esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Classification with Shapelet and Canonical Features
## 5 Experimental Analysis
### Design Strategy Validation  
The biggest innovation in this paper is to provide a novel idea for embedding typical time series features in randomly selected Shapelets. To verify the effectiveness of this strategy, we compared the accuracy of RSFCF and Naive Random Shapelet Forest (RSF) on 112 UCR datasets. RSF is a simplified version of RSFCF that does not embed typical time series features in Shapelet (this simplification can be achieved by setting the parameter \(a\) to 0 or commenting on lines 9-12 of Algorithm 2) (Figure 9).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Comparison of Algorithms** & **Win/Draw/Loss** \\ \hline HIVE-COTE & 36/12/64 \\ STC & 65/10/37 \\ ResNet & **54**/13/45 \\ CIF & **62**/19/31 \\ WEASEL & **63**/11/38 \\ Proximity Forest & **64**/14/34 \\ gRSF & **60**/12/40 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Pairwise comparison of RSFCF and 6 comparison algorithms in terms of accuracy over 112 UCR datasets.  
Figure 10 shows the results of the comparison of RSFCF and RSF, with RSFCF winning on 69 datasets, while RSF won on only 31 datasets. The reason why RSF is significantly weaker than RSFCF is that RSF does not embed other features, so it can only use the overall shape of Shapelet as a basis for distinguishing different categories of time series, but the randomly selected Shapelet is only a very small subset of all possible Shapelet,  
Figure 8: Comparison of RSFCF and 4 classifiers in terms of average training time and average ranks over Small60 datasets.  
Figure 9: Comparison of random Shapelet forest classifiers with or without canonical time series features (RSFCF versus RSF) in terms of accuracy over 112 UCR datasets.  
so the probability of taking a discriminating \"exact shape\" is very low, thus affecting the classification accuracy. RSFCF has two significant advantages over RSF.  
First, because the value of Shapelet on one or more features can reflect the discriminating shape information contained within it (as shown in Figure 1), RSFCF relaxes the requirements for Shapelet and thus reduces the risk of random selection of Shapelet, resulting in a loss of accuracy; second, in addition to the sequence shape, RSFCF can also capture discriminating information from multiple angles such as the numerical distribution characteristics, autocorrelation, and periodicity of the sequence, which is the most critical reason for which RSFCF surpasses RSF and STC. Based on the comparison results of RSFCF and RSF and the above analysis, we believe that embedding typical time series features in Shapelet plays a key role in improving classification accuracy.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion
### Limitations and Future Directions  
The main limitation of this study is that the proposed system collects data from different sources and sends them to the cloud for further analysis. The IoT-based system can be further expanded with different types of wearable medical healthcare devices that can be operated on smart devices such as smartphones, digital assistants, or tablets, which are common among medical workers. These devices provide local data storage and have only fundamental processing capabilities. The security of such devices is also low, which can compromise the confidentiality and privacy of patients' data. Wearable and implanted IoT devices can provide continuous monitoring of patients and allow for theeard diagnosis of possible health issues.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stacking Classifiers** & _Precision_ & _Recall_ & _F1 Score_ \\ \hline KNN, XGB, ADA [73] & 0.888 & 0.890 & 0.889 \\ \hline KNN, XGB, SVM [73] & 0.878 & 0.878 & 0.878 \\ \hline KNN, XGB, MLPC [73] & 0.910 & 0.910 & 0.910 \\ \hline KNN, XGB, MLPC, ADA [73] & 0.871 & 0.875 & 0.873 \\ \hline XGB, MLPC, ADA, SVM [73] & 0.882 & 0.886 & 0.884 \\ \hline KNN, XGB, MLPC, ADA, SVM [73] & 0.872 & 0.873 & 0.872 \\ \hline
**Proposed CNN** & 0.934 & 0.975 & 0.956 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison with existing approaches from [73].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & _Accuracy_ & _Precision_ & _Recall_ & _F1 Score_ \\ \hline CNN & 0.9534 & 0.93 & 0.97 & 0.95 \\ MLP & 0.9302 & 0.91 & 0.93 & 0.92 \\ RNN & 0.9449 & 0.92 & 0.92 & 0.92 \\ LSTM & 0.9329 & 0.91 & 0.92 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Performance of deep learning models using the real-time dataset.
