  
Recurrent long short-term memory (LSTM) cells [13] are used for video processing [14] and object detection [15]. Lidar point cloud data, unlike video data, does not have a fixed structure over consecutive timeframes. When we look at a specific lidar point at a given time, in the past time frame there might be no lidar return originating from the same location. The association of consecutive point cloud frames is therefore less intuitive than that of video data. [16] adapts LSTM cells for point cloud processing. They associate each point from the current point cloud with the past point cloud by learning a relation over the _k-nearest neighbors_ and their relative distance to the current point. These association functions are used as the gate functions in the LSTM cell to create their PointLSTM to predict future point clouds from a time series.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 5 Experiments and Analysis
### Experimental Setting  
In the experiments of this paper, we randomly selected \(T\) sample data from each database as the training sample set and the remaining \(C\) sample data as the test samples. All experiments were repeated 10 times, and the average of their classification accuracy was calculated. In order to find the best parameters for the proposed method, a grid search method [47] was used. For the three balance parameters \(\alpha\), \(\beta\), and \(\lambda\), the values of \(\alpha\) and \(\beta\) were set in the range [0, 0.01, 0.1, 1, 10, 100, 1000, and 10,000], and \(\lambda\) was set as large as possible (i.e., 100,000) in this work. For the feature dimensions, we set the range from 60 to 560 with intervals of every 100 dimensions.
## 3 Results
### Optimizing the Model
#### 3.1.3 K-Nearest Neighbors (KNN)  
To optimize the k-nearest neighbors (KNN) model, the values of the number of neighbors, weights, algorithm, and power parameter are adjusted. Number of neighbors is a parameter that specifies the number of nearest neighbors and is usually chosen as an odd value for applying majority voting rule. In this study, the number of neighbors is set to three and five. Weights are a parameter that determines the weight of neighbors, and two options, uniform and distance, are chosen. Uniform assigns equal weight to all neighbors, while distance assigns weights inversely proportional to the distance. Algorithm is used for neighbor search, and auto is used in this study as it automatically selects the most suitable algorithm. Power parameters are methods of distance measurement, where in this study, Manhattan distance and Euclidean distance are used.  
The results show that for CV values ranging from 2 to 10, for the test set, the R\({}^{2}\), MAE, and RMSE values are consistent for CV values 2 to 7 and 10, which yielded the highest R\({}^{2}\) and the lowest RMSE, Delta RMSE, and MAE. Therefore, the optimal KNN model is selected with a CV value of 2, 5 neighbors, weights as distance, algorithm is set to auto, and Euclidean distance for power parameter, as summarized in Table 3. CV value as 2 shows an R\({}^{2}\) of 0.74, an RMSE of 8.48 MPa, an MAE of 5.91 MPa with the Delta RMSE as 6.91 MPa.
# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 4 Results
### Technology behind V-NLIs  
In this section, we briefly explore the software technologies used in the reviewed works. We can distinguish between those that directly use _NLP-toolkits_ and those that use _chatbot frameworks_. For the former, we found multiple examples. The most used NLP-toolkit is open-source CoreNLP in Java [93]. For instance, Snowy [40], Miva [90] and Evizeon [39] all use it. Others use CoreNLP in combination with other toolkits, such as [15], which combines CoreNLP with NLTK [94] and AIML [95], and ConvisQA [69], which integrates CoreNLP with an ANTLR parser [96]. Some works use other NLP-toolkits; for example, Valetto [92] uses spaCy toolkit [97]. Finally, Data@Hand [84], which focuses on speech recognition, uses Apple speech framework [98] and Microsoft Cognitive Services [99] for IOS and Android devices, respectively, and Compromise NLP toolkit [100] to perform part-of-speech tagging. Among the V-NLIs that use chatbot frameworks, running independently from the visualisation module, we find: ACUI [80] using Rocket Chat open-source software [101]; Boomerang [82] based on IBM Watson Assistant [102]; GeCoAgent [87] based on Rasa [103]; and TransVis [63] employing Google Dialogflow4 [104].  
Moreover, other works proposed customised solutions. Gamebot [86] uses rule-based word matching. Iris [89] uses domain-specific language that transforms Python functions into an automata (finite state machine). Ava [81] employs a state machine to control natural language conversations. FlowNL [70] uses a declarative language to filter and combine data to derive structures and translates natural language queries into declarative specifications to render visualisations. Finally, the latest contributions to the field: Chat2 Vis [83] uses LLMs, while Talk2 Data [64] uses a novel decomposition model that is extended from sequence-to-sequence (deep neural networks) architectures.
# Preparation of Few-Layer Graphene Dispersions from Hydrothermally Expanded Graphite
## 2 Materials and Methods  
Natural graphite flakes were purchased from Pingdu Huadong Graphite Co. Ltd., and the CTAB (H5882-100G, 98%, M.W. 364.45) was purchased from Sigma Aldrich. The graphite and the CTAB were used as received, without further purification. In a typical experiment, 100 mg of natural graphite flakes were added into 50 mL of CTAB aqueous solution at different surfactant concentrations (i.e., 0.1, 0.5, and 1.0 mg mL\({}^{-1}\)). The resulting mixture was transferred into a sealed Teflon vessel (100 mL) and reacted at 180 \({}^{\circ}\)C for 15 h (optimal parameters reported in [15; 21]) to obtain pretreated graphite. After the hydrothermal treatment (HT), pretreated graphite was immediately dispersed in the very same CTAB aqueous solution by sonication, employing a tip sonicator (UP100H-Hielscher, 50 watts, 30 kHz) in continuous operation. To reduce the damage to the graphene sheets by ultrasonic processing, the maximum sonication time was 3 h. The sonication produced black suspensions that were left to stand overnight and then centrifugated for 90 min at 2000 rpm (optimal parameters reported in [22]) to remove nonexfoliated graphite flakes. After centrifugation, the pipette-extracted 40 mL supernatant was semitransparent without the presence of agglomerates (Supplementary Figure S1). The concentration of dispersed graphene was determined by UV-visible absorption spectroscopy and the absorbance (\(A\)) was adapted to calculate the concentration using the Beer-Lambert law (a widely used approach [10; 22; 23]):  
\[C_{G}=\frac{A}{l\cdot\alpha_{660}}\]  
where, \(l=0.01\) m, is the path length and \(\alpha=1390\) mL mg\({}^{-1}\) m\({}^{-1}\), is the absorption coefficient of the dispersed graphene in the surfactant water solutions [10]. The characteristic absorption spectra of the dispersed graphene were recorded using UV-vis spectroscopy (UV-160A, Shimadzu Corporation, Japan). The morphologies of the samples were observed using a scanning electron microscopy (SEM, Quanta Feg 400 F7) with accelerating voltage ranging from 5 to 35 kV. Raman spectra were obtained using a Jobin Yvon LABRAM spectrometer, with a 514 nm laser wavelength.