esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 3 Definition of the User Requirements
### Precision Requirements
#### 3.3.2 Co-Registration Requirements
SNR refers to the Signal to Noise Ratio, NESR refers to the Noise Equivalent Spectral Radiance, and TOA refers to Top of Atmosphere.
# Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood
## 3 Results and Discussion
### Single-Shot Py(HMDS)-GC/ms  
In this section, the ability of Py-GC/MS with in situ silylation to estimate wood preservation is discussed by presenting several case studies. The main advantage of Py-GC/MS over techniques that are directly coupled with mass spectrometry is the selectivity in the identification of pyrolysis products, thanks to a chromatographic separation step before MS analysis. This enables the abundance of specific pyrolysis products to be related to different degradation patterns. Figure 7 shows the chromatographic profiles obtained by Py(HMDS)-GC/MS of sound and archaeological wood samples from Biskupin, La Marmotta, and the Oseberg collection. Table 3 shows the  
Figure 6: Percentage ratios indicative of syringyl lignin oxidation (S index), guaiacyl lignin oxidation (G index), and formation of dimers (D index) in EGA-MS analysis of the wood pile 2212 from La Marmotta site at different depths from the surface, where A—surface, G—core, Ref—reference, (sound non-degraded oak wood sample). identification of trimethylsilyl derivatives of wood pyrolysis products, which can also be found in recent publications [7; 8; 62; 92]. The wood pyrolysis products were attributed to holocellulose (H) and lignin (L). In addition, guaiacyl- (G) and syringyl-lignin (S) can be easily distinguished (Table 3). From a qualitative point of view, all archaeological samples showed different profiles, indicating different preservation states of the wood components. A relatively simpler chromatographic profile, showing mainly lignin pyrolysis products with a very low abundance of pyrolysis products deriving from holocellulose, was obtained for the Oseberg sample (Figure 7d). Vanillic (#85) and syringe (#95) acids were the most abundant peaks in the chromatogram.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 4 MPI Malleability  
The dynamic creation of processes, in other words, spawning processes from a running application, is available since MPI standard version 2[18]; however, most RMS operate in ways that make it difficult to add dynamic processes support, which prevents its usage in production systems. For this reason, jobs run with a fixed number of processes and may use load balancing techniques that do not rely on spawning processes. The straightforward integration of processes management in RMS would allow for different load balancing strategies and more efficient use of resources.  
The API function provided by the MPI standard to support dynamic processes is MPI_Comm_spawn, which creates processes with an inter-communicator connected to the original invoking communicator. We can also find a variant of this function named MPI_Comm_spawn_multiple, which additionally, supports different binaries or configurations in the same call.  
As it is reported in [19], the current process spawning in MPI Standard involves several limitations:  
* The spawn operations are synchronous for all processes involved in the invocation of this operation and the new set of processes created. Therefore, they are implemented as collective operations that block the processes they invoke while the new ones are created and initialized.
* These operations produce inter-communication based on disjoint processes groups. The majority of applications consider a single flat communicator (MPI_COMM_WORLD), from which other intra-communicator could be defined and used in their MPI codes.
* Subsequent creation of processes results in multiple process groups. Communication between them is not straightforward to manage.
* Processes can only be terminated on the entire process group. Processes in a group are not destroyed until all of them invoke MPI_Finalize.
* Processes created with spawn operations are run in the same resource allocation by default.  
Finally, another important issue to consider in this approach is how to redistribute the data among existing and new processes, which is usually performed on the memory of each process during resizing. As a consequence, without proper management, it could affect the performance of the application.  
This section describes various efforts that deal with these issues.
# Measuring Efficiency and Effectiveness of Highway Management in Sustainability
## 2 Theoretical Considerations  
### Sustainability and Highway Management  
Sustainability is generally defined as \"the capacity to maintain\" [12]. In the field of highway management, suitability management is discussed with four aspects: environment, economy, financial management, and society [13]. U.S. Federal Highway Administration (FHWA) also presents three dimensions--environmental, economic, and social values--for the concept of the sustainable highway [14]. Similarly, the center for sustainable transportation defines a sustainable highway system as the one with safety, human and ecosystem health, and affordable costs, and one that is an environmentally friendly system [15].  
