esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Preprocessing
#### 3.2.1 Denosing  
The ECG signals are prone to be contaminated by background noise and bioelectrical inference, such as power-line noise and muscle movement. The denoising step could clean the ECG signals to prevent overwhelming micro features in signals and help DL models focus more on the ECG features [30]. Based on the selected studies, only about 38% of selected works (138 out of 368) specifically mentioned their denoising methods, and those methods can be mainly categorized into three types, i.e., traditional filter-based denoising methods (45.9%, 62 out of 138), wavelet-based denoising methods (38.4%, 53 out of 138), and hybrid denoising methods (16.7%, 23 out of 138). The traditional denoising filters, such as lowpass, bandpass, and notch filters, assume that the noise and useful signals lie in different frequency bands. Other denoising filters include smoothing filters such as the median filter and the Savitzky-Golay (S-G) [23; 31; 32; 33] and adaptive filters [34; 35]. The discrete wavelet transform (DWT) could project ECG signals onto the time-frequency domain based on wavelet basis functions [36]. To remove the noise, the wavelet coefficients at high-frequency bands can be simply set to zero or apply a thresholding process to set the modest wavelet coefficients to zero [19; 20; 21; 22] based on the assumption that the useful ECG signal is similar to the selected wavelet basis function. A combination of different types of denoising methods can be applied for noise removal, e.g., [37; 38] combines DWT, median filters, or S-G filters for denoising. However, this type of method will induce higher processing latency.  
The frequency counts of the three types of methods in each year are presented in Figure 3a. The number of works considering denoising has been increasing in recent years. The traditional filter-based methods are more popular than the other two denoising methods because of their effectiveness but easier implementation. In addition, there have been increasing works that consider wavelet-based methods for ECG signal denoising in recent years.
# AHC: Advanced Network-Hiding Access Control Framework
## 3 Preliminaries  
**Definition 1**.: _Zero Trust is a network security concept that prioritizes resource protection. It assumes that trust is not inherent in any part of the network, and access to resources should not be based on the trustworthiness of the subject or the resource itself. Instead, trust connections between subjects and resources should be established from scratch through continuous environmental awareness and dynamic trust assessment to implement access control._  
**Definition 2**.: _Port Knocking is a communication technique where a requesting party sends packets containing information about its identity, device, and target resource. These packets are used for identity and privilege legitimacy verification before establishing a data communication connection._  
**Definition 3**.: _A Noise Protocol is a set of algorithms that introduce randomness into the encryption and decryption processes during communication. This randomness helps to avoid a fixed computational paradigm, making it difficult for attackers to crack. It enhances security for data transmission by providing a dynamic and unpredictable encryption scheme._  
**Definition 4**.: _Network-Hiding Access Control is a security measure designed to protect resources in a computer network from unauthorized access. This access control method hides critical resources, such as servers, databases, or sensitive information, from potential attackers. Techniques such as network isolation, encryption, and authentication are used to ensure that only authorized users or devices can access these resources, thereby enhancing network security and preventing unauthorized access and data breaches._  
**Definition 5**.: _Mutual Authentication, also known as two-way authentication or client authentication, is a network security mechanism used to ensure that the connection established betweentwo parties in communication is trustworthy. In Mutual Authentication, not only does the server authenticate the client's identity, but the client also authenticates the server's identity. This two-way verification helps prevent man-in-the-middle attacks and other security threats, ensuring that both parties in communication are legitimate and trustworthy._
## 1 Introduction
combined the fractional programming theory and the framework of continuous convex optimization problems, to propose a fractional programming power allocation scheme [27]. [PERSON] et al. proposed an optimization method for antenna selection and power allocation, based on genetic algorithms [28]. The Pearson correlation coefficient is widely used to measure the degree of correlation between two variables, and its value is between \(-1\) and \(1\). The larger the absolute value, the stronger the correlation [29]. In this paper, we propose to use the Pearson coefficient to express the correlation between the central luminous intensity of the LED and the illuminance received by the photodetector, and then to select the optimal antenna combination. The aforementioned studies, despite their characteristics, exhibited a poor scope of application or high complexity. Therefore, this study proposes a multi-array visible-light OGSMP-MIMO system design scheme, based on Pearson coefficients. The basic principle of this scheme is to adopt the Pearson coefficient correlation between the photoelectric detector (PD) end at different positions and the active transmitting antenna, to select the optimal antenna combination, independent of the accuracy of channel estimation, realize multiplexing of the time and space domains, and improve the bit-error performance.
