esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Advantages of Synchronous Machines with Electric and Hybrid Excitation without Rare Earth Magnets  
Generators with rare-earth permanent magnets are widely used when operating with a controlled rectifier [1]. At the same time, many researchers are looking for alternative designs that do not contain rare earth magnets, since these magnets are expensive, and their use makes the manufacturer dependent on a few suppliers. The price of rare earth magnets can fluctuate greatly and change several times, depending on the state of the international market. The extraction of raw materials for rare earth magnets is harmful to the environment [2]. In addition, electric machines with rare earth magnets have disadvantages in operation. Nd-Fe-B rare earth magnets are not well suited for high temperature applications due to their tendency to irreversibly demagnetize at temperaturesabove 120 \({}^{\circ}\)C and strong demagnetizing fields. While more temperature resistant Sm-Co magnets are available, due to the high cost and difficulty in machining, they are typically only used in aerospace systems [3].  
The electrical conductivity of rare earth magnets is relatively high, therefore, at a high operating frequency, large eddy currents are induced in rare earth magnets, which leads to their high heating and complicates their use in high-speed applications. Magnetomotive force (MMF) from permanent magnets is uncontrollable and cannot be turned off in emergency situations, which complicates the use of the machines with rare earth magnets in many applications [4]. An alternative is wound rotor electrically excited synchronous generators (WRSG). However, their conventional designs cannot be used in many applications due to the presence of slip rings and brushes feeding the excitation winding on the rotor, which are subject to rapid wear [5]. In addition, the excitation winding on the rotor produces significant losses, which makes it difficult to cool the WRSG.  
In a number of applications, for example, for turbo generators connected directly to the grid and for electric aircraft, wound rotor synchronous generators with a brushless exciter are used [6; 7]. However, the use of a brushless exciter leads to an increase in the cost, size, and weight compared to traditional generators. In addition, in railway applications, the low resistance of the brushless exciter to shaking and shock loads, as well as the complexity of repairing a generator with such an exciter, are critical. Another alternative with an electrically excited brushless design is synchronous homopolar machines (SHMs).
## 5 Evaluation Scenario
### Evaluation Metrics
#### 5.3.2 Normalized Lifetime  
In the following, we focus on the LCs lifetime. Specifically, we exploit a metric, called Acceleration Factor (AF) [3], to express the normalized lifetime, i.e., the current lifetime of the LC normalized by the one assuming that the LC always operates at full power. More in depth, when \(AF<1\) the current LC lifetime is higher than the reference one. As a result, the application of SM is beneficial for the LC. On the contrary, when \(AF>1\) the current lifetime is higher than the reference one, i.e., SM is not beneficial for the lifetime. The actual AF value depends on HW parameters, which are related to the components used to build the LC as well as SM parameters, which instead depend on the strategy used to put the LC into SM/AM. More formally, the AF of the k-th LC installed at node \(i\in V\)at time \(t\in T\) is expressed as:  
\[AF_{i}^{k}(t)=1-\left(1-AF^{sleep}\right)\cdot\tau_{i}^{k}(t)+\chi\cdot\
u_{i}^{k }(t)\quad\text{(units)}, \tag{13}\]  
where \(\tau_{i}^{k}(t)\) denotes the time spent by the k-th LC at node \(i\in V\) in SM up to time \(t\in T\), \(AF^{sleep}\) is a HW input parameter, \(\
u_{i}^{k}(t)\) represents the number of SM-AM-SM cycles of the k-th LC at node \(i\in V\) up to time \(t\in T\), and \(\chi\) is another HW input parameter. Intuitively, higher values of \(\tau_{i}^{k}(t)\) tend to decrease the AF, and consequently increase the lifetime. On the contrary, the increase in the number of cycles \(\
u_{i}^{k}(t)\) tends to increase the AF, and therefore to decrease the lifetime. The ideal condition would be therefore to keep the LC always in SM in order to maximize its lifetime. However, this is impossible in practice due to the experienced traffic and its variations, which impose changes of power states of the LCs.  
