esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spatially Fixed Virtual Sensing Algorithms
### The stochastically optimal tonal diffuse field virtual sensing method
Control at a virtual microphone, using the measured pressure and pressure gradient at a point, achieved a maximum attenuation of 24 dB at the virtual location and generated a 10 dB zone of quiet with a diameter of \(\lambda/10\). This is the same sized zone of quiet as that achieved by [PERSON] et al. [1], when minimising the measured pressure at the physical sensor location with a single secondary source. Similar control performance was obtained using two closely spaced physical microphones to estimate the pressure at the virtual location. Minimising the pressure and pressure gradient at virtual location with two control sources, using the measured pressures and pressure gradients at two points, achieved a maximum attenuation of 45 dB and extended the zone of quiet to a diameter of \(\lambda/2\). This is the same sized zone of quiet as that achieved by [PERSON] and [PERSON] [3], when minimising the measured pressure and pressure gradient with two control sources. Similar control performance was also obtained using physical microphones at four closely spaced points to estimate the pressure and pressure gradient at the virtual location.
# Forced Circulation of Nitrogen Gas for Accelerated and Eco-Friendly Cooling of Metallic Parts
## 2 Materials and Methods
### Methods  
#### 2.2.1 Design of the Dry Nitrogen Cooling System  
Figure 2 shows the nitrogen cooling system developed in this study. The gaseous nitrogen, which was transported via a specially-designed nozzle of the cryogenic liquid nitrogen tank, is passed into the cooling chamber. As the surge tank with a volume of 15,000 L was filled with nitrogen under a pressure of 8.0 bar, nitrogen could be rapidly filled in the cooling zone. In this study, the cooling chamber was controlled to be filled with nitrogen at a pressure of 5 bar. This nitrogen gas exchanged heat with the workpieces it contacted, resulting in an accelerated cool-down. Cooling of the workpiece resulted from the forced convective heat transfer between the workpiece surface and nitrogen.  
As shown in Figure 3, the heated nitrogen gas is forcibly circulated and transported to the chiller in a closed loop, where the heat is removed, and the nitrogen gas is cooled. This cooled nitrogen gas is rapidly circulated again using a circulation pump rotating at a speed of 1000 rpm.  
Figure 1: Process sequence for the THA tube fabrication.  
Figure 2: Schematic drawing of the proposed nitrogen cooling system.  
Figure 4 presents the operating sequence of furnace with the nitrogen cooling system. Figure 4a shows the workpiece before the heat treatment. For heat treatment, workpieces are loaded in heating zone and the loading gate is closed. The workpiece positioned at the heating zone is heated under a nitrogen atmosphere, as shown in Figure 4b. Then, the heat-treated workpieces are transported to the nitrogen cooling zone and cooled by nitrogen, as shown in Figure 4c.  
Figure 4: Operating sequence of the furnace with the nitrogen cooling system: (**a**) Before workpiece loading; (**b**) workpiece loading and heating; (**c**) nitrogen cooling.  
Figure 3: (**a**) Schematic drawing of closed loop nitrogen circulation; (**b**) photograph of the developed nitrogen cooling system.  
As the environmental conditions of the heating and cooling zones must be precisely controlled, the door between the heating and cooling zones as well as the external door is specially designed to prevent leakage [45]. Figure 5 shows the developed door system for the high-quality process cooling. A roller-type moving system is designed to enable faster movement of the workpiece. The time to move the workpiece from the heating chamber to cooling chamber is less than 15 s.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology  
In this section, we introduce _MPGT-Align_ for unsupervised EA, which employs a multi-hop pruning-based attention mechanism and Graph Transformer to facilitate the unsupervised alignment process. Figure 2 illustrates the structure of _MPGT-Align_, with further details presented below. Given two KGs, _MPGT-Align_ executes the EA task through the following five steps:  
* **Textual embedding** (Section 4.1). MPGT-Align first constructs the textual embedding of entities based on their name and context-dependent information within KGs. In this module, \(\textbf{E}^{nc}\) represents the textual embeddings for all entities across two KGs, while \(\textbf{E}^{n}\) denotes the entity name embeddings and \(\textbf{E}^{c}\) denotes the entity-context embeddings.
