esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction
#### 2.2.2 Liquid-Solid Phase Extraction  
For the liquid-solid phase extraction, Diaion(r) HP-20 (SUPELCO, Sigma-Aldrich) resin was used. An amount of 6.75 g resin was soaked in methanol (MeOH, HiPerSolv, VWR, Radnor, PA, USA) for 30 min for activation and transferred to pH\({}_{2}\)O to wash out MeOH (-4.5 g resin was used for 100 mL sample or culture). After 20 min of washing in pH\({}_{2}\)O, the resin was added to the spiked fermentation broth and shaken for 72 h at 10 \({}^{\circ}\)C for extraction. The resin was filtered from the sample using a cheese-cloth filter (1057, Dansk Hjemmeproduktion, Ejstrupholm, Denmark) placed on a Buchner funnel and subsequently washed in pH\({}_{2}\)O. Thereafter the resin was eluted twice using 50 mL MeOH. Extraction was done by eluting the resin in MeOH for 1 h and by subsequently removing the resin by  
Figure 1: Structures of the natural products used for spiking within this study. **(1)** Colchicine; **(2)** Ampicillin; **(3)** Tetracycline; **(4)** Gentamycin; **(5)** Vancomycin; **(6)** Cyclosporine A; **(7)** Paclitaxel; **(8)** Rifampicin.  
vacuum filtration. The extracts were reduced to dryness at 40 \({}^{\circ}\)C under reduced pressure, and the yield was determined gravimetrically.  
Preparation and Extraction of Spiked Water for Quantitative Comparison and Investigation of the pH Effect on Extraction Yields  
Stock solutions of tetracycline (10 mg/mL aq.), cyclosporine (10 mg/mL in EtOH), colchicine (5 mg/mL in EtOH) and gentamicin (10 mg/mL aq.) were prepared to spike the five water samples. Five 200 mL samples of pH\({}_{2}\)O were set to a pH of 4.0, 5.0, 6.0, 7.0 and 8.5, respectively, using 1 M HCl (EMSURE, Merck), 0.3 M NaOH (VWR) and a pH meter (WTW InoLab pH 720, Xylem Inc. Rye Brook, NY, USA) equipped with a SenTix(r)41 electrode (WTW). The five conditions were spiked with the natural products to reach a final concentration of 10 \(\upmu\)g/mL for tetracycline, cyclosporine A and gentamicin and 5 \(\upmu\)g/mL for colchicine. Thereafter the five conditions (each 200 mL) were split into two aliquots of 100 mL each for parallel extraction using liquid-liquid phase extraction and liquid-solid phase extraction. For liquid-liquid phase extraction, the 100 mL aliquots were extracted with 100 mL EtOAc using a separation funnel. After shaking the two phases for \(\sim\)1 min, the funnel was left to rest until the two phases separated properly. The EtOAc fractions were reduced to dryness at 40 \({}^{\circ}\)C under reduced pressure. The other aliquot for each water sample for resin or liquid-solid phase extraction was extracted using resin as described above (using \(\sim\)4.5 g resin/100 mL of sample).
# A Novel Intelligent IoT System for Improving the Safety and Planning of Air Cargo Operations
## 1 Introduction
To address these issues, the \"INTELLICONT\" project integrates the best available technologies with novel solutions to enhance situational awareness and support relevant crew and technical staff in the execution of their duties and involvement. In our previous work [33], we described from the software point of view the intelligent human-machine interface that provides a novel visualization tool for loading and flight crew. The objective of the current work is to describe from a hardware and embedded software perspective, the novel intelligent remote monitoring system (RMS) that enables the accurate acquisition of the status and condition of the ULD as well as the fast and secure transportation of relevant data to the HMI and the loading/unloading robotic platform and status placement of aircraft cargo. The platform's design was based on LAROS [34] and PrismaSense [35] systems, which were redesigned to comply with the specifications of the INTELLICONT project and the standards of the Aviation industry [36] and includes the five layers of modern IoT [37]. Focus has been given to the validation (lab-scale testing) and verification (field testing) of the system. The main innovation of our work is the development of a system capable of integrating a hybrid cloud-fog-edge-mist data fusion pipeline in the aircraft and cargo-loading environment that is also capable of receiving a feed from sensors using any protocol in real-time, running IoT-enabled applications in a scalable manner for real-time control and analytics, providing transient storage, performing analysis on the IoT data collected from the sensors and data from other sources (e.g., external, third party) to gain business insight and handling application rules to the fog nodes based on these insights. The rest of the article is structured as follows: Section 2 presents a detailed description of the system's concept. Section 3 demonstrates the integration and the testing undertaken. Finally, in Section 4, the conclusions from this study are summarized.