## 2 Results and Discussion
### Odor activity values (OAVs)
In our study, alcohols were the predominant groups which constituted the aroma compounds rather than acids as in the previous study [8]. Thirteen aroma compounds whose concentrations are higher than threshold values from the Chardonnay wine produced in Changli County of China were identified [9]. In contrast, 12 aroma compounds whose concentrations are higher than threshold values were identified in present study, among which seven aroma compounds, namely ethyl octanoate, ethyl hexanoate, isoamyl acetate, ethyl decanoate, octanoic acid, isoamyl alcohol, and phenethyl acetate overlapped with ones in the study above. The characteristics of local climate and soil could lead to the discrepancy between our results and others. Based on the OAVs and the ROC values of aroma compounds, ethyl octanoate, ethyl hexanoate, and isoamyl acetate are able to exert a strong influence on wine aroma: they are responsible for a major part of the aroma characteristics of young wines, but it does not mean they are the most significant aroma compounds in terms of sensory evaluation, and sensory studies are necessary to further confirm the impact of the odor-active compounds already identified. In contrast, the contributions of phenylethyl acetate and 2-phenylethanol were minimal. Based on their ROC, phenylethyl acetate and 2-phenylethanol each accounted for less than 1% of the global aroma perceptions of the three wines. According to [PERSON] _et al_. [32], even if they were present at a concentration higher than their threshold values, compounds such as fused alcohols, acids, esters and volatile phenols are not able to affect individually the flavor of the wines. This is because these compounds are common in any kind of fermented alcoholic beverages that share similar aromatic properties. Moreover, the aromatic buffer would be caused by the presence in wine of relatively high concentrations of ethanol, ethyl esters, fused alcohols, volatile phenols, \(\beta\)-damascenone, and fatty acids and can be broken only by the presence of an aroma with very different aroma properties, such as 4-methyl-4-mercaptoethanol-2-one.
## 2 Results and Discussion  
### Protein Immobilization Yield  
Enzyme immobilization yield of amino-, epoxy- and adsorption-immobilized Endo BI-1 was evaluated by quantifying the unbound protein remaining in the supernatant and resin washing buffer post-immobilization. The fluorescence method employed herein (Qubit Protein Assay Kit) demonstrated the high immobilization yield (>98%) of the amino-based resin, whereas divinylbenzene (DVB)-based hydrophobic interaction adsorption immobilized 70% of the enzyme (Figure 1). Epoxy-based covalent binding yielded the lowest immobilization yield at 12%. These results were corroborated by SDS-PAGE, where denser Endo BI-1 bands in the supernatants were present in epoxy and adsorption methods, while amino-based immobilization had no visible band (Figure S1). Differences between immobilization yields for covalent and adsorption immobilization methods have been observed previously and can possibly be attributed to the strength of interaction between hydrophobic interactions and covalent bonds [18]. However, low binding using epoxide-activated resin may be due to non-optimal methods to identify the ideal pH, time, and temperature for increased binding [19]. Further investigation into those conditions may improve protein immobilization yield for the epoxide-activated resin.
## 3 Results and Discussion
### NEs Design and Physicochemical Characterization
Figure 7: Representative images of the transmission electron microscopy observations of selected empty NEs (**A**) and NEsL (**B**) samples. lipophilic compartment of the nanocarrier. By fluorescence characterization, anisotropy values have been obtained. Due to the hydrophobic nature of DPH, it was localized into the oil core of the NEs. The low anisotropy value indicates that the probe rotational diffusion is not hindered, which can indicate that the fluidity of the local environment is elevated [68]. Both samples, empty and loaded NEs, showed a low and similar anisotropy value (0.24 for empty NEs and 0.26 for loaded ones) and this result suggests a quite fluid oil droplet that could assure a significant release ability of a loaded drug [24].
###### Abstract  
The evaluation of buildings damage following disasters from natural hazards is a crucial step in determining the extent of the damage and measuring renovation needs. In this study, a combination of the synthetic aperture radar (SAR) and light detection and ranging (LIDAR) data before and after the earthquake were used to assess the damage to buildings caused by the Kumamoto earthquake. For damage assessment, three variables including elevation difference (ELD) and texture difference (TD) in pre- and post-event LIDAR images and coherence difference (CD) in SAR images before and after the event were considered and their results were extracted. Machine learning algorithms including random forest (RDF) and the support vector machine (SVM) were used to classify and predict the rate of damage. The results showed that ELD parameter played a key role in identifying the damaged buildings. The SVM algorithm using the ELD parameter and considering three damage rates, including D0 and D1 (Negligible to slight damages), D2, D3 and D4 (Moderate to Heavy damages) and D5 and D6 (Collapsed buildings) provided an overall accuracy of about 87.1%. In addition, for four damage rates, the overall accuracy was about 78.1%.  
