esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spaceborne Mounting Method for Fixing a Cubic Fabry-Perot Cavity in Ultra-Stable Lasers
## 2 Model for Mounting the FP Cavity  
To meet the requirements of the space-based strontium optical clock in the China Space Station, the frequency instability of the ultra-stable laser should be lower than 2 \(\times\) 10\({}^{-15}\)/s. Therefore, a homemade FP cavity with a cubic shape was first fabricated, as shown in Figure 1. The spacer of the FP cavity, which is made of ultralow-expansion (ULE) glass, has a size of 100 \(\times\) 100 \(\times\) 100 mm. To obtain minimum acceleration sensitivity, the spacer's eight vertices were all truncated to a depth of 18.3 mm toward the center of the spacer. Two fused silica mirrors with radius curvatures of infinity and 1000 mm were optically attached to the spacer, along with Two mirrors, both with a diameter of 25.4 mm and a thickness of 6 mm. Additionally, two ULE rings, with an outer diameter of 25.4 mm, an inner diameter of 10 mm, and a thickness of 6 mm, were optically attached to the mirrors to compensate for the thermal mismatch between the ULE spacer and the fused silica mirrors.  
As shown in Figure 2a, the FP cavity was first fixed into a titanium bracket with eight titanium screws supporting its eight vertices. Gaskets, which were made of PEEK, were installed between the screws and the spacer to isolate heat conduction. The contact faces between the gaskets and the spacer and between the gaskets and the screws were glued together. The squeeze forces of all of the screws were set to 100 N\(\cdot\)m. To ensure the reliability of the cavity, the screws and the titanium bracket were laser welded. Subsequently, the bracket was fixed to the inner heat shield with four titanium screws. As depicted in Figure 2b, three heat shields were used to avoid the influence of temperature fluctuation of environment. Gaskets made of PEEK were placed between the different heat shields to isolate heat conduction. Finally, heat shields were fixed to the vacuum house using titanium screws. Two ion pumps and a getter pump were used to maintain a high vacuum (<2 \(\times\) 10\({}^{-6}\) Pa) in the vacuum hose. A total of 12 vibration isolation pads were also attached to the vacuum hose to isolate high-frequency vibrations, as shown in Figure 2c.  
Figure 1: Homemade FP cavity with cubic shape.  
Figure 2: Model of mounting system for the FP cavity showing: (**a**) cavity with bracket, (**b**) cross-sectional view, and (**c**) overall system.
## 3 Wireless Sensor Network Model for the Internet of Things
### The Positioning Mechanism of the Internet of Things Based on a Sensor Network  
As shown in Figure 3, the positioning structure based on the Internet of Things has four levels. When wireless sensors are located in the Internet of Things system, canonical correlation analysis (CCA) can establish the mapping between two datasets and maximize the correlation between them, which is an effective modeling method. However, the CCA can only mine a linear relationship between the data, which is usually difficult to apply to the actual environment. The nonlinear kernel CCA (KCCA) is used to mine the nonlinear relationship between data to achieve the required nonlinear mapping, and the LE-KCCA sensor location algorithm has been proposed. However, the KCCA model adopts a unified global nonlinear mapping, without considering the local structural characteristics of the network [20]. On the other hand, the Internet of Things has a complex and topology-changeable form, which makes it extremely difficult to select the core parameters in the KCCA. The key to the effective construction of the KCCA model is the quality of the selection. The LPCCA method is recommended to mine the local topology data of signal space and physical space, and the LE-LPCCA sensor location model has been established on this basis. The node location accuracy can be greatly improved.  
Figure 3: Positioning hierarchy of the Internet of Things based on sensor networks.  
Figure 2: The architecture of a wireless sensor network based on the Internet of Things.
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 6 Conclusions and Future Works  
In this paper, we propose a new pseudolinear Kalman filtering method based on TMA with available state constraints by combining the PL-MMSE and state constraints. The mean square and estimation projection methods are enveloped with PL-MMSE to address the linear constrained state estimation problem. The linear approximation and second-order approximation methods are used to refine PL-MMSE estimates under nonlinear constraints. The merged algorithms can effectively solve the bearings-only TMA problem under Gaussian mixture noise. Simulations show that the constrained PL-MMSE has better performance than other filters. In particular, when the target velocity is small, the algorithms combined with the linear approximation method perform better than those combined with the second-order approximation method under the circular road. It turns out just the opposite when the velocity is large.  
Analyzing the statistical properties of the PL-MMSE with state constraints will be one topic of our future research. Applying the designed algorithms in actual engineering practice is the other direction we endeavor to study for the next step.
