esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 5 Conclusions  
The plane alignment parameter has a great influence on the change of the flow-field structure near the embankment, which not only changes the movement trajectory of the wind-sand flow on the embankment surface, but also affects the movement mode and gathering positions of the blown sand particles. Using numerical simulation, combined with the analysis of the sand accumulation form in the field, the following conclusions are obtained:  
(1) When the oblique wind-sand flow crosses the embankment, the separation scale of the moving wind-sand flow can be reduced, and the severity of the wind-speed change around the road decreases with the decrease in the angle between wind direction and line direction.  
(2) The change of the flow-field structure of the plane alignment embankment is not only related to the characteristics of the embankment itself, but the flow-field structure of the road at different locations along the road is also different due to the diversion effect generated when the oblique airflow passes through the embankment.  
(3) The convex windward embankment has the effect of dredging the wind-sand flow outward, and the concave windward embankment collects the wind-sand flow inward, and the dredging and collection effects both weaken with the increase in the radius.  
(4) In the design of plane alignment in desert areas, preliminary investigations should be fully conducted to grasp the characteristics of local wind and sand, and a straight-line parallel to the dominant wind direction. If a flat curve needs to be set to avoid obstacles, a convex windward curve is preferred, and a small radius curve should be avoided.  
Conceptualization, L.L. and Z.T.; methodology, Z.T.; software, Z.T.; validation, W.Y., Z.L.; investigation, W.Y.; writing--original draft preparation, L.L. and Z.T.; writing--review and editing, W.Y., Z.L; funding acquisition, L.L. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Transportation of Ningxia, grant number 20200173.  
This study did not involve humans or animals.  
This study did not involve humans.  
This study did not report any data.  
The authors declare no conflict of interest.
## 7 Future Research Directions and Projects  
Basically, the research in the field of WSNs started very recently with respect to other areas of the wireless communication society, as examples like broadcasting or cellular networks. The first IEEE papers on WSNs were published after the turn of the Millennium.  
The first European projects on WSNs were financed after year 2001: During the sixth and seventh Framework Programmes, some Projects were financed by the EC, with explicit activities dedicated to communication protocols, architectural and technological solutions for embedded systems: among them, the first to be launched were WISENTS [41], e-SENSE [42], CRUISE [43] and CONET [44]. In the US the research on WSNs was boosted few years before.  
Standardization is a key issue for success of WSN markets. The possible options for building HW/SW platforms for WSNs have been identified in the previous section. For low data rate applications (250 kbit/s on the air), IEEE 802.15.4 seems to be the most flexible technology currently available, while also Bluetooth LE can be attractive for applications demanding higher data rates. However, IEEE is also currently developing a new standard specifically oriented to WSNs for Body Area Networks, through the Task Group 802.11.6. While this shows the perceived relevance of standards in the research arena, it will also set the basis for the possible creation of an heterogeneous WSN environment, and opens the field to new technical solutions: in fact, many technical topics of WSNs are still considered by research, as the current solutions are known to be non optimized, or too much constrained.  
Then, it is also relevant to mention that there exist two European Technology Platforms, gathering all stakeholders in the field, related to the area of WSNs: e-Mobility and ARTEMIS. They have drawn research agendas that will drive the selection of large cooperative projects in the next years in Europe.  
In particular, ARTEMIS (Sub-Programme 7) is currently financing (2009-2011) the project eDIANA (Embedded Systems for Energy Efficient Buildings), addressing the need of achieving energy efficiency in buildings through innovative solutions based on embedded systems, described in Section 3.  
For a proper discussion on future WSN research directions, it is useful to split the issue in two: some research efforts are in fact application-agnostic, while others are mainly driven by market trends and future application needs.
