esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 4 Results
### Experiment  
In this experiment, we first train a network model without the attention mechanism by batch-inputting 80% of the randomly chosen training set into the neural network model without the self-attention module, with the batch size set to 16. Following the introduction of the attention module, a different network model is trained, and its performance metrics are compared.  
As shown in Table 4, the results tested on the Cinc2011 database show that the addition of the self-attention module improves the accuracy of the model by 1.39, recall by 0.85, specificity by 7.64, precision by 1.41, and F1-score by 1.14. The results indicate that, compared to the original Resnet18 structure, the addition of the self-attention module can further improve the signal quality evaluation performance of the network.  
The attention matrix of the acceptable sample and the attention matrix of the unacceptable sample are shown in Figure 7 below, corresponding to the attention matrices of the two samples shown in Figure 8, respectively, and it can be seen that there is a clear difference between them.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Accuracy** & **Recall** & **Specificity** & **Precision** & **F1-Score** \\ \hline Resnet18 & 91.43\% & 98.32\% & 52.87\% & 90.51\% & 94.26\% \\ SA-Resnet18 & 92.82\% & 99.17\% & 60.51\% & 91.92\% & 95.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of network indicators without attention module and with attention module.  
Figure 7: (a) Attention matrix of an acceptable sample; (b) attention matrix of an unacceptable sample.
## 3 Sensor Data Security Estimator
### Proposed Security Metrics
#### 3.2.3 Legitimacy Probability of the Intrusion Detection System
That is, this type of IDS, in addition to performing distributed, collaborative detection in real-time, can also perform centralized, offline detection, which is responsible for providing \(P_{M}\). In centralized detection, a great amount of traffic information is sent to the base station, a central point on the network that, differently from sensor nodes, has no resource constraints and can then perform more sophisticated and accurate traffic analyses than those performed by collaborative real-time detection [10]. Although this detailed analysis performed in the base station can be quite time-consuming, it allows not only detecting attacks that have already occurred, but also predicting the network base rate at any given moment based on the historical traffic information. This allows SDSE to obtain, from the IDS, the expected current network base rate in real time (_i.e._, at the moment it calculates the legitimacy probability \(P_{L}\) for a given node). Actually, we see the fact that SDSE can only work with hybrid IDSs as an advantage, because, as discussed in [34], hybrids IDSs are the future of intrusion detection in sensor networks, since they have the strengths of both centralized and distributed IDSs.
## 4 Case Studies
### Three-Dimensional Point Cloud of San Nicola in Montedoro Church  
#### 4.2.1 Three-Dimensional Survey of the Church  
The survey of the church was carried out through the use and integration of active and passive sensors, terrestrial and aerial. In particular, the external facade was surveyed using a TLS, the inner part using a digital single-lens reflex (DLSR) camera with fish-eye lens and the upper part of the building (i.e., the roof and other architectural elements not visible through a terrestrial survey) through the use of a camera mounted on a UAV platform.  
Before performing the surveys with photogrammetric techniques and laser scanners, a survey with a total station was performed. The survey was carried out by TS30 Leica Geosystems. This total station allows discrete points to be acquired with an angular precision of 0.5\" (0.15 mgon) and to acquire distance with prism (precision of 0.6 mm + 1 ppm) and without prism (2 mm + 2 ppm).  
In this case study, the survey was carried out by two base stations. In this way, it was possible to obtain horizontal and vertical angular observations of the ground control points (GCPs). The GCPs, inside and outside the building, were chosen so as to be easily recognizable even on the image (Figure 4). The post processing of the data was carried out in LGO (Leica Geo Office) developed by the Leica Geosystem company.