* **Pruning-hop2 Token** (Section 4.2). We enhance the hop2 Token method to develop a novel multi-hop pruning-based neighborhood aggregation method, called Pruninghop2 Token. Pruning-hop2 Token can be executed offline prior to model training, producing a multi-hop feature matrix derived from the KG structure. In this module, \(\textbf{E}=(\textbf{E}_{0}^{\prime};\textbf{E}_{1}^{\prime};\dots;\textbf{E}_{K}^ {\prime})\) represents the aggregated feature matrix obtained from the neighborhoods of all entities at hop distances ranging from 0 to \(K\), inclusive of the entities' own features.
* **Attention-based Transformer encoder** (Section 4.3). We employ an attention-based Transformer to construct an encoder for entity features. According to the design of the contrastive learning mechanism, we apply two different perturbation ratios, \(\gamma^{q}\) and \(\gamma^{k}\), to mask the multi-hop neighborhood features **E**, thereby obtaining two views, \(\textbf{E}^{q}\) and \(\textbf{E}^{k}\). Subsequently, \(\textbf{E}^{q}\) and \(\textbf{E}^{k}\) are fed into two Graph Transformer models, which output two different entity feature matrices, \(\textbf{H}^{q}\) and \(\textbf{H}^{k}\), respectively. In addition, \(\theta^{q}\) and \(\theta^{k}\) represent the parameters of two Graph Transformer encoders (a query encoder and key encoder), respectively.
* **Unsupervised learning** (Section 4.4). _MPGT-Align_ utilizes graph contrastive learning to facilitate an unsupervised learning process for EA task. In this module, \(\mathcal{L}_{infoNCE}\) is the InfoNCE loss function [45], which is utilized for the contrastive learning in this task.
* **Alignment** (Section 4.5). We introduce a novel metric, known as consistency-similarity function, which calculates the similarity between candidate entities in order to achieve alignment results with enhanced accuracy.
## 4 Numerical Modelling and Calculation Methodology  
### Establishment of Numerical Model of the Surrounding Rock-Initial Support  
#### 4.1.1 Model Size and Boundary Conditions  
The schematic of the model size is illustrated in Figure 4a. The total model length is 100 + 6 \(L_{\mathrm{dism}}\), where \(L_{\mathrm{dism}}/2\) = 10 + (i \(-\) 1) \(\times\) 7.5, with i = 1 \(\sim\) 8, and the width is 130 m. The entire multi-factor model contains the same number of meshes and mesh layout to minimize the impact of grid differences on the overall results. The model employs a regularized grid comprising 80,511 nodes and 53,184 hexahedral elements.  
Normal constraints are applied to the sides and bottom of the surrounding rock. The overall structure of the lining consists of surrounding rock, shotcrete, steel arch (Figure 4b), and a system of rock bolts collectively bearing the load. By continuously releasing the initial load of the original rock, the overall deformation of the corresponding lining structure is calculated, and the GRC of the overall lining structure is drawn. In the model, the shotcrete is represented by solid elements, while the steel arch is modelled using beam elements. The shotcrete is assumed to be in close contact with the steel arch, and no relative displacement  
Figure 4: Model size diagram. (**a**) Overall model. (**b**) Schematic diagram of the surrounding rock-initial support overall bearing capacity.  
between the steel arch and the shotcrete is considered throughout the entire loading process. Systematic rock bolts are represented by cable elements, and the reinforcement effect of grouting on the rock mass is also incorporated.
# Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches
## 6 Trends and Promises
### AIS Hybrid Solution Challenges in the IoT  
Implementing a method based on AIS techniques is difficult. For instance, clonal selection algorithms are adaptive but computationally expensive. Moreover, clonal selection suffers from high false-positives, and the degree of damage cannot be inferred instantly. On the other hand, the negative selection algorithm has high false-negatives and is not suitable for dense environments. Combining two or more AIS algorithms might be the solution to overcome some of these challenges, such as applying negative selection and neural network techniques in NShN, which results in fault-tolerant, adaptive, and distributed solutions; however, it is not lightweight [54]. Furthermore, negative and positive selection algorithm techniques were combined in MNSA to improve the detection rate in the IoT [51]. Even though the goal of implementing this method was met, the solution does not meet all the IoT system's requirements, such as robustness. The same scenario applies to PCSA, which is not fault-tolerant as well [37]. Based on the characteristics of AIS methods and IoT system properties, we contemplated the reviewed AIS solutions in IoT and investigated which properties are applied in each solution. Table 5 below shows the result of this analysis.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Property** & **Definition** \\ \hline Robust & The capability of a system to cope with issues during execution and continue operating despite data conditions \\ \hline Lightweight & The capability to operate and execute with minimal computational complexity \\ \hline Fault tolerance & The capability to function given a defect within hardware or software in the system, and adapt to the changing environment to build up a trustworthy network \\ \hline Adaptive & The capability to adapt and learn the system behavior over runtime \\ \hline Distributed & The capability to run and communicate within a distributed environment \\ \hline \hline \end{tabular}
\end{table}
Table 4: IoT Systems’ Properties.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & \(M\) & \(L\) & \(N_{S}\) & \(N_{R}\) \\ \hline NPS & 2 & 16 & 1000 & 60 \\ MNSA & 2 & 12 & 1000 & 170 \\ NShN & 2 & 7 & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Space and time complexity calculations.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis  
Building accurate mathematical models is expensive due to the steady developments in the complexity of control system structures and functions [15]. Yet, it is necessary to present their fault models in the model-based fault diagnosis procedure [16]. The development that broke this deadlock of structural analysis is the graphical representation of a system model that evaluates only the interactions between the variables and equations [17]. Structural analysis collects the required information for fault detection and fault-tolerant control by leveraging the structural model of the system. It offers superior computational efficiency compared to other diagnostic approaches, particularly those for large-scale complicated control systems. Due to its unique properties, structural analysis is indispensable for diagnosability [18].
## 3 Results and Discussions
### Accident Number Prediction
#### 3.2.1 Monthly Forecast
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Months** & & & & & & & & & & & \\
**in 2019** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**Raw Values** & 41 & 13 & 69 & 83 & 74 & 93 & 85 & 62 & 89 & 61 & 57 & 46 \\ \hline
**Predictive** & 43 & 27 & 70 & 78 & 80 & 86 & 79 & 78 & 77 & 66 & 58 & 56 \\ \hline
**Relative Errors \%** & 4.9 & 107.7 & 1.4 & \(-\)6.0 & 8.1 & \(-\)7.5 & \(-\)7.1 & 25.8 & \(-\)13.5 & 8.2 & 1.8 & 21.7 \\ \hline
**Months in 2019** & **1 + 2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**Corrected** & & & & & & & & & & & \\
**Predictive Values** & 56 & 66 & 82 & 79 & 85 & 84 & 76 & 76 & 66 & 59 & 62 \\ \hline
**Relative Errors \%** & 3.7 & \(-\)4.3 & \(-\)1.2 & 6.8 & \(-\)8.6 & \(-\)1.2 & 22.6 & \(-\)14.6 & 8.2 & 3.5 & 34.8 \\ \hline
**Months** & & & & & & & & & & & \\
**in 2020** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline
**Uncorrected** & & & & & & & & & & & \\
**Predictive Values** & 43 & 37 & 75 & 81 & 93 & 93 & 85 & 80 & 84 & 74 & 63 & 59 \\ \hline
**Corrected** & & & & & & & & & & & \\
**Predictive Values** & 62 & 77 & 84 & 90 & 99 & 83 & 89 & 79 & 73 & 69 & 68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monthly forecast results for accidents. focus on larger changes in monthly data and strengthen safety management during that period. In particular, although the amount of work in some months has been reduced because of holidays, and accidents have been reduced accordingly, it is often accompanied by the relaxation of safety awareness or the lack of supervision. This is also the key reason for the large variation and is consistent with the prediction results, which proves the validity of the prediction. Due to the impact of the COVID-19 pandemic, the true data reference value for 2020 is small, so no comparison between the predicted value and the true value is made.
