esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Control of Satellite Formations: A Survey
## 5 Conclusions  
This paper provides an overview of the results on the control of a formation flight of spacecraft, expanding and supplementing the review [1], related to 2004, with modern publications. Three types of formation architectures are considered: MIMO, in which the formation is treated as a single entity with multiple inputs and multiple outputs; LF, in which individual spacecraft controllers have linked hierarchically, and a virtual structure in which each spacecraft in formation is considered as a rigid body, embedded in a common virtual structure. The MIMO architecture differs from other ones in its optimality and stability. However, the use of all system states leads to the need to introduce high requirements on the information exchange, so such algorithms are usually unstable to local failures. The leader-follower architecture uses information about leaders only, which simplifies formation navigation. The problem of reliability is solved by increasing the number of leaders. However, this approach is inferior in optimality, and for ensuring high system stability, information requirements can reach those of the MIMO case. The cyclic architecture is a middle ground between LF and MIMO architectures. The information requirements are as high as those of MIMO case, and connections between some devices are allowed. However, round-robin algorithms can be completely decentralized. There is no coordinating agent and no instability due to single failures. In the process of research in this area, a number of directions have appeared in which, in particular, an increase in the stability and reliability of systems, reducing requirements for information restrictions, and increasing the spacecraft autonomy in formations. In the paper, examples of modern group spacecraft missions are also outlined.  
Conceptualization, B.A and A.M.P.; data curation, J.F. and I.K.; formal analysis, B.A. and A.M.P.; funding acquisition, A.M.P. ; investigation, J.F. and I.K.; methodology, A.M.P.; project administration, A.M.P.; software, J.F. and I.K.; supervision, A.M.P.; writing--original draft, B.A. and A.M.P. writing--review and editing, B.A. and A.M.P. All authors have read and agreed to the published version of the manuscript.  
The work was carried out in BSTU \"VOENMEH\" with financial support from the Ministry of Science and Higher Education of the Russian Federation (Government contract agreement No 075-03-2020-045/2 of 9 June 2020).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ATI & Along-Track Interferometry \\ AMDS & Autonomous Microsatellite Docking System \\ ATO & Along Track Orbit \\ CCN & Cooperative Circumnavigation \\ CMKF & Converted Measurement Kalman Filter \\ DGPS & Differential GPS \\ EKF & Extended Kalman Filter \\ EMFF & Electromagnetic Formation Flight \\ ESA & European Space Agency \\ FFG & Formation Flying Guidance \\ GCO & General Circular Orbit \\ GNSS & Global Navigation Satellite System \\ \end{tabular}
# Influence of Source Shape on Semi-Airborne Transient Electromagnetic Surveys
###### Abstract
[19] further explored source geometry effects on short-offset transient electromagnetic measurements. Their research established an inverse relationship between subsurface resistivity and the relative difference in electric field responses between straight and distorted source geometries. [PERSON] et al. [20] calculated frequency-domain electromagnetic responses for several typical source configurations, concluding that the source geometry exerts greater influence on frequency-domain electric fields compared to magnetic fields. [PERSON] et al. [21] investigated the impact of various acquisition parameters on three-dimensional (3D) inversion outcomes for SATEM data. Their results emphasized the importance of source length. Current research on source geometry effects has predominantly relied on 1D simplified models, with analyses focusing primarily on electromagnetic responses along with the 1D receiver profiles or inversion results derived therefrom. There remains a significant research gap regarding comprehensive 3D modeling approaches and the spatial distribution of electromagnetic fields in complex geological environments. Building upon the groundbreaking work of [PERSON] et al. [15] on grounded source configurations, this study establishes a representative 3D SATEM survey model to address this knowledge deficit. We implement an advanced 3D modeling algorithm [22] based on finite volume spatial discretization [23] and model order reduction [24] techniques to compute the comprehensive distribution of electric and magnetic fields generated by both linear and curvilinear source geometries. Through systematic comparative analysis, this paper provides a comprehensive assessment of source geometry influences on SATEM survey outcomes in realistic 3D contexts.
## 3 Experiments and Results
### RGB Benchmark  
In the last experiments, the spectral features with the features extracted from RGB-transformed radiance images are benchmarked. The RGB radiance data are transformed from the HS data cubes as described in Section 2.8. For each subject, the transformed data (Figure 6) are employed to detect the features during the different breathing phases and to estimate the 2D feature locations.  
