esa-sceva/satcom-chunk-collection huggingface dataset:
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Empirical Mode Decomposition (EMD) on PPG Signals  
EMD is a technique that decomposes a signal into its fundamental components, termed IMFs [14]. It employs a sifting process to identify peaks and troughs in the data to create upper and lower envelopes. The mean of these envelopes is then removed from the signal, isolating an IMF, which represents a basic oscillatory mode within the signal. Essentially, EMD aims to represent a signal as the sum of these IMFs and a residual. The 'EMD' class from the 'PyEMD' library was used to extract IMFs from the processed PPG signal. Each RGB channel of the segmented PPG data undergoes filtering before EMD is executed on each channel. Every IMF has certain characteristics: its upper and lower boundaries are symmetrical, and the number of points where they cross zero and the number of peaks (or troughs) differ by at most one. This means that the IMF must have at least one zero crossing and one peak (or trough). This condition ensures that the oscillatory characteristic of the IMF is consistent and regular. Figure 2 shows the decomposition of a blue channel signal of the first segment of subject 1 into its intrinsic mode functions (IMFs) as an example.  
The initial IMF (IMF 1 at Figure 2b) captures the highest frequency oscillations with amplitudes around \(-\)20 to 20. Subsequent IMFs show progressively lower frequency oscillations and smaller amplitude ranges. IMF 2 at Figure 2c oscillates between about \(-\)10 to 10, IMF 3 at Figure 2d oscillates with smoother oscillations between \(-\)5 and 5, and IMF 4 depicted in at Figure 2e shows much wider oscillations within an amplitude of \(-\)2 to 2. The fifth IMF (IMF 5 at Figure 2f) has a very narrow amplitude ranging from \(-\)1 to 1, indicating very smooth oscillations. The last IMF or residual (Figure 2g) shows a nearly constant trend with amplitudes around 4750, indicating a low-frequency drift in the signal.
## 2 Materials and Methods
### Calibrations  
Before applying the system, radiometric calibration of the optical paths in the four spectrometers was performed using the cross-calibration method described by [PERSON] et al. [29]. This method employs a pre-calibrated spectrometer as a reference to calibrate other spectrometers. In this study, a halogen lamp (Lowel Pro-light; Lowel-Light Manufacturing Inc., New York, NY, USA; 350-2500 nm, 250 W, 230/240 V AC-) was employed as the light source, and reflected radiance from a standard reflectance panel (Jingyi Optoelectronics Technology, Guangzhou, GD, China; \(\sim\)2% reflectance) was measured simultaneously usinga pre-calibrated spectrometer (QE Pro; Ocean Optics Inc., Dunedin, FL, USA) and the target spectrometers, which required calibration (Figure 2a,b). The calibration factor (_Cal_) was subsequently derived as follows:  
\[Cal=\frac{Rad}{\text{DN}_{\text{raw}}-\text{DN}_{\text{dark}}} \tag{1}\]  
where _Rad_ (mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\)) represents the radiance of the light source, as measured by the pre-calibrated spectrometer; DN\({}_{\text{raw}}\) designates the digital number recorded by each target spectrometer with the light source active; DN\({}_{\text{dark}}\) represents the dark current recorded by each target spectrometer with the light source inactive.  
Figure 1: Overview of the developed system. (**a**) Schematic of the system setup. (**b**) Schematic and photograph of the custom metal plate.  
Owing to the high signal-to-noise ratio of the spectrometers, the calculated _Cal_ exhibited an approximately linear relationship with the integration time (Figure 2c,d). As a result, _Cal_ normalized by the integration time was used to convert the recorded digital numbers into radiometric values (Figure 2d). It is crucial to highlight that, once the radiometric calibration is completed, the optical setup must remain unchanged, with the optical fiber firmly connected to the spectrometer until the experiment is completed.