## 5 Technologies Stack
### Infrastructure
#### 5.3.3 Storage  
Storage is the backbone of every software system that generates data. The proposed system design contains entities that must be stored to provide a continuously available system: user accounts, notification data, document content, and metadata.  
In a microservices architecture, it is recommended that the databases be segregated into multiple, smaller, and independent databases to serve only one function. This approach simplifies the structure of data but creates other problems  
By dividing the application data schema into smaller independent applications, the possible relationships between data are impossible to maintain at the database level. Those constraints need to be maintained at runtime with application code. The advantage of this pattern is the diversity of options in terms of storage solutions. In some cases, a relational model might be necessary, while in other use cases, a non-relational model might provide more flexibility. In this system, two main storage solutions were used: MongoDB and PostgreSQL.
## 4 Experiments and Results
### Implementation Details  
In this paper, the algorithm was built based on the PyTorch framework, the experimental platform was the Ubuntu 18.04 operating system, and the graphics card was ab NVIDIA GeForce RTX2080 Ti. In the training process, the input image was always set to 256 \(\times\) 256, and the network was optimized using the Adam optimizer. The initial learning rate of the network was 0.0002, the total number of iterations was 200, the learning rate of the first 100 iterations was unchanged, and the learning rate of the last 100 iterations decreased linearly to 0. The value of the weight coefficient \(\lambda\) in the loss function was chosen to be unchanged from the default value of 10 in CycleGAN. Meanwhile, after experiments, the best results were obtained when \(\mu\) and \(\gamma\) were selected to be 1 and 0.5, respectively. The  
Figure 7: Patch-wise contrastive loss.  
parameter settings of the other dehazing algorithms were kept the same as those in the original papers.
# Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling
## 2 YOLOv8  
YOLOv8 was designed by Ultralytics and is based on the previous models of YOLO. The overall architecture is separated into four sections, namely the input, backbone, neck, and head. Figure 1 shows this network structure. The input section is concerned with image preprocessing, adjusting the provided images to the necessary training size according to a certain ratio, and Mosaic data enhancement [20]. The backbone section is the module utilized for the extraction of primary information. It is made up of CBS, C2f, and SPPF. Using the ELAN structure concept, the C2f convolution module is designed in YOLOv7 [21], which guarantees a light weight and obtains richer gradient flow information by parallelizing more branches of the gradient flow. The neck part adopts an FPN (feature pyramid network) together with a PAN (path aggregation network) to facilitate the merging of features of different dimensions. The head part is in charge of the final target detection and classification functions, including a recognition head and a classification head. A decoupled header scheme is implemented to increase the accuracy of each by assigning the classification and regression tasks to be processed by different network layers, allowingeach task to focus on its objective. In the loss functions, the BCE (binary cross-entropy) function is used for the loss of the classification branch, and the DFL (distribution focal loss) and CIoU (complete intersection over union) functions are used for the loss of the regression branch. Considering the model's detection capability and the adaptability of the dataset's feature map size, this article uses YOLOv8 as the baseline model and introduces the attention mechanism EMA (efficient multi-scale attention) [22] and the pyramid network model BiFPN [23].
## 2 Methods  
Ellipsometry is a typical model-based technology, which usually involves a multiparameter forward model and the solution of an inverse problem. The former describes the interaction between the probing light and the sample, while the objective of the latter is fitting the measured data with the theoretical outputs of the forward model. Determining an appropriate model to accurately calculate the polarization state change of the incident beam induced by the light-nanostructure interaction is the premise of a successful metrology.  
Different to common grating structures consists of ridge and groove that are typically made of two different materials (such as air and grating material), the volume grating is essentially a specific distribution of refractive indices in the material, which can be depicted as Figure 1a. The fundamental principle of such distribution formation is based on the photoinduced property changes like photopolymerization induced diffusion, or ultraviolet radiation and thermal development, giving rise to expected refractive index distribution in the recording material, such as polymer-nanoparticle composites as well as PTR glasses, etc. As in a polymer-nanoparticle system, the photo-active monomer consumption occurs in bright regions during holography because of photopolymerization, which results in the movement of monomers towards the bright regions.  