## 4 Experimental Results
### Parameters Selection for ELM  
For ELM, there are two parameters that should be determined. The first one is the number of hidden neurons which is denoted by \(L\), while the other is the activation function. To make a qualitative comparison, the mean absolute error of the values of nonexistent pixels in an image, named \(MAD\), is defined as Equation (4).  
\[MAD=\frac{1}{n}\big{|}N\_img_{c}-N\_img_{p}\big{|} \tag{4}\]  
where \(n\) is the number of nonexistent pixels in a color component, \(N\_img_{c}\) is the actual contone value of a nonexistent pixel in a continuous image, \(N\_img_{p}\) is the corresponding estimated value of a nonexistent pixel.  
Figure 3 illustrates the variety of \(MAD\) with \(L\) for image Lena and Baboon. From Figure 3, we can see the fitting precision of nonexistent pixels has a considerable improvement when \(L\) goes from 10 to 100, and then it increases gradually with \(L\) from 100 to 300. In the end, it starts to decrease when \(L\) is greater than 300. Thus, the number of hidden neurons is determined as 300. For the activation function, Figure 4 shows the experimental results of three common activation functions, which are sigmoid, sin, and hardlim. It is obvious that the sigmoid activation function is the best choice.  
Figure 3: _Cont_.
###### Abstract  
This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4 DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.  
adaptive identification; dynamic neural networks; tracking control; underwater vehicle +
Footnote †: journal: Applied Sciences
# Analysis of Azure Zero Trust Architecture Implementation for Mid-Size Organizations
## 8 Gaps Between Azure ZTA Theory and Practice  
This paper rigorously analyzes the discrepancies between Azure's ZTA theoretical construct and its application in commercial settings, with particular emphasis on mid-sized enterprises. The analysis shows a substantial disparity between ZTAs potential and its practical implementation, underscoring the difficulties stemming from the operational context.  
A primary concern pertains to the intricacy of administering Azure ZTA. The theoretical framework presupposes uniformity among cloud platforms; however, Azure's execution of ZTA necessitates customized configurations, especially concerning micro-segmentation and identity management. These configurations frequently diverge from standard ZTA recommendations. Although ZTA ostensibly utilizes seamless continuous verification, the frequent prompts for multi-factor authentication can exasperate users and hinder productivity. This research tackles these issues by providing customized guidelines for Azure-specific ZTA configurations and connecting theory with practice.  
The balance between security and user experience is a significant concern when implementing Azure ZTA. While continuous verification is essential, it can lead to delays in high-traffic settings, causing user fatigue. In practice, the identity verification mechanisms of Azure ZTA often need to improve their theoretical efficiency, leading to user interruptions that disrupt operations. This user dissatisfaction with what they perceive as unnecessary complexities of ZTA is a practical issue that needs to be addressed.  
Scalability is a significant challenge in implementing ZTA. The theoretical model does not adequately address the complexities of scaling in dynamic, cloud-oriented environments. Case studies of two different hybrid cloud approaches to ZTA illustrate this issue, where conventional ZTA guidelines led to performance deterioration during scaling. The solution was to adopt adaptive security policies and automated responses, which allowed these hybrid scenarios to scale their ZTA implementation while maintaining security integrity successfully.  
Literature frequently exaggerates ZTAs efficacy in countering Advanced Persistent Threats. Although ZTA is designed to mitigate such attacks, empirical testing has uncovered vulnerabilities as innovative APT techniques circumvent conventional ZTA defenses [46]. This study fills the gap by creating an improved threat detection algorithm that integrates with Azure ZTA, thereby more efficiently identifying and mitigating advanced threats. The algorithm was effectively utilized in a security evaluation for a financial services company, thwarting an advanced attack that prior ZTA implementations had been unable to identify.
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 3 Materials and Methods
### Methodology
#### 3.1.2 Development and Assessment of an Experimental Virtual Museum  
We designed an experimental virtual museum and developed it using the Unity game engine. The virtual museum was dedicated to the Antikythera Mechanism, and it hosted the fragments of the artifact. The 3D digital representations were artificially reconstructed by using the CT-scans dataset. In addition, we designed, developed, and integrated the functionality of the X-ray viewer [107] (RQ2). By using the virtual reality head-mounted display with its touch controllers, the users can interact with the 3D representations, as well as with the internal structure of the fragments of the Antikythera Mechanism [54] (RQ3).  