## 3 Robustness and Reliability: New Challenges in CMR Segmentation
### Challenges to Reliable Segmentation
#### 3.2.2 Loss Formulation  
State-of-the-art CMR segmentation is performed through supervised learning techniques. During supervised training, the loss functions measure the dissimilarity between the ground truth and the predicted segmentation. There is a vast offer of loss functions for medical image segmentation (e.g., the cross-entropy loss, the soft-Dice loss) [66], which can be used independently or combining multiple losses together. An inherent disadvantage of most of these loss functions is that they are typically pixel-wise objective functions, which measure dissimilarity in terms of correctly classified pixels over the total. This formulation does not optimize the model towards the final problem task since it does not reward segmentation results that better reflect the anatomy, i.e., the shape of the heart. Instead, it favors similarity among pixel intensities and, eventually, it leads to incomplete and unrealistic segmentation results both at training and at inference. In particular, predictions may contain holes inside the structures, abnormal concavities, or duplicated regions, typically located in the most basal and apical slices [67]. Being caused by intrinsic limitations of DL-based algorithms, anatomical failures can occur at inference without any possibility of inferring the quality of the model outcome. Therefore, the model becomes unpredictable, intractable for model verification, and ultimately unreliable.
## 3 Case Study and Results  
### Basic Information of a Typical Concrete Gravity Dam  
A typical gravity dam located in Yunnan Province of China was selected. The gravity dam is a roller-compacted concrete gravity dam. The dam is 106.00 m high, 17.50 m wide at the top, and 90.10 m wide at the base. The normal storage level is 1472.00 m, with a corresponding downstream water level of 1408.20 m. The designed flood level is 1478.35 m, with a corresponding downstream water level of 1423.46 m. The check flood level is 1480.26 m, with a corresponding downstream water level of 1425.23 m and an upstream dead water level of 1429.00 m. The foundation of this project is mainly hard slate with an elevation of 1375.00 m. A non-overflow dam section was selected for analysis. The profile of the section is shown in Figure 2. The statistical characteristics and value ranges of the influencing factors considered are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Type of Probability Distribution** & **Mean** & **Standard Deviation** & **Value Range** \\ \hline Upstream water level (m) & Truncated Normal Distribution & 102.00 & 5.10 & 97.00–105.26 \\ Downstream water level (m) & Truncated Normal Distribution & 43.20 & 2.59 & 33.20–50.23 \\ Concrete elasticity modulus (GPa) & Truncated Normal Distribution & 28.0 & 2.8 & 25.2–30.8 \\ Foundation elasticity modulus (GPa) & Truncated Normal Distribution & 26.0 & 2.6 & 23.4–28.6 \\ Anti-shear friction coefficient & Truncated Normal Distribution & 1.00 & 0.22 & 0.90–1.10 \\ Anti-shear cohesion (kPa) & Truncated Normal Distribution & 900 & 360 & 700–1100 \\ Concrete compressive strength (MPa) & Truncated Normal Distribution & 11.20 & 2.24 & 6.80–13.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical characteristic and value ranges of the considered factors.  
Figure 1: Flow chart of calculating the reliability of concrete gravity dams.
## 3 Results and Discussion
### Characterization of Essential Oils
#### 3.1.1 Chemical Composition
Other components can be considered as minor ones (linalool, clavicol, \(\alpha\)-cubebene, \(\gamma\)-cadinene and carophyllene oxide). Similar results were found in samples from Slovakia, Saudi Arabia, Brazil and Iraq, with the percentage of the main compound eugenol ranging from 59.87 to 84.63% [15; 39; 40; 41]. Star anis oil had _trans_-anethol as the major compound, reaching 92.56% of total amount. Only limonene and anisaldehyde were present in amounts higher than 1%, while all other components were in percentages under 1%. The samples from China, Egypt, France, and USA showed similar chromatographic profiles, with percentages of _trans_-anethole varying from 77.47 to 93.39% [16; 42; 43; 44]. Although the chromatographic profile was similar, the variability in the proportion of _trans_-anethole was significant. Oregano essential oil primarily consisted of carvacrol (67.20%), \(p\)-cymene (13.02%), and thymol (8.22%), with 99.29% of components identified. The literature data on the oregano essential oil differed greatly due to the essential oil chemotype. The Greek oregano was rich in carvacrol (67.98%) [9], thus belonging to the carvacrol chemotype, while oregano from Serbia was of the thymol chemotype, with 41.6% of thymol [45]. Sabinene, \(\alpha\)- and \(\beta\)-pinene were major compounds of the nutmeg essential oil with 36.89, 13.20, and 11.26%. The intermediate compounds were limonene (6.20%), \(\gamma\)-terpinene (2.63%), terpinene-4-ol (5.16%), myristicin (2.77%), myrcine (2.26%) and \(\alpha\)-thujene (1.83%). A total of 96.11% of compounds were identified. Samples from Romania and Serbia were also rich in sabinene (21.71-42.30%) while nutmeg essential oil from Grenada was rich in \(\beta\)-pinene (26.61%) [8; 46; 47]. The great variability in the percentages of the essential oils' main components indicates the need for reliable, simple and fast methods for main component identification. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**N.**} & \multirow{2}{*}{**RI\({}^{*}_{\text{exp}}\)**} & \multirow{2}{*}{**RI\({}_{\text{ref}}\)**} & \multirow{2}{*}{**Compound**} & \multicolumn{5}{c}{**\%**} \\ \cline{5-9}  & & & & **LEO** & **BLEO** & **CEO** & **SAEO** & **OEO** & **NEO** \\ \hline
1 & 926 & 930 & \(\alpha\)-Thujene & 0.52 & 0.61 & - & - & 0.18 & 1.83 \\
2 & 933 & 939 & \(\alpha\)-Pinene & 2.12 & 4.28 & - & 0.38 & 0.66 & 13.20 \\
3 & 948 & 954 & Camphene & 0.06 & 0.13 & - & - & 0.17 & 0.24 \\
4 & 973 & 975 & Sabinene & 1.00 & 8.0 & - & 0.02 & 0.17 & 36.89 \\
5 & 976 & 979 & \(\beta\)-Pinene & 8.11 & 3.24 & - & 0.05 & - & 11.26 \\
6 & 978 & 986 & 1-Octen-3-ol & - & - & - & 0.44 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Chemical composition of essential oils obtained by GC/MS.
# MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
## 4 Experiments
### Comparison Methods  
In order to reflect the advancement of our proposed method, we chose DANN [30], OSDAFD [31], BWAN [32], UDA [33], EVT-OSFD [32], DAN-COSFD [34] and other methods for comparison. These methods are based on transfer learning methods, and a brief introduction to each method is as follows:  
1. DANN: A classical transfer learning approach, based on adversarial neural networks, and oriented to domain adaptation problems.
2. BWAN: A representative method of open set domain adaptation, using extreme value theory to reject unknown class samples.
3. UDA: Universal domain adaptation is a classical open set data mining method. The domain similarity and prediction uncertainty of each sample are used to achieve shared class adaptation and unknown class detection.  
Figure 6: Variable Mechanical Structure Bearing Experimental Platform.  
* EVT-OSFD: This method uses the output of additional domain classifiers and proposes a bilateral weighted adversarial network model, which assigns large weights to shared classes and small weights to outlier classes in the feature alignment process.
* OSDAFD: An open set domain adaptive method for rotating machine fault diagnosis, which uses a state classifier to identify known machine health states and a binary classifier to identify unknown machine health states.
* DAN-COSFD: An adaptation network based on dual adversarial learning. The auxiliary domain searcher assigns similarity weights to each target sample to distinguish between known and unknown fault modes, and employs weighted adversarial learning to selectively adapt the domain distribution.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Node Security  
Worker nodes constitute the execution layer of a Kubernetes cluster, tasked with operating the containers that provide application functionality. Every node interacts with the control plane and comprises essential components, including the Kubelet and container runtime, both require safeguarding to avert lateral movement or privilege escalation. Due to nodes frequently managing external workloads and directly interfacing with the underlying host operating system, vulnerabilities at this level can result in complete node compromise. This section emphasizes the fortification of the Kubelet through the implementation of stringent authentication and authorization protocols, the deactivation of anonymous access, and the assurance of encrypted communications, all intended to mitigate the danger of unauthorized acts on the node.  