Consequently, the nanoparticles will be squeezed into the dark regions due to the chemical potential effects. As a result, a sinusoidal refractive index distribution as shown in Figure 1a is expected in the formed polymer nanocomposite. In order to accurately describe such a system, a distributed dielectric constant-based RCWA is proposed as the forward model for volume grating characterization.
## 2 System Description
### Data Acquisition and Preprocessing  
We configured the measuring system to acquire data at a frame rate of 200 to quickly collect sufficient data. Figure 4 presents the data acquisition and preprocessing flowchart within the measurement system. The intensity measurement of atmospheric optical turbulence requires statistical calculation of sequential data. Consequently, each measure employs 10 s of data, equal to 2000 frames. To facilitate real-time data reception and computation, we developed a first-in-first-out (FIFO) data container with a capacity of 4000 frames of data. Once the FIFO container accrues an additional 200 data frames, it  
Figure 3: The UI of the real-time turbulence intensity and distribution measurement system is attached to the GLAO control software.  
triggers a follow-up computation. Consequently, the most recent 2000 data frames are used for current processing. This FIFO approach enables the measurement system to compute results every second.  
The data obtained from the RTC contain various errors and noise. Preprocessing is required before the statistical calculations. This preprocessing includes removing obvious errors and noise and selecting suitable guide regions for computation.  
During the tracking of targets using the solar telescope, tracking errors inevitably occur. Moreover, the telescope is also subject to slight shaking due to wind influence, introducing judder errors. Such errors have a pronounced characteristic: the overall judder of the pupil plane. On the MD-WFS, these error features manifest as the overall tilting judder of all sub-apertures. Therefore, these errors can be eliminated by removing the total tilt of the Hartmann wavefront sensor.  
In optical systems, all the optical devices inevitably introduce aberrations, which are time-invariant. In daytime solar observations, the primary mirror or other optical devices are indeed affected by solar energy, thus producing minute deformations. This kind of deformation is very complicated, but the variation time of this deformation is long. It can be considered that the aberrations introduced by this deformation also have the property of being time-invariant in a short time. For errors with time invariance, we eliminate them by removing the static aberrations of the MD-WFS.  
It is a significant challenge to sense wavefront information with low-contrast targets in the solar atmosphere, such as granulations. The accuracy of the wavefront is not good when using mage-shift measurement algorithms [28]. Therefore, it is necessary to pre-process the data to improve the accuracy of the measurement. In addition, the measurement system could update the measurement parameters based on the data situation.
## 2 Materials and Methods
### Multi-Scale Superpixel Segmentation  
Applying the shadow index calculation to the image produces the corresponding shadow index map. However, directly thresholding this index map to extract shadows can result in numerous dense separation points and false alarms. In order to overcome the effect of noise and to protect the integrity of shadow edges, ensuring the continuity and integrity of shadow superpixel, superpixel segmentation can be performed on the image. The superpixel segmentation of images may have the problem of over-segmentation and under-segmentation, which is difficult to balance. In the shadow detection task, under-segmentation may cause the shadows and non-shadows to be mixed in the same object, and the over-segmentation will unnecessarily increase computation costs. Therefore, we adopt a multi-scale segmentation method. We first use the SLIC [42] method to generate an initial large number of superpixels. Then, we use FNEA [43] to merge spectrally similar and spatially close SLIC superpixels to form a multi-scale segmentation result. FNEA is a bottom-up superpixel segmentation method that views the image as a four-neighborhood Region Adjacency Graph (RAG) based on the idea of graph theory. It combines the spectral features and shape features of multiple bands to describe the node characteristics. The neighboring nodes are then merged according to the merging cost in a Nearest Neighbor Graph (NNG). We use the superpixels obtained by over-segmentation of SLIC as the starting nodes for merging by FNEA. This way, we can avoid the problem of mixing shadows and non-shadows to the greatest extent while having a suitable number of superpixels.  
