esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CO Device Implementation
### Electronic Circuit
#### 2.4.1 Signal Adaptation Circuit (Wheatstone Bridge Calibration)  
In the calibration stage, the chemical sensor is installed in atmospheres with no presence of CO, the sensor terminals are connected to the Wheatstone bridge according to Figure 2 and the Wheatstone bridge is calibrated by varying \(R_{x}\) until the following condition is met:  
\[V_{AB}=V_{A}-V_{B}=0 \tag{1}\]  
and then the next condition \(V_{A}=V_{B}\) is true.  
Based on the Equation (1) and Figure 2, we will have  
\[V_{A}=V_{B}\rightarrow\frac{V_{CA}}{V_{AD}}=\frac{V_{CB}}{V_{BD}} \tag{2}\]  
Observing Figure 2, \(V_{CA}\), \(V_{AD}\), \(V_{CB}\), and \(V_{BD}\) are defined as  
\[\frac{V_{CA}}{V_{AD}}=\frac{I_{1}R_{1}}{I_{s}R_{s}}\;and\;\frac{V_{CB}}{V_{BD}} =\frac{I_{2}R_{2}}{I_{x}R_{x}} \tag{3}\]  
Figure 2: The conditions required for the calibration of a Wheatstone bridge.  
Since the Wheatstone bridge is calibrated, then \(I_{1}=I_{s}\) and \(I_{2}=I_{x}\) are true and as a consequence, Equation (2) can express through  
\[V_{A}=V_{B}\rightarrow\frac{R_{1}}{R_{s}}=\frac{R_{2}}{R_{x}} \tag{4}\]  
From Equation (4), the resistance \(R_{x}\) can be determined by  
\[R_{x}=R_{s}\frac{R_{2}}{R_{1}} \tag{5}\]  
When Equation (5) is satisfied, then the Whetstone bridge is completely calibrated.
# Evolution towards Smart and Software-Defined Internet of Things
## 3 Smart or Cognitive IoT Architecture
### Application Layer  
The application plan is in charge of providing the user with application-specific services. It defines several applications for the IoT, including smart homes, smart cities, and smart health. There are billions of IoT devices capable of sensing, processing, actuating, and communicating with the internet. They are communicating with each other through cellular networks or a gateway in the personal wireless local area network. Device-to-device (D2D) communication in an IoT environment is carried out through multiple application layer protocols and application programming interfaces. The communication protocols at the application layer are application-dependent and rely on multiple choices, such as security goals, data integrity, data confidentiality, and data availability.
## 3 Methods
### Statistical Model Setup and Accuracy Metrics  
Regardless of the considered statistical algorithm, the backscattering coefficients were first estimated by considering the following explanatory variables: the incidence angle for the SAR images; the topsoil moisture; the fractions of clay, silt and sand for the texture; and the standard deviation of roughness heights and the autocorrelation length collected in the directions parallel and perpendicular to the tillage orientation for the surface roughness. Regardless of the considered SAR signal configuration, the dataset was randomly divided into a training set and a test set, each containing half of the data. The statistical algorithms were calibrated on the training set and validated on the independent test set, repeating the procedure ten times. The average values of the coefficient of correlation, root-mean-square error and bias were finally derived from the comparison between the observed and estimated values of the backscatter coefficients (Equations (2)-(4)).  
\[r=\frac{\text{COV}(o,p)}{\sigma_{o}\sigma_{p}}, \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(p_{i}-o_{i})^{2}}{n}}, \tag{3}\]  
\[Bias=\frac{1}{n}\sum_{i=1}^{n}(p_{i}-o_{i}), \tag{4}\]  
where \(n\) is the number of observed values, \(o\) is the observed value, \(p\) is the predicted value, COV is the covariance, and \(\sigma_{o}\) and \(\sigma_{p}\) are the standard deviations of observed and predicted data, respectively.  
The values of the multiple linear regression parameters are summarized in Appendix A for each studied satellite configuration, and the trained random forest models are available by request from the authors or can be downloaded by following the link provided in the section \"Supplementary Materials\".
