esa-sceva/satcom-chunk-collection huggingface dataset:
# A Redactable Blockchain Scheme Supporting Quantum-Resistance and Trapdoor Updates
## 3 Hierarchical Identity-Based Chameleon Hash with Revocable Subkey  
The hierarchical identity-based chameleon hash with revocable subkey (HIBCH-RS) model extends the IBCH model, introducing a hierarchical structure of user identities. In this section, we give a formal definition of the lattice-based HIBCH-RS scheme. Then, we analyze its correctness and security.  
At level \(\ell\), let \(\mathbf{id_{\ell}}=(d_{1},d_{2},\ldots,d_{\ell})\) represent the identity of a specific user while its parent node's identity is represented by \(\mathbf{id_{\ell-1}}=(d_{1},d_{2},\ldots,d_{\ell-1})\) or \(\mathbf{id_{\ell}}\). This ensures that the composition of the vector serves as a prefix to the IDs of its child nodes. To satisfy the massive user base in the blockchain, we can even assume that there is no upper bound on \(\ell\).  
In the traditional hierarchical identity encryption scheme, the parent node generates the trapdoor information of the child node and hence the problem of the parent node forging the identity of the child node occurs. This scheme is only applicable for mutual communication between nodes. In contrast, in a redactable blockchain, the trapdoor between nodes should be confidential and independent and at the same time constrained. Based on the above description, this paper realized this secure key delegation method by adding a slave key algorithm. When a user with delegation rights assigns a key, executing the _SlaveKey_ algorithm generates a slave key pair for the subordinate user. This slave key pair is then employed to generate the key pair for the subordinate user. This procedure ensures that the keys between the user and the sub-user remain confidential. Additionally, the user can utilize the slave key pair to recover, update, and disable the trapdoors of sub-user. In the event of key abuse, the system can execute the _Update_ algorithm to regenerate a new key.  
Eventually, the HIBCH-RS model consists of the following six algorithms: initial algorithm _Setup_, slave key algorithm _SlaveKey_, delegated algorithm _DelKey_, i.e., extracting the algorithm in HIBCH, key updating algorithm _Update_, chameleon hash algorithm _CH_, and collision-finding algorithm _Forge_.
# Crawling Magnetic Robot to Perform a Biopsy in Tubular Environments by Controlling a Magnetic Field
## 3 Actuating Principle of the CMR
### Crawling Motion under External Precessional Magnetic Field  
Figure 3 shows the external precessional magnetic field used to control the crawling motion of the CMR. The external precessional magnetic field can be expressed as follows:  
\[\mathbf{B_{e}}=B_{e}(cos(\delta)\mathbf{N}+sin\delta\cos(2\pi ft)\mathbf{U}+ \sin\delta\sin(2\pi ft)\mathbf{N}\times\mathbf{U}) \tag{3}\]  
where \(\mathbf{B}\)_e_, \(\delta\)_f_, \(\mathbf{N}\), and \(\mathbf{U}\) are the magnitude, precessional angle, rotational frequency of the external precessional magnetic field, a unit vector of the rotating axis, and a unit vector normal to \(\mathbf{N}\). The CMR performs a crawling motion using an external precessional magnetic field to reach the target location along the tubular environment. The magnetic torque of Equation (2) is generated in the CM by the external precessional magnetic field of Equation (3), and the repetitive oscillation of the crawling bodies is generated by this magnetic torque. In this process, the angle of the oscillation is determined by the precession angle (\(\delta\)). Due to this oscillation, as shown in Figure 4, the asymmetric frictional force between the flexible leg and the wall of the tubular environment is formed to generate the propulsive force of the CMR [20].  
As shown in Figure 4a, when the crawling body rotates counterclockwise, positive frictional force is generated in the upper flexible leg, and negative frictional force is generated in the lower flexible leg. The positive friction force is greater than the negative friction force, so that the CMR moves forward. Figure 4b shows that the crawling body rotates clockwise to the opposite direction. Then, the CMR moves forward by the same mechanism, as shown in Figure 4a. However, the propulsive force generated by the asymmetric friction force is degraded when the external oscillating magnetic field is not parallel to the driving plane. To constantly apply the oscillating external magnetic field even in any posture of the CMR, we applied an external precessional magnetic field as shown in Figure 3. Furthermore, the  
Figure 4: Principle of crawling motion with an external precessional magnetic field. (**a**) Counterclockwise rotation of the crawling body. (**b**) Clockwise rotation of the crawling body.  