Figure 4 illustrates the multi-scale segmentation method we use and its segmentation results on the two stretch-processed images. We set the number of over-segmented superpixels \(n_{o}\) as the iterative stopping control quantity for SLIC and the number of merged superpixels \(n_{m}\) to control the iteration of FNEA. We define the average area of merged superpixels \(a_{m}\), then \(n_{m}=M\times N/a_{m}\), where \(M\) and \(N\) are the number of rows and columns of the image.  
The shadow and the background in the linear-stretched image are more likely to be mixed in the same object, and the superpixel contour cannot closely adhere to the real shadow edge, while the superpixels in the logarithmic-stretched image are able to segment shadows more accurately. Compared with single-scale, the multi-scale segmentation results are more hierarchical, with more superpixels and smaller sizes in the dark region containing  
Figure 3: Images and their histograms after different degrees of logarithmic stretching (from left to right: before stretching: \(v=30\) and \(v=200\).  
shadows and fewer superpixels and larger sizes in the non-shadowed background. All in all, multi-scale superpixel segmentation of the logarithmic-stretched image can separate the shadow and background more clearly in the form of image objects. It can ensure the continuity and integrity of shadows, which creates a good condition for the subsequent shadow processing.
# ORC Optimal Design through Clusterization for Waste Heat Recovery in Anaerobic Digestion Plants
## 2 Case Study
### Modified Layout  
The study aims at analyzing a new system configuration to improve plant performance and profit. The proposed solution implies a further exploitation of the thermal content of the microturbine exhaust gases. The exhaust gasses are used to heat the sewage, as in the original configuration, and power the ORC. Figure 3 shows the modified system layout.  
mGT exhaust gases provide the heat to the fluid of an intermediate circuit, which, conversely to the original scenario, powers the ORC evaporator and then heats the incoming sewage. The incoming sewage is preheated by the sewage exiting the digesters, which is mostly recirculated into the reactors. This internal regeneration reduces the heat requirement of the digesters, even though only to a small extent, and allows a slight increase in the available thermal energy to the ORC. The sewage heating is regulated through an air cooler inserted into the intermediate circuit, upstream of the circulation pump. This equipment rejects the excess of heat to the environment. Compared to a bypass, this solution may allow the heat exchanger of sewage heating to work at a constant flow rate under optimal heat-transfer conditions.  
Figure 1: Reference anaerobic digestion biogas facility layout. For further details, the reader should refer to [11].  
Diathermic oil is used in the intermediate circuit to match the operating temperatures. In the analyzed case, Therminol 66, a widely used synthetic fluid, was selected. Such a thermal oil can operate in a vast temperature range (from around \(-3\) to \(345\)\({}^{\circ}\)C) with high thermal stability and long operating life [31].  
Concerning the ORC, a subcritical, internally regenerated configuration was chosen. Internal regeneration might be convenient in the analyzed case, as the mGT waste heat is used for the ORC and meets the digester thermal demand.  
Pentafluoropropane (R245 fa) was selected as the ORC working fluid. In the literature, R245 fa is referred to as one of the most appropriate fluids for medium-low temperature ORC applications, from both economic and performance points of view [32]. Furthermore, R245 fa has quite good safety characteristic and optimal flammability limits. The main characteristics of the selected fluid are shown in Table 1 ORC cooling is obtained through a water-cooled condenser using purified wastewater from the purification plant. In this  
Figure 3: Anaerobic digestion biogas facility with the upgraded layout. The turbine exhaust gases power an organic ranking cycle (ORC) before heating the sewage.  
Figure 2: Histograms of biogas facility operating conditions (average hour values). (**a**) Heat flow rate required by the digester \(\dot{Q}_{dig}\) in kW\({}_{\text{th}}\). (**b**) mGT exhaust mass flow rate \(\dot{m}_{f}\) in kg/s. (**c**) Micro gas turbine exhaust temperature \(T_{f}\) in \({}^{\circ}\)C. Data from [11].  
way, the auxiliary consumption associated with air coolers is saved, thus allowing for more stable condensation conditions.