Focusing instead on the HW parameters \(AF^{sleep}\) and \(\chi\), they depend on the components used to build the device. Specifically, \(AF^{sleep}\) is the ratio between the failure rate of the device aways in SM and the one always in AM (which is always lower than 1, according e.g., to the Arrhenius law [41]). In addition, \(\chi\) acts as a weight for the frequency of the power cycles. Since actual measurements of these parameters are not (yet) available, we perform a sensitivity analysis w.r.t. them. More in depth, we set \(AF^{sleep}=\{0.2,0.5,0.8\}\) (units) and \(\chi=\{0.5,1.0,5.0\}\) (h/cycle). By default (unless specified otherwise), we assume \(AF^{sleep}\) equal to 0.5 unit and \(\chi\) equal to 0.5 h/cycle [3].  
By extending our reasoning to the whole set of LCs, we compute the average AF as:  
\[AF_{agg}=\frac{\sum_{i\in V}\sum_{k=1}^{X_{i}^{LC}}AF_{i}^{k}(|T|\cdot\Delta t )}{\sum_{i\in V}X_{i}^{LC}}\quad\text{(units)}. \tag{14}\]  
Specifically, results are reported in terms of average AF over all LCs in the network after 14 days of network operation, and assuming that the traffic demands are repeated in each day.
## Results and Discussions: The Influence and Benefits of PPGIS
### Creating Accountability  
PPGIS also helps ensure the implementation of transparent policy decisions [23; 31]. In the process of PPGIS, empowerment is fostered, stakeholders' capacity is built, and opportunities delivered by policy decisions are increased through this shift of power [25; 28; 35]. In the fieldwork for this study, at the platform meetings, PPGIS was used to help stakeholders clarify their visions and objectives, to discuss the division of labor, and to review the results so that stakeholders could understand the roles and functions of one another in the PA management. As a result, the transparency of the management process and decision making were improved and mutual accountability was achieved. This promoted the integration of mutual resources and enhanced the public sector's willingness to entrust community members to carry out management work and to discuss budget allocations, thereby contributing to the realization of a mature partnership between the public and private sectors. Subsequently, community members continued to participate in the monitoring work and to routinely review management status; thus, they became the supervisors for the refuge. Overall, PPGIS helped with the sharing of rights and liabilities in governance and it also constructed an accountability mechanism in the governance process.  
\"After monitoring the substratum elevation these last two years, we found that the sedimentation in the refuge can be cleared out once. The records of these two years can be considered as the worst-case scenario and then serve as a reference point after complete dredging. By monitoring the siltification in the refuge for a long time, we can understand the status and speed of the siltification in the refuge.\" (AC01) \"When the county government is wrong, we can correct them. When they have an inappropriate view of the refuge, we can correct them and make them change the plan. When the government needs to carry out any management actions involving the refuge, we can provide assistance, supervision, and cooperation.\" (LC02)
# A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
## 2 Theoretical Considerations
### Influence of Rotational Raman Scattering  
As briefly mentioned in the beginning of this section, the spectral ranges of Thomson scattering and rotational Raman scattering signals overlap. Hence, the influence of the latter on the results should be evaluated. According to [PERSON] et al. [32], an overlap occurs when the total density of the Raman scattering particles \(n_{I}\) is \(10^{4}\) times larger than electron density, since Raman scattering cross sections are about \(10^{-5}\) times smaller than the Thomson scattering cross section at the used laser wavelength. If the spectral distribution of the measured Raman signal \(M_{RaS}(\lambda^{*})\) is expressed similarly to Equation (12), both \(M_{THS}(\lambda^{*})\) and \(M_{RaS}(\lambda^{*})\) signals can be put into relation as they are calibrated with the Rayleigh scattering signal \(M_{RJS}(\lambda^{*}=\lambda_{0})\). Since the Raman spectrum for a given plasma gas at a specific temperature of heavy particles can be calculated if the instrumental profile of the detection apparatus is known, as described in [26], the influence of rotational Raman scattering can be estimated by comparing the experimentally measured signals of \(M_{RJS}(\lambda^{*})\) and \(M_{THS}(\lambda^{*})\) with the calculated \(M_{RaS}(\lambda^{*})\) signal. Such a comparison is presented in Figure 2 for nitrogen, which is used in this work.  