Figure 3: External precessional magnetic field to control the CMR.  
CMR can change its direction of movement by the rotation of a uniform magnetic field with respect to the y-axis by 180\({}^{\circ}\). The design factors affecting the crawling motion are the magnitude, precessional angle, and rotational frequency of the external precessional magnetic field [21].
## 4 Conclusions  
This techno-economic analysis explored the potential of using solar photovoltaics in cooling remote underground mines. The proposed solar system provides electric energy to operate a refrigeration unit capable of generating 200 tonne of ice per day. The study finds that to generate a cooling effect of 8.3 MWh per day, 2.5 MW of solar photovoltaics should be installed in a place with an average sun-peak hour of 6. The Levelized Cost of Energy from solar PV for a project with a life of 20 years is found as 0.06 USD/kWh. The penetration of the solar system could save the carbon footprint by 3100 tonne per year in addition to 1180k litters of diesel. Two scenarios are developed in storing the  
Figure 4: Cash flow diagram for ITES.  
Figure 5: Cash flow diagram of BS.  
electric energy from the solar system: Ice Thermal Energy Storage System (ITES) and Battery Storage system (BS). Economic analysis suggests that capital cost of USD 11.2 million is required to establish the BS which is about USD 2.9 million greater than ITES. While both systems return enough to pay back the investment during their lifetime, a higher initial investment cost leads to a relatively slower payback period for BS. While ITES pays back in 10 years, BS takes 13 years to pay back the amount invested. In addition, ITES and BS generate cumulative savings of USD 7.8 million and USD 4.8 million respectively at the end of the project lifetime.  
In conclusion, this paper recommends the use of solar PV for mine cooling applications in places with average sun peak hours of 6 or greater. Besides, Ice Thermal Energy Storage System is recommended over Battery System due to lower capital investment cost, higher savings, and a smaller payback period.  
S.P.: Conceptualization, investigation, methodology, software, validation, writing--original draft, writing--review & editing. A.F.K.: conceptualization, validation, writing--review & editing. H.K.: conceptualization, validation, writing--review & editing. S.A.G.-M.: conceptualization, validation, resources, supervision, writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
## 2 Dynamic Modeling for Morphing Aircraft with SJA
### Modeling for Roll Moment for SJA
\begin{array}{rl}\text{d}M_{x}^{\text{jet}}&=C_{y\text{t}}qy\text{d}S-C_{y \text{t}}q\text{d}S+C_{y\text{,jet}}q\text{d}y\text{d}S-C_{y\text{,jet}}q \text{d}y\text{d}S\\ &=[k_{1}(\alpha-\alpha\alpha)+b-k_{1}(\alpha+\Delta\alpha)+b]q\text{v}q\text{d }y+C_{y\text{,jet}}q\text{d}y-C_{y\text{,jet}}q\text{v}\text{d}y\\ &=-2k_{1}\Delta\alpha q\text{v}\text{d}y+(C_{y\text{,jet}}^{\text{jet}}, \delta_{\text{jet}}-C_{y\text{,jet}}^{\text{jet}},\delta_{\text{jet}})q\text {v}\text{d}y\\ &=-2k_{1}\frac{\omega_{y}y}{U_{\text{s}}}q\text{v}\text{d}y+(C_{y\text{,jet} }^{\text{jet}}\delta_{\text{jet}}-C_{y\text{,jet}}^{\text{jet}},\delta_{\text {jet}})q\text{v}\text{d}y\end{array} , \tag{2}\]  
where \(C_{y}\) and \(C_{y\text{,jet}}\) are roll moment coefficients caused by the rolling motion and SJA, respectively. Here, the coordinate \(y\) along the wing span is as shown in Figure 1, and \(\delta_{\text{jet}}\) is the intermediate control input relative to SJA with \(C_{y\text{,jet}}^{\delta_{\text{jet}}}\) as coefficients. Subscripts \(l\) and \(r\) denote the left and right wings, respectively. \(q=\frac{1}{2}\rho_{\text{air}}U_{\infty}^{2}\) is the dynamic pressure, \(S\) is the surface area of the fixed wing, \(\omega_{x}\) is the roll angular velocity, and \(k_{1}=0.0258\) is a constant  
Figure 4: The effectiveness of synthetic jet-based control [28]. Figure 5: The influence of the blowing magnitude on the lift coefficient. when the AoA is around \(\alpha=18^{\circ}\). Integrating Equation (2) along the span of the fixed wing gives the roll moment  