## 3 Technical Preliminaries
### Elliptic Curve Cryptography (ECC)  
The ECC is an asymmetric cryptosystem, which offers better performance compared with traditional systems because it employs a smaller key size with the same security [31]. Therefore, ECC-based authentication protocols are highly suitable for mobile devices in many applications scenarios. The security of the ECC is based on the elliptic curve discrete logarithm problem (ECDLP) and the elliptic curve computational Diffie-Hellman problem (ECCDHP), which are two security assumptions used in the proposed protocol. Suppose there is an elliptic curve over a finite field \(Fp\;Ep(a,\;b):\;y^{2}=x^{3}+ax+b(mod\;p)\).  
**Definition 2**.: _Given an integer \(k\in Z_{p}\) and a point \(P_{(x,\;y)}\in E_{p}\), it is easy to compute \(Q_{(x,\;y)}=k.P_{(x,\;y)}\in E_{p}\). However, due to the ECDLP, it is computationally difficult to find the scalar \(k\), such that \(Q_{(x,\;y)}=k.P_{(x,\;y)}\) given \(P_{(x,\;y)}\) and \(Q_{(x,\;y)}\)._  
**Definition 3**.: _Given two integers \(s,t\in Z_{p}\) and three points \(P_{(x,\;y)}.s.P_{(x,\;y)},t.P_{(x,\;y)}\in E_{p}\), the ECCDHP is used to find the point \(s.t.P_{(x,\;y)}\in E_{p}\) given \(s.P_{(x,\;y)}\) and \(t.P_{(x,\;y)}\)._
###### Abstract  
Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope. However, heart sounds can be masked by visceral sounds such as organ movement and breathing, and a doctor's level of experience can more seriously affect the accuracy of auscultation results. To improve the accuracy of auscultation, and to allow nonmedical staff to conduct cardiac auscultation anywhere and anytime, a hybrid-type personal smart stethoscope with an automatic heart sound analysis function is presented in this paper. The device was designed with a folding finger-ring shape that can be worn on the finger and placed on the chest to measure photoplethysmogram (PPC) signals and acquire the heart sound simultaneously. The measured heart sounds are detected as phonocardiogram (PCG) signals, and the boundaries of the heart sound variation and the peaks of the PPG signal are detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between PCG and PPG signals, an automatic heart sound analysis algorithm based on calculating the time interval between the first and second heart sounds (S1, S2) and the peak of the PPG was developed and implemented via the manufactured prototype device. The prototype device underwent accuracy and usability testing with 20 young adults, and the experimental results showed that the proposed smart stethoscope could satisfactorily collect the heart sounds and PPG signals. In addition, within the developed algorithm, the device was as accurate in start-points of heart sound detection as professional physiological signal-acquisition systems. Furthermore, the experimental results demonstrated that the device was able to identify S1 and S2 heart sounds automatically with high accuracy.  
Keywords:finger-ring shape; heart sounds; phonocardiogram; photoplethysmogram; Shannon entropy; stethoscope +
Footnote †: journal: _Sensors_
###### Abstract  
In construction projects, conducting dynamic load tests on all piles proves impractical. Selective testing estimates bearing capacity, while the remaining piles rely on penetration depth for management. This approach, however, faces reliability issues due to varying conditions among piles. Technological advancements, such as non-contact hammers and sensors, have enhanced the accuracy of penetration depth measurements during final driving. Nonetheless, relying solely on penetration depth for construction and quality management remains problematic. This study, therefore, focuses on enhancing the use of driving formulas to improve pile quality management, particularly for the widely used pre-stressed high-strength concrete (PHC) piles. To improve pile quality management, existing driving formulas underwent review and refinement. Utilizing 258 dynamic load test data from various sites, the Hiley, Gates, and Danish formulas underwent validation through statistical analysis and graphical comparison. Enhancements to the Gates formula, achieved through curve fitting with actual data and the application of segment-based coefficients, demonstrated increased accuracy in bearing capacity estimation. These improvements offer a more reliable approach to pile quality management in construction projects.  
pile driving formula; Hiley formula; Gates formula; dynamic load test; PHC pile; pre-bored pile; bearing capacity +
Footnote †: journal: Applied sciences