## 4 Evaluation
### Experimental Environment
#### 4.1.2 Simulator Configuration  
Our proposed model is evaluated with an in-house memory system simulator which is trace-driven. The detailed configuration of the simulator is described in Table 2. We place an eDRAM-based prefetch buffer between the LLC and the main memory. As shown in Table 2, the processor consists of four cores, and L1 and L2 caches are privately allocated to each core. The L1 cache consists of an instruction cache and data cache, and each cache has 32 KB of space with an 8-way set associativity. The L2 cache has unified cache structure and is composed of 256 KB with a 4-way set associativity per core. The L3 cache (LLC) is shared by all cores and consists of 8 MB with a 16-way set associativity. All caches store data as cache block granularity (64-byte) and replace cache lines with the LRU replacement policy. The eDRAM-based prefetch buffer has similar configurations to the LLC. The prefetching operation is only carried out in the DRAM. Additionally, the DRAM capacity is 8 GB, and it stores data with 4 KB page granularity.  
To generate prefetch candidates, we designed a history table to store information on the recently accessed memory requests. The history table consists of three main modules. First, the page table is configured with 64 entries to store information of the page. The history table uses the LRU replacement policy to retain information on the correlated line table belonging to the page table for as long as possible. The correlated line table belonging to the page table consists of 4 KB. Our prefetcher decides whether to apply a prefetching by tracking accesses corresponding to the same correlated line based on the information of correlated line table. Finally, the picture table is updated only when four or more accesses occur on the same correlated line or the same page granularity; it consists of 64 entries. The 64 entries are divided into one half (32 entries) that stores page-based access patterns  
Figure 8: Memory access patterns of graph processing.  
and another half that stores correlated line-based access patterns. To store the pattern related to the most redundant access for a long period of time, the picture table applies a replacement policy based on the number of times the duplicates are accessed. In addition, Table 3 shows the performance parameters of memories and the disk which are used for our in-house simulator.
# Scaling of Rain Attenuation Models: A Survey
## 3 Existing Scaling Techniques
### Pathlength Scaling  
The pathlength is an essential parameter of rain attenuation, and an error arises if the proper estimated value is not considered developed through the rain attenuation estimation process. In [10], pathlength scaling techniques along with frequency scaling were presented. The cumulative rainfall distribution from the chosen reference path for the frequencies employed can be recalculated in the other two links through (35)-(37):  
\[A_{1}=U\cdot V \tag{35}\]  
\[U={k_{1}L_{1}/\left(1+{l_{2}}/r\right)} \tag{36}\]  
\[V=\left[\frac{A_{2}(1+{l_{2}}/r)}{k_{2}L_{2}}\right]^{({\alpha_{1}}/{\alpha_{2 }})} \tag{37}\]  
where \(A_{1}\) is the attenuation on the base path, \(A_{2}\) is the attenuation on the target path, \(k_{1}\) and \(\alpha_{1}\) are coefficients dependent on the frequency for the base path, \(k_{2}\) and \(\alpha_{2}\) are coefficients for the target path, \(L_{1}\) is the base link's pathlength, \(L_{2}\) is the target link's pathlength, and \(r\) is the path reduction factor. However, this technique is limited by the operating frequency from 38 to 58 GHz.
## 4 Application Testing and Analysis
### Access to Evaluation Indicators  
Terrain morphology and ecological environment category indicators for the two side slopes were obtained for September 2023 using the Geospatial Data Cloud ([[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)), accessed on 28 April 2024). For Zhangcungou, the metrics were as follows: slope, F\({}_{1}\), was 18.9%; curvature, F\({}_{2}\), was 0.0024 rad/m; vegetation coverage, F\({}_{5}\), was 45.55%; and the NDVI, F\({}_{6}\), was 0.62. For Longtan, the metrics were as follows: F\({}_{1}\) was 22.3%, F\({}_{2}\) was 0.0029 rad/m, F\({}_{5}\) was 56.31%, and F\({}_{6}\) was 0.83.  
For meteorological conditions, soil moisture conditions, reservoir water levels, and slope deformation types requiring real-time monitoring, sensor sites were deployed in the potential landslide area of the slope by installing sensors at different monitoring points and different depths; the installation scheme is shown in Figure 7. Three surface monitoring areas were divided along the landslide body area in the downslope direction based on different elevations, with one to three measurement points distributed in each monitoring area, equipped with a variety of sensors to provide a comprehensive setup for capturing critical data.  