## 1 Introduction
According to [PERSON] [46], in the case of a diffractive sail, a membrane covered by EOCPs, which are switchable between two different functioning states, is potentially able to invert the direction of the in-plane component of the thrust vector while maintaining its radial component unchanged. In fact, assuming that all EOCPs change their state at the same time, the result is that the in-plane component of the thrust vector essentially flips with respect to the Sun-spacecraft line, as illustrated in Figure 4 (note that in this paper, the two states will be denoted as 1 and 2). In addition, [PERSON] [46] points out that a suitable selection of a subset of EOCPs that change their state allows the in-plane component of the thrust vector to be varied (nearly) continuously between two equal but opposite values. Starting from the mathematical model discussed in the recent literature [53], and assuming a Sun-facing configuration to allow the spacecraft attitude control to be passively maintained, this paper presents a simplified thrust model of a diffractive sail with EOCPs that simultaneously change their state. Using a suitable initial sail orientation in which the grating momentum unit vector (and so the thrust vector) belongs to the plane of the  
Figure 4: Diffractive sail equipped with EOCPs: conceptual sketch of the thrust vector variation as a function of EOCPs state. Figure 3: Conceptual scheme of JAXA’s Interplanetary Kite-craft Accelerated by Radiation Of the Sun (IKAROS) spacecraft, showing active LCD panels to execute attitude control maneuvers. parking orbit, the proposed thrust model can be employed for the preliminary trajectory design of a diffractive solar sail. The transfer performance is calculated in a heliocentric mission in which a spacecraft moves between two circular and coplanar orbits. As usual, the transfer trajectory is determined by minimizing the total flight time. This paper is organized as follows. Section 2 introduces the mathematical model used to evaluate the optimal performance of the diffractive sail-based spacecraft. It also describes the simplified thrust vector model and its integration in the two-dimensional equations of motion, which are used to optimize the transfer trajectory with an indirect approach. The optimization model is then numerically simulated, and the results are discussed in Section 3, which analyzes potential interplanetary transfers toward Mars, Venus and Jupiter. Finally, Section 4 summarizes the main results of the work.
# Privacy Framework for the Development of IoT-Based Systems
## 5 Proposed Methodological Approach: Planning, Requirements, and Design (PRD)
### PRD Phases
#### 5.2.2 Phase 2--Requirements Elicitation  
The requirements elicitation phase in the PRD approach, integrated within TpM-Pro, highlights the essential role of accurately defining actionable technical and operational privacy guidelines. As a cornerstone of software development, requirement elicitation enables engineers to capture user needs and translate them into the system's foundational design. However, this process is often complex and iterative, as not all requirements can be fully identified during the initial stages of development. To effectively address this challenge, the approach leverages Agile methodologies, which promote iterative adjustments, accommodate continuous client feedback, and refine requirements throughout the development cycle. This adaptability ensures that evolving user needs, privacy concerns, and technical constraints are continuously addressed, resulting in a more robust and user-centered system design.  
In this phase, tools such as user stories, scenarios, and use cases are utilized to gather and analyze both functional and non-functional requirements, with a particular focus on privacy considerations. To enhance the effectiveness of this process, the approach integrates extracted privacy guidelines and domain-specific methods, providing developers with structured guidance to address privacy needs throughout the requirements elicitation process, as outlined in Table 3. These tools facilitate a comprehensive understanding of user stories, enabling a clear and precise articulation of both system and software requirements, while ensuring alignment with user needs and project objectives.  
Aligned with TpM-Pro, the requirements elicitation phase translates strategic privacy goals into detailed technical specifications that guide subsequent design and implementation stages. During the \"Considering the Business\" phase of TpM-Pro, broad privacy objectives are transformed into initial requirements, ensuring their alignment with the overall project goals. In the \"Gathering the Requirements\" phase of TpM-Pro, these initial objectives are further refined into detailed user stories, enriched with privacy considerations to capture both technical and operational needs, while maintaining adaptability to evolving challenges.  
By the time the project reaches the implementation phase, these refined requirements serve as the blueprint for system configuration, privacy feature deployment, and iterative validation. This integrated and domain-specific approach ensures that privacy is systematically embedded into the IoT project lifecycle, effectively addressing dynamic user needs while simultaneously mitigating potential risks and strengthening regulatory compliance.