\[ \begin{array}{ll}M_{x}^{\text{jet}}&=-2k_{1}\frac{\omega_{x}}{\Omega_{\text{ so}}}qc\int_{0}^{L}y^{2}\text{d}y+(C_{y,\text{jet}}^{\delta_{\text{jet}}}\delta_{ \text{jet}}-C_{y,\text{jet}}^{\delta_{\text{jet}}}\delta_{\text{jet}})qc\int_{ \frac{\delta}{\delta}L}^{\frac{\delta}{\delta}L}y\text{d}y\\ &=-\frac{2}{3}k_{1}\frac{\omega_{x}}{\Omega_{\text{so}}}qcL^{3}+\frac{3}{10}qcL ^{2}(C_{y,\text{jet}}^{\delta_{\text{jet}}}\delta_{\text{jet}}-C_{y,\text{jet} }^{\delta_{\text{jet}}}\delta_{\text{jet}})\end{array} . \tag{3}\]  
According to Figure 5 and the nonlinear least squares method, one can obtain the relationship between the roll moment coefficient \(C_{y,\text{jet}}\) and blowing magnitude \(\overline{U}_{\text{jet}}\),  
\[C_{y,\text{jet}}=-0.5027e^{-0.1766\overline{U}_{\text{jet}}}+0.5027, \tag{4}\]  
with \(C_{y,\text{jet}}=C_{y,\text{jet}}^{\delta_{\text{jet}}}\times\delta_{\text{ jet}}\in(0,0.5117]\), \(\overline{U}_{\text{jet}}\in(0,45]\)m/s, and \(\delta_{\text{jet}}\in(0,45]\)m/s. It follows that  
\[C_{y,\text{jet}}^{\delta_{\text{jet}}}=0.5117/45=0.0114. \tag{5}\]  
According to Equations (4) and (5), the relationship between \(\delta_{\text{jet}}\) and \(\overline{U}_{\text{jet}}\) can be obtained, i.e.,  
\[ \begin{array}{ll}\delta_{\text{jet}}=C_{\text{jet}}/C_{y,\text{jet}}^{ \delta_{\text{jet}}}\\ =-44.10\times\text{e}^{-0.1766\overline{U}_{\text{jet}}}+44.10\end{array} . \tag{6}\]
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Existing ML-Based approaches  
#### 8.2.1 Classical Machine Learning  
ML algorithms are typically categorized as supervised, unsupervised, semi-supervised, and reinforcement learning. Supervised ML algorithms learn the inputs and their corresponding outputs to perform the learning process. Supervised algorithms are subdivided into regression and classification; well-known models include Logistic Regression (LR), K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Decision Trees (DT), Gaussian Naive Bayes (NB), Artificial Neural Network (ANN), and Random Forests (RF). Unsupervised learning ML algorithms only use the inputs while learning, as the associated outputs are not provided; the learning process is performed by classifying the provided input data into groups called clusters, and any new input is classified within its corresponding group. Clustering and dimensionality reduction are the two main categories of unsupervised learning. Semi-supervised learning works by combining a small amount of labeled data with a large amount of unlabeled data. In reinforcement learning, neither the inputs nor their corresponding outputs are provided, and the relationship between the input and the output is learned by interaction with the surrounding environment and a reward scheme. The reward scheme depends on the learning algorithm's performance when achieving a certain task such that a reward is provided if it achieves high performance. A popular example of reinforcement learning is Q-learning.  
Several studies have discussed the efficiency of using classical ML techniques to tackle different cyberattack types [47; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71]. For instance, research has examined well-known network layer DoS attacks (blackhole, grayhole, flooding, and TDMA scheduling). Another study used ANN and SVM to target common MAC layer attacks (collision, unfairness, and exhaustion) [72]. Other research work has considered the efficiency of RF techniques for the detection of physical layer clone attack [73]. The authors of [74] used a reinforcement learning (RL)-based IDS to detect DoS, remote-to-local, user-to-root, and probe attacks.
# Analysis of Lightweight Structure Mesh Topology of Geodesic Domes
## 6 Comparative Analysis of the Obtained Results
### Nodal Displacements  
Based on calculations in the serviceability limit state, Figure 15 presents the values of nodal displacements in the analyzed domes. Vertical and horizontal displacements are caused by gravitational forces. The value of vertical displacements indicates lower stiffness along the vertical axis of the dome made according to the second method.