The next step is securing the Kubelet component. Kubelet offers significant node and container control via an HTTPS endpoint, which is exposed unauthenticated by default. Kubelet authentication and authorization are recommended for production clusters. Any kubelet HTTPS endpoint requests not denied by other authentication mechanisms are anonymous, and they are assigned the username system:anonymous, group system:unauthenticated.  
Kubelet's default configuration with \(-\)anonymous-auth\(=\)true treats any request not matched by other authentication mechanisms as an anonymous request and allows it if authorization policies do not explicitly deny it. That often gives unauthenticated access to the Kubelet API. The Kubelet denies unauthenticated requests with --anonymous-auth=false, so the client must be authenticated and authorized to act on the node. Disabling anonymous access restricts Kubelet endpoint requests to authenticated principals (users or service accounts) with the correct permissions. To disable anonymous access and return \"401 Unauthorized responses for unauthenticated requests,\" start kubelet with --anonymous-auth=false, as shown in Figure 5:  
Securing the kubelet is vital as it directly governs the workloads operating on the node. Any misconfiguration or security vulnerability may result in significant repercussions, such as unauthorized access to sensitive workloads, data exfiltration, or total compromise of worker nodes.  
In addition to safeguarding the infrastructure that executes workloads, it is crucial to analyze the workloads themselves, particularly the container images and runtime configurations. If inadequately assessed, container images may contain known vulnerabilities or excessive rights, rendering them a possible access point for attackers. This section outlines optimal techniques for container image scanning, privilege limitation, and secure integration into CI/CD pipelines to guarantee that only trusted and fortified workloads are deployed within the cluster.
# Ion Mobility-Mass Spectrometry Approach for the Comparison of Sheep and Goat Milk Lipidomes
## 2 Materials and Methods
### Milk Samples  
Morning milk samples from ten mature goats and sheep were obtained by pooling the raw milk of 120 pluriparous animals for each species. Bulk-tank milk samples were collected in June; both ewes and goats had access to the same pastures. The milk samples provided by the Sardinian Regional Animal Farmers Association (Associazione Regionale Allevatori, ARA, Sardegna) within the International Committee for Animal Recording (ICAR) program were collected in sterilized tubes; bronopol (2-bromo-2-nitro-1,3-propanediol) was added, and samples were then stored at \(-80\)\({}^{\circ}\)C until analysis. Each bulk-tank milk sample was analyzed as 15 single samples.
# X-ray Photoelectron Spectroscopy in Mineral Processing Studies
## 4 Towards in Situ XPS
### XPS Studies of Solid-Liquid Interfaces
#### 4.1.1 Ambient Pressure XPS  
(Near) ambient pressure photoelectron spectroscopy ((N)AP-XPS) is a fast-developing technique allowing to characterize a sample at pressure up to about 100 mbar by using differentially pumped spectrometers [172, 173, 174, 175, 176, 177]. Such equipment first advanced by [PERSON] and co-workers [172] can work as backfilling system, in which the whole chamber is filled with gas, or the enhanced pressure is created in a reaction cell connected to the analyzer. Usually, the brilliant synchrotron radiation is utilized because of the photoelectron attenuation, but laboratory instruments equipped often with high-energy X-ray sources (e.g., Cr anode) are available now too [173, 179].  
Several types of the cells have been proposed for samples containing a liquid [173, 174, 33]. The liquid microjet set-up, in which the liquid stream is injected with a high speed through the nozzle having a diameter of 20 \(\upmu\)m or smaller, is used for XPS studies of aqueous solutions, liquid-vapor and liquid-solid (nanoparticles) interfaces [179]. A \"dip and pull\" method has been utilized for a solid surface coated with a thin liquid layer up to 10 nm which has been fabricated by partially pulling up from a liquid reservoir placed in the analysis chamber; both the solution species and underlying substrate with adsorbates can be analyzed with XPS (HAXPES) [178]. In a technique based on SR or conventional X-ray tube sources, an environmental cell containing liquid, gas, or multiphase samples is sealed with a thin membrane (graphene, Si, Si\({}_{3}\)N\({}_{4}\)) as a window transmitting photoelectrons and separating it from the environment [173, 172, 178, 179, 33].  