## 5 Conclusions  
The electrical conductivity within anisotropic materials exhibits remarkable differences when flowing in various directions. The disparity in electrical conductivity between the longitudinal and transverse directions causes eddy currents to encounter distinct resistances when traversing the material, thereby forming complex and variable trajectories. This reflects the intricate physical structure within anisotropic materials. Low-frequency signals can penetrate the inspected material more deeply to detect deep-seated defects, while high-frequency signals are more applicable to the detection of surface or minor defects. An increase in the number of coil turns enhances the magnetic field strength and elevates the sensitivity. The smaller the inner diameter of the coil, the smaller the gradient of the coil impedance variation with the lift-off height. The larger the outer diameter of the coil, the greater the gradient of the impedance variation with the lift-off height. A larger outer diameter can expand the detection range. The electrical conductivity of the braided carbon fiber model surpasses that of the unidirectional carbon fiber model. Under the same excitation frequency gradient, the measured real part of the coil impedance of the braided carbon fiber plate is considerably larger than that of the strip-shaped carbon fiber model.  
Future research will validate the simulation model and design method proposed in this paper through experiments. The relationship between impedance changes obtained from simulations and actual detection results will be verified. CFRP samples with different structures, such as unidirectional and braided carbon fibers, will be compared in experiments to further validate the predicted current density distribution and electromagnetic response of the materials. Additionally, the effects of skin effect and proximity effect on the detection results will be considered. Probes with different frequencies and numbers of turns will be selected in the experiments to evaluate their impact on sensitivity and signal strength. These experimental validations will help optimize probe design, improve detection accuracy, and provide more reliable practical evidence for nondestructive testing technologies of CFRP materials.  
[PERSON] and [PERSON] conceived the research idea, [PERSON] wrote the paper; [PERSON] and [PERSON] processed the data; and [PERSON] reviewed the paper and made suggestions for improvement. All authors have read and agreed to the published version of the manuscript.  
The paper is supported by the Shanghai Engineering Research Center of Marine Renewable Energy (Grant No. 19 DZ2254800) and Shanghai Higher Education Young Teachers Training Program (Grant No. A1-2007-23-000215).  
**Institutional Review Board Statement:** Not applicable.  
Figure 13: The relationship between the real part of the coil impedance and the modulus of the current density.
## 5 Fault Location in Direct Current (DC) Grid Having Cable and Overhead Line Segments
### Identification of Dominant Natural Frequency
#### 6.3.1 Mode Extraction  
Bipolar HVdc system is considered as in [83]. One transmission tower is erected for two lines of bipolar HVdc system in order to make it economical. Thus, current coupling exists. There is a need to decouple the current into an independent modulus by mode transformation matrix [83]. The telegraph equation of transmission line is:  
\[\left\{\begin{array}{l}\frac{\partial u}{\partial x}=-\frac{\partial i}{ \partial t}-Ri\\ \frac{\partial i}{\partial x}=-\frac{\partial i}{\partial t}-Gi\end{array}\right. \tag{44}\]  
where \(u=\left[\begin{array}{l}u_{R+}\\ u_{R-}\end{array}\right]\), \(i=\left[\begin{array}{l}i_{R+}\\ i_{R-}\end{array}\right]\), \(L=\left[\begin{array}{l}L_{s}\\ L_{m}\end{array}\right]\), \(R=\left[\begin{array}{l}R_{s}\\ R_{m}\end{array}\right]\), \(C=\left[\begin{array}{l}C_{s}\\ -C_{m}\end{array}\right]\), \(C_{s}=\begin{array}{l}C_{o}+C_{m}\), \(C_{s}=\begin{array}{l}C_{o}+C_{m}\).  
\(L_{s}\) and \(L_{m}\) are self and mutual inductances of line, respectively. \(R_{s}\) and \(R_{m}\) are self and mutual resistances, respectively. \(G_{o}\) and \(G_{m}\) are line to earth and line-line conductances, respectively. \(C_{o}\) and \(C_{m}\) are line to earth and line-line capacitance of the TL, respectively. By applying [PERSON] phase mode transformation matrix, mode form of voltage and current are:  
\[\frac{\partial u_{m}}{\partial x}=-S^{-1}LS\frac{\partial i_{m}}{\partial t}-S ^{-1}RSi_{m} \tag{45}\]  
where \(i_{m}=\left[\begin{array}{l}i_{0}\\ i_{1}\end{array}\right]=S^{-1}\left[\begin{array}{l}i_{R+}\\ i_{R-}\end{array}\right]=\frac{\sqrt{2}}{2}\left[\begin{array}{l}1\\ 1\end{array}\right]\)\(\left[\begin{array}{l}i_{R+}\\ i_{R-}\end{array}\right]\)\(i_{0}\) and \(i_{1}\) are zero mode and one mode currents of dc transmission line, respectively. \(i_{R+}\) and \(i_{R-}\) are the positive and negative line currents, respectively. Since the transient energy of one mode current is higher than the transient energy of zero mode current, therefore, information of one mode current is utilized for the extraction of dominant component of natural frequency. Algorithm of protection based on natural frequency is presented in Figure 8.
