esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations
## 5 Experimental Setup and Study Methodology
### JIT Access  
Giving permanent administrative privileges to users presents a considerable danger of privilege exploitation or inadvertent misuse. The organization chooses JIT access to resolve this issue. JIT access, established with Azure PIM, facilitates the temporary elevation of privileges alone when necessary. Unlike static, permanent administrative permissions, this method minimizes the attack surface and adheres to the Zero Trust principle of least privilege. JIT was chosen because it provided granular access control and assurance that enhanced privileges are issued just temporarily. This decision aims to reduce exposure to insider threats while preserving operational flexibility, particularly concerning third-party contractors. This reduces the potential attack surface and aligns with the Zero Trust principle of \"least privilege.\" PIM was chosen to deploy Just-in-Time access due to its comprehensive and unified methodology for controlling privileged access. PIM enabled the temporary escalation of privileges for administrative functions, with privileges automatically expiring after a specific duration. This decision was motivated by the necessity to mitigate the possibility of enduring administrative privileges being exploited during a breach. Moreover, PIM facilitates audit logs and just-in-time reporting, allowing monitoring of the utilization of administrative privileges, thus enhancing accountability and security posture. Let us prepare one of the Azure roles for JIT access, as seen in Figure 15:  
Figure 15: Preparing Azure Application Administrator role for JIT.  
The maximum duration of the activation has been set to 8 h; the duration depends on the organization's needs. For extra security, we have also checked off \"Require justification\" on activation\" and \"Require approval to activate.\"  
After configuring some of the settings, we can continue to add the assignment to our test user, as shown in Figure 16:  
We can view all added assignments through the Assignments tab for the role, where we can also remove, update, or extend them.
## 3 VLC for Underwater Communication
### Key Components and System Design of UVLC  
#### 3.3.1 Transmitters  
The primary light sources used are light-emitting diodes (LEDs) and laser diodes (LDs), each with distinct advantages and limitations. LEDs are cost-effective, reliable [22], and suited for mobile underwater networks due to their broad divergence angle, which simplifies alignment. However, their lower power output limits communication range and data rates [22; 23]. LDs, on the other hand, provide high power, narrow beams, and longer communication distances, making them ideal for high-speed data transmission. Nonetheless, LDs are more expensive, sensitive to temperature variations, and require precise alignment [22].  
To enhance the performance of transmitters, advanced techniques such as signal modulation, error correction coding, and adaptive modulation are often employed [23]. Optical components like lenses are also integrated to shape and direct light, reducing temporal  
Figure 3: Configuration types for underwater wireless optical communication: (**a**) LOS, (**b**) DLOS, (**c**) RLOS, and (**d**) NLOS.  
pulse spreading and improving transmission efficiency. For instance, narrow-beam LDs reduce scattering, enabling efficient and reliable data transmission over longer distances, while wide-beam LEDs are better suited for collaborative tasks in turbid environments [23].
###### Abstract
These insights contribute to the understanding of how macroscopic biomechanical factors affect the healthy or pathological growth and remodeling of cardiovascular and engineered tissues. However, the replication of in vivo physiological conditions in vitro is cumbersome, and the results of in vitro experimentation can be challenging to extrapolate to patient-specific situations. As a result, the clinical application of inverse analyses of in vitro experiments remains limited. This article aims first to serve as a referential document for concepts and methods from all involved disciplines on patient-specific in vivo inverse modeling; and secondly, to highlight the potential clinical application of patient-specific inverse modeling in the cardiovascular research field. Specifically, we review the fundamentals of cardiovascular tissue and blood biomechanics, modeling and simulation, and medical imaging, as they relate to the inverse modeling approach and its applications in cardiovascular medicine. In Section 2, we review the application of the principles of classical continuum mechanics to the study of blood and tissue motion, with special emphasis on the constitutive equations that have been proposed to describe the mechanical behavior of cardiovascular tissue and blood. Section 3 briefly summarizes the fundamentals and main features of the finite element method (FEM) and finite volume method (FVM), as the most popular formulations for the numerical solution of biomechanical models. Next, we review the general definition of inverse problems and the available alternatives to solve inverse mechanics problems in Section 4. In Section 5, we review the working principles and main features of ultrasound (US), magnetic resonance imaging (MRI), and computational tomography (CT) imaging, giving special attention to the available techniques for the resolution of tissue and blood kinematics. Finally, in Section 6, we present a comprehensive review of the applications of imaging-based inverse modeling approaches to patient-specific human cardiovascular mechanics, including the resolution of the unloaded configuration and the estimation of tissue properties and stresses. Reviewed applications include healthy and diseased heart valves, cardiac and arterial walls, and hemodynamics of large arteries. To highlight the potential application of the inverse-modeling approach in cardiovascular medicine, we focus herein mostly on developments made in human studies, with a few mentions of relevant and pioneering studies in animals.