A sustainable approach seeks to meet all of these needs while hitting economic targets for cost-effectiveness throughout a highway's life cycle [14]. Again, a sustainable approach to highway management means helping stakeholders and decision makers reach balanced goals among environmental, socioeconomic, and public values--the triple bottom line of sustainability [13; 14]--that will benefit current and future road users. This approach considers public access as more than just enhancing mobility and movement of people and goods, more than just managing vehicles and provision of transportation choices, e.g., optimized routes for walking, bicycling, and transit. That is, highway management in the field of sustainability includes various concepts including efficiency and effectiveness of funding, incentives for construction quality, regional air quality, resilience considerations, livability, and environmental management systems [14].
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 4 Fuzzy Logic in User Experience Assessment  
In this section, we first provide an overview of the applicability of fuzzy logic in quality assessment algorithms, focusing on its advantages over other comparative approaches. Second, we continue this section by discussing the model development process, which involves the fuzzification of scalar input and output values, the development of the set of rules for the fuzzy inference system (FIS), and the defuzzification of the fuzzy output to obtain crisp results, i.e., VQA for specific input values (video properties).
# Optimizing Aircraft Routes in Dynamic Conditions Utilizing Multi-Criteria Parameters
## 4 Results
### Practical Modeling
Thus, our improved solution not only outperforms the classical algorithms in reducing flight time and fuel consumption but also demonstrates significant advantages in adapting to changing weather conditions, which makes it more effective in real-world conditions compared to the results of previous studies. In general, the improved solution reduces flight time and fuel consumption in all cases considered, which indicates its high efficiency and advantages in real-world conditions compared to other algorithms. This confirms that the improved solution is optimal for reducing costs and improving the time of route execution in dynamic and diverse conditions.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 5 Discussion
### Time Cost Analysis of the Proposed Method  
The time consumption of the entire processing of the four groups of data is assembled in Table 4. The unit of measurement is seconds. It can be seen that the Perspective-LJL method does not significantly reduce the processing efficiency (as demonstrated by the first and second image pairs) by adding a resampling and back-projection process. It even takes less time than the LJL method (as demonstrated by the third and fourth image pairs).  
In this section, the time complexity of the LJL algorithm is briefly analyzed. Since the left and right image processing is equivalent, it is assumed that the numbers of line segments extracted from the two images are both \(M\). When constructing a LJL structure in the left image, it is necessary to select  
Figure 16: Comparison of the number of mismatched line segments between two different areas.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time Cost (s)** & **Image Pair 1** & **Image Pair 2** & **Image Pair 3** & **Image Pair 4** \\ \hline LJL & 12.3 & 18.0 & 20.5 & 24.8 \\ Perspective–LJL & 12.4 & 19.5 & 19.8 & 24.6 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of processing time consumption of the two methods.  
Figure 17: Comparison of the number of correctly matched line segments in fluctuant areas.  
a reference line first, and then find a line that can form a LJL structure with the reference line from the remaining \(M-1\) lines. There are \(C_{M}^{2}\) combinations. Similarly, there are also \(C_{M}^{2}\) combinations for the right image. Then, the function that completes the matching is \(T(M)=C_{M}^{2}*C_{M}^{2}\). The time complexity is \(O\big{(}M^{4}\big{)}\). Due to reduction in the number of line segments on the rectified images, that is, a reduction of \(M\), the line matching by Perspective-LJL takes less time. Although Perspective-LJL takes extra resampling and back-projection stages, linear time consumption is used. Resampling is a linear, pixel-by-pixel transformation through an inverse projection matrix. The back-projection of a line segment is a linear transformation of two endpoints with a projection matrix. Their time consumption is very small; therefore, the overall time costs of processing are better than those of the LJL method.
## 2 Advanced Persistent Threat
### Attribution Problem
#### 2.4.1 Actors  
The cyberattacks carried out by governments and nation-states are becoming more frequent. The suspicions of interference in elections or interruption of electricity supply in other countries are generating widespread public concern due to the high cybernetic capabilities of these actors.  
* China: Chinese cyberattacks have been observed as being focused on industrial espionage, and aimed to steal intellectual property. APT1 have been the most persistent cyberthreat of this actor [14].
