esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Plsr Model Using Optimal Wavelengths  
PLSR models were built using the optimal wavelengths selected by four different methods. The results of PLSR models are shown in Table 3. SPA-PLSR, _Bw_-PLSR, and CARS-PLSR models obtained better performances, with \(r_{c}\) and \(r_{p}\) over 0.9. RF-PLSR obtained slightly worse results, with \(r_{c}\) and \(r_{p}\) over 0.8. The _Bw_-PLSR model obtained the highest \(r_{p}\) and lowest RMSEP (shown in Figure 4). A fact that the number of wavelengths reduced from 190 wavelengths of the full spectrum to 15, 21, 24, and 26 wavelengths of the optimal wavelengths. The number of wavelengths was significantly reduced at least 86.3% by optimal wavelength selection. It was noticed that the PLSR models using selected optimal wavelengths obtained similar or slightly worse performance to the full spectrum PLSR model. The results showed that the selected optimal wavelengths carried useful information relating to the quality parameters. Although the number of optimal wavelengths was small compared with the full spectrum, informative wavelengths could be used for calibration instead of the full spectrum with similar results. As for spectral analysis, use of informative wavelengths relating to chemical meaning and quality parameters had great potential for practical application instead of the full spectrum. The overall results indicated that optimal wavelength selection could be used to detect SO\({}_{2}\) residual content in sulfur-fumigated _Fritillaria thunbergii_ Bulus by HSI.
## 2 Related Work  
### Introduction of Existing Display Wall Systems  
Early display wall systems mostly used projection display methods [14], such as the CAVE [15] system. With the development of display equipment, the display screen using LCD panels has become the mainstream display solution for building a display wall system because of its better display effect, higher cost performance ratio and lower maintenance cost. As shown in Table 1, many large-scale high-resolution display wall systems have been used in practical application fields. Although these display wall systems have different shapes and layouts, they are all equipped with powerful computing and rendering systems that can visualize data with extremely high detail and quality. These display wall systems belong to scientific research institutions such as universities, and are mainly used for visualization and analysis of data-intensive scientific research tasks.  
The structure of large-scale display wall systems is complex and needs to be composed of various hardware systems and software systems. Therefore, there are many research directions for large-scale display wall systems, including: research appropriate visualization methods, supporting software for large-scale high-definition display wall systems, and human-computer interaction and collaboration methods. As can be seen from Table 1, most display wall systems are equipped with a tracking system, and the research on human-computer interaction technology for large-scale high-definition display wall systems is a hot spot in this field, and it is also one of the characteristics of display wall systems that are different from traditional display systems.
## 3 Results
### Comparing Dose Calculations and Personal Measurements  
In Figure 4 dose predictions are compared with personal measurements. With respect to total dose (first row of Figure 4) there was a slight tendency that the group median of the personal measurements increased with increasing predicted dose. The Spearman correlation between the dose and the mean of the personal measurements was 0.10 (\(p\)-value = 0.34) for the brain dose and 0.05 (\(p\)-value = 0.63) for the whole-body dose. For the far-field dose the picture was similar, but with a slightly higher correlation of 0.18 (\(p\)-value = 0.08) for the brain far-field dose and 0.17 (\(p\)-value = 0.09) for the whole-body far-field dose (second row of Figure 4). If taking into account only the downlink dose and the downlink measurements (third row of Figure 4) the mean and the median of the measurements were clearly increased for increasing predicted dose. The Spearman correlation was 0.53 (\(p\)-value \(<0.0001\)) for the brain downlink dose and 0.52 (\(p\)-value \(<0.0001\)) for the whole-body downlink dose.  
Figure 3: Total brain and whole-body dose for the three HERMES study participants (non-user, normal user, heavy-user); Percentages of the far-field dose on the total dose are indicated above the bars.  
Figure 4: Comparison of predicted dose measures and personal measurements using the three dose categories \(<\)50 th percentile (low), 50 th–90 th percentile (medium) and \(>\)90 th percentile (high); First row: total dose _vs._ total personal measurements; Second row: far-field dose _vs._ total personal measurements; Third row: downlink dose _vs._ downlink personal measurements.
