esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise Source Identification in Training Facilities and Gyms
## 5 Classification of Noise Sources  
The acoustic measurements conducted during common exercises chosen based on our own observations are summarized in Figure 9 in each case as an average of three measured occurrences. Acoustic measurements were conducted during barbell drops as in previous present measurements and during a pylemetric box jump of an 80 kg trainee, with the height of the plyo box set to 61 cm. All measurements were carried out at a 2 m distance from the source. It can be assumed that the trainees preforming the exercises may be exposed to higher levels.  
Figure 8: Acceleration generated at the floor during a barrel drop of a 90 kg deadlift barbell drop.  
Figure 7: Acceleration generated at the floor during a barrel drop of 60 kg from the overhead locked position.  
The highest LAeq and LApeak were registered during the dropping of the 60 kg barbell onto the jerk boxes. The second highest levels were observed in the case of the 90 kg deadlift top position barbell drop. The data presented in Figure 9 should be used as an indicator of potential exercises causing increased noise emissions. From the perspective of group workouts, the deadlift should be considered as potentially noisy. Jerk boxes are used mostly by one trainee only, rather than in a group workout session. Deadlifts, box jumps, and snatches or clean and jerks are commonly implemented in group workouts. The legal limit values applicable for this case study are LAeq D < 55 dB(A) A-weighted Leq sound level during the day time and LAeq N < 45 dB(A) A-weighted Leq sound level during the night time.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### Recommendations for Possible PPPM Improvement  
Despite the increasing interest in privacy-preserving process mining, several obstacles persist. A fundamental challenge involves reconciling privacy guarantees with model utility and the preservation of behavioral fidelity. Anonymization frequently alters control-flow semantics, thereby diminishing the fitness and precision of the models derived from the anonymized data. Moreover, current models encounter difficulties in handling high-dimensional event attributes, extended trace variants, and infrequent behaviors, which are susceptible to either suppression or excessive generalization. Computational efficiency represents another significant challenge, especially in the context of intricate differential privacy mechanisms. Potential improvements in PPPM models may involve the exploration of adaptive anonymization techniques that explicitly account for process behavior, the integration of semantic-aware utility preservation mechanisms, and the implementation of privacy-by-design strategies throughout the process discovery phase. Furthermore, the development of scalable and interpretable differential privacy algorithms specifically designed for process data structures is essential to improving practical applicability while preserving formal privacy guarantees.  
Although differential privacy mechanisms are theoretically robust, they face challenges in maintaining behavioral patterns due to the introduction of noise in event frequencies, trace variants, and model parameters. This frequently results in diminished fitness, precision, and generalization in the process models derived from the discovered data. Furthermore, real-world event logs containing extended or distinctive traces are particularly susceptible to overgeneralization and a diminished level of interpretability when subjected to differential privacy constraints. Runtime scalability presents a significant challenge, as the intricacy of differential privacy budget allocation and noise calibration increases proportionally with log size and attribute dimensionality. To mitigate these challenges, potential advancements may center on the development of behavior-aware differential privacy algorithms and hybrid models that integrate both syntactic and semantic privacy principles. Additionally, conducting benchmarking on real-world logs encompassing diverse domains is crucial for assessing the robustness, fairness, and generalization of the proposed models within practical applications.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 3 Barrier Function-Based Multivariable Super-Twisting Guidance Law Design