* United States: This actor could have perpetrated the most sophisticated cyberattacks. Attacks have been harmful, and advanced technologies have used, which means considerable resources for the development of this type of attack. The APT campaigns have mainly been used to enforce geopolitical interests. One example is the world-famous operation Stuxnet [10], which targeted SCADA (Supervisory Control and Data Acquisition) systems to cause substantial damage in the nuclear program of Iran.
* Russia: This actor is very active in terms of state-sponsored APT activity. These groups have been involved in high profile intrusions and because of this has been the subject of intense investigations [4]. Recently, spear-phishing attacks from APT28 have been detected by Microsoft; their targets have been the employees of the German government. This group has attempted to gain access to employee credentials and infect sites with malware [20].
* Iran: In the Middle East, this actor controls the most attack capacity attributed to the country with several incidents perpetrated by diverse groups [4]. Experts have monitored APT33 operations because this group has recently upgraded its infrastructure. The main objectives of this group have been the aviation industry and energy companies with connections to petrochemical production. The latest malware campaigns have been targeted at organisations in the United States, the Middle East, and Asia [21].
* North Korea: The cyber groups associated with this actor have conducted numerous operations, including conventional espionage, banking hacks, and destructive attacks [3]. One example employed by this actor is the ransomware WannaCry [22].  
Israel: This actor has been identified as a possible co-author of the Stuxnet [10] attack. It is publicly known as the high potential of the intelligence services of this country, one example is Unit 8200 [23] of the Israeli army, the equivalent to the US intelligence agency NSA. The Duqu 2.0 [24] attack was state-sponsored by this actor and it has infected numerous systems in several countries in recent years. This malware used zero-day vulnerabilities, and for sending data to the command and control (C&C) servers; different techniques were used to take the computers on the network.
## 3 Results and Discussion
### Device Structure Dependence of RTN
#### 3.6.2. Asymmetry Source and Drain Width MOSFETs
In trapezoidal MOSFETs with \(W_{\text{D}}<W_{\text{S}}\), \(V_{\text{DS}}\) dependance is the same as others at less \(V_{\text{DS}}\) than a pinch-off voltage of 0.3 V. However, the opposite dependency is obtained at larger \(V_{\text{DS}}\) than the pinch-off voltage. It is considered that the apparent gate width of MOSFETs (\(W_{\text{D}}<W_{\text{S}}\)) increases when the pinch-off point reaches the source, and then, the size effect of \(V_{\text{RMS}}\) shown in Figure 6 is obtained. These data imply that the noise strength depends heavily on operation conditions, which means that the location and electron density in a channel are critical for RTN generation. Although using trapezoidal MOSFETs in real electronic devices is difficult, changing the shape of MOSFETs is very useful to obtain much information about RTN characteristics. For example, the effect of trap at the isolation edge can be evaluated using octagonal MOSFETs, which have only a gate edge and no shallow trench isolation edge [62].
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 2 Failure Mechanism of High-Speed Solenoid Valve and Modeling
### Modeling
The increased resistance results in slower spool movement, which reduces the action time \(t_{2}\) and current drop \(\Delta i\). From Table 2, we can see that the influence of these two fault factors on current characteristics is different. In theory, we can judge the fault type by the change of these five physical characteristics. However, in the practical application of the solenoid valve, its degradation process is usually the result of the combined effect of two failure factors. Therefore, it is necessary to extract more information from the current to predict the type of failure. In this paper, the wavelet packet decomposition is used to process the current data to obtain more failure information. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Physical Index** & **Aging of Coil** & **Drying up of Lubricant** \\ \hline Trigger current \(i_{1}\) & - & \(\uparrow\) \\ Stable current \(i_{2}\) & \(\uparrow\) & - \\ Reaction time \(t_{1}\) & \(\downarrow\) & \(\uparrow\) \\ Action time \(t_{2}\) & \(\downarrow\) & \(\downarrow\) \\ Current drop \(\Delta i\) & \(\downarrow\) & \(\downarrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relationship between physical indices and failure types. Figure 2: Dynamic driven current and core displacement of the high-speed solenoid valve.