# Accurate Modeling of Conductor Rough Surfaces in Waveguide Devices
## 2 Theories
### Surface Impedance Boundary Condition with Effective Conductivity  
The electromagnetic wave attenuates exponentially when entering a conductor and the signal is concentrated only near the conductor surface within a few skin depths [19]. For metal-based waveguide components, if the air medium and conductor are both discretized with grids, the cell length will be very fine due to the rapidly varying signal inside the conductor. This will then exhaust a large amount of computer resources. Fortunately, a surface impedance boundary condition can be employed to handle the conductor boundary. For a conductor boundary in the \(x-z\) plane, as shown in Figure 1, the surface impedance \(Z_{\mathrm{s}}\) can be expressed as  
\[Z_{\mathrm{s}}=\ E_{z}/H_{x}=j\omega\mu_{0}/\gamma \tag{14}\]  
where \(\gamma\) is the complex propagation constant in the conductor with \(\gamma=(1+j)/\delta\), and \(\delta\) is the skin depth of the conductor. For conductors with a surface roughness, the equivalent frequency dependent effective conductivity \(\sigma_{eff}\) has been obtained. Then, the effective surface impedance is  
\[Z_{\mathrm{s}}=(1+j)/(\sigma_{eff}\delta_{eff}) \tag{15}\]  
where \(\delta_{eff}=1/\sqrt{\pi\mu_{0}\sigma_{eff}}\) is the effective skin depth for a conductor with surface roughness. In commercial field solvers, the effective surface impedance can be incorporated to characterize the conductor boundary. Hence, it is not necessary to mesh and solve inside the conductors. Since the effective conductivity is frequency dependent, a practical approach is to import a data file containing the frequency dependent values for effective conductivity from Section 2.3 to the material library in commercial field solvers such as HFSS(tm). Using the procedure outlined, the numerical simulation will be able to consider the effects of conductor surface roughness on the performance of waveguide components at high frequencies. In the following section, several examples for metal-based waveguide components will be demonstrated.
###### Abstract  
This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.  
moving force identification; damage detection; acceleration; gross vehicle weight; structural health monitoring; bridge
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 2 Materials and Methods
### Wind Power Prediction Model
#### 2.2.2 Graph Attention
The multi-head attention mechanism splices the outputs of multiple nodes into column vectors to obtain the final new node features, and the new node features are calculated using the Equation (10):  
\[{h_{i}}^{\prime}=||_{k=1}^{K}\sigma(\sum_{j=N_{i}}a_{ij}^{k}W^{k}h_{j}), \tag{10}\]  
where \(||\) denotes the vector splicing operation, \(a_{ij}^{k}\) represents the normalized value of the attention score calculated by the \(k\)-th attention mechanism, and \(W^{k}\) is the weight matrix of the linear transformation. Using the graph attention mechanism to deal with the pair of spatial feature information, combined with the multi-head attention mechanism, different attention score weights are calculated for the target node and its neighboring nodes [42]. It is beneficial to improve the model's representation of spatial dimensions and reduce the risk of overfitting.
## 2 Audio Data Acquisition  
### Recording Device  
The audio of the database was recorded using a noise-monitoring sensor developed by NOISMART, which includes a recording module (Figure 1). The noise-measuring sensor registers the ambient sound pressure level (SPL) and can also record audio clips. It is based on a commercial printed circuit board (PCB) and includes an omnidirectional pattern microphone with a frequency response of 20 Hz-20 kHz that replaces the standard micro-electro-mechanical (MEM) microphone on the PCB. The microphone is externally mounted.  
Each audio file was recorded in wav format with constant gain settings, mono configuration, and a sampling rate of 48 kHz and 16-bit coding.