# FnmOS-ELM: A Flexible Neural Network Mixed Online Sequential Elm
###### Abstract  
The learning speed of online sequential extreme learning machine (OS-ELM) algorithms is much higher than that of convolutional neural networks (CNNs) or recurrent neural network (RNNs) on regression and simple classification datasets. However, the general feature extraction of OS-ELM makes it difficult to conveniently and effectively perform classification on some large and complex datasets, e.g., CIFAR. In this paper, we propose a flexible OS-ELM-mixed neural network, termed as fnmOS-ELM. In this mixed structure, the OS-ELM can replace a part of fully connected layers in CNNs or RNNs. Our framework not only exploits the strong feature representation of CNNs or RNNs, but also performs at a fast speed in terms of classification. Additionally, it avoids the problem of long training time and large parameter size of CNNs or RNNs to some extent. Further, we propose a method for optimizing network performance by splicing OS-ELM after CNN or RNN structures. Iris, IMDb, CIFAR-10, and CIFAR-100 datasets are employed to verify the performance of the fnmOS-ELM. The relationship between hyper-parameters and the performance of the fnmOS-ELM is explored, which sheds light on the optimization of network performance. Finally, the experimental results demonstrate that the fnmOS-ELM has a stronger feature representation and higher classification performance than contemporary methods.  
Keywords:extreme learning machine; CNN; RNN; feature; extraction; classification +
Footnote †: journal: Applied Sciences
## 3 Experiment I: Minimal SBR for Full Sentence-Level Intelligibility
### Stimuli  
Speech materials were drawn from the SCRIBE corpus [36]. This corpus consists of speech sentences (e.g., 'the EARTH USED to BE FLAT but NOW IT'S a SPHERE', with the keywords capitalised) uttered by eight (four male and four female) native British English talkers. As many studies (e.g., [37; 38]) have shown great variation in speech intelligibility between different talkers, having speech from many talkers makes the experiment more ecologically valid. In order to control the size of the experiment, speech produced by two male (M5 and M8) and two female (F6 and F7) talkers were used based on a pilot test, in which keyword recognition rate of listeners was measured as the intelligibility in speech-shaped noise (SSN) at \(-6\) and \(0\) dB SBR. The results suggested that the chosen talkers are representative of the most and the least intelligible cases of each gender in the corpus under the tested background conditions.  
The background sounds covered a wide range of types--both stationary and fluctuating--including: SSN, which has the long-term average spectrum of the speech corpus; cafeteria babble noise (BAB); competing speech (CS) drawn from news broadcasting; a rock song (SNG, 'Song for Someone' by U2), and the same song with vocal track being removed (MSC, i.e., instrumental sounds only). Note that in order to reduce the effect of informational masking caused by the same gender [16], CS from a person of the opposite gender to the target talker was used. For each speech-background stimulus pair, the background signal was randomly cropped from a 5-min long signal, and preceded and followed the speech by 300 ms. In total, this setup led to 20 conditions (5 background sounds \(\times\) 4 talkers).
# Intelligent Fault Diagnosis of Rotating Machinery Using Hierarchical Lempel-Ziv Complexity
###### Abstract  
The health condition monitoring of rotating machinery can avoid the disastrous failure and guarantee the safe operation. The vibration-based fault diagnosis shows the most attractive character for fault diagnosis of rotating machinery (FDRM). Recently, Lempel-Ziv complexity (LZC) has been investigated as an effective tool for FDRM. However, the LZC only performs single-scale analysis, which is not suitable to extract the fault features embedded in vibrational signal over multiple scales. In this paper, a novel complexity analysis algorithm, called hierarchical Lempel-Ziv complexity (HLZC), was developed to extract the fault characteristics of rotating machinery. The proposed HLZC method considers the fault information hidden in both low-frequency and high-frequency components, resulting in a more accurate fault feature extraction. The superiority of the proposed HLZC method in detecting the periodical impulses was validated by using simulated signals. Meanwhile, two experimental signals were utilized to prove the effectiveness of the proposed HLZC method in extracting fault information. Results show that the proposed HLZC method had the best diagnosing performance compared with LZC and multi-scale Lempel-Ziv complexity methods.  