## 3 Results
### Climate-Optimised Intermediate Stop Operations  
A minimisation of the climate impact was performed by selecting the optimal combination of ISO airport and flight level from the available set of simulated configurations. For almost all of the flights (99.98%), an improvement of the climate impact can be achieved by introducing the ISO concept. In this course, the average flight length of a mission increases moderately (+3.58%), while the duration of the average mission is extended by almost 10% (+9.57%). The amount of required fuel also rises significantly by 17.96%. Emission quantities change as displayed in Figure 4. CO\({}_{2}\) and H\({}_{2}\)O emissions rise proportionally with the additional fuel consumption (+17.96%) compared to the reference case, but NO\({}_{\rm x}\) and SO\({}_{2}\) increase to a lesser extent. While NO\({}_{\rm x}\) emission indices vary with the different flight phases and altitudes, SO\({}_{2}\) emissions mainly depend on the sulphur content of the fuel, which varies geographically and is influenced by re-locating refuelling locations through ISO. HC and CO emissions quantities rise disproportionally due to the additional on-ground times caused by the refuelling stop.  
Figure 4: Relative increase in emission quantities for climate-optimised ISO compared to the non-stop reference case, additional relative fuel consumption of 17.96% is indicated by the dashed line as a reference.  
Despite the changes in emissions, a mitigation potential of up to 40.3% in ATR100 (38.9% in ATR20) can be achieved with the climate-optimised ISO concept in comparison to the reference case for an average flight of the investigated sample. Figure 5 illustrates changes in ATR100 caused by the implementation of climate-optimised ISO. We confirm that the resulting increase in ATR100 caused by additional CO2 emissions proportional to the rise in fuel consumption is overcompensated by the reduction in the impact of all non-CO2 effects. This is caused by changed emission locations both horizontally and vertically. Consequently, the climate impact of contrail cirrus is reduced by 53.0%, and the aggregated impact induced by emissions of NOx is reduced by 48.8%. The small contribution of H2O decreases by almost 90%.  
Generalising the analysis from individual flights to an investigation of the full flight sample, we incorporate changes in background emissions and saturation effects with regards to contrail cirrus. By doing so, we confirm significant mitigation potentials if ISO is implemented where it reduces ATR. The mitigation potential is estimated to be between 38.3 and 42.0% for ATR100 (36.9 to 40.3% for ATR20).
# Applying Deep Learning to Medical Imaging: A Review
## 6 Ethical Considerations for Using DL Methods
### Data Privacy and Security  
One of the main ethical concerns associated with DL in medical imaging is the need to protect patient data privacy and ensure data security. Medical images contain sensitive information about patients, and their unauthorized use or disclosure could have serious consequences for their privacy and well-being. Therefore, it is essential to implement appropriate measures to protect the confidentiality, integrity, and availability of medical images and associated data. Several studies have proposed various methods for enhancing data privacy and security in medical imaging, including encryption, anonymization, and secure data sharing protocols [167]. These methods can help to protect patient data privacy and reduce the risk of data breaches or cyberattacks.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
###### Abstract
However, despite these advances, a gap remains in the literature regarding the comprehensive integration of edge attributes, route choice behavior, and intelligent recovery mechanisms in cascading failure analysis and restoration. Therefore, this paper aims to develop a sequential recovery model that considers the characteristics of network edges and traveler behavior, thereby improving the recovery process after cascading failures. Motivated by the above studies, the objective of this paper is to improve the cascading failure model to explore how edge characteristics and route choice behavior may affect the traffic network. Additionally, it aims to propose a sequential recovery model to restore the network after a cascading failure. The research framework encompasses the development of the methodology, the experimental design, and the evaluation of network performance. The primary contributions of this work are summarized as follows:  
1. Cascading Failure Model: An edge-based cascading failure model is developed to simulate failure propagation in transportation networks, explicitly incorporating realistic route choice behavior in freeway systems. We conduct simulations for a specific freeway network to observe the changes in network performance during the cascade process. 2. Identification and Ranking of Critical Edges: We propose a method to identify and rank important edges within the network. The proposed method aims to identify critical edge combinations and prioritize their recovery sequence to enhance overall network performance during restoration. 3. Recovery Strategy: Based on the identified critical edges, a targeted recovery strategy is developed. Simulations conducted on a real-world freeway network dataset show that the proposed strategy significantly outperforms existing benchmark methods in terms of recovery efficiency. The main innovations of this research are as follows: (1) the development of a cascading failure model that integrates travelers' route choice behavior using SUE; (2) the proposal of an effective edge-recovery strategy by ranking and prioritizing critical edges based on combined topological and traffic features; and (3) the validation of the methodology using empirical data from a real-world freeway network under multiple failure scenarios. The remainder of this paper is organized as follows. Section 2 reviews related work from existing studies. Section 3 details the proposed cascading failure model, defines critical edges, and presents the recovery strategy. Section 4 provides a case study based on the freeway network of Hunan Province to validate the model. Section 5 concludes the paper and discusses potential applications.