### Stability Analysis of the Proposed Guidance Law
\right] \tag{35}\]  
Since \(\left|\ \dot{\Delta}\right|\leq\mathbf{M}\), the following expression holds:  
\[\frac{\left|\ \dot{\Delta}\right|}{L_{b}^{2}}\leq\frac{\mathbf{M}}{L_{b}^{2}}=\frac{ \mathbf{M}}{b^{2}(1+2C_{0}\|\mathbf{z}_{1}\|)}\leq\frac{\mathbf{M}}{b^{2}} \tag{36}\]  
Consider following Lyapunov function candidate:  
\[\mathbf{V}=\frac{\ln(1+2C_{0}\|\mathbf{z}_{1}\|)}{2C_{0}}\circ\left(1-\frac{1}{4} \frac{z_{1}}{\|\mathbf{z}_{1}\|}\circ\varpi(\mathbf{z}_{2})\right)+\frac{\mathbf{z}_{2}^{ 2}}{2} \tag{37}\]  
where \(\varpi(\mathbf{z}_{2})\) is a saturation function given by:  
\[\varpi(\mathbf{z}_{2})=\frac{\mathbf{z}_{2}}{\|\mathbf{z}_{2}\|}\text{min}(\|\mathbf{z}_{2}\|,1) \tag{38}\]  
The time derivate of (37) can be formulated as:  
\[\mathbf{V}^{\prime}=\frac{\mathbf{z}_{1}\circ\mathbf{z}_{1}^{{}^{\prime}}}{1+2C_{0}\|\mathbf{ z}_{1}\|}^{2}+\mathbf{z}_{2}\circ\mathbf{z}^{{}^{\prime}}-\frac{\mathbf{z}_{1}\circ\varpi( \mathbf{z}_{2})}{4(1+2C_{0}\|\mathbf{z}_{1}\|)}-\frac{\varpi^{\prime}(\mathbf{z}_{2})\circ \mathbf{z}_{1}\circ\ln(1+2C_{0}\|\mathbf{z}_{1}\|)}{8C_{0}\|\mathbf{z}_{1}\|} \tag{39}\]  
\[\mathbf{V}^{\prime}\leq-\frac{1}{2}\|\mathbf{z}_{1}\|^{1/2}-\|\mathbf{z}_{2}\|\circ\left( \frac{1}{4}-\frac{\mathbf{M}}{b^{2}(1+2C_{0}\|\mathbf{z}_{1}\|)}\right)+\varpi^{\prime }(\mathbf{z}_{2})\circ\frac{\mathbf{M}}{8b^{2}C_{0}}\circ\frac{\ln(1+2C_{0}\|\mathbf{z}_{ 1}\|)}{1+2C_{0}\|\mathbf{z}_{1}\|} \tag{40}\]For the Lyapunov function (37), the following inequality exists:  
\[\frac{3\ln(1+2C_{0}\|\mathbf{z_{1}}\|)}{8C_{0}}+\frac{\mathbf{z_{2}^{2}}}{2}\leq\mathbf{V} \leq\frac{5\ln(1+2C_{0}\|\mathbf{z_{1}}\|)}{8C_{0}}+\frac{\mathbf{z_{2}^{2}}}{2} \tag{41}\]  
Consequently, \(\mathbf{V}\) is positive-definite. Then the theorem will be proved if the following expression is true:  
\[\text{limsup}_{t\rightarrow\infty}V\leq V^{*} \tag{42}\]  
It can be concluded that (42) holds true when \(V^{*}\) satisfies:  
\[V^{*}=\frac{16M^{2}}{b^{4}}+\frac{5\ln(1+8(4M/b^{2}-1/8))}{4C_{0}} \tag{43}\]  
This completes the proof.
# Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series
## 5 Experiments
### Prediction Performance  
Figures 4 and 5 show the meaning of precision, recall, and F1-measure for four kinds of states on the four datasets' test sets. Commonly, the form \((r,c)\), \(r,c\in[0,9]\), indicates the indices of row and column metrics, respectively. For example, \((3,8)\) means that the row metric is Levenshtein and that the column metric is Jaccard. Second, with increasing \(k\), the precisions of the state, tri-state, IFS-based tri-state, and RFS-based state are decreased. Third, the precision of state is better than that of the others. Moreover, the precision of tri-state is slightly better than that of the IFS- and RFS-based ones. The precisions of the IFS- and RFS-based tri-states are almost consistent. This is because three kinds of tri-states provide two additional symbols for each variable. However, tri-state may be incomplete while the IFS- and RFS-based ones are complete. Therefore, the precision of the tri-state is between the state and the IFS- and RFS-based tri-states. This can be observed in Figure 4b,c.  
In Figure 5, the recalls of the three kinds of tri-states are better than that of the state. Moreover, the recalls of the IFS- and RFS-based tri-states are the highest. Similarly, the recall of the tri-state is also between that of the state and the IFS- and RFS-based tri-states. Interestingly, the recall of the IFS- and RFS-based tri-states on the Stocks (Dataset II) can reach 95% and 93%, respectively.  