feature extraction; fault diagnosis; Lempel-Ziv complexity; rotating machinery
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
###### Abstract  
Improving the accuracy of power load forecasting is an important link in the optimization of power systems. Most of the existing studies in the short-term load forecasting task at present have the problem of insufficient extraction of multi-scale features. Therefore, in order to improve prediction accuracy, this study designs a short-term power load forecasting model integrating multi-scale GCN and the improved Transformer, as well as the prediction method based on this model. First, multi-feature power load data were collected. Second, the random forest algorithm was used to preprocess the data. Next, multi-scale GCN was utilized to model the multi-scale spatio-temporal features in the power load data. The data processed by the multi-scale GCN were input into the improved Transformer module based on MLLA to extract long-term temporal dependencies. Subsequently, comparative experiments and ablation experiments were conducted on three public power datasets. The experimental results show that, compared to the comparative model, for the ETH1 dataset, the RMSE index of this model decreased by up to 0.314, the MAE decreased by up to 0.304, and the R\({}^{2}\) index result improved by up to 9.45%. For the ETTm1 dataset, the RMSE index of this model decreased by up to 0.266, the MAE decreased by up to 0.231, and the R\({}^{2}\) index result improved by up to 3.3%. For the Australian dataset, the RMSE index of this model decreased by up to 494.366, the MAE decreased by up to 493.127, and the R\({}^{2}\) index result improved by up to 54%, verifying the superiority and effectiveness of the proposed model.  
inproved transformer; GCN; short-term power load forecast; multiple features 2025  
[PERSON], 1,2, [PERSON], 1, [PERSON], 3, 2025
## 3 Results and Discussion
### Spectroscopic Studies of Ligand (L1) Cu (II) Complex
#### 3.1.2 Fourier Transforms Infrared Spectroscopy  
The FTIR spectra in Figure 2a of the organic ligand (L1) showed a strong stretching band of the secondary amine N-H group in the 3421-3249 cm\({}^{-1}\) region for both the ligand and the metal complex. The disappearance of the 3495 cm\({}^{-1}\) peak of the ligand confirms the attachment of the metal with the N-H via coordination bond as the electron delocalization takes place in keto-enol forms of the synthesized organic ligand, L1. The neutral ligand indicates that the proton is bonded to the nitrogen atom instead of the oxygen and exhibits tautomeric structures (Scheme 2) which evidenced that the ketone form is more stable than the hydroxyl form. The peaks in the 3057-2905 cm\({}^{-1}\) region were assigned to the C-H stretching bands present in the synthesized oxindole-based ligand. The slightly lower shifting of the C=O stretching band of the ligand from 1640 cm\({}^{-1}\) to 1638 cm\({}^{-1}\) in the metal complex might be due to the coordination bonding (ligand to metal bond) of the O atom to the Cu ion [28]. The characteristic C=S peak of the dithiocarbamate ligand appeared in the region of 1328 cm\({}^{-1}\), which shifted to the lower frequency of 1319 cm\({}^{-1}\) on complexation with metal, indicating the S coordination attachment of the ligand. After complex formation with metal, the C=N stretching peak (Table 1) also decreased to a low frequency from 1569 cm\({}^{-1}\) to 1556 cm\({}^{-1}\), suggesting N coordination towards the metal ion. The dentity of various ligands can ascertain the unsymmetrical monodentate or bidentate coordination via IR spectroscopy [29]. The single peak in the frequency region of 1020-950 cm\({}^{-1}\) implies the bidentate attachment of the dithiocarbamate moiety with the metal ion. Additionally, the presence of a thioureide (N-CSS) peak around 1520 cm\({}^{-1}\) in the metal complex indicates the presence of adjacent C-S and N-C bonds of the thiocarbamate ligand with a supporting C-S peak in the 1012 cm\({}^{-1}\) region [30].