## 2 Materials and Methods
### Genotoxicity Evaluation
#### 2.6.2 Comet Assay  
The Comet assay is a sensitive, fast and versatile genotoxic test, capable of detecting DNA breaks in a single cell, which are still within the range of what a cell can repair. In Drosophila, the most used cell types are neurooblasts, midgut cells and haemocytes [54]. Neuroblasts were used in this study, and the assay was performed based on the method described by [PERSON] et al., 2014 [55], with some modifications. For each BPA concentration tested (1, 10, 20, 50 mM), as well as for the control, neuroblasts from 3 larvae were isolated, macerated and centrifuged in Ringer solution for 5 min at 268\(\times\)_g_. After removing the supernatant, 140 \(\upmu\)L of 1% low-melting-point (LMP) agarose (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) at 37 \({}^{\circ}\)C was added to each microtube. Two 70 \(\upmu\)L drops of this solution were placed in a slide precoated with 1.5% normal-melting-point agarose (NMP; Sigma-Aldrich, Lisbon, Portugal), and each drop was covered with an 18 \(\times\) 18 mm coverslip, spreading the solution. The slides were stored at 4 \({}^{\circ}\)C for 5 min and, after agarose solidification, the coverslips were removed. The slides were then immersed in cold, fresh lysis buffer (2.5 M NaCl, 0.1 M EDTA, 10 mM Tris-base, 1% Triton X-100, pH 10), for 2 h at 4 \({}^{\circ}\)C, in order for lysis to occur, after which they were placed in an electrophoresis chamber with their frozen ends facing the cathode and without empty spaces among them. The electrophoresis chamber was filled with an electrophoresis buffer (0.3 M NaOH, 1 mM EDTA pH 12.6) until the microgel slides were covered, and kept at 4 \({}^{\circ}\)C for 30 min. The next step was electrophoresis, in the dark, at 0.8 V/cm and 300 mA for 30 min at 4 \({}^{\circ}\)C (Cleaver Scientific Ltd., Warwickshire, UK). In the end, the slides were neutralized with PBS 1x and distilled water (at 4 \({}^{\circ}\)C, 10 min each), left to air-dry, and posteriorly stained with the blue-fluorescent DNA stain DAPI (1 \(\upmu\)g/mL, 20 \(\upmu\)L in each gel). A total of 100 cells per gel/condition were visually scored with a fluorescence microscope (Nikon Eclipse E400, Nikon Corporation, Tokyo, Japan), and DNA damage was quantified by visual classification of nucleoids into five comet classes, according to tail intensity and length, from 0 (no tail) to 4 (almost all DNA in tail).
## 3 Model
### Validation of Model  
The model is simulated in a MATLAB environment (R2018b, MathWorks, Natick, United States). Because the compound parabolic collector (CPC) is employed to feed absorption subsystems of SASCHCS prototype [10], the validation is divided into two sections. Firstly, the ETC model is verified by the experimental data from the reference [15]. The experiment is implemented at the College of Technological Studies, PAAET Kuwait. The collector flow rate was varied from 10 kg/(h m\({}^{2}\)) to 60 kg/(h m\({}^{2}\)). The comparison is exhibited in Table 1 and the maximal deviation is 6.3%. Secondly, the modeling of SASCHCS is verified by the experiment data of the prototype [10] in the same condition. It is noted that the CPC is modeled in terms of the reference [29]. As shown in Figure 2, the simulation results are in good agreement with the measurement data, and the deviation is within 10%. In addition, the simulation and experimental daily mean COP of the absorption subsystem are 0.594 and 0.567, respectively. The deviation is only 4.8%. The simulation and experimental daily mean COP of the compression subsystem are 6.59 and 6.41, respectively. A good agreement is obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \((T_{c,i}-T_{a})/I\) & **Model** & **Experimental** & **Relative Error** \\ \hline
0 & 0.68 & 0.69 & 1.4\% \\
0.01 & 0.64 & 0.66 & 3.0\% \\
0.02 & 0.61 & 0.62 & 1.6\% \\
0.03 & 0.58 & 0.60 & 3.3\% \\
0.04 & 0.55 & 0.57 & 3.5\% \\
0.05 & 0.51 & 0.52 & 1.9\% \\
0.06 & 0.48 & 0.50 & 4.0\% \\
0.07 & 0.45 & 0.48 & 6.3\% \\
0.08 & 0.41 & 0.42 & 2.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation of the ETC model.  