## 2 Theoretical Background
### The Forward Model
#### 2.2.2 Statistical Model  
Assuming that the error term \(\mathbf{e}_{d}\) follows a multivariate Gaussian distribution:  
\[P(\mathbf{m}|\mathbf{d})=exp\bigg{(}-\frac{1}{2}(\mathbf{d}-g(\mathbf{m}))^{T} \mathbf{\Sigma_{d}}^{-1}(\mathbf{d}-g(\mathbf{m}))\bigg{)}, \tag{5}\]  
where \(\mathbf{\Sigma_{d}}\) is the covariance matrix of the error and \(g(\mathbf{m})\) is the forward model. Introducing low-frequency information, such as well-log data, into the model can be accomplished by introducing it as an extra constraint through regularization techniques such as Tikhonov regularization. The resultant solution will demonstrate enhanced accuracy, specificity, and less ambiguity. When comparing a solution that includes low-frequency information to one that does not, we have the following equation:  
\[P(\mathbf{m})=\exp\bigg{(}-\frac{1}{2}(\mathbf{m}-\boldsymbol{\mu}_{m})^{T} \mathbf{\Sigma_{m}}^{-1}(\mathbf{m}-\boldsymbol{\mu}_{m})\bigg{)}, \tag{6}\]  
where \(\boldsymbol{\mu}_{m}\) represents a vector formed by combining the low-frequency models of the logarithmic elastic properties and \(\mathbf{\Sigma}_{m}\) is the covariance matrix that describes the spatial relationship between the properties and quantifies the difference in the prior distribution.
## 3 Distance-Aware Replica Adaptive Data Delivery Scheme
### Network Model  
Without loss of generality, we consider a network that consists of \(N\) sensors randomly deployed in a square area \(A\). Each sensor and gathering point has the same radius \(r\). And we assume the mobile sensor network has the following character:  
1. All sensors' movement accord with the Community-based Mobility model [15], which can be described as follows: in the community-based mobility model, the whole network area \(A\) consists of a gathering place (e.g., in reality it can be a market for people, or a feeding ground for animals), and some communities. Each node has one home community that it is more likely to visit than other places, and for each community there are a number of nodes that have it as home community. Each node selects a destination and moves to it at a selected speed \(v\) (\(v_{min}\), \(v_{max}\)), and then repeats the process. The destination is selected such that if a node is a home community, there is a high probability that it will go to the gathering place, of course it is also possible for the node to go to other places); and if it is away from home, it is very likely that the node will return home. The mobility of every mobile node follows the process described above and is mutually independent each other, as shown in Figure 1.
2. The sink node is located at the gathering place. When the sink node is immobile, we assume that its location is known to all sensor nodes.
3. Through either GPS (Global Positioning System) or other GPS-less technique such as those described in [16-18], each sensor node can compute its physical position.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Classification of Fracturing Fluids
#### 5.1.1 Water-Based Fracturing Fluids
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Base Fluid** & **Fluid Type** & **Composition** & **Used for** \\ \hline  & water–oil emulsion & water + oil & water-sensitive formations, \\ \cline{2-4}  & CO\({}_{2}\)–methanol & CO\({}_{2}\) + water + methanol & unconventional (low permeability) formations, but no direct usage for shale gas stimulation \\ \cline{2-4}  & others & - & direct usage for shale gas stimulation \\ \cline{2-4}  & & & Advantages \\ \cline{2-4} Emulsion & Depending on the type of components used to formulate the emulsion, these fluids can have potential advantages such as: water usage is very reduced or eliminated, fewer (or no) chemical additives are required, the increased productivity of the well, better rheological properties, and fluid compatibility with shale reservoirs. \\ \cline{2-4}  & Disadvantages & \\ \hline \hline \end{tabular}  
* There are potentially higher costs. The costs could potentially be higher when compared to water-based hydraulic fracturing, depending on the type of emulsion formulation.  
\end{table} 
Table 9: Emulsion-based fracturing fluids. fluids. The performance evaluation shows that delay time, shear and thermal stability can be enhanced by manufacturing the appropriate crosslinker chemical structure, thus reducing additional additives required and saving cost. They also concluded that zirconium crosslinker reactivity in a solution is influenced by the polymer type used [26]. **Slickwater fluid** contains mainly water and sand, so it represents the simplest and most common form of water-based fluid. Generally, slickwater treatments use low viscosity fluids pumped at high rates to generate narrow, complex fractures with low concentrations of added proppant (24-660 kg/m\({}^{3}\)). The most significant disadvantages of slickwater fracturing are proppant settling, high water consumption, and smaller fracture widths. **Viscoelastic surfactant gel fluid** (VES) is composed of water, electrolytes and surfactant. It is based on ionic structures and can be increased by mixing more electrolytes or surfactants. Surfactants are used with inorganic salts to produce ordered structures that make them more viscous, so this fluid has high zero-shear viscosity and, with less loading, it can transport a proppant efficiently. [PERSON] et al. (2020) showed that viscoelastic surfactants (VES) can be used as a proppant carrier in order to replace polymers which possess a high potential for formation damage. Although the limiting factor for the use of VES is their low thermal stability, to enhance rheological properties and extend thermal stability, the authors have designed a novel VES-based hydraulic fracturing fluid assisted by functionalized carbon nanotubes (CNTs). Through the addition of CNTs in concentrations between 0.04 and 0.2 wt% to a solution of 6 wt% VES, rheological properties were enhanced by 40% [28].