damage assessment; airborne SAR; LIDAR; Kumamoto; earthquake +
Footnote †: journal: Applied Sciences
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 2 Resilience Metrics
[38] highlight among their concerns the structures of systems and the cascading effects that will occur in the presence of events and how to take this into account in defining resilient systems. ISO standard ISO-14224 [39] defines a general hierarchy using terms such as system, subsystem, and component which can be used to establish an appropriate structure to determine the resilience behavior in a complex system. Figure 1: Concept of system resilience (adapted from [37]). Another important concept related to the resilience quantification is the gradual or abrupt loss of functionality. Several authors do not consider the effect of a shock only as a binary situation (functional or not: on or off) [40]. [PERSON] et al. [15] assume that the initial system is full of functionality (100% functional), however, this is not always the case. The system may not be fully functional before a performance shock. A system's response may be expressed as a given minimum level of service, or as an acceptable range within which the system operates. This situation is recurrent in complex engineering systems that, even in the presence of local failures, and thanks to complex topologies that combine parallel, redundant and stand by equipment, the functionality of the system is not completely shut down, but it may occur in a gradual performance degradation process. The Figure 2 (adapted from [14]) depicts that graduality. Similarly, the system will not necessarily recover its full functionality after it is recovered. This may be due to two important aspects: other disruptive events may continue to influence the system performance [16] or the organization may not be able to sustain the costs of full recovery. This is why resilience must consider that the output or service level of the system must be compared to certain levels of acceptance. Initially we addressed the following question, what is the goal of maintenance and what is its measure? [PERSON] [41], as an analogy with the productivity ratio, defines as the main goal of maintenance as the correct balance between the \"maintenance output\" and \"maintenance input\". This reinforces the idea that the performance of maintenance actions (efficiency and effectiveness) should be measured. Thus, it is widely accepted that the availability is the most effective metric to determine the result of the operational condition of an asset. That is, availability determines the operating condition of the equipment and the effectiveness of the maintenance being applied to that equipment.
## 3 Experimental Design and Implementation
### Experimental Parameter Setting and Optimization
#### 3.4.3 Algorithm Parameter Optimization  
1. Data storage format: Providing two storage options,'manytable' and 'onetable', where the former is suitable for detailed segment-by-segment analysis and the latter facilitates overall comparison and statistics.
2. Parallel processing settings: When processing large-scale data, reasonably setting the number of parallel processing threads to balance computational resources and processing efficiency.
3. Visualization parameters: Including histogram bin numbers, chart color schemes, and annotation detail levels. These parameter settings must ensure sufficient information display while avoiding visual clutter.
# Investigating Wearable Fitness Applications: Data Privacy and Digital Forensics Analysis on Android
## 5 Discussion
### Health Data Privacy Concerns  
Some previous research [30] argue that profiling of users could lead to discrimination using information learned from health wearables. Due to the plethora of sensors being used and thus the multitude of varying health data points available to companies, this data may indicate more intricate details about a person's mental or physical state. All three fitness apps investigated in the current study provide varying degrees of recoverable heart rate and activity data, potentially pointing to the wearable device user's medical condition. Such data could and have been used to determine heart diseases and pregnancy. Consider a hypothetical situation in which wearable IoT data are used to determine a person's acceptance for an advertised job position; a woman may be denied the job based on data from her wearable device, which may be able to predict (or _infer_) when a woman is pregnant, based on her resting heart rate, as research has shown that during pregnancy, resting heart rate increases by 30-50% in women [37]. Such a scenario is not far-fetched, as currently, companies refer to the applicant's social media before deciding whether to hire the person or not [38]. Companies that have access to this level of wearable user data may also misuse the data to discriminate against their employees by denying promotions. Insurance companies may also gain access to such wearable data, particularly heart rate, activity, and sedentary data, and use this information to deny or inflate their customers' premiums. Moreover, the availability of wearable technologies might pave the way for more frequent user/patient monitoring. However, many products can now be connected to the smartphone of the user, which can potentially allow hackers to access information by exploiting software vulnerabilities [26].  