Figure 2: Validation of SASCHCS.
## 5 Security Requirements  
During the design of the authentication scheme, it is essential to ensure both the integrity and confidentiality of messages exchanged during transmission, as well as the protection of device identity information. To achieve robust security in an Industrial Internet environment, the protocol must satisfy the following security requirements:  
**(1) Mutual Authentication and Key Agreement**  
The protocol must ensure mutual authentication between the device and the authentication server (e.g., edge gateway or anchor node). Only after both parties have authenticated each other should they engage in a secure key agreement phase to derive a session key used for subsequent communication.  
**(2) Resistance to Man-in-the-Middle (MitM) Attacks**  
The protocol must be designed to prevent attackers from intercepting, modifying, or injecting messages during transmission over public or insecure channels.  
**(3) Resistance to Impersonation Attacks**  
The protocol should be resilient against adversaries attempting to impersonate a legitimate device or authentication entity. This requires robust identity verification methods, often involving cryptographic proofs bound to unique device credentials.  
**(4) Device Anonymity and Privacy Preservation**  
The protocol should preserve device anonymity, ensuring that a device does not expose its real identity during authentication. Techniques such as pseudonym-based identifiers or zero-knowledge proofs can be employed to achieve unlinkability and identity concealment.  
**(5) Resistance to Replay Attacks**  
The protocol must prevent attackers from reusing previously captured messages or authentication exchanges to gain unauthorized access. Common countermeasures include nonces, timestamps, and session tokens that ensure message freshness and temporal uniqueness.  
**(6) Resistance to Ephemeral Key Leakage**  
Even if an adversary obtains temporary (ephemeral) session-specific keys, they should not be able to derive the session key or any other sensitive long-term credentials. This property, often referred to as ephemeral key compromise resilience, ensures the confidentiality of session keys under partial exposure scenarios.  
**(7) Forward Secrecy (Perfect Forward Secrecy, PFS)**  
The protocol must ensure that the exposure of long-term private keys does not affect the confidentiality of either past or future session keys. Forward secrecy protects earlier session keys from being compromised, even if a device's long-term credentials are revealed at a later point in time.
# Physics-Informed Neural Networks for Cantilever Dynamics and Fluid-Induced Excitation
## 3 Experiments
### Flutter of a Slender Cantliever in Axial Flow
#### 3.2.3 Neural Networks' Architecture  
The PINN architecture was constructed as a sequence of the following layers, as schematically illustrated in Figure 3: the global scaling layer at the top, which adjusts the input to the range of \(-1\) to \(1\); two parallel structures reflecting decomposed bases; a merging layer; and the final global linear scaling with \(\omega_{\text{Ref}}\). Whereas the global scaling layers are identical to those used for the free vibration problem, the parallel structures in the middle were designed to take both scaled space and time, in contrast to STD in the free vibration problem. Therefore, the parallel structures in the middle can be denoted as \(G(\widetilde{x},\widetilde{t}|\theta)\) and \(H(\widetilde{x},\widetilde{t}|\theta)\). Fully connected neural networks composed of two hidden layers and 20 neurons are employed for each of the parallel structures with \(x+\sin^{2}x\) activation. The outcomes of each structure are merged using elementwise multiplication as \(g_{i}(\widetilde{x},\widetilde{t})h_{i}(\widetilde{x},\widetilde{t})\), where \(i\) corresponds to the number of hidden neurons, which is 20. The outputs are superimposed to produce a single value, \(\widetilde{\omega}\), using a fully connected layer without activation, leading to \(\widetilde{\omega}=\sum_{i}W_{i}g_{i}(\widetilde{x},\widetilde{t})h_{i}( \widetilde{x},\widetilde{t})+b\), where \(W_{i}\) represents the weights, and \(b\) represents the bias. The superimposed value is linearly scaled at the last layer to produce the final prediction as \(\omega_{\text{Ref}}\widetilde{\omega}\).  
Figure 3: Neural networks’ architecture adopting two decomposed bases to resolve fluid-induced flutter.