# Computational Simulation of 3D Fatigue Crack Growth under Mixed-Mode Loading
###### Abstract  
The purpose of this research was to present a simulation modelling of a crack propagation trajectory in linear elastic material subjected to mixed-mode loadings and investigate the effects of the existence of a hole and geometrical thickness on fatigue crack growth and fatigue life under constant amplitude loading. For various geometry thickness, mixed-mode (I/II) fatigue crack growth studies were carried out to utilize a single edge cracked plate with three holes and compact tension shear specimens with various loading angles. Smart Crack Growth Technology, a new feature in ANSYS, was used in ANSYS Mechanical APDL 19.2 to predict the cracks' propagation trajectory and their consequent fatigue life associated with evaluating the stress intensity factors. The maximum circumferential stress criterion is implemented as a direction criterion under linear elastic fracture mechanics (LEFM). According to the hole position, the results demonstrate that the fatigue crack grows towards the hole due to the unbalanced stresses on the hole induced crack tip. The results of this simulation are verified in terms of crack growth paths, stress intensity factors, and fatigue life under mixed-mode load conditions, with several crack growth studies published in the literature showing consistent results.  
stress intensity factors; fatigue life; failure analysis; influence of hole position; geometry thickness; loading angles; ANSYS Mechanical
## 2 Heat Equation
### Extended Heat Equation  
As discussed in the introduction, an example of the systems targeted by the present study is sketched in Figure 1. In the effective media approach, this heterogeneous system, with parameters \(\rho_{\text{NP}},\kappa_{\text{NP}}\) (for the nanoparticles or NMs) and \(\rho_{\text{m}},\kappa_{\text{m}}\) (for the matrix), is replaced by a homogeneous effective system with the effective parameters \(\rho_{\text{eff}},\kappa_{\text{eff}}\) (the subscript \"eff\" is dropped in the sequel), as sketched in Figure 2.  
Figure 2: Effective medium approach: replacing the heterogeneous system with parameters \(\rho_{\text{NP}},\kappa_{\text{NP}}\) (for the nanoparticles) and \(\rho_{\text{m}},\kappa_{\text{m}}\) (for the matrix) by a homogeneous system with the effective parameters \(\rho_{\text{eff}},\kappa_{\text{eff}}\).  