From the experimentally measured \(M_{RJS,\varepsilon}(\lambda_{0})\) and \(M_{THS,\varepsilon}(\lambda_{0})\) signals, a heavy particle temperature of 3500 K was determined for a pulsed, low-current nitrogen discharge operated at atmospheric pressure (described in Section 3.1). The \(M_{RaS}(\lambda^{*})\) signal was then calculated for this temperature and compared with the experimentally measured signals. As can be seen in Figure 2, the measured Thomson scattering signal is three orders of magnitude smaller than the measured Rayleigh signal, whereas the calculated rotational Raman spectrum is approximately two orders of magnitude smaller than the Thomson signal. Taking into account that the intensity of the \(M_{RaS,\varepsilon}(\lambda_{0})\) signal decreases with increasing \(T_{h}\), while the spectral range becomes broader [26], it can be assumed that the rotational Raman signal does not significantly influence the Thomson scattering signal and hence the determined plasma parameters for the experimental setup proposed in the next section.
# A Novel Deep Learning Stack for APT Detection
## 5 Detection Engine  
The purpose of the proposed method is to process outliers as well as to detect and identify APT attacks. For this purpose, we define data flow as follows:  
\[F =\text{data flow}\] \[K =\text{known attacks}\] \[N =\text{normal traffic}\] \[O_{1} =\text{unknown legitimate outliers}\] \[O_{2} =\text{unknown attack outliers}\] \[K_{T} =\text{traces of known attacks}\] \[N_{T} =\text{traces of normal traffic}\]  
\[F=\{K,N,O_{1},O_{2}\}\]  
The first and second layers remove known data. However, as mentioned the Sections 4.1 and 4.2, outlier data can still contain traces of known attacks and normal traffic.  
After the first layer, the data flow is:  
\[F_{1}=\{K_{T},N,O_{1},O_{2}\}\]After the second layer, it is:  
\[F_{2}=\{K_{T},N_{T},O_{1},O_{2}\}\]  
Historical events are tested with RNN-LSTM where outlier features flow through it. If there is no earlier detection, it is a new outlier. When an outlier is detected in this phase, it is added to the database. Then, this information becomes available for the classifier.  
The classifier organises outliers to the clusters in GHSOM, based on data packets. As GHSOM does not limit the growth, wecan detect slight differences in outliers. It can identify new types of clusters if they appear in data, since it is an unsupervised method.  
Afterwards, outliers are mapped with a graph method, which reveals their hidden interconnections. These hidden connections can reveal how widely the APT has spread and which are the communication protocols and ports. In the case an attack uses internal routing communication channel, it can also be tracked.  
The last phase is data fusion, where outlier data are mapped in such that they can be visualised for investigation and analysis purposes.  
For the final tests, there are four different stack models as described in Table 1.
## 3 Comparison and Impact Analysis of Pouring Schemes for UHPC Layer on Bridge Deck
### Analysis of the Impact of Setting up a Post-Pouring Strip in Scheme I without Counterweight Pouring  
According to the analysis in Section 3.1, the tensile stress of the UHPC bridge deck near the auxiliary pier is relatively high, mainly caused by the negative bending moment. It is considered to set up post-cast sections within a range of about 20 m near the bridge tower and auxiliary pier to reduce the considerable tensile stress of the UHPC bridge deck. According to the overall construction organization, the post-pouring section is poured before the dead load of the second phase of bridge deck construction. The schematic diagram of the scheme is shown in Figure 6.  