The results of the RGB-transformed color images are illustrated in Table 3. The amount of matched inliers used for geometric verification was not sufficient to perform robust feature matching (number of inliers \(<2\)). An error larger than 1 mm is found in four cases. The algorithm failed when applied to subjects 7, 8, 9 and 12. This means that, in the geometric verification step, we could not find matched features. Apart from the noise introduced by the RGB transformation, it is easier to track skin features in the spectral images, which are enhanced when light interacts with deeper tissue layers. It is clear that HSI is much more powerful in identifying skin surface features than conventional RGB imaging.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Handcrafted Features (SURF)**} & \multicolumn{2}{c}{**Deep Local Features (DELF)**} \\ \cline{2-7}  & **PCA** & **VGBS** & **SSBS** & **PCA** & **VGBS** & **SSBS** \\ \hline Subject 1 & 0.40 & 0.39 & 0.25 & 0.38 & 0.25 & 0.22 \\ Subject 2 & 0.41 & 0.39 & 0.21 & 0.46 & 0.25 & 0.21 \\ Subject 3 & 0.44 & 0.38 & 0.20 & 0.32 & 0.24 & 0.24 \\ Subject 4 & 0.52 & 0.42 & 0.34 & 0.47 & 0.40 & 0.32 \\ Subject 5 & 0.60 & 0.41 & 0.28 & 0.46 & 0.29 & 0.35 \\ Subject 6 & 0.57 & 0.42 & 0.30 & 0.30 & 0.28 & 0.20 \\ Subject 7 & 0.46 & 0.41 & 0.28 & 0.35 & 0.25 & 0.15 \\ Subject 8 & 0.50 & 0.44 & 0.30 & 0.40 & 0.26 & 0.19 \\ Subject 9 & 0.44 & 0.43 & 0.34 & 0.40 & 0.27 & 0.23 \\ Subject 10 & 0.47 & 0.41 & 0.31 & 0.60 & 0.24 & 0.28 \\ Subject 11 & 0.41 & 0.44 & 0.23 & 0.37 & 0.24 & 0.25 \\ Subject 12 & 0.47 & 0.48 & 0.45 & 0.30 & 0.27 & 0.19 \\ Subject 13 & 0.34 & 0.44 & 0.34 & 0.46 & 0.27 & 0.31 \\ Subject 14 & 0.47 & 0.43 & 0.20 & 0.35 & 0.30 & 0.28 \\ Subject 15 & 0.34 & 0.45 & 0.20 & 0.37 & 0.28 & 0.20 \\ Subject 16 & 0.45 & 0.44 & 0.44 & 0.40 & 0.31 & 0.25 \\ Subject 17 & 0.44 & 0.38 & 0.41 & 0.31 & 0.32 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the mean error per subject (in mm) in 2D skin feature localization. Handcrafted local features are compared with deep local features when PCA (three principal components) and band selection algorithms are used (10 selected bands).  
Figure 9: The barplot presents the average number of matched inliers per subject when the band selection algorithms are applied. The number of matched inliers using SURF and DELF are indicated and visualized in the plots above.  
Figure 10 illustrates an example of feature matching when the rendered RGB image is used for subject no. 3.
## Case Studies
### Case Study 1: Edmodo
#### 5.2.2 Implementation of DDPLM  
Implementing the Data-Driven Personalized Learning Model (DDPLM) within the Edmodo platform involved several critical steps to integrate sophisticated data analysis capabilities into the existing educational framework. This process was pivotal in transforming Edmodo into a more adaptive learning environment that responds dynamically to the needs of individual students.  
The first step in the implementation involved establishing a comprehensive data integration system. This system was designed to aggregate and analyze data continuously collected from Edmodo's various user interactions, such as homework submissions, quiz results, and participation in discussion forums. Integrating this system with DDPLM's analytical engine allowed for the real-time processing of student data, enabling the identification of learning patterns and potential educational barriers.  
Once the data integration was in place, the next step focused on developing adaptive learning algorithms tailored to the language learning context. These algorithms utilized machine learning techniques to analyze performance trends and tailor educational content to students' individual learning styles and paces. For example, if a student struggled to understand certain grammatical structures, the system automatically adjusted the curriculum to provide additional targeted exercises and explanatory content to address these specific challenges.  
The implementation of DDPLM on Edmodo required the development of a user interface that could present personalized learning paths and feedback in an intuitive and accessible manner. The interface was enhanced to display personalized dashboards where students could view their progress, receive recommendations, and access learning resources that matched their needs.  