As part of the evaluation of the virtual museum for the Antikythera Mechanism, in 2019, we conducted a series of pilot tests with end users from different backgrounds. Specifically, in the context of four pilot tests, 135 users experienced the virtual reality application on an Oculus Rift S device with second-generation controls. Voluntarily, 62 of our participants (female: 42, male: 20) provided anonymous ratings on specific factors through a survey of 25 questions. The factors evaluated were primarily usability, usefulness, and user satisfaction [49]. Regarding the integrated functionality of the CT scans in the virtual reality application, we included two questions related to its direct evaluation (RQ1). Simultaneously, the effectiveness of the proposed combined representation was also evaluated indirectly with specific questions that related to the wider user experience (RQ2). A further analysis of the results of the integrated functionality is presented for the first time in the current research (RQ3).
## 4 Discussion  
The data obtained in this study refer to two years of operation of two coke batteries. The data collected show a trend in the behavior of the refractory of the ovens. It is important to clarify that the refractory material of these ovens has been affected by the sticky ovens and hard pushes, not only during these two years, but also during their entire operating life. At the same time, there are also other factors for which no data have been collected, which can affect the integrity of the refractory, such as incorrect operations, impacts due to errors, etc. That said, this period does prove the influence of these phenomena on the refractory lining of coke ovens.  
Batteries are designed with over-dimensioned margins in terms of mechanical resistance to counteract the stress suffered by the walls in the pushes. However, during the operation of the ovens, the mortar used to bind the refractory pieces is gradually lost and, as a result, their mechanical stability decreases, leaving the walls of the ovens more vulnerable to the hard pushes. At the same time, the areas repaired with ceramic welding lose mechanical strength compared to the original lining, and they are less able to withstand hard pushes.  
The data tells us that, in general, ovens exposed to less hard pushes have less damage on their refractory.  
Hard pushes are reduced after the repair of a wall by ceramic welding or even with pieces of fused silica. This permits to eliminate deformations in the oven wall and oven soles, which represent one of the main causes of hard pushes, guaranteeing their reduction in the future.  
The data also shows that the effect is not immediate, as there are ovens with hard pushes which did not need to be repaired until the following year. Nor is it proportional, that is, more hard pushes do not mean more damage; it is rather a question of how hard the pushes are.  
In all, 95% of the ovens suffered sticky ovens. The majority of them underwent reparation during the two years of study. The other 5% of the ovens had either received major repair in the previous period or received it in the following period.  
The negative effects of sticky ovens are not immediate, but they usually appear with a delay. The drop in temperature during removal of coke can provoke the proliferation of cracks, which may weaken the structure.  
As with hard pushes, not all the sticky ovens have the same consequences. If extended over time, the manual removal process will overexpose the oven refractory to ambient temperature, with a higher risk of lining degradation.
# Effect of Residual Stress in Surface Layer on Plastic Yield Inception
## 4 Discussion
### Discussion on Effect of Residual Stress on the Critical Yield Load
With compressive residual stress (Figure 10c), \(\sigma_{rr}\) and \(\sigma_{\theta\theta}\) become more compressive (or less tensile), and the differences between, \(\sigma_{rr}\), \(\sigma_{\theta\theta}\), and \(\sigma_{zz}\) are reduced at the axis. Although the differences between \(\sigma_{rr}\), \(\sigma_{\theta\theta}\), and \(\sigma_{zz}\) are enlarged at part of the surface near the contact edge, the maximum differences between \(\sigma_{rr}\), \(\sigma_{\theta\theta}\), and \(\sigma_{zz}\) are reduced, along  
Figure 9: Contact stress field in surface layer without residual stress, with tensile, compressive, and excessive compressive residual stress. The parameters are \(E_{s}=200\) GPa, \(E_{l}=400\) GPa, \(Y_{s}=600\) MPa, \(Y_{l}=952\) MPa, \(t/R=0.006\), and \(P=0.05\) N.  
with the maximum von Mises equivalent stress. Therefore, compressive residual stress makes the contact system harder to yield. With excessive compressive residual stress (Figure 10d), the \(\sigma_{zz}\) became more tensile than \(\sigma_{rr}\) and \(\sigma_{\theta\theta}\) at both axis and surface. The maximum difference between \(\sigma_{rr}\), \(\sigma_{\theta\theta}\), and \(\sigma_{zz}\) are located at the contact edge of the surface, and are larger than the contact system without residual stress, as is the maximum von Mises equivalent stress. That is to say, excessive compressive residual stress makes the contact system easier to yield and may cause the performance of the system to deteriorate. From the above analysis, it can be found that, on one hand, compressive residual stress can reduce the maximum von Mises equivalent stress on the axis, and on the other hand, compressive residual stress will enlarge the maximum von Mises equivalent stress at the contact edge. Hence, the residual stress to obtain maximum yield load is a range, or the value lies on moderate compressive residual stress.