## 3 Results and Discussion  
### Chemical Composition of Rose Petals Based on SPME Arrow  
The SPME Arrow results revealed high variation among the studied rose cultivars. The main components of the rose petal oil were phenethyl alcohol, citronellol, and geraniol (Table 3). Phenethyl alcohol ranged from 69.28% in B215 to 77.58% in A104, while in previous research, it ranged from 44.5% to 47.2% according to the distillation method [2]. Phenyl ethyl alcohol is known for a fragrant rose-like odor with lilac honey note [17]. Citronellol was also in the range of 4.48% in A104 to 9.61% in A105. Geraniol also ranged from 1.36% in D234 to 3.49% in A104. The highest amount of citronellol/geraniol (C/G) was observed in D234 (4.52%) and D237 (4.30%) followed by A105 (4.26%), while the lowest amount belonged to A104 (1.28%). [PERSON] et al. [2] also reported higher ranges for C/G (3.3-6.9%) compared to our studied cultivars based on distilled oil analysis. These researchers highlighted that damasks that grow in semi-arid, cold condition possessed a higher C/G ratio, while those that grow in the same condition with low altitude had a lower C/G ratio [2]. Similarly, in the present investigation, the highest C/G ratio based on the SPME method was obtained in D234 from Bardsir in high altitude, cold temperature, and semi-arid condition (Table 3). Rose oxide is one of the most important factors that determines the odor of rose petals. In the present investigation, it ranged from 0.062% in D237 from Maymed, Fars with the lowest altitude to 0.15% in D211. It might be suggested that the rose oxide odor increases in high altitudes and arid conditions. This amount is much higher than those reported by Bulgarian rose petals [12], but less than Indian [10] and Chinese [19]. [PERSON] et al. [17] also reported a similar range for rose oxide in Egyptian rose petals. These researchers also reported a relatively similar range for geraniol in Egyptian rose petals, suggesting that SPME provides the real aroma of the rose petals. However,there are no reports regarding the SMPE of Iranian rose petals for comparison. Aroma is one of the most important factors determining the quality of rose petals, especially for the production of perfumes. Among the compounds, rose oxide is considered the unique scent of damask rose that exerts a sweet floral aroma [3; 17]. However, citronello1/geraniol (C/G) is also crucial for the aroma and quality of rose petals. These compounds can be highly affected by harvesting time, plant growth stage, temperature, and drying methods [22]. In the present investigation the samples were harvested at full bloom stage, in the morning at 25 \({}^{\circ}\)C. In most cases, harvesting in the early stage of petals leads to a decrease in citronello1 and geraniol content [17].
# Image-Based Scratch Detection by Fuzzy Clustering and Morphological Features
## 4 Experiments  
The algorithm consists of three steps: image enhancement utilizing FCM, detection of abnormal regions based on the morphological features, and judging scratch defects, as in Figure 6. Image enhancement was realized by a membership table lookup and normalization. The membership table was composed of the foreground membership degree and interpolation method of offline FCM clustering. Abnormal region detection was obtained for the enhanced image by the threshold technique, eliminating small areas, and merging and deleting regions based on the mathematical morphology. The area size, average value, and skewness were used as criteria to judge whether the abnormal area was a scratch defect.  