## 5 Experimental Results
### Expanded Scenarios for Validation
#### 5.3.1 Scenario 1 (Existing): Static User Location  
* **User Location**: Tunisia City (latitude: 40.7128, longitude: \(-\)74.0560). **Stores**: **Store AA** (Ariana): Rating 4.5, Distance 3.92 km. **Store BB** (Ben Arous): Rating 4.0, Distance 5.0 km. **Store CC** (Manouba): Rating 3.5, Distance 7.88 km.
* **Results:** Store AA is recommended due to its high rating and proximity, achieving the highest score of **3.20**.
# DTN and Non-DTN Routing Protocols for Inter-CubeSat Communications: A comprehensive survey
## 4 DTN Routing Protocols in Disjointed Satellite Networks
### DTN Flooding-Based Techniques  
The well-known flooding-based Epidemic routing is a baseline routing algorithm in the DTN world. The bundles in Epidemic routing are mainly distributed through the network in the same manner as the spread of a viral epidemic [46], which can result in a high probability of messages reaching their destinations in the shortest period if the storage and bandwidth of the nodes are unlimited. However, epidemic routing does lead to wasteful usage of resources in terms of power, bandwidth, and buffers at each node. Besides, message replicas may continue exhausting network resources as a consequence of the lack of negative acknowledgements, even after a copy has already been delivered to its destination. The simple epidemic scheme was enhanced by introducing some bundle dropping policies [50]. In this scheme, the buffer size of each node has been restricted to only be able to store a limited number of bundles based on the assigned dropping policy. Four types of dropping policies were investigated, including Drop-Random (DRA); Drop-Least-Recently-Received (DLR); Drop-Oldest (DOA); and, Drop-Least-Encountered (DLE). The results of their simulation illustrate that DLE and DOA produce the best performance. The DLE policy focuses on dropping bundles according to information concerning node mobility and location, whereas the DOA policy focuses on dropping bundles according to a message delivered duration where the oldest bundles have the highest probability of already having been delivered to their planned bundle endpoints. A (p,q)-Epidemic routing is proposed as another modified form of the simple Epidemic [52]. The (p,q)-Epidemic protocol is designed for networks with random mobility and unpredictable future meetings that make it unreasonable for satellite networks. However, a VACCINE recovery technique that is used to delete needless replicas from the network can be adapted for satellite networks. New schemes were proposed to control flooding in sparse mobile DTNs [51]. These probabilistic schemes are based on Time-To-Live (TTL), in addition to an expiration time that is associated with every bundle. Moreover, after a message is delivered to its destination, a healing process is established to clear all unnecessary message replicas from the network. This healing process is based on a death certificate concept in the context of replicated database maintenance, which was originally introduced by [75].
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 5 Experimental Results
### Feature Selection Using Wrapper Method  
#### 5.3.1 Recursive Feature Elimination  
RFE is a feature selection method supported by linear or tree-based models that assigns weights, such as feature importance and coefficients, to the features. Hence, among the four classification algorithms used in this study, DT and RF were applied. Table 6 shows the multiclass classification evaluation results of each algorithm using a subset of 5 or 10 features selected through RFE, as well as using all features. The results showed that high scores were recorded for the DT algorithm using five features and the RF algorithm using 10 features. When using the five features selected by the RFE method, the accuracy and F1-score of the DT algorithm increased relative to those of the base model. In contrast, the accuracy and F1-score of the RF algorithm were reduced by 1.07% and 0.82%, respectively, when using the 10 features selected by the RFE method. This is a relatively minor decrease compared with the ratio of the decrease in the number of features (approximately 82.82%), indicating the usefulness of the features selected by the RFE method.