## 3 Experiments and Functioning Tests
### Quadruped Robot Dog Wall Inspection Experiment  
To demonstrate the effectiveness of the QRD for the inspection of the walls, an experiment was conducted to calculate the curvature of the wall using the LiDAR mounted on the QRD, and its results were compared with the manual measurements and the measurements performed with Xbox Kinect in 2014. The setup in this experiment is that the QRD tilts, as displayed in Figure 9, to point the 2D LiDAR at the area where the bulge is the most visible and the area is suspected for damage.  
Figure 8: **(a)** RedRock 16 G CO2 cartridge, connected to the syringe and denoted by the arrow, mounted underneath a confined enclosure, **(b)** QRD maneuvers into a confined enclosure where a syringe directs compressed CO2. Sensors on the board collect environmental measurement data which are transmitted via Wi-Fi to a remotely located computer.  
Figure 9: A tilting QRD aimed at the direction of the arrow to inspect the damaged area of the bulged wall using LiDAR.
# Frequency-Dependent Acoustic Reflection for Soil Classification in a Controlled Aquatic Environment
## 4 Discussion
### Physical Mechanisms Behind the Observed Spectral Differences  
The results indicate that the higher return spectrum observed in SP (compared to GP) can be attributed to several physical mechanisms affecting energy absorption and scattering:  
The differences in spectral reflectance can be attributed to combined impedance mismatch and microstructural scattering. SP soils exhibit a denser packing structure, leading to higher impedance contrast and greater reflection intensity. With their coarser grains and larger pore spaces, GP soils facilitate increased energy dissipation and scattering, resulting in a lower coherent reflection.  
Grain size vs. wavelength ratio also significantly influences the spectrum's shape. The average grain size of SP (-0.4 mm) is considerably smaller relative to the acoustic wavelengths (100-400 kHz). Hence, the classic theory of [PERSON]'s law is more dominant, with a more substantial coherent part and a weaker backscatter. As expected, the coherent part is more significant for the lower range of frequencies (with a larger wavelength), as evidenced by the results. GP, with a coarser grain size (-5 mm) and, hence, a coarser relief concerning the wavelength, interacts differently with the acoustic wave, with a much more significant scattering of the acoustic waves leading to a much smaller coherent reflection.  
Also, here, we see an amplification of the coherent part for the lower frequencies; however, this is much less prominent than in the case of SP.  
This study provides a clear physical basis for spectral-based seabed classification by identifying the dominant factors influencing spectral variation.
# Experimental Study of 6 LoPLC for Home Energy Management Systems
## 5 Results and Discussion  
In this section, we present and discuss the simulation results of the system under consideration. Using the parameters in Table 1, as well as adopting 6 LoPLC, extensive simulations were conducted to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Available spectrum & 0–32 MHz \\ Used bandwidth & 2–28 MHz \\ Impedance & 50 ohms (fixed) \\ Number of OFDM carriers & 128 (86 active) \\ Carrier spacing & 250 kHz \\ Symbol length & 4 μs \\ Modulation & DBPSK \\ Bits per carrier & 1 \\ Background noise PSD & \(-\)96.52 dBm/Hz (measured) \\ Network layer & IPv6 \\ Transport/application model & UDP/server-client \\ Adaptation layer & 6 LoPLC \\ Distance between sockets & 2.5 m \\ Transmission line length between nodes & 5.5 m \\ Maximum Transmission Unit (MTU) & 127 bytes (power line) \\ CoAP application size (controller-lamp) & 30 bytes \\ CoAP application size (lamp-controller) & 8 bytes \\ CoAP application size (controller-sensor) & 28 bytes \\ CoAP application size (sensor-controller) & 15 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters. OFDM: orthogonal frequency-division multiplexing, PSD: power spectral density, UDP: user datagram protocol, DBPSK: differential binary phase shift keying.  
assess the performance of the basic HEMS applications (monitoring of temperature and lamp control) over the power line. The idea here is to use the model developed, to assess various HEMS scenarios and use-cases that cannot be easily investigated with the three units of Hanadu kits available.