In the experiments, the images were gray scale images employing \(400\times 800\) pixels. The enhanced image was converted to binary by the threshold 179. Then, the area less than 16 in the binary image was deleted. Some regions were merged by the morphological method. Some non-scratched areas needed to be further excluded, as shown in Figure 7. It can be found that the region with scratches can be distinguished by two parameters: the size and the ratio of mean to skewness, as in Figures 8 and 9. According to the application requirements of piezoelectric ceramic plate surface scratch detection, the size and ratio of mean to skewness thresholds were set to 100. According to the size, regions 1, 6, and 8 were scratch areas. According to the skewness, regions 2, 9, 10, and 11 were scratch areas. Figure 10 shows the scratch detection results of the piezoelectric ceramic chip by the method proposed in this paper.  
Figure 5: Region merging method for discontinuous scratches. (**a**) The region merger sketch map of discontinuous scratches. (**b**) Linear fitting coefficients of the region. (**c**) On a larger scale, scratches merging.  
Figure 8: Ratio of mean to skewness for each region.  
Figure 6: Outline of the working steps.  
Figure 7: Region merging results for scratch detection.
## 2 Related Work
### Comparison of Our Model with Existing Models  
We compare the strengths and weaknesses of the proposed research model and the three existing models mentioned before. MaskRCNN uses RPN (region proposal network) to obtain a series of candidate regions and classifies the image regions after feature extraction using a classifier. The strengths and weaknesses of MaskRCNN are listed as follows:  
* Strengths: 1. Can generate candidate regions using object detection techniques, is capable of simultaneously performing object detection and image semantic segmentation tasks; 2. The ROI (region of interest) align layer is introduced to make the alignment of the feature map and the original image more accurate by bilinear interpolation.
* Weaknesses: 1. Insufficient consideration of global semantic information in the image; 2. It is easy to make mistakes when detecting small target objects.  
Deeplab-v3 [18] is an improved full convolutional network. The strengths and weaknesses of Deeplab-v3 are listed as follows:  
* Strengths: 1. The ASPP (atrous spatial pyramid pooling) module is introduced to increase the perceptual field of the network and reduce the loss of the feature image size; 2. Optimized edge segmentation by using a decoder to refine the segmentation results.
* Weaknesses: 1. Large computational volume and slow segmentation speed; 2. Less-accurate segmentation of small-scale objects.  
GAMNet [29] introduces an attention module with a gating module and designs a composite loss function that adds an auxiliary loss to achieve deep supervision of GAM. The strengths and weaknesses of GAMNet are listed as follows:  
* Strengths: 1. The introduction of the GAM module can improve the segmentation performance of small targets while imposing boundary constraints; 2. The designed compound loss function facilitates model optimization and improves the performance of integrated modules.
* Weaknesses: 1. Large computational volume and slow segmentation speed; 2. Shaded areas are easily misclassified on the ISPRS dataset.  
CloudRCNN (our method) has improved on MaskRCNN and introduces feature fusion, a spatial attention module, and an auxiliary branch with a decoder structure. The strengths and weaknesses of CloudRCNN are listed as follows:  
* Strengths: 1. The backbone of MaskRCNN is too long, which is prone to the gradient explosion problem. The auxiliary branch introduced by this model is a good correction of the problem; 2. The model performs additional focused segmentation on the subject of the input image, so the model segments the subject's edges more carefully; 3. The introduced spatial attention module refines the boundary information for semantic segmentation.
* Weaknesses: 1. The number of pixels in the background is often larger than the segmented subject, and the additional supervision of foreground-background information causes the model to prefer to classify pixels as background.
# Interaction Strength Analysis to Model Retweet Cascade Graphs
## 3 The Interaction Strength-Based Network (ISN) Approach to Generating Retweet Cascade Graphs
### Users without Interactions and Sparse Nodes  
As a complementary step, when there are no available interactions by a user \(u\), and thus no IS values between \(u\) and any other user, we attempt to find a link from the \(u\) to another user in the cascade according to the state-of-the-art method based on social networks. More precisely, we collect the user's friend list by using the Twitter API, and every user's friend that has retweeted at an earlier point in time is considered as a potential influencer [6; 16; 34]. To identify the influencer that more likely spread the tweet to user \(u\), we consider the most recent influencer, i.e., \(u\) is linked to the last friend that retweeted the message. In Figure 1 the friends list has been investigated to find the link from [PERSON] to [PERSON]; in fact, [PERSON] was a sparse node during the first analysis based on users' interactions. Users that still remaining without an edge after this second step are denoted as sparse nodes (SN).  