The real-time monitoring of the slopes and data collection for the multi-dimensional risk assessment indicators were accomplished through sensors that monitor changes in various slope parameters in real time and provide important data support for assessing slope landslide risk. To ensure timely notification to nearby residents and power station staff in the event of a landslide, wireless loudspeakers were installed in the village at the foot of the slope and on top of the power station. These wireless speakers can be automatically triggered in the event of a landslide, emitting early warning signals and playing relevant emergency alerts to minimize human casualties and economic losses.  
Data for key indicators (F\({}_{1}\)-F\({}_{10}\)) for the Zhangcungou and Longtan slopes, collected from 1 September to 30 September 2023, at 12:00, are presented in Figure 8. These data highlight the variations over the monitoring period, illustrating the impact of environmental factors on slope stability.  
Figure 7: Layout of the monitoring sensor network. (a) Zhangcungou layout; (b) Longtan layout.  
The figure shows that there was continuous rainfall on 10, 11, and 12 September, resulting in a significant increase in soil moisture. This increase may have implications for slope stability and could alter the risk level of the slopes. Subsequently, heavy rainfall on 20 September washed away surface soil layers, further compromising slope stability. However, as temperatures rise and plant transpiration resumes, soil moisture content is expected to return to its normal range, thereby contributing to the rebound of slope stability.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Advantages of Using DDPM in Biomedical Image Segmentation
#### 3.4.4 Reversibility Facilitating Model Interpretability  
The reversible nature of DDPM is a unique feature that supports interpretability. In biomedical image segmentation, understanding how a model arrives at a segmentation result is essential for clinical acceptance. The reversibility property allows for tracing back the denoising diffusion process, providing insights into the features and transformations contributing to the final segmentation [13; 44].
## 4 Results and Observations
### Envelope Spectral Indicators  
Based on the results of the averaged envelope spectrum, the bearing fault characteristic frequencies can be identified, the spectrum amplitudes of which are combined to compute the proposed ESI for measuring the bearing degradation level. Accordingly, the ESIs of the test bearings are shown in Figure 9. For each test bearing, the first detection time of faults, \(t_{FDT}\), identified based on the inspection of the averaged envelope spectrum, is marked by the black dotted line.  
It is seen that each of the ESI plots maintains a stable platform at the healthy stage while exhibiting an overall ascending trajectory from the time when the bearing fault initiates, indicative of an accelerating trend of bearing degradation. However, it is noteworthy that these ESIs are not strictly monotonic and display substantial fluctuations as they approach the end of machine operation. Except Bearings 3 and 4, the ESI trajectories of other test bearings show noticeable declines at the end of the experiments. This could be explained by the theory of 'healing phenomena' during the bearing deterioration [5], where the defect surfaces caused by wear or fatigue may partially restore the original smoothness due to several potential factors such as the redistribution of lubrication or the removal of debris, leading to a small performance improvement in friction and vibration. However, it should be noted that this phenomenon does not guarantee the prevention of subsequent degradation.  
As a representation of the functional failures of rolling bearings, the end-of-life time is formulated as the time when the extracted ESI reaches the maximum, indicating the peak energy of the vibration components at the FCFs, as defined in Equation (4). Respectively, the end-of-life time \(t_{EOL}\) for each bearing is listed in Table 4 and marked with a black dashed line in Figure 9.  
Based on the computation of the ESI for all the available test bearings, the average level of the ESI at \(t_{EOL}\) is utilized as the failure threshold for further prediction of RUL, which is \(\overline{\gamma}=3.24\).  
Figure 8: The averaged envelope spectra indicating the fault initiation of Bearing 6: (**a**) The envelope spectrum at the 1417 th minute. (**b**) The envelope spectrum at the 1418 th minute when the inner-race fault is identified.
# Closing Editorial for Computer Vision and Pattern Recognition Based on Deep Learning
## 5 Application  
[PERSON] et al. [23] developed a method integrating improved YOLOv5s target detection with Anylog emergency evacuation simulation to enhance the efficiency of emergency evacuations in field stations. It leverages YOLOv5s with an SE attention mechanism to detect pedestrians and determines their locations based on head detection, considering crowded conditions. Anylogic employs closest distance evacuation principles to guide pedestrians to the nearest exit.  