Figure 4: The precisions of four state prediction strategies. (**a**) Dataset I. (**b**) Dataset II. (**c**) Dataset III. (**d**) Dataset IV.  
Compared with the state, the three kinds of tri-states have better recall but worse precision. Although the improvement of IFS- and RFS-based tri-states is not significant compared to the tri-state, more information can be provided. In most cases, \(k=1\) is the first choice for precision and recall.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 3 Smart Grid Cyber-Physical Attacks
### Denial of Service (DoS)  
Data absence attacks in smart grid control systems are mainly referring to Denial of Service (DoS) attacks that comprise the data availability. A DoS attack refers to a type of network overflow attack, which will jam the traffic and prevent legitimate users or services from using the channel to communicate. In [13], the author summarised the DoS attack differences between smart grids and traditional networks: first, DoS attacks in smart grids are not targeting only timely and reliable access assets but also the control system, computing process, communication channels, or the power itself. Second, DoS attacks could affect state estimation. Combined with other attacks, DoS attacks can cause cyber-physical damage.  
The DoS attack targets in the smart grid can be mainly classified into three categories: 1. smart grid communication protocols, 2. smart gird physical devices, and 3. smart grid applications [13]. There are several pieces of research regarding DoS attacks against smart grid communication protocols, such as IEC 61850 (substation automation) [14], ANSI C12.22/IEEE 1703 (AMI) [15], IEEE C37.118 (PMU) [16], and IEC 60870 (SCADA) [17]. Also, DoS attacks will prevent users from changing the routing protocol to mitigate the risks [18].  
DoS attacks' main focus is on smart grid devices, like smart meters, generators, PMUs, IEDs, RTUs, and PLCs; DoS and Distributed Denial of Service (DDoS) attacks can start from one specific or several distributed sources by transmitting malformed packages to exhaust the target's network bandwidth and processing capacity [19]. One of the famous DoS attack cases happened in 2015 against a Ukrainian substation, resulting in a power outage [20].  
The data sources that smart grid applications use, like Advanced Metering Infrastructure (AMI), Distribution Management System (DMS), and Wide Area Monitoring, Protection, and Control Systems (WAMPAC), are some of the DoS attack targets as well [13].  
To sum up, the DoS attack's main purpose in smart grids is to prevent the smart grid control system from receiving the control signals, or PMUs from receiving the data from sensors, which will lead to cascading blackouts and loss of availability to many infrastructures in the smart grid sectors [13].
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 3 Xperience Similarity-Based Q-Learning (XSQ-Learning)
### Theoretical Analysis  
The update rule maintains two key properties:  
1. **Controlled Update Magnitude**:  
\[|Q_{t+1}(s,a)-Q_{t}(s,a)|\leq\beta_{\text{clip}}\cdot|\text{TD-error}| \tag{8}\]
2. **Convergence Guarantee**: When \(\delta=1\), XSQ-Learning reduces to standard Q-learning, inheriting its convergence properties. For \(\delta<1\), convergence is maintained, provided that  
\[\sum_{k=1}^{\infty}\beta_{k}=\infty\quad\text{and}\quad\sum_{k=1}^{\infty} \beta_{k}^{2}<\infty \tag{9}\]  
These conditions ensure that (1) updates continue indefinitely (allowing convergence to optimal values), while (2) update sizes decrease sufficiently to stabilize the learning process. The similarity weighting naturally satisfies these conditions through the \(\alpha\) decay.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Dataset  
In this work, we used the public SAR image ship detection dataset from the Key Laboratory of Digital Earth Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences [31], which was labeled by [PERSON] et al, part of the sample images is shown in Figure 7. It contained 102 Gaofen-3 images and 108 Sentinel-1 images, in which there were 59,535 ships in 43,819 ship chips. The images varied in terms of polarization, resolution, incidence angle, imaging mode, and background. The dataset was created for vari  
\begin{table}
\begin{tabular}{c c} \hline
**Index** & **Value** \\ \hline Orbit & 400–500 km \\ Center frequency & 5.4 GHz \\ Bandwidth & 60–300 MHz \\ Incidence angle range & 20\({}^{\circ}\)–35\({}^{\circ}\) \\ Polarization & VV, HH \\ Resolution and swath & 3 m @ 20 km, 20 m @ 100 km, 10 m @ 50 km, 1 m @ 5 km * 5 km \\ Positional accuracy & \(\leq\)200 m \\ Absolute radiometric accuracy & 2.0 dB \\ Antenna size & 4.0 m (A) \(\times\) 0.64 m (R) \\ Average power & \(\leq\)250 W \\ Total mass & 185 kg \\ \hline \end{tabular}
\end{table}
Table 1: HISEA-1 mission parameters.  
Figure 6: On-board intelligent processing platform processing flow.  
ous SAR image applications and the size of each image in the dataset was 256 \(\times\) 256 pixels. In this dataset, each image target in the dataset has a ground truth boundary and label, and each target corresponds to an Extensible Markup Language (XML) file, which is consistent with the PASCAL VOC data set. The dataset was divided into three parts: training set, validation set, and test set at a 7:2:1 ratio.
# A Road Truncation-Based Location Privacy-Preserving Method against Side-Weight Inference Attack
## 4 Problem Formulation and Proposed Scheme
### Proposed Scheme
#### 4.3.1 Workflow  
The workflow of the proposed scheme is shown in Figure 5 and the detailed scheme is shown as follows. When a user launches a query request, it firstly communicates with the base station to get the background knowledge of road network topology and user distribution. Then the user runs the Road Truncation(R-T) Algorithm 1 to generate an anonymous set. As our proposed scheme achieves not only \(k\)-anonymity and \(l\)-diversity, but also resist the side-weight inference attack, it may take a long time to generate an anonymous set. In order to ensure the quality of service(QoS), we define a tolerant time \(\delta_{l}\). If the anonymous set is not successfully generated within the tolerant time \(\delta_{t}\), the constraint Equation (6d) is neglected and the anonymous set is generated by algorithm GA [30], which meets the demand of \(k\)-anonymity and \(l\)-diversity. The tolerant time \(\delta_{l}\) can be described as \(t_{n}-t_{0}<\delta_{t}\), where \(t_{n}\) is the current time and \(t_{0}\) is the starting time of the algorithm.  
```
0:\(e_{i},k_{0},l_{0},\lambda_{0}\)
0: anonymous set \(S_{i}\)
1: add \(e_{i}\) to candidate set \(C_{i}\)
2:while\(N(C_{i})<\alpha\)do
3:if\(e_{j}\) is adjacent to \(C_{i}\)then
4: add \(e_{j}\) to candidate set \(C_{i}\)
5:endif
6:endwhile
7: compute the average edge weight \(\widetilde{w}\)
8: sort all edges in \(C_{i}\) by the weight
9: add \(e_{i}\) to candidate set \(S_{i}\)
10:while\(k<k_{0}||<l_{0}||\)do
11:if\(e_{h}(e_{h}\in C_{i})\) is adjacent to \(S_{i}\)then
12: add \(e_{h}\) to anonymous set \(S_{i}\)
13:endif
14:endwhile
15:while\(k<k_{0}||<l_{0}||\lambda>\lambda_{0}\)do
16:if\(e_{k}\) is the bigger \(\beta\) edges in \(C_{i}\)then
17: truncate \(e_{k}\)
18: refresh \(C_{i}\)
19:endif
20:endwhile
```  
**Algorithm 1** The R-T algorithm.
## 1 Introduction  
An inductor is a fundamental component for electronic devices, which is used for energy storage and filtering, but it dominates in size and loss. To achieve its miniaturization and aid lowering the cost of the power supply on chip, monolithic integration of power inductors is essential [1; 2; 3; 4]. Lots of design methodologies and fabrication technologies have been proposed for potential monolithic integration.  
The increase in switching frequencies of power circuits up to megahertz can significantly reduce the inductor's size [5; 6]. A tiny 0.47 uH inductor has been used in a commercial 8 MHz PWM synchronous Buck regulator [5]. A 250 MHz buck regulator has been implemented with a 12 nH wire-bond power inductor in [6]. However, such a high frequency will cause high power switching and result in poor efficiency (e.g., 71% [6]). Furthermore, the power inductor with low inductance easily introduces a high ripple current. Various magnetic core materials have been adopted to enhance the inductance density for monolithic integration of power inductors