Alternative Approach: Information Strength-Based Network with Author's Followers Evaluation (ISN-AF)  
As a further version of the model based on the IS concept (ISN approach) we also propose a modified algorithm that first exploits the tweet's author followers network. In fact, Twitter preferably shows original content, and thus if a user directly follows the tweet's author, he/she will retweet the original tweet without seeing the retweets from intermediate nodes. To exploit this Twitter feature, the ISN-AF approach first explores if a retweeter belongs to the tweet's author followers network: followers are linked directly to the root in the cascade graph. For remaining nodes, ISN-AF uses the same approach as in the ISN model, namely, IS analysis and friends network for sparse nodes. An example of the differences generated by ISN and ISN-AF models in retweet cascade graphs is proposed in Figure 1, where [PERSON] is connected to [PERSON] according to the ISN procedure, whereas he is linked to the tweet's author [PERSON] for the ISN-AF model ([PERSON] belongs to [PERSON]'s followers list). This algorithm version allows one to reduce the computation costs related to the IS step but, at the same time, requires additional information which is the tweet's author followers list, which can be time consuming if the account has a lot of followers. Moreover, this ISN-AF version suffers from the limitation reported in Section 1 about the possible divergence in terms of followers relationship between the date in which the retweet has been done and the time when the followers list is fetched.
# Failure Warning at the End of Service-Life of Lead-Acid Batteries for Backup Applications
## 2 Linear Superposition-Voltage Aging Model
### Analysis of Deep-Discharge Curve by Linear Superposition  
The principle of linear superposition was used to sequentially calculate parameters of the Thevenin circuit model. First, the open-circuit voltage was read from the terminal ports before discharge as per Equation (8). Second, \(r_{\Omega}\) was calculated at the first discharge point as per Equation (9). Third, \(\varepsilon_{bulk}\) was averaged among the electrode utilization coefficient interval of [0.1, 0.3] as per Equation (10). In Equation (10), internal resistance was approximated with \(r_{\Omega}\) and influence of morphology correction was ignored. Fourth, while terminal voltage (\(V_{tij}\)) was approaching the end of the voltage (1.80 V), \(\xi_{\Omega}\) was calculated by Equation (11). If there were many samples around 1.80 V, \(\xi_{\Omega}\) could be calculated by least square method. Figure 2a marks points cited by above algorithm. In the figure of the deep-discharge curve, the horizontal axis is the rated state of charge and the vertical axis is the terminal voltage. Figure 2b plots the fitting curve of the proposed Thevenin model.  
\[V_{OCP}=\ c_{bulk}(t_{0}) \tag{8}\]  
\[r_{\Omega}\ \cong\ \frac{r_{\Omega}}{1-EUC_{1}^{\xi_{\Omega}}}\Bigg{|}_{EUC \ \cong\ 0}=\ \frac{V_{OCP}-V_{t1}}{I}\Bigg{|}_{Q\ \cong\ 0} \tag{9}\]  
\[c_{bulk}=\frac{1}{(l-m)}\sum_{i=m}^{l}\frac{Ah_{o}EUC_{i}}{V_{OCP}-V_{ti}-Ir_{ \Omega}},\ EUC_{m}=0.1,\ EUC_{L}=0.3 \tag{10}\]  
\[\xi_{\Omega}=\frac{\ln\!\left(1-\frac{Ir_{\Omega}}{V_{OCP}-V_{ti}-\frac{Q_{i} }{c_{bulk}}}\right)}{ln\ EUC_{j}},\ \lim V_{tj}=1.80\ V \tag{11}\]
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
###### Abstract
In this paper, we explore its potential for anomaly detection, as its ability to compress and reconstruct functional data makes it a promising approach for identifying anomalous patterns in a time series. Despite their success, machine learning and deep learning models are often sensitive to noise and irrelevant patterns present in time series data, which can lead to reduced accuracy in detecting anomalies. To address this, preprocessing techniques, such as filtering, have been introduced to clean the data before applying models [20; 21] for time series tasks. Bandpass, bilateral, and other filtering approaches have shown promise in isolating specific frequency ranges that contain the most relevant information while removing noise, improving the performance of downstream anomaly detection [8]. In this paper, we propose a novel two-step approach that combines bandpass filtering with deep learning-based Autoencoders for anomaly detection in time series data. The data processing step uses a bandpass filter to help remove noise and irrelevant frequency components, allowing the deep learning models to focus on capturing the essential dynamics of the time series. We leverage the Functional Neural Network Autoencoder, which is particularly adept at handling complex temporal patterns, to detect anomalies. We perform extensive experiments on various benchmark datasets to demonstrate the effectiveness of our method in improving the accuracy of anomaly detection compared to other approaches. This paper is structured as follows: Section 2 provides a comprehensive review of the literature relevant to time series anomaly detection. Section 3 shares the proposed methodology and key concepts used in this work. Section 4 dives into the results across different datasets along with discussing key observations. Finally, Section 5 offers conclusions and suggests avenues for future research.