# A Review of the Development and Future Challenges of Case-Based Reasoning
## 2 Basic Framework and Concept of CBR  
The CBR framework [9] is shown in Figure 2. The main functions of each step are as follows:  
1. Case retrieval: One or more source cases most similar to the new case are retrieved from the case base.
2. Case re-use: Information and knowledge from similar cases are re-used to establish solutions adapted to new case.
3. Case revision: The proposed solution is evaluated, and the solution is adjusted if it does not meet the requirements.  
Figure 1: The changing trend of quantity of literature around CBR.  
(4) Case retention: The parts of this experience that may be useful for solving problems in the future are retained.  
From the problem-solving process of CBR, it can be seen that the initial description of the problem will be defined as a new case. Then, the similarity measurement between the new case and the source case in the case base is carried out to achieve case retrieval. Therefore, the case is the basis of CBR, and the representation form of the case plays an important role in CBR. The solutions for similar cases are then directly and appropriately adapted as solutions for new cases through the retrieval and re-use steps, and new solutions are evaluated to determine whether revision is needed. Finally, whether a new case and solution need to be retained for the solution of subsequent problems is considered. Several key links affecting the reasoning performance are case representation, case retrieval, case re-use, case retention, and case base maintenance.  
It can be seen from the recent collected papers from the International Conference on Case-Based Reasoning (ICCBR) that articles on similarity measurement, retrieval, adaptation, and other methods have increased, which indicates that research on CBR key technologies is still very important to the CBR community. According to the basic concept of CBR and the principle of problem solving, we retrieve some keywords in the theme, title and abstract of the literature based on CBR in the EI database, which includes 'case representation','similarity', 'case retrieval' or'retrieve', 'case adaptation' and 'case revision', and 'case base maintenance' or 'case maintenance'. Literature retrieval was carried out from these dimensions. The number of relevant literatures is shown in Figure 3. Figure 3 shows that case representation, similarity measurement, case retrieval, case adaptation, and case base maintenance have received sufficient attention. The following will review and summarize these dimensions.  
Figure 2: CBR cyclic structure.
## 5 Flight Test Validation  
Prior to the validation of the control law, a significant amount of hardware-in-the-loop (HIL) simulation is performed, which encompassed the processing of control law switching and the formulation of contingency plans for a spectrum of potential abnormal situations. The actual flight validation is executed after a thorough series of ground tests. The flight control computer operates at a frequency of 200 Hz, with the control parameters for the roll and yaw channels presented in Table 4.  
Real-time computation is utilized to allocate control according to the guidelines presented in Section 3.5.2. This involves considering prior aerodynamic data calculations and the aircraft's current dynamic pressure value. To achieve coordinated flight, it is necessary to set the lateral acceleration command to zero while issuing a roll angle command to correct the aircraft's lateral deviation from the intended flight path. The aircraft's lateral maneuver flight, as depicted in Figure 20a,b, is successfully executed within the altitude range of 8000-12,000 m and the Mach range of 0.8-1.25.  
During the flight, the roll angle tracking remains normal, displaying remarkable tracking performance characterized by a peak roll angular velocity of 40 deg/s. Additionally, the maximum absolute value of the lateral acceleration does not exceed 1 m/s2 throughout the entire flight, indicating a decoupled control for roll and yaw. Table 6 shows the root mean square (RMS) of the tracking error.  
The inner-loop rate controller is effective in tracking the roll and yaw rate, demonstrating stable performance with negligible variations in steady-state oscillation amplitude across a range of speed and altitude changes. However, it is worth mentioning that a low-frequency limit cycle oscillation exists in the steady-state, which is caused by the impact of the actuators' backlash.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tracking Parameter** & \(\mathbf{a_{y}}\) **(m/s2)** & \(\mathbf{r}\) **(deg/s)** & \(\mathbf{p}\) **(deg/s)** & \(\mathbf{\phi}\) **(deg/s)** \\ \hline \(RMS\) & 0.2 & 0.57 & 2.53 & 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMS of the tracking error for the flight test.  
Figure 19: Variation of stability margins at the breaking points of the actuators with time delay: (**a**) Aileron and (**b**) Rudder.  
Figure 20: Flight test results: (**a**) Roll channel response and (**b**) Yaw channel response.
# Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events
## 3 Materials and Methods
### Components of the System
#### 3.1.2 Syslog Server  
This component is responsible for receiving, filtering, transforming, storing, or redirecting log records generated by the managed network equipment. It is relevant to highlight that, to operate, the network equipment must activate a Syslog event notification service based on RFC 5424 [23], a widely accepted standard. Typically, the configuration process is straightforward. In our case, the Syslog server was installed in the DRACSC system. An open-source version of Syslog-ng has been chosen for this work, specifically version 4.1.1. This software can easily filter the event logs by utilizing attributes such as the severity level, IP address, or payload. This possibility of filtering optimizes log delivery, enhances profitability, and enables the adaptation of our solution to public blockchain performance. Eventually, the data received by the Syslog server is forwarded to the message broker via the advanced message queuing protocol (AMQP) for further processing in subsequent stages.
## 2 Materials and Methods
### Dataset, Aging Trajectories, and Synthetic Cycle Generation
To assess the robustness of our approach under more realistic conditions, we generated noisy versions of cycle 1 by adding Gaussian white noise with increasing standard deviations, ranging from 0.1% to 0.5% of the signal amplitude. This allows us to simulate the data acquisition noise that would eventually occur in real-world applications. Similarity metrics estimated under these conditions allowed us to evaluate the noise tolerance of the proposed approach. Our computational experimental setup mimics features of real-world second life battery behaviour, including a combination of stress factors, nonlinear degradation, and noise. This controlled validation framework provides proof of concept for our proposed approach and is illustrated in Figure 2.  
Figure 2: Controlled validation framework. In aging data generation, we can use experimental or synthetic data in various combinations of stress factors, as well as electrochemical models that incorporate aging. The knee point detection stage allows the identification of the end of life (EoL) and the battery discarding threshold or end of second life. This stage frames the second life window between the knees. Trajectories are then fitted using a kinetics-based approach. In the generation of the synthetic cycle stage, we build a cycle from known trajectory weights; this is the cycle from which the similarity method will recover the true weights. The similarity computation is executed by comparing the generated cycle against the reference trajectories. Normalized contributions are estimated by time series and tree-based similarity methods. In the noise robustness test, we add Gaussian noise to the cycle to evaluate the performance of the methods under realistic noise conditions. The output and diagnosis stage displays the contribution percentage of each aging trajectory, thereby validating our approach in a controlled setting.
## 3 Results and Discussion
### Surface Damage Characteristics
#### 3.2.1 Effects of Early Damage on Destruction Patterns  
The strain concentration belt during loading reflects the expansion of cracks, and the effects of different early damages on the macrodestruction pattern of the sample can be analyzed by observing the main strain and position cloud maps of the surface [29]. Figure 4 indicates that as the early damage increases, the destruction pattern on the sample surface exhibits a change in the trend of the pull-cut mixed destruction. For 0% (Figure 4a,b) and 20% early damage (Figure 4c,d), the sample surface damage is a multidirectional parallel-loading expansion, indicated by the distortion pattern shown. With increasing early disruption, 40% early damage (Figure 4e,f) extends in a certain perpendicular angle with the load direction, and the destruction pattern turns to cutting destruction, and 60% early damage (Figure 4g,h) turns into cutting mixed destruction.
# An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality
## 4 Experimental Results and Discussion
### Experiment for Evaluation of Proposed Method  
In this section, we describe the experiment to evaluate the proposed method. Figure 12 shows the procedure of applying the proposed adaptive UI based on the results of Section 4.2.  
The black dashed arrow in Figure 12 is an example of a case without the proposed method, and the interaction information of the MR-application user is directly input to the UI-display part. On the other hand, blue arrow is an example of the proposed-method case, and MR-user-interaction information is input to the user-satisfaction-prediction part. In the user-satisfaction-prediction part, the input value is used for feature-vector extraction, and the MLP model predicts user satisfaction with the extracted feature vector as an input. The predicted user satisfaction is input to the UI-position-correction part and compared with the threshold. If the predicted user satisfaction is higher than the threshold, no correction is performed. Otherwise, the UI position is corrected in the UI-position-correction part, and the result is used as an input for the adaptive-UI-display part.  
For comparison of the user satisfaction with and without the proposed method, seven subjects in their twenties to thirties were recruited [29]. Of the recruited subjects, six were male, and one was female. In addition, two of the subjects used MR devices more than ten times, four of the subjects used MR devices less than ten times, and one of the subjects had no experience of using MR devices at all. In the experiment, three selecting tasks and three moving tasks were given to each subject, with and without proposed method, respectively, so that one subject performed a total of 48 interactions. During the experiment, a user satisfaction (Likert 5 scale) input for interaction performance was requested from the subject. User satisfaction with and without the proposed method was measured as shown in Figures 13 and 14.