# Incremental Canonical Correlation Analysis
## 4 Experiments
### Settings
** This section compares ICCA with several representative trackers, such as robust fragments-based tracking (Frag) [46], visual tracking decomposition (VTD) [47], incremental learning for robust visual tracking (IVT) [42], real time robust L1 tracker using accelerated proximal gradient approach (L1 APG) [48], visual tracking with online multiple instance learning (MIL) [49], correlation-based incremental visual tracking (TLD) [41] on a series of video sequences, including basketball, car4, cardark, carscale, deer, Dudek, faceocc1, fish, football, and 18 other video sequences to verify the effectiveness of ICCA. Of the trackers compared, VTD selects multiple image feature models and motion models at the same time, and tracks them respectively. Finally, the optimal feature and motion model are combined as the optimal target area in real time; IVT is a video tracking algorithm based on the incremental update appearance model and maintains a template in the whole tracking process, and dynamically updates the template through incremental PCA, so that it can effectively adapt to changes in appearance. L1 APG is based on sparse representation and uses accelerated proximal gradient (APG) to solve the L1 minimization problem. The MIL tracker treats the visual tracking problem as the multi-instance learning problem, in which the bag is composed of many instances instead of a single instance. TLD combines the traditional tracking algorithm and detection algorithm to solve the problems of deformation and occlusion in tracking. Frag is recognized by partial matching. Its target template is described by multiple fragments and blocks of the image. Blocks are arbitrary, not based on the target model. These trackers used here represent different types of trackers and are the most representative shallow learning trackers, which are widely used for comparison purposes in research reports. More importantly, such main tracking algorithms are integrated into a tracking framework [50], which provides the evaluation and figure codes for fair comparison. This paper also implements the ICCA algorithm on the basis of this tracking framework. The video sequences used here, which include different appearance changes of tracking objects in different scenes, can be downloaded from this tracking framework as well.
## 3 Results and Discussion
### SDX Transformation Products  
Samples taken at early time intervals of the photocatalytic experiments (within the first 10 min) were analysed by LC-HRMS to detect and identify both SDX and its transformation products. Previous studies have investigated some degradation pathways of SDX by photolytic and microbial treatment [53], but only two of the TPs produced by heterogeneous photocatalysis were included in their results. This indicates that different degradation mechanisms occur in a photocatalytic process leading to completely different intermediates.  
Since SDX is a pollutant that has not been extensively studied, the fragmentation mechanisms (Figure 4) resulting from the MS2 spectra were taken into serious consideration in the prediction, identification, and interpretation of the detected TPs.  
As shown in Figure 5, the 11 detected TPs can be formed by three distinct transformation mechanisms, bond cleavage, hydroxylation, and desulfonation. Among the identified TPs, TP156 and TP327 are the only previously reported ones [53], while the remaining ones were documented for the first time. The summary of TPs, their molecular formula, chemical structure, fragmentation pattern, and important MS data are summarized in the Supplementary Materials (Table S2). The proposed mechanisms for the formation of TPs are explained in detail as follows.  
\begin{table}
\begin{tabular}{c c c} \hline  & **k (min\({}^{-1}\))** & **R\({}^{2}\)** \\ \hline UW & 0.1972 & 0.9973 \\ TW & 0.0407 & 0.9969 \\ SW & 0.0757 & 0.9964 \\ \hline \end{tabular}
\end{table}
Table 2: Kinetic parameters of SDX degradation with heterogeneous photocatalysis, in UW, TW, and SW.  
Figure 4: Main fragmentation pathway for SDX.  
Figure 3: SDX-UV-A/TiO\({}_{2}\) kinetic analysis for UW, TW, and SW.