## 7 Metabolic Adapations of _Lactobacillus_ spp. to PPCs  
### Nitrogen Metabolism  
The _L. plantarum_ transcriptome responses to GA [26], RSV [27], HXT [29], and \(p\)-CA [25] share a conserved profile that entail the downregulation of genes involved in nitrogen metabolism (Figure 3). These genes, all under the control of GlnR regulator [54], include the glutamine (Gln) synthetase (\(glnA\)), aspartate ammonia lyase which produces ammonium (\(asnB\)), a Gln ABC transporter (_lp_0802_ and _lp_0803_), and the ammonium transporter protein (\(amtB\)). The observed profile agrees with a tight control of intracellular ammonia levels, the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Phenolic Compound** & **Transporter Type** & & **Locus Tags a and Fold Change b,c** & **References** \\ \hline  & ABC (BceAB-like) & & & & \\ GA & & \(lp\_2739\) & \(lp\_2740\) & & & [26] \\ GA & & \(-7.2\) & \(-6.7\) & & [25] \\ \(p\)-CA & & \(-6.4\) & \(2.0\) & & [25] \\ RSV & & \(-12.4\) & \(-2.7\) & & [27] \\ OLE & & \(-20.4\) & \(-16.6\) & & [28] \\ HXT & & \(-4.7\) & \(-3.8\) & & [29] \\ \hline  & MFS & & & & \\ \(p\)-CA & & \(lp\_0989\) & \(lp\_0990\) & \(lp\_0991\) & \(lp\_0992\) & \\ \(p\)-CA & & \(3.0\) & \(19.5\) & \(17.5\) & \(16.7\) & [25] \\ RSV & & \(5.8\) & \(5.6\) & \(5.5\) & \(5.7\) & [27] \\  & & \(\text{LmRA-like}\) & & & & \\  & & \(lp\_2393\) & \(lp\_2394\) & \(lp\_2395\) & & \\ \(p\)-CA & & \(10.2\) & \(8.7\) & \(9.8\) & & [25] \\ RSV & & \(3.8\) & \(4.6\) & \(6.5\) & & [27] \\ \hline \hline \end{tabular}  
* Designated gene number for the annotated _L. plantarum_ WCFS1 genome; b fold change refers to growth in MRS supplemented with the corresponding PPC relative to growth in MRS without supplement; c FDR \(\leq\) 0.05; \(p<\) 0.05.  
\end{table}
Table 2: Drug efflux and ABC transport systems of _Lactplantobacillus plantarum_ WCFS1 differentially expressed in presence of plant phenolic compounds.  
previously proposed regulatory function of GlnR in _Lactobacillus_, which would be achieved by constraining the import of ammonia/amino-containing compounds and controlling the production of intracellular ammonia and glutamine [55]. The regulation of nitrogen metabolism triggered by PPCs could contribute to control the bacterial access to host glutamine, which is a hub for nitrogen metabolism in plants and bacteria [56] and can be important for plant-bacterial combinations. It is to note that OLE, a PPC proposed to conduct a close association between _L. plantarum_ and Oleaceae plant hosts [28], does not modulate the GlnR regulon which could facilitate nitrogen transfer from the plant to the microbial symbiont, an essential requirement for symbiotic relationships. This knowledge opens the possibility to use suitable PPCs to better control the bacterial access to host glutamine. In the case of some human pathogens, preventing access to host glutamine is required to attenuate its virulence [57; 58].