###### Abstract  
Proper determinations of light use efficiency (LUE) and absorbed photosynthetically active radiation (APAR) are essential for LUE models to simulate gross primary productivity (GPP). This study intended to apply the photochemical reflectance index (PRI) to track LUE or APAR variations in a subtropical coniferous forest using tower-based PRI and GPP measurements. To improve the ability of using PRI to track LUE or APAR, a two-leaf approach differentiating sunlit and shaded leaves was used to process the remote sensing and flux data. However, penumbra region, the 'grey region' between sunlit and shaded leaves, increases the difficulty for quantifying the fractions of sunlit and shaded leaves. Firstly, three methods with different ways on treating the penumbra region were investigated for estimating the fraction of sunlit leaves (\(P_{T}\)). After evaluating the correlations between observed PRI (PRI\({}_{\text{obs}}\)) and inversely retrieved PRI (PRI\({}_{\text{inv}}\)) from estimated \(P_{T}\) using the three methods, we found that treating a substantial portion of penumbra region as sunlit leaves was reasonable and using the ratio of canopy reflectance to leaf reflectance as \(P_{T}\) was accurate and efficient. Based on this, we used the two-leaf approach to estimate the canopy-level PRI, aiming to evaluate the ability of using PRI as a proxy for LUE or APAR. Results showed that PRI was able to capture half-hourly and daily changes in LUE and APAR, and the two-leaf approach could enhance the correlations between PRI and both LUE and APAR at both half-hourly and daily time steps. Strong diurnal correlations (averaged \(R=0.82\) from 173 days) between two-leaf PRI and APAR were found on more than 80% days and the relationship between them over the whole study period was also very significant (\(R^{2}>0.5\), \(p<\)0.0001) regardless of different climate conditions, suggesting that the two-leaf PRI was probably a better proxy for APAR than for LUE at short-term scale as PRI mainly represented the absorbed energy allocated to photoprotection at short time scale and was a direct outcome driven by APAR. However, the scattered relationships of PRI with LUE and APAR indicated there were still many limitations in usage of PRI to accurately estimate physiological parameters affected by changing weather conditions, pigment pool size, etc., which needed further exploration.  
photochemical reflectance index; light use efficiency; absorbed photosynthetically active radiation; two-leaf; multi-angle; hyperspectral
# Multi-Pedestrian Tracking Based on KC-YOLO Detection and Identity Validity Discrimination Module
## 3 Multi-Pedestrian Tracking Method Based on IVDM
### Construction of KC-YOLO Detection Model
#### 3.1.2 Deep Feature Extraction  
Deep features extracted by convolutional neural networks can provide an effective description of the high-level semantic information of an image, and the CBAM is an attention mechanism module used to enhance the performance of convolutional neural networks with significant results. In order to improve the feature extraction capability of the detection network [32], we introduce the CBAM [20] into the detection model.  
The CBAM depicted in Figure 3 comprises both the Channel Attention Module (CAM) and the Spatial Attention Module (SAM). The CAM is employed to enhance the weights of important features while reducing the weights of irrelevant features. It begins by subjecting the input feature map to max pooling and average pooling along the channel dimension. The output results are then fused through an MLP network, and subsequently, weight coefficients are obtained by applying the Sigmoid activation function.  
\[M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F)))=\sigma\Big{(}W_{1}\Big{(}W_{ 0}\Big{(}F_{avg}^{c}\Big{)}\Big{)}+W_{1}(W_{0}(F_{\text{max}}^{c}))\Big{)} \tag{2}\]  
where \(\sigma\) is the Sigmoid activation function; \(w_{0}\in R^{C_{r}\times C}\) and \(w_{1}\in R^{C\times C_{r}}\) are the weights, and \(r\) is the contraction rate; _MLP_ stands for a neural network; \(M_{c}(F)\) is obtained by performing element-wise summation and applying the Sigmoid activation operation on the shared fully connected layer; and \(F_{avg}^{c}\) and \(F_{\text{max}}^{c}\) are the two features obtained by pooling the extracted features.  
Figure 3: The structure of CBAM (Global Pooling is the global maximum pooling layer; FC is Rectified Linear Unit; Sigmoid is an S-type activation function).  