According to the calculation results of the above scheme, after setting the post-pouring strip, the tensile stress of the UHPC bridge deck during the completion stage decreases by about 2 MPa, and the additional stress during the construction stage decreases by about 0.3 MPa. Other indicators are not significantly different from those without the post-pouring strip.  
The main reason for the small effect of setting up a post-pouring strip is that a post-pouring section can only solve the first-stage stress during the UHPC pouring period and cannot solve the second-stage stress caused by the large second-stage dead load (73 kN/m, about twice the weight of the UHPC bridge deck) after UHPC pouring is performed.
## 3 Materials and Methods  
Our model compensates for the shaded information automatically in monocular true color images in Red, Green, and Blue (RGB) color space. The flow chart of our automatic shadow compensation process proceeds as follows and is also is illustrated in Figure 1. Initially, the image in RGB space is transformed into a normalized Hue, Saturation, and Intensity (HSI) color space to gather the Hue (\(H\)), Saturation (\(S\)), and Intensity (\(I\)) components. \(I\) is the only component compensated for Then, typical shadow spectral features, such as low brightness and high normalized blue component, are employed to detect the shadows; and each shadow and non-shadow area is optimized and confirmed by their morphology. The means of the Red (\(R\)), Green (\(G\)), Blue (\(B\)) components in shadow areas and non-shadow areas are also calculated, respectively, to acquire their difference, including \(\Delta R\), \(\Delta G\), and \(\Delta B\), which are used to correct the results later in the process. The mean and standard deviation of component \(I\) of each shadow and non-shadow area are calculated. Then, the feature points around the shadow boundaries are extracted and matched to calculate the values of the unknown compensation parameters. The mean and variance of component \(I\) of each shadow and non-shadow area and the compensation parameters then are the input to build a regional improved Wallis model. Meanwhile, the mean and variance of the local window centered on each shadow pixel are calculated to establish the window compensation model. Then, the original \(I\) is heightened by combining the regional compensation with the local window compensation. In the final step, the new \(I\), the initial \(H\), and \(S\) are converted into RGB color space; and \(\Delta R\), \(\Delta G\), and \(\Delta B\), which represent the difference in \(R\), \(G\), and \(B\) between the shadow area and non-shadow area, are subtracted from the converted \(R\), \(G\), \(B\) components later on so that the colors in the shadow areas are better matched with their original colors. Each step of our methodology is discussed in detail in the subsequent sections.
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
## 2 Theory and Principle
### Results and Discussion
Finally, to understand the computational complexity of the proposed algorithm, we calculated the CPU running time required to extract the features of a single constellation diagram and compared it with the other three algorithms, as shown in Figure 9. CPU running time tests were conducted on an Intel Personal Computer with Processor Core i5-10210U CPU at 1.60 GHz, 16 GB Random Access Memory, and Windows 10 Home Edition operating system. Combined with Figure 8, it can be seen that the proposed MFI scheme achieves a significant improvement in identification accuracy and stability at the cost of minor computational complexity and is the result of balancing accuracy and stability optimally. Figure 8: Comparison of the overall identification accuracy of the seven signals with different MFI schemes. The fiber distance is set as (**a**) 80 km, (**b**) 120 km. Figure 9: Comparison of CPU running time of different algorithms for extracting constellation diagram features.