Another crucial aspect of DDPLM's implementation was ensuring that all adaptations and personalized content were pedagogically sound. This involved collaboration between educational technologists, language experts, and classroom teachers to ensure that the modifications made by the DDPLM algorithms were effective and conducive to language learning.  
The integration of DDPLM into Edmodo not only personalized the learning experience for students but also provided educators with deeper insights into student performance and engagement. Teachers could use these insights to further refine their teaching strategies and provide more targeted support where needed, thereby enhancing the overall educational impact of the platform.
## 5 LPWAN Application Requirements
### Coverage  
The range of operations requires both long range as well as short range communications. Typically, LPWAN needs to provide long range communication up to 10-40 km in rural/desert zones and 1-5 km in urban zones with +20 dB gain over the legacy cellular networks [6; 9]. For applications involved in monitoring and collecting data, coverage may need to be provided for indoor, hard to reach locations like underground locations and basements. Coverage needs to be consistent with expectations on adaptable data rates and managed data error rates. Use of the sub-GHz band helps most of the LPWANs to achieve robust and reliable communication with a lower power budget because the lower frequencies of the sub-GHz band have better propagation characteristics as compared with the 2.4 GHzband and higher bands. Additionally, the low modulation rates used for LPWAN put more energy for each bit, and hence increase the coverage. A lower modulation rate also helps the receivers in demodulating the signal correctly.
# Detecting and Responding to Concept Drift in Business Processes
## 3 Preliminaries
### Markov Decision Process (MDP)  
The MDP models sequential decisions in a stochastic environment that can be represented by the five-tuple \(\left\langle\mathbf{S_{D}},\mathbf{A_{D}},\mathbf{P_{D}},\mathbf{R_{D}}, \mathbf{\Pi_{D}}\right\rangle\) referring to the state space, action space, transition probability matrix, reward matrix and action selection policy.  
**Definition 5** (\(\mathbf{S_{D}}\)): _The state space is a set of states that is used to describe a system/problem._  
**Definition 6** (\(\mathbf{A_{D}}\)): _The action space is a collection of actions that is able to control the system. Considering a queueing system, actions can be (1) 'open a counter', (2)'maintain the current situation' and (3) 'close a counter', dependent on the current state, for example, the number of people in the queue._  
**Definition 7** (\(\mathbf{P_{D}}\)): _The transition probability from the current state \(i\) to the next state \(j\) under the impact of action \(a\) is denoted as \(p_{D}(i,a,j)\). \(\mathbf{P_{D}}=\{p_{D}(i,a,j),i,j\in\mathbf{S_{D}},a\in\mathbf{A_{D}}\}\)._  
\[p_{D}(i,a,j)=\frac{N(i,a,j)}{\sum_{v\in\mathbf{S_{D}}}N(i,a,v)}. \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case ID** & **Activity** & **Timestamp** \\ \hline case0 & New & 11 February 2015 16:30:00 \\ case0 & FIN & 11 February 2015 16:30:00 \\ case0 & Release & 11 February 2015 09:59:02 \\ case0 & Code OK & 12 February 2015 09:59:02 \\ case0 & Billed & 12 February 2015 10:21:01 \\ \hline case1 & New & 11 May 2015 12:07:00 \\ case1 & Change Diagnosis & 12 May 2015 12:30:40 \\ case1 & Release & 17 May 2015 12:54:09 \\ case1 & Code OK & 21 May 2015 15:22:14 \\ case1 & Billed & 12 June 2015 09:21:29 \\... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 1: An example event log.  
\(N(i,a,j)\) is the number of transitions from state \(i\) to \(j\) under the influence of action \(a\)._  
Definition 8 (\(R_{D}\)): The immediate reward (positive or negative) \(r_{D}(i,a)\) is obtained when the Markov chain stays at state \(i\) and makes action \(a\). \(\mathbf{R_{D}}=\{r_{D}(i,a),i\in\mathbf{S_{D}},a\in\mathbf{A_{D}}\}\).  
Definition 9 (\(\mathbf{\tau_{D}}\)): A policy at state \(i\), denoted as \(\pi_{D}(i)\), is a mapping from \(\mathbf{S_{D}}\) to \(\mathbf{A_{D}}\). That is, selecting the action that has the highest long-term reward.