The three applications considered in this study provided varying degrees of recoverable health activity data. Regarding the Amazon Halo user's data, a forensic investigator would be able to paint a detailed picture of the user's daily habits for at least the last 13 days the device was used. Similarly, forensic investigators may be able to recover Garmin Connect users' activity-related data for at least the last 15 days, in some cases. Concerning user privacy on the Mobvoi app, one of the biggest concerns is storing user PII related to the user's health. However, it is essential to note that even though these data were found, it appears that the app developers tried to obfuscate the data so that it was not easily identifiable, though given enough time and enough data collected, it was still possible to identify some of this information.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Iron-Based NPs
#### 4.3.3 Attenuation of Abiotic Stress  
Spraying of salt-stressed (100 mM of NaCl) _Dracocephalusum maladavica_ L. plants with Fe\({}_{2}\)O\({}_{3}\) NPs considerably increased the leaf area, content of secondary metabolites (TPC, TFC, anthocyanins), as well as activities of antioxidant enzymes (GPX, APX, CAT, GR), suggesting improved antioxidant defense. Moreover, under salinity stress, the Fe\({}_{2}\)O\({}_{3}\) NPs can serve as a source of Fe for this medicinal plant [224]. Application of Fe\({}_{3}\)O\({}_{4}\) NPs was also able to greatly attenuate Cd and Pb induced toxicity in coriander plants grown in soil artificially contaminated with these toxic metals [225]. Treatment of _Mentha piperita_ plants under salt stress with 10-30 uM Fe\({}_{2}\)O\({}_{3}\)NPs improved the fresh and dry biomass of leaves, contents of essential elements such as P, K, Fe, Zn, and Ca, although did not affect Na content. Moreover, Fe\({}_{2}\)O\({}_{3}\) NPs pronouncedly reduced lipid peroxidation and proline contents and decreased activities of CAT, SOD and GPX in salt-stressed plants [226]. In _Melissa officinalis_ L. plants exposed to drought stress (80%, 60%, and 40% field capacity (FC)) considerable increase in MDA, H\({}_{2}\)O\({}_{2}\), as well as proline content and electrolyte leakage was observed with decreasing FC, while TPC, activities of antioxidant enzymes, Chl content and relative water content were reduced. Although increasing of water deficit from 80% to 60% FC resulted in improved yield of EO, further reduction to 40% already showed an opposite effect. However, application of FeNPs (5-20 uM) had beneficial impact on drought-stressed plants, which was reflected in further increase of EO yield, increased proline levels and mitigation of oxidative stress by activation of antioxidant defense system of plants and, thus, FeNPs protected the plants against oxidative stress damages [142].
## 3 CGST Codec Simulation for Processing Images
### Combining CGST with JPEG Compression  
To test the compatibility of our proposed method with traditional lossy compression methods, we converted the collected dataset of images into the JPEG format at 100% quality.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Neural Network** &
\begin{tabular}{c} **Response** \\ **Type** \\ \end{tabular} & **MSE** & **MN** & **SSIM** & **CW-SSIM** \\ \hline \multirow{4}{*}{Fully connected} & 13 & 36.214 & 67.014 & 0.1813 & 0.4493 \\  & 20 & 35.943 & 65.891 & 0.1875 & 0.4636 \\  & 113 & 31.212 & 58.337 & 0.2162 & 0.4845 \\  & 140 & 25.613 & 46.611 & 0.3688 & 0.5175 \\ \hline \multirow{4}{*}{Recurrent} & 13 & 36.454 & 68.845 & 0.1802 & 0.4263 \\  & 20 & 29.814 & 42.674 & 0.4091 & 0.5343 \\  & 113 & 27.034 & 47.013 & 0.3825 & 0.5772 \\  & 140 & 22.613 & 37.421 & 0.6012 & 0.6553 \\ \hline \multirow{4}{*}{Convolution} & 13 & – & – & – & – \\  & 20 & 30.614 & 57.437 & 0.2605 & 0.4858 \\  & 113 & 21.360 & 38.594 & 0.5790 & 0.6342 \\  & 140 & 18.714 & 34.803 & 0.6475 & 0.7716 \\ \hline \multirow{4}{*}{Convolution in Fourier space} & 13 & – & – & – & – \\  & 20 & – & – & – & – \\ \cline{1-1}  & 113 & 20.018 & 37.020 & 0.6047 & 0.6642 \\ \cline{1-1}  & 140 & 17.810 & 33.331 & 0.6759 & 0.8082 \\ \hline \hline \end{tabular}
\end{table}
Table 2: NN performance in restoring PNG images compressed by modified CGST method.  
Figure 7: Compressed image restoring using NNs: (**a**) image compressed by modified CGST; (**b**) recurrent NN (CW-SSIM = 0.6553); (**c**) CNN (CW-SSIM = 0.7716); (**d**) CNN in Fourier space (CW-SSIM = 0.8082).  
Then, we applied the modified CGST algorithm to compress the images and restored the compressed data using the previously described sequence of actions. We used the convolution in Fourier space NN, as it showed the best restorative ability. The compression ratio obtained was approximately 4.8.  
Table 3 illustrates the restored images' quality characteristics compared to ordinary JPEG compression with an image quality of 89, which provided the same compression ratio. NN-assisted CGST showed significantly better results according to MN and MSE and was only slightly inferior to JPEG in CW-SSIM. This difference can be explained by the JPEG algorithm selectively removing high-frequency information from an image, resulting in a loss of pixel-level precision [64]. These losses may be insignificant to human vision but can reduce information for computer vision algorithms.  
The quality metrics of the compressed and restored images prove their suitability for computer vision systems as training data for machine learning models [65]. Classical machine learning models, particularly those that perform low-level image processing functions, require a better MSE value to be trained successfully [66].