# In Situ Neutron Radiography Investigations of Hydrogen Related Processes in Zirconium Alloys
## 3 Examples of In Situ Neutron Radiography Investigations
### Hydrogen Diffusion at Temperatures between 900 and 1400 \({}^{\circ}\)C
#### 3.2.1 Materials and Measurements  
The measurements were performed using 20 mm long segments of as-received commercial cladding tubes made of Zircaloy-4 (outer diameter, 10.75 mm; wall thickness, 0.725 mm) and E110 (Zr-1% Nb; outer diameter, 9.62 mm; wall thickness, 0.75 mm). The in situ neutron radiography investigations were performed at the ICON facility (SINQ, PSI, Villigen, Switzerland) and at the ANTARES facility (FRM II, TU Munich, Munich, Germany). The measurement parameters are given in Table 1.  
Figure 8: Arrhenius plots of the temperature dependence of the determined diffusivity and temperature correlations given in the literature.  
The oxidation tests were performed in a flowing Ar/steam mixture (30 L/h Ar, 30 g/h steam) at temperatures between 900 and 1300 \({}^{\circ}\)C in 100 K steps using the INRRO furnace. The duration of one oxidation test was between 1 and 6 h depending on the test temperature.
## 2 Material and Methods
### Time Domain Inversion Method  
The surface dielectric permittivity of the soil is estimated via the time domain inversion of Green's function, with a focus on a time window containing the ground reflected first arrival [41]. The inverse Fourier transform is used to transform the measured and modeled frequency domain Green's functions into the time domain. The inversion procedure is defined as a classical, non-linear least-squares problem, and the objective function to be minimized is formulated as follows:  
\[\phi(\mathbf{p})=\left(\mathbf{g}_{\mathbf{xx}}^{\uparrow*}-\mathbf{g}_{ \mathbf{xx}}^{\uparrow}\right)^{T}\left(\mathbf{g}_{\mathbf{xx}}^{\uparrow*}- \mathbf{g}_{\mathbf{xx}}^{\uparrow}\right) \tag{5}\]  
where:  
\[\mathbf{g}_{\mathbf{xx}}^{\uparrow*}=\left.g_{xx}^{\uparrow*}(t)\right|_{t_{ min}}^{t_{max}}\quad\text{and}\quad\mathbf{g}_{\mathbf{xx}}^{\uparrow}=\left.g_{ xx}^{\uparrow}(t)\right|_{t_{min}}^{t_{max}} \tag{6}\]  
are the vectors representing the observed and simulated time domain windowed [PERSON]'s functions, respectively (see Figure 1); \(\mathbf{p}=[\varepsilon_{r},h]\) is the parameter vector to be inversely estimated; \(\varepsilon_{r}\) is the soil surface relative dielectric permittivity and \(h\) the distance between the soil surface and the antenna phase center. In the far-field region of the antenna, the spherical divergence of the radiated field is initiated from the virtual source point, which is the antenna source point. [PERSON] _et al._[35] proposed a procedure to estimate the phase center of an off-ground monostatic antenna. Furthermore, [PERSON] _et al._[43] reported that the frequency-dependent phase center position of the antenna is inherently accounted for in the antenna transfer functions of the GPR forward model. Although the magnetic permeability, electrical conductivity and soil layering can be taken into consideration in the inversion process, in this study, we assume them to have a negligible effect on the estimation of \(\varepsilon_{r}\), which is a commonly-adopted assumption [41].  
The misfit between the measured and modeled Green's function (5) is minimized by the Levenberg-Marquardt optimization algorithm. The surface reflection arrival time \(t_{i}\) (see Figure 1) is detected automatically and used to drive the initial guess for the \(h\). A fast evaluation of Green's function was used [40], and model inversion is achieved in less than one second, which permits real-time processing applications.
## 3 Mechanical Vibration Tests
### Vacuum Fastening  
The charts in Figure 22 present the results of the experiments of the transmittance function for the vacuum clamp at measurement point 2. The ordinate axis represents transmittance as a dimensionless quantity, and the abscissa axis represents frequency in Hz. The results of the transmissibility function are much better aligned with the standard compared to electromagnetic clamp showing an almost straight line. The vacuum clamps for 1 g, 2 g, 4 g, and 8 g amplitudes of acceleration meet the requirements of the IEC 60068-2-6 standard.