The SAM focuses on the intrinsic relationships within the spatial dimensions of the input feature map. It takes the output from the CAM and performs max pooling and average pooling along the channel direction. The results obtained are then processed through a convolutional layer with a kernel size of \(7\times 7\). Finally, the SAM's feature map is obtained by applying the Sigmoid activation function. The calculation is performed with the following equation:  
\[M_{s}(F)=\sigma\Big{(}f^{7\times 7}([AvgPool(F);MaxPool(F)])\Big{)}=\sigma \Big{(}f^{7\times 7}\Big{(}\Big{[}F^{c}_{avg};F^{c}_{\max}\Big{]}\Big{)}\Big{)} \tag{3}\]  
where\(f^{7\times 7}\) denotes the convolution kernel size; \(M_{s}(F)\) is obtained by the logistic activation function.
###### Abstract  
Objective: This work presents an implementation of a stable algorithm that recovers sources located at the boundary separating two homogeneous media in field-programmable gate arrays. Two loop unrolling architectures were developed and analyzed for this purpose. This inverse source problem is ill-posed due to numerical instability, i.e., small errors in the measurement can produce significant changes in the source location. Methodology: To handle the numerical instability when recovering these sources, the Tikhonov regularization method in combination with the Fourier series truncation method are applied in the stable algorithm. This stable algorithm is implemented in two different architectures developed in this work: The first architecture (Mode 1) allows for different operating speeds, which is an advantage depending on whether we work with fast or slow signals. The second one (Mode 2) reduces resource consumption by exploiting the characteristics of the source identification algorithm, which is an advantage for multichannel problems such as inverse electrocardiography or electroencephalography. Results: The architectures were tested on four devices of the 7 Series of Xilinx: Spartan-7 xc7s100 fga484, Virtex-7 xc7v585 ffg1157, Kintex-7 xc7k70 thg484, and Artix-7 xc7a35 tcpg236. The two hardware implementations of the stable algorithm were validated using synthetic examples implemented in MATLAB, which shows the advantages of each architecture. Contributions: We developed two efficient architectures based on a loop unrolling design for source identification problems. These are effective strategies to divide and assign tasks to the configurable hardware, and they appear as an appropriate technique for implementing the algorithm. The first one is simple and allows for different operating speeds. The second one uses a control system based on multiplexors that reduce resource consumption and complexity of the design and can be used for multichannel problems. From the numerical test, we found the regularization parameters. The synthetic examples developed here can be considered for similar problems and can be extended to concentric spheres.  
Keywords:field-programmable gate arrays (FPGAs); loop unrolling architectures; inverse source problem; ill-posed problems; Tikhonov regularization +
Footnote †: journal: Applied Sciences
## 4 Proposed Scheme
### Node Registration Phase  
Before sending the real-time authentic credentials, the communication node which is a sensor device receives registration in the server and then transmits information. The server maintains the identification and status of communication nodes composed of sensors. At the sensor node registration, RA assigns a unique identity as \(SN_{ID}\) to every sensor node in the healthcare system. The server saves the identity and status of the sensor node in the server's database.
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 2 Materials and Methods
### Biochemical Composition
#### 2.4.3 Lipid Content Determination  
The total lipids were quantified using the Bligh and Dyer method [47], as detailed by [PERSON] et al. [48]. Briefly, 0.2 g of dried biomass underwent an extraction process employing a mixture containing 1 mL of chloroform, 2 mL of methanol, and 0.8 mL of distilled water (a ratio of 1:2:0.8 (\(v/v/v\))). The suspension was then stirred (vortex) for 2 min. The lower organic phase, containing the extracted lipids, was recovered and transferred into pre-dried and weighed glass tubes, while the remaining aqueous phase and algal residues were re-extracted with a mixture consisting of 2 mL of chloroform and 2 mL of distilled water (a ratio of 2:2 (\(v/v\))), followed by an additional 2-min stirring. The resulting mixture was centrifuged (8000\(\times\)\(g\), 10 min) to separate the lower chloroform layer containing the extracted lipids. The chloroform phases were combined, and the solvent was evaporated until the lipid mass stabilized. The lipid content (%) was then determined using Equation (1).  
\[\text{Lipid content}\%=\frac{\text{Lipid mass(g)}}{\text{Mass of microalgal biomass(g)}}\times 100 \tag{1}\]