# Fault Classification and Localization Scheme for Power Distribution Network
## 2 Wavelet Transforms and Feature Extraction
### Kurtosis  
The kurtosis \((K)\) of a signal is the measure of the signal's uniformity. The kurtosis \((K)\) of the signal is expressed mathematically as:  
\[K(t_{1},t_{2})=\int_{t_{1}}^{t_{2}}\biggl{(}\frac{x(t)-\overline{x}}{\sigma} \biggr{)}^{4} \tag{6}\]  
Under normal conditions, \(K=3\), and \(K>3\) under fault conditions.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 4 APT Analysis Model
### Attack Chain Model  
The attack chain model [81; 82; 83; 84; 85; 86] is based on the attacker's analysis of the attack chain. The attack process is broken down into multiple stages, taking into account the different methods and techniques that an attacker may use. By modeling and analyzing the attack chain, potential attack paths and vulnerabilities can be identified, providing guidance for defense and response.  
Different attack chain models have different divisions and naming conventions, but they typically include the following common steps.  
* Reconnaissance: The attacker gathers information about the target, such as IP addresses, operating systems, and vulnerabilities.
* Weaponization: The attacker creates malicious payloads, such as Trojans, backdoors, and ransomware, and combines them with transmission carriers such as emails, web pages, and documents.
* Delivery: The attacker sends the malicious payload to the target or lures the target to visit a malicious website.
* Exploitation: The malicious payload exploits vulnerabilities in the target system or user actions to execute malicious code.
* Installation: Malicious code installs backdoors or other malware on the target system for persistent control.
* Command and control: The malware communicates with the attacker's remote server, receiving instructions, or sending data.
* Execution: The malware executes the final goal according to the attacker's instructions, such as stealing data, destroying the system, or spreading ransomware.  
Different attack chain models may divide or merge these steps or add additional steps to adapt to different scenarios and requirements. Table 4 shows some common attack chain models and their developments.  
The attack chain model is a useful way to analyze APT attacks, which can help us better understand the attackers' behavior and intent and detect and respond to attack activities in a timely manner. By using the attack chain model, we can gain insight into the attackers' attack strategy, improve overall security, identify threats, discover vulnerabilities, and take preventive measures in time.
## 3 Results and Discussion  
### Potentially Toxic Elements Analysis  
Table 2 reports the potentially toxic elements content (mg kg\({}^{-1}\) dry weight, d.w.) for the analyzed pyroclastic products, as obtained through ICP-MS analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \cline{2-13}  & & & & & & & & & & & & \\ \cline{2-13}  & & & & & & & & & & & & \\ \hline C\({}_{\text{As}}\) & 1.73 & 1.74 & 1.72 & 2.09 & 1.85 & 2.11 & 1.98 & 2.32 & 1.97 & 2.14 & 20 \\ C\({}_{\text{Cd}}\) & 0.10 & 0.15 & 0.10 & 0.11 & 0.10 & 0.13 & 0.10 & 0.11 & 0.12 & 0.12 & 2 \\ C\({}_{\text{Cu}}\) & 76.91 & 76.83 & 76.04 & 74.02 & 82.61 & 87.23 & 82.45 & 85.23 & 85.08 & 92.91 & 120 \\ C\({}_{\text{Hg}}\) & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.04 & 0.04 & 1 \\ C\({}_{\text{Ni}}\) & 16.61 & 14.95 & 14.32 & 15.06 & 15.91 & 17.45 & 17.81 & 21.95 & 18.82 & 18.32 & 120 \\ C\({}_{\text{Pb}}\) & 4.93 & 5.02 & 4.67 & 5.17 & 5.18 & 5.55 & 5.23 & 5.16 & 5.02 & 5.46 & 100 \\ C\({}_{\text{Sb}}\) & 0.08 & 0.08 & 0.08 & 0.15 & 0.09 & 0.12 & 0.09 & 0.11 & 0.35 & 0.10 & 10 \\ C\({}_{\text{T}}\) & 0.11 & 0.09 & 0.08 & 0.07 & 0.08 & 0.09 & 0.09 & 0.08 & 0.09 & 0.09 & 1 \\ C\({}_{\text{Zn}}\) & 62.15 & 58.45 & 56.56 & 58.41 & 62.32 & 69.32 & 63.39 & 80.23 & 68.65 & 69.05 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Potentially toxic elements content (mg kg\({}^{-1}\) dry weight, d.w.), as obtained through ICP-MS analysis for the analyzed samples.  