Equation (2) may be extended to include the contribution from the interface, leading to the following more general equation (here \(L\) is measured in \(\mathrm{W}/\mathrm{K}/\mathrm{m}^{3}\))  
\[\rho c_{v}\frac{\partial T}{\partial t}(\mathbf{r},t)+\rho c_{v}\mathbf{v}(\mathbf{r},t) \cdot\
abla T(\mathbf{r},t)-\kappa\
abla^{2}T(\mathbf{r},t)=\frac{\mathcal{P}}{V_{\rm ff }}-L[T(\mathbf{r},t)-T_{0}]. \tag{4}\]  
Dividing Equation (4) by \(T_{0}\), we rewrite it in terms of \(\theta(\mathbf{r},t)\equiv(T(\mathbf{r},t)-T_{0})/T_{0}\), the _relative temperature elevation_, as follows:  
\[\rho c_{v}\frac{\partial\theta}{\partial t}(\mathbf{r},t)+\rho c_{v}\mathbf{v}(\mathbf{r}, t)\cdot\
abla\theta(\mathbf{r},t)-\kappa\
abla^{2}\theta(\mathbf{r},t)=q[\theta], \tag{5}\]  
where we have defined the source as a function of \(\theta\):  
\[q[\theta]\equiv\frac{1}{T_{0}}\frac{\mathcal{P}[\theta]}{V_{\rm ff}}-L\theta. \tag{6}\]  
Next, dividing by \(L\), we note that \(\rho c_{v}/L\) has the dimensions of time and accordingly introduce the characteristic time  
\[t_{s}\equiv\frac{\rho c_{v}}{L}. \tag{7}\]  
Then, we rewrite Equation (5) as follows:  
\[\frac{\partial\theta}{\partial\tau}(\mathbf{r},\tau)+\tilde{\mathbf{v}}(\mathbf{r},\tau) \cdot\mathbf{\
abla}\theta(\mathbf{r},\tau)-\tilde{\kappa}\mathbf{\
abla}^{2}\theta(\mathbf{r },\tau)=\tilde{q}(\theta). \tag{8}\]  
Here, we also introduce the (dimensionless) time \(\tau=t/t_{s}\) and the new quantities  
\[\tilde{v}\equiv t_{s}v,\quad\tilde{\kappa}\equiv\frac{\kappa}{L},\quad\tilde{ q}(\theta)\equiv\frac{q}{L}=-\theta+\frac{1}{LT_{0}}\frac{\mathcal{P}[\theta]}{V_{ \rm ff}}. \tag{9}\]  
We may rewrite \(\tilde{q}\) using the SAR \(\mathfrak{S}=\mathcal{P}/m_{\rm Fe}\) (in \(\mathrm{W}/\mathrm{kg}\)) as follows:  
\[\tilde{q}(\theta)=-\theta+\frac{\mathfrak{S}}{LT_{0}}\rho_{\rm Fe}. \tag{10}\]  
The quantity \(\frac{\mathfrak{S}}{LT_{0}}\rho_{\rm Fe}\phi\) is the dimensionless SAR \(\Xi[\theta]\) introduced in Ref. [39].  
Consequently, we have the final (dimensionless) heat equation  
\[\frac{\partial\theta}{\partial\tau}(\mathbf{r},\tau)+\tilde{\mathbf{v}}(\mathbf{r},\tau) \cdot\mathbf{\
abla}\theta(\mathbf{r},\tau)-\kappa\mathbf{\
abla}^{2}\theta(\mathbf{r},\tau)= -\theta+\Xi[\theta]. \tag{11}\]
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
###### Abstract  
Recently, there has been a growing interest in underground construction safety, during activities such as subway construction, underground mining, and tunnel excavation. While Internet of Things (IoT) sensors help to monitor these conditions, large-scale deployment is limited by high power needs and complex tunnel layouts, making real-time response a critical challenge. A delay-sensitive multi-sensor multi-base-station routing scheduling method is proposed for the IoT in underground mining. First, a mixed network topology of wireless and wired networks is formed based on the irregular distribution characteristics of multiple tunnels in the mine construction environment. Based on this topology, a multi-sensor and multi-base-station real-time routing scheduling problem is proposed, proving that the problem is NP-hard. Secondly, the corresponding solving algorithms are designed based on the greedy strategy and the heuristic strategy. Finally, an experimental platform is built, and the performance of the proposed algorithm is compared and analyzed.  
Keywords:real-time schedule; delay-sensitive; underground mine; Internet of Things; multi-hop routing scheduling +
Footnote †: journal:
# Model-Driven Approach to Cloud-Portability Issue
## 4 Entities of Existing IaaS Platforms
### OpenStack
#### 4.2.4 Security Groups  
The Security Group entity in the OpenStack system uses a similar security logic as in the AWS platform. It can be defined either via the web interface, through a command prompt or using a script where its name is OS::Neutron::SecurityGroup. It has no mandatory parameters, only three optional ones. The first is a description that serves only as a description of the group. The second parameter is its name, which is unique within a single project and forms the same identifier of a given group. The last parameter is rules, which are security rules. Each rule has five parameters. The first is a direction that indicates whether the rule will be applied to incoming or outgoing packets. The other four parameters are the same as in AWS. Protocol refers to the transport protocol carried in the body of the packet. Further, the pair of parameters port_range_min and port_range_max indicate the port intervals that will be compared within the rule. The last rule is remote_ip_prefix, which specifies the range of IP addresses compared in the rule.