[PERSON] et al. [24] present the application of supervised learning and image classification for the early detection of late blight disease in potato using convolutional neural network and support vector machine (SVM). Initially, a dataset of crop images is collected and pre-processed to extract disease characteristics. Subsequently, classification models are trained and evaluated using various performance metrics to identify healthy and infected potato plants.  
[PERSON] and [PERSON] et al. [25] addressed the challenge of separating overlapped space-based ADS-B signals, crucial for air traffic control, by proposing a deep learning approach using a Multi-Scale Conv-TasNet (MConv-TasNet). Specifically, a Multi-Scale Convolutional Separation (MCS) network is introduced to fuse temporal features from overlapping ADS-B signals, enabling accurate signal separation.  
[PERSON] et al. [26] explored the application of deep learning in cooperative vehicle-infrastructure systems (CVISs) for video applications, highlighting its ability to handle high-dimensional datasets and improve performance compared to traditional approaches.
## 4 Data and Examples
### Parameter Prediction and State Analysis  
In this section, we predict the parameters after the multivariate traffic parameter phase space reconstruction. Based on the predicted results, we calculate the spatiotemporal state value of the road traffic in the example section using Formula (12).  
LSTM is utilized to construct the evolution of phase space phase points and predict multivariate traffic parameters [48]. We referred to the LSTM network structure design parameter rules in MATLAB R2020a, and we set the input size to 52 combined with the feature dimensions after multi-parameter phase space reconstruction. We set the fully connected layers to 1, and we set the hidden layers to 200 and the dropout rate to 0.2 with a RELU activation function after multiple rounds of numerical experiments. The results meet the prediction accuracy requirements. The deep learning network structure is depicted in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Speed at Section 1** & **Headway** & **Speed at Section 2** & **Speed at Section 3** & **Travel Time** \\ \hline Embedding Dimension, m & 22 & 20 & 9 & 44 & 3 & 25 \\ Time Delay & 1 & 1 & 1 & 1 & 2 & 1 \\ Largest Lyapunov index & 0.002 & 0.003 & 0.001 & 0.004 & 0.002 & 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multivariate variable phase space reconstruction with an embedding dimension and a time delay.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Name** & **Type** & **Activations** \\ \hline
1 & Sequence input & Sequence Input with 52 dimensions & Sequence Input & 52 \\
2 & LSTM with 200 hidden units & LSTM & 200 \\
3 & Dropout & Dropout & 200 \\
4 & Relu & ReLU & 200 \\
5 & fc & Fully Connected & 1 \\  & One fully connected layer & Regression output & \\
6 & Mean squared error with response & Regression Output & - \\  & ‘Response’ & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Structure of the long short-term neural network.
# IoT-Ready Temperature Probe for Smart Monitoring of Forest Roads
## 5 Conclusions  
The growing need for higher-quality transportation in a specific natural setting, typically characterized by significant temperature changes, puts pressure on local civil engineers to improve quality. The interest in more efficient forest environment management, a more pleasant approach to fire interventions, and finally, the attempt to expand recreational use of the forest in the development of tourism are the major causes for forest management. The installation of a network of stationary measuring sites on designated road sections is also part of the criteria, with the goal of providing valuable inputs for the accurate evaluation of the gathered data on the structure and road surface. This includes, for example, unique sensors that assess the temperature profile of roads in both the vertical and horizontal directions, soil sensors put along the road, frost sensors, road pollution sensors, and different meteorological sets for use on the forest road network. Our approach opens the potential of fusing data from such professional equipment with data from our suggested temperature probe for measuring the temperature profile of the soil to create a measurement set with additional value that contributes to the achievement of the above purposes.  