To the best of our knowledge, NAP-XPS has not been used in mineral processing research to date; the main tasks here seem to be a creation of experiment methodology and proper cells.
## 3 Results
### Expression Patterns of RcTPS Genes in Different Tissues of R. chinensis  
The transcriptome data of nine rose tissues, including roots, stems, leaves, stamens, pistils and flowers, at different developmental stages were downloaded from the NCBI to analyze the tissue-specific expression patterns of _RcTPS_ genes. _RcTPS17_, _RcTPS29_ and _RcTPS45_ were not expressed in any of the nine analyzed tissues. Sixteen percent (8 of 49), 6% (3 of 49) and 18% (9 of 49) of the _RcTPS_ genes showed the highest transcript abundance in roots, stems and leaves, respectively. Notably, 53% (26 of 49) of the _RcTPS_ genes showed the highest transcript abundance in floral organs. Among them, _RcTPS23_ showed the highest transcript abundance in the stem; _RcTPS02_, _RcTPS08_, _RcTPS11_, _RcTPS13_, _RcTPS22_, _RcTPS27_, _RcTPS31_, _RcTPS33_, _RcTPS40_ and _RcTPS41_ in the pistil; _RcTPS12_ in the green petals of the flower bud; _RcTPS20_ and _RcTPS37_ in the color-changing petals of the flower bud; _RcTPS14_ and _RcTPS19_ in the pink petals of the flower bud; and _RcTPS01_, _RcTPS06_, _RcTPS09_, _RcTPS16_, _RcTPS26_, _RcTPS34_, _RcTPS35_, _RcTPS36_, _RcTPS44_ and _RcTPS46_ in the pink petals of the open flower (Figure 7). These data show that the rose _RcTPS_ genes are expressed in different organs and in petals at different developmental stages, suggesting their potential roles in the biosynthesis of terpenoids in different organs and during petal development.  
Figure 6: _Cis_-acting elements distribution in the promoter region of _RcTPS_ genes. Different color boxes represent different _cis_-acting elements. _Cis_-acting elements related to plant growth and development, stress response and phytohormone response are shown by triangular, oval or rectangular boxes, respectively.
# A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine
## 2 Inductive Wear Debris Sensor  
The most representative inductive sensor is MetalSCAN from Gastops Ltd., which is applied to the aero-engine and wind turbine successfully, whose schematic diagram is illustrated as Figure 1[19; 20]. Due to the structural characteristics, this type of sensor is named as the inverse dual-excitation three-coil differential inductance sensor [6; 21]. It consists of two field coils and a central sensing coil. The two field coils are supplied with a high frequency AC voltage and induce the opposite magnetic fields, which are balanced at the sensing coil. If a ferromagnetic (non-ferromagnetic) debris passes through the sensor, the magnetic field on its side will be stronger (weaker) than that of the other side, the reverse magnetic fields will not be balanced, and as a result, a characteristic signature will be output. The amplitude and phase reflect the debris size and ferromagnetic properties, respectively.  
As Figure 1 shows, two field coils form part of resonant circuit and are supplied with a high frequency AC voltage that induces the opposite magnetic fields, which are balanced at the midpoint between the coils, that is, the sensing coil. The resonant circuit enables significantly greater sensitivity to a passing metallic particle. The passage of the metallic  
Figure 1: Schematic diagram of the triple-coils inductive sensor.  
particle causes a characteristic signature, whose amplitude and phase are utilized to obtain the information of the passed particle [8; 12].  
In this paper, a domestic triple-coils inductive sensor is utilized, as illustrated in Figure 2. The raw monitoring signal is acquired by the domestic inductive monitoring system in the test runs of an aero-engine of a certain type. However, the amount of debris detected by the sensor is more than the actual amount of debris, which means a false alarm. Therefore, a new signal processing method is proposed to reduce the false alarm rate. The raw monitoring signal in a test is illustrated in Figure 3, of which the sample rate is 24.5 kHz.