## 2 Materials and Methods
### Algorithm
#### 2.1.4 Artifact Removal  
The artifact removal is similar to MARA but was extended in order to preserve physiological information. MARA includes the interpolation of the MA segment with a spline function and the subtraction of the interpolated segment from the original segment to preserve the high frequency information of the signal. In the current implementation we used an \(n\)th-order Savitzky-Golay filter [65] since this allowed to model sharp rises in the signal (_i.e._, an artifact) while being computationally inexpensive. However, during an \"ok\" period of the signal, it will also exclude true physiological information. To overcome this caveat, the Savitzky-Golay filtered signal \(x_{k,SG}\) was weighted according to \(\textit{MSD}_{k}\), the MSD of \(x_{k}\), but with the window length \(p\) (substitute \(q\) with \(p\) in Equation (1)) and thus different from \(\textit{MSD}_{x}\). \(\textit{MSD}_{k}\) was further normalized to a range [0, 1] by \(\textit{MSD}_{k}/\text{max}(\textit{MSD}_{k})\). \(x_{k,SG}\) was complemented with a finite infinite response (FIR) low-pass filtered signal \(x_{k,FIR}\). \(x_{k,FIR}\) is weighted complementary with \(1-(\textit{MSD}_{k}/\text{max}(\textit{MSD}_{k}))\). The complete signal is recovered by adding \(x_{k,FIR}\)and the \(x_{k,SG}\). Put simply, the Savitzky-Golay filter was applied to abrupt signal changes (_i.e._, the artifacts) and the FIR filter during the stable parts of the signal. The window lengths (parameter \(p\)) of the FIR filter, the Savitzky-Golay and the MSD were set to \(p=f_{s}\times 1\) s samples (Table 1). Furthermore, the order of the Savitzky-Golay filter was set to 3 and the low-pass cutoff frequency \(f_{c,FIR}\) of the FIR filter to 0.5 Hz if the heart beat (approximately 1 Hz) was of interest (preservation of the desired physiological information). This filtered signal was subtracted from the original signal and the following vector was obtained:\[x^{\prime}{}_{MA,m}=AR\big{\{}x_{MA,m}\big{\}},\text{for}\ m=\{1,...,(N-1)/2\} \tag{12}\]  
The dash and the operator \(AR\) (artefact removal) indicate that the procedure outlined above was applied. The mean of the high-pass filtered segment is zero and will be adapted to the \"ok\" segments of baseline during the following reconstruction.
## 2 Background Knowledge
### Ontology-Based Representation and Reasoning  
An ontology refers to a formal representation of knowledge within a domain. It defines a set of concepts and the relationships between them, providing a structured and organized framework for understanding a specific domain of knowledge. It is tightly related to the Semantic Web. Key components of an ontology are:  
* Concepts (domain entities organized in hierarchies, regarded as classes)
* Relationships (between concepts)
* Properties (attributes of the concepts that define them)
* Axioms or Rules (logical statements that hold true in the domain)
* Individuals (specific entities, regarded as instances of classes)  
Reasoning with ontologies means making explicit the implicit knowledge represented in the ontology. Ontologies are based on the description logic model. The types of reasoning that can be accomplished in an ontology are:  
* Class hierarchy reasoning
* Concept subsumption
* Property restriction inference
* Consistency checking
* Rule-based reasoning
* Logic-based reasoning  
OWL is a semantic web language for constructing ontologies on the web. SWRL is a semantic web language for constructing first-order rules. SWRL rules are often used for producing new knowledge based on the knowledge explicitly represented in an ontology. For a comprehensive introduction to ontological reasoning, see [18].