Our proposal represents a comprehensive solution with possibilities for future extensions. We have addressed the design of the mechanical solution in two variants for use in several types of environments, considering the requirements for environments with mechanical, chemical, and environmental stresses. The electrical wiring of the sensors used is straightforward, but it was necessary to design their arrangement in the probe so that their positions were guaranteed. We proposed two solutions, of which the solution using PCB elements that can be adapted to the desired length, currently with a 50 mm grid, proved to be the most suitable. Such wiring allows data collection via a standalone logger, or the probe can be connected to an IoT device supporting a 1-wire bus. Based on the proposed solution, we have created a simulation model, which then enters the verification process. We designed and implemented a set of experiments in a water bath and a climate chamber. Subsequently, we discussed and compared the results of the experiments and simulation in Chapter 4. The presented figures clearly confirm the substantial agreement between the experimental and simulation data. Our proposed probe will primarily be used to measure the temperature profile of forest roads as input to models used in their management. However, its application is much broader in different areas of the economy, such as agriculture, energy, technological processes, automation, smart city solutions and more with requisite for measuring temperature profiles.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 7 Opportunities for Organizational Adoption of the Research
### Methods of Defending Organizational Systems--Business Recommendations
#### 7.1.1 NIDS or HIDS for a Business Strategy  
A network-based intrusion detection system gives the organization significantly more monitoring capability than the host-based intrusion detection system, as seen in Section 3--this will aid in the business noticing cyber-attacks (in real-time), providing a higher level of cyber threat intelligence. Moreover, the HIDS technology can only understand a cyber threat once a setting or file has already been altered, meaning that it can only mitigate the problem once the problem has happened, causing a HIDS to be a problematic solution in comparison. The recommendation for businesses is to deploy a NIDS as the priority and, if possible, use both technologies as a hybrid solution but avoid using a HIDS as a standalone IDS.
## 4 Measurement Results
### Measurements Related to under Ballast Mats  
Measurements at Raron in Switzerland [23] with and without under-ballast mats (static stiffness 0.06 N/mm\({}^{3}\)) are here analysed for the reduction effects at different train speeds. Figure 8a shows the ground vibration of the reference track without under-ballast mat. For a distance of 8 m, a maximum can be found at 20 Hz for 175 km/h, at 16 Hz for 160 km/h, at 12 Hz for 120 km/h and at 8 Hz for 80 km/h. This maximum is reduced by the under-ballast mat (Figure 8b) and a reduction of up to 10 dB is evaluated in Figure 8c. An amplification around 40 Hz can be observed in Figure 8b,c, and the frequencies above 64 Hz are reduced by about 10 dB. Other measurements in Switzerland (Raron 2, Nuziders and Rothrist) with softer under-ballast mats (static stiffness between 0.025 and 0.06 N/mm\({}^{3}\)) show a lower resonance frequency and a stronger ground vibration reduction; see Annex A2 in [14].
## 2 Materials and Methods
### Essential Oils Characterization by Gas Chromatography-Mass Spectrometry (GC-MS)  
The volatile profiles of the obtained EOs of _C. citratus_, _L. angustifolia_, _T. gummifera_, _M. pulegium_, _M. suaveolens_, _T. villosa_ and _A. foeniculum_ were characterized by gas chromatography coupled with mass spectrometry (GC/MS SCION-SQ 456 GC, Bruker Corporation, Massachusetts, United States of America). The separation was achieved on an HP-5 MS capillary column (30 m \(\times\) 0.25 mm id \(\times\) 0.25 \(\upmu\)m film thickness, Agilent J&W, Folsom, CA, USA). Helium was the carrier gas used with a flow rate of 1 mL/min. Firstly, EOs were diluted (5:200) in the solvent n-pentane, and then injected (1 \(\upmu\)L) using a split ratio of 1:100 and analyzed using electron impact ionization mass spectrometry (EI-MS) at 70 eV. The compounds were identified in scan mode with the positive polarity of ions 20-300 _m/_z with a time of 250.0 ms. The initial oven temperature was programmed to 45 \({}^{\circ}\)C, gradually increasing 3 \({}^{\circ}\)C per minute to 175 \({}^{\circ}\)C, and finally reaching 300 \({}^{\circ}\)C with a heating rate of 15 \({}^{\circ}\)C per minute and maintaining this final temperature for 10 min. The injector and the detector were kept at 220 and 250 \({}^{\circ}\)C, respectively. The identification of the compounds was based on a comparison of their mass spectra with the mass spectra of the MS library (NIST library 17 version 2.3) included in the equipment software. The relative amount of each compound was expressed as a percentage of the relative peak area of the compound, relative to the total area of the peaks identified in the EO samples. The _T. mastichina_ EO used in this assay was previously characterized by GC-MS in a different work [48].