## 3 Problem Formulation
### Assumptions and Threat Model
#### 3.2.1 Main Assumptions  
1. **Data privacy and locality:** The local dataset \(D_{i}\in\mathcal{DS}\) for every edge device \(ed_{i}\in\mathcal{ED}\) remains on the device. According to healthcare data standards and patient privacy, only model updates \(w_{i}\in\mathcal{W}\) are shared. Formally: \[\forall ed_{i}\in\mathcal{ED},\text{ share }w_{i}\text{ but not }DS_{i}\] (5)
2. **Device heterogeneity and intermittent connectivity:** The edge devices, \(\mathcal{ED}=\{ed_{1},ed_{2},\ldots,ed_{N}\}\), are heterogeneous in terms of processing capability and network reliability. We represent this heterogeneity by defining a time-varying subset of devices that are active, \(\mathcal{ED}_{t}\subseteq\mathcal{ED}\) in each round \(t\): \[\mathcal{ED}_{t}=\{ed_{i}\in\mathcal{ED}\mid\text{device }ed_{i}\text{ is active at time }t\}\] (6)
3. **Semi-honest participants:** While implementing the steps, participants have the opportunity to learn from the data they have received. We assume the reliability assessment function \(REL:\mathcal{ED}\times\mathbb{T}\rightarrow[0,1]\) represents this behavior over time: \[\forall ed_{i}\in\mathcal{ED},t\in\mathbb{T}:0<REL(e_{i},t)\leq 1\] (7)
# A Comparative Analysis of the TDCGAN Model for Data Balancing and Intrusion Detection
## 4 Results and Discussion
### Discussion
The significant improvements in the performance of all machine learning models--decision tree, random forest, multilayer perceptron (MLP), and Naive Bayes--after data balancing strongly indicate that the use of the TDCGAN model for data balancing has substantially contributed to enhancing classification accuracy, as well as metrics like precision, recall, and F1-score. The TDCGAN model effectively tackles class imbalance by generating synthetic instances of the minority class while maintaining the underlying data distribution. This augmentation of the dataset ensures a more balanced representation of both classes, thereby enabling the machine learning models to learn more effectively from the data and make fairer and more accurate predictions. The consistent improvements across all models and datasets further highlight the efficacy of the TDCGAN approach in mitigating class imbalance issues and enhancing the overall performance of the machine learning classifiers. When comparing the classification performance of each model across the four datasets, the results indicate overall improvement in performance for all models, with slightly superior results observed for the TDCGAN model. Conversely, the Naive Bayes model exhibited comparatively lower performance, particularly evident in the BOT-IOT dataset. The Naive Bayes model assumes conditional independence among features. However, the BOT-IOT dataset likely contains complex correlations between features that violate this assumption, leading to suboptimal performance of the Naive Bayes model. Specifically, in network intrusion detection tasks, certain features such as packet size, protocol type, and connection duration are often interdependent, and these correlations are critical for accurately distinguishing between normal and malicious traffic. The limitations of the Naive Bayes model in capturing these correlations may explain its lower performance on the BOT-IOT dataset. Furthermore, while TDCGAN is designed to handle high-class imbalance and generate synthetic samples, its effectiveness in preserving complex feature correlations during generation could also influence the overall performance when used in conjunction with models like Naive Bayes.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 3 Results
### Deep Learning-Based Detection of Water Adultration in Milk  
Figure 11 presents the efficiency levels achieved in detecting different water concentrations (20%, 40%, 60%, and 80%) in whole and lactose-free milk samples under three NaCl conditions (0, 2, and 4 wt%). In general, droplet pattern analysis using the deep learning-based model proves to be an effective tool for detecting milk adulteration. Although a slight decrease in efficiency is observed in certain specific cases compared to Figure 9, the deep learning-based model maintains precision values above 90% in most of the evaluated scenarios, exhibiting a more homogeneous and consistent performance across different NaCl concentrations and water content. This suggests that the method not only optimizes water detection in milk but also improves accuracy across a broader range of experimental conditions. Specifically, with 4% NaCl, the efficiency in detecting 20% added  
Figure 10: Accuracy of lacunarity-based water detection in milk. Accuracy of differentiation between 100% milk (whole lactose and lactose-free milk) and milk with added distinct water concentrations (0, 20, 40, 60, and 80%) at \(\phi_{N}=0\) wt% (**a**), 2 wt% (**b**), and \(\phi_{N}=4\) wt% (**c**).  
Figure 9: Lacunarity of milk deposits with water. Lacunarity \(\lambda\) as a function of natural logarithm of the normalized box size \(\ln(\epsilon)\) for whole and lactose-free milk at \(\phi_{N}=0\) wt% (**a**), 2 wt% (**b**), and 4 wt% (**c**) at different added water concentrations 0, 20, 40, 60, and 80%. The lacunarity of dried drop patterns decreases systematically with normalized box size and higher NaCl concentrations, which indicates structural sensitivity to changes in sample composition.  
water in lactose-free milk increases from 0.78 (lacunarity) to 0.99 (machine learning), while for 40% water, it improves from 0.83 to 0.96. The observed differences indicate that machine learning enables more precise classification in scenarios where lacunarity shows lower sensitivity. However, for higher water concentrations (60% and 80%), both approaches maintain high performance.