With noting is that the obtained concentrations are lower than the threshold limits [37], also reported in Table 2, for all detected metals. Therefore, they cannot be considered as pollutants, and thus do not compromise the welfare of the environment. For this reason, they do not constitute a risk to human health.
## 1 Introduction  
To ensure the safety and reliability of conventional and nuclear power plants, it is essential to adopt a proactive approach to preventing component failures. The failure of a single component can result in the shutdown of the entire plant. Securing the structural integrity of a piping system leans on two key elements: ensuring a sufficient pipe wall thickness and installing appropriate supports. A pipe elbow is one of the most common pipeline fittings. It contains higher flexibility compared to a straight pipe and can be used to reduce reaction forces and moments within the piping system. Pipe elbows are subjected to various damage mechanisms, manifested on internal or external surfaces, intrados, crowns, or extrados, and exposed to fluids and the external environment. In a long-term operation, piping as pressure equipment may contain crack-like defects [1; 2; 3; 4; 5; 6; 7].  
Applying fracture mechanics principles is crucial when determining a structural integrity assessment of flawed pipelines. In this context, the limit load (limit pressure) and the stress intensity factor are essential parameters while evaluating structural integrity. To prevent pipe failure, the internal pressure (\(P\)) must remain below the plastic limit pressure (\(P_{\text{L}}\)), indicated as (\(P<P_{\text{L}}\)). In scenarios involving flawed pipes, the plastic limit load corresponds to the local limit load, representing a loading level at which gross plasticity occurs in the un-cracked ligament. This may have implications for ligament failure. Numerous studies have investigated the plastic limit pressure on straight pipes featuring circumferential, axial, and part-through or through-wall cracks that appear on thin- and thick-walled geometries [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Various solutions rely on experimental data, while others are examined using finite element analysis. However, these solutions cannot be implemented on pipe elbows due to the impact of the elbow curvature, leading to an increase in hoop stress [19].  
In the late 1970s, [PERSON] (1970) published experimental data on pipe elbows under in-plane bending, which were relatively limited. [PERSON] and [PERSON]'s publications from the late 1980s rely on [PERSON]'s experimental data ([PERSON] and [PERSON], 1980; 1981). [PERSON] et al. (1981) verified that in-plane opening bending, contributing to crack circumferential opening, poses a higher risk to the elbow than closing bending. In later studies, numerous publications focused research on pipe elbows with cracks, subjecting them to in-plane opening or closing bending loads (K
## 3 Evaluation of Ultimate Flexural Limit State
### Iterative Evaluation Procedure  
The tendon forces (\(T_{t,c}\), \(T_{t,t}\)) in Equation (2) are first assumed. For example, \(T_{t,c}\) and \(T_{t,t}\) might be assumed at the yielding limit by considering the yielding strain \(\epsilon_{y}\). Then, by substituting \(\sigma_{B}\) for \(\sigma_{c}\), the initial value of the neutral axis depth (\(x_{n}\)) and the coefficient \(\beta\) can be calculated by Equations (2) and (8). Based on the obtained \(x_{n}\) and \(\beta\), and by substituting \(\epsilon_{cu}\) (=0.003) for \(\epsilon_{n}\), the concrete axial shortening \(\Delta_{c,ex}\) can be obtained from Equation (7). The tendon strains (\(\epsilon_{t,c}\), \(\epsilon_{t,t}\)) thus can be obtained by Equations (5) and (6), and the corresponding tendon forces \(T_{t,c}\) and \(T_{t,t}\) are calculated by Equation (1). Then the initially assumed values of \(T_{t,c}\) and \(T_{t,t}\) are replaced by the calculated ones, and this is repeated until the calculated values are sufficiently close to the values in the last step.  
After the calculated values of \(T_{t,c}\) and \(T_{t,t}\) converge, accurate \(x_{n}\) and \(\beta\) can be obtained by Equations (2) and (8), respectively. They are then substituted into Equation (7) to calculate the accurate \(\Delta_{c,ex}\). Subsequently, the ultimate bending moment \(M_{b,u}\) and shear force \(P_{b,u}\) can be obtained by Equations (9) and (10), and the beam deflection angle \(R_{b,u}\) can be calculated by Equations (11), (12) and (20).  
Similar iterative methods have been adopted by the authors in [21; 22], while other design methods based on iterative processes were also proposed in previous studies (e.g., [57; 58]), but not aimed at the PC connections with debonded partial tendons.  
Figure 8: Load–deflection angle of RC member.