## 4 Discussion on Enabling Technologies
### Shaft End Seals  
Suitable shaft end seals for sCO\({}_{2}\) cycles can limit the leakage of the supercritical CO\({}_{2}\) fluid to minimise cycle efficiency penalties. They are required to separate the high pressure turbine from the rest of the ancillary components. Knowing that the temperature at turbine inlet is 900 \({}^{\circ}\)C, the seal at the end of the high-temperature/high-pressure side needs to be placed as far as possibly permissible from the turbine inlet without affecting rotordynamics. General Electric and the Southwest Research Institute [50] concluded that labyrinth seals are insufficient for providing the required low-leakage characteristics for the high-pressure fluid because the physical clearance is optimised for a single operating condition, and thus with the change of clearance gaps during different operation modes, leakage losses are aggravated. Non-contacting, dry gas lift-off face seals are suggested for high-temperature operation, with experimental testing by Sandia National Laboratories in collaboration with Flowserve Corporation at conditions of 700 \({}^{\circ}\)C, 4400 psi (\(\approx\)303 bar) and a shaft speed of 40,000 rpm [51]. In the case of the operating temperature of the modified NET Power cycle, Joule-Thomson or throttle cooling will need to be provided at that shaft end from compressor discharge to decrease the temperature to around 700 \({}^{\circ}\)C and allow safe and effective seal operation. Commercially-ready dry gas seals are limited to shaft sizes in the range of 37.5 mm for high rotational speeds (near 50,000 rpm) and for slower speeds, they can reach up to 10 times bigger shaft diameters [45]. Looking back at the conditions of the designed turbine in terms of temperature (900 \({}^{\circ}\)C), pressure (297 bar), rotational speed (\(\approx\)20,000 rpm) and shaft diameter (\(\approx\)120 mm), dry gas lift off seals are not a far measure from use for a 60-70 MW\({}_{e}\) supercritical CO\({}_{2}\) radial turbine.
# Application of Bonded-Block Models to Rock Failure Analysis
## 3 Generation and Application of Bonded-Block Models
### Mechanical Micro- and Macro-Properties
The Poisson's ratio of the sample is rather dependent on the ratio of the joint shear and normal stiffness parameters, so the numerical tests may provide a refinement of the estimate of the shear stiffness. For systems with deformable inner blocks, only a fraction of the deformation is assigned to the joints; therefore, the stiffnesses values will be proportionally higher. Comparisons of rigid-block models with deformable-block models in which the block material was assigned 50% and 95% of the total deformation have been presented [56]. The deformable-block model generally leads to a more realistic deformation pattern, but at a computational cost. If a fine discretization is sought to have a detailed fracture network, then rigid inner blocks would be the first choice. The micro-properties of the strength parameters can be obtained by performing numerical tests involving uniaxial tension, uniaxial compression and biaxial compression setups. Many authors employ simple brittle failure models for the inner-block contacts, typically defined by the uniaxial tensile strength and the Mohr-Coulomb parameters, cohesion and friction angle. These micro-parameters can be varied until the desired macro-properties are achieved in a sample with the intended block geometry and size. The use of contact models considering post-peak softening is discussed in the following section.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 2 Backgrounds  
### Graph Neural Networks  
In recent years, Graph Neural Networks (GNNs) have experienced rapid development and emerged as a deep learning architecture widely applied in modeling and learning non-Euclidean data with applications in various fields, including social network analysis [2], drug discovery [3], and traffic network analysis [4]. Currently, most GNN frameworks can be categorized as either spectral-based [5] or spatial-based [6]. Spectral-based methods utilize the Laplacian spectrum to define graph convolution, and the graph convolutional operation is performed in the spectral domain. In contrast, spatial-based methods define graph convolution as a message-passing process, updating node representations by aggregating their neighborhood representations. Since the introduction of the Graph Convolutional Neural Network (GCN) [7], which interrelates the spectral- and spatial-based methods, various GNN models have been proposed, including GAT [8], GraphSAGE [9], and GIN [10]. Due to their high flexibility and efficiency, these models have achieved noticeable success in various application domains recently. Moreover, the graph autoencoder [11] has been proposed as an extension of traditional autoencoders for unsupervised learning on graph-structured data. The model aims to embed node attributes in a low-dimensional vector space using a multi-layered GCN encoder. The training task is set to reconstruct the original graph topology, represented as an adjacency matrix, through a structure decoder with the encoded latent representations.