# Modeling and Control of an Articulated Multibody Aircraft
## 2 Development of the Multibody Equations of Motion
### Kinematics  
The velocities of each of the bodies will be defined in this section using the rotational time derivative operator and displacement vector between points. The velocities are ultimately expressed in the preferred \(B\) frame. Given that the displacement vector of the central body relative to the inertial frame is \(s_{\mu|_{z}}\), the translational velocity of the central body regarding the inertial frame is expressed as \(D^{I}s_{\mu|_{I}}\). The translational velocity of the central body in the body frame can then be obtained using the inertial to body transformation matrix  
\[[D^{B}s_{\mu|}]^{B}=[R]^{BI}\ [D^{I}s_{\mu|_{I}}]^{I} \tag{9}\]  
where the term \([D^{B}s_{\mu|}]^{B}=[u,v,w]\), and \(u,v,w\) are the body velocity components.  
Using the abdominal joint \(j\) as the reference point, the displacement of the joint with respect to the inertial frame can be expressed as a summation of displacement vectors as in  
\[s_{\mu|_{I}}=s_{\mu|_{B}}+s_{\mu|_{I}} \tag{10}\]  
Hence, the relative translational velocity of the joint regarding the inertial frame is expressed as  
\[D^{I}s_{\mu|_{I}}=D^{I}s_{\mu|_{B}}+D^{I}s_{\mu|_{I}} \tag{11}\]Since \(D^{I}s_{j_{\mu}}\) is not readily available for measurement, a simpler expression is established by applying a Euler transformation of \(D^{I}s_{j_{\mu}}\). Equation (11) becomes  
\[D^{I}s_{j_{\mu}}=D^{B}s_{j_{\mu}}+\Omega^{BI}s_{j_{\mu}}+D^{I}s_{j_{\mu}} \tag{12}\]  
Noting that \(D^{B}s_{j_{\mu}}=0\), since \(s_{j_{\mu}}\) is associated with frame \(B\), simplifies Equation (12) to  
\[[D^{I}s_{j_{1}}]^{I}=\Omega^{BI}s_{j_{\mu}}+D^{I}s_{j_{\mu}} \tag{13}\]  
Similar to Equation (9), the abdominal joint relative velocity can be obtained using the inertial to body transformation matrix.  
Rotational kinematic equations are mathematical representations of the relationships between the rate of change of kinematic parameters and the angular velocities. Let the Euler angle derivatives for the central body and the abdomen be \((\dot{\phi},\dot{\theta},\dot{\psi})\) and \((\dot{\phi}_{T},\dot{\theta}_{T},\dot{\psi}_{T})\), respectively. The angular velocity of the central body relative to the inertial frame, expressed in the \(B\) frame, is introduced as \([\omega^{BI}]^{B}=[p,q,r]\), while that of the abdomen relative to the body frame and expressed in the \(T\) frame is introduced as \([\omega^{TB}]^{T}=[p_{{}_{T}},q_{{}_{T}},r_{{}_{T}}]\). The relationships between the rate of change of kinematic parameters and the angular velocities follow the sequence of rotations and are calculated according to  
\[[\omega^{BI}]^{B}=\begin{bmatrix}\dot{\phi}\\ 0\\ 0\end{bmatrix}+R_{\phi|x}\begin{bmatrix}0\\ \dot{\theta}\\ 0\\ 0\end{bmatrix}+R_{\phi|x}R_{\phi|y}\begin{bmatrix}0\\ 0\\ \dot{\psi}\end{bmatrix} \tag{14}\]  
\[[\omega^{TB}]^{T}=\begin{bmatrix}\dot{\phi}_{T}\\ 0\\ 0\end{bmatrix}+R_{\phi_{T|x}}\begin{bmatrix}0\\ \dot{\theta}_{T}\\ 0\\ 0\end{bmatrix}+R_{\phi_{T|x}}R_{\theta_{T|y}}\begin{bmatrix}0\\ 0\\ \dot{\psi}_{T}\end{bmatrix} \tag{15}\]
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 1 Introduction
The YOLOv8n-CBAM-EfficientNetV2 model proposed in this paper is based on 'You Only Look Once' (YOLO) [16; 17; 18; 19; 20], incorporating the submodules from the EfficientNetV2 [21] model and the Convolutional Block Attention Module (CBAM) [22]. It achieves the high-precision and real-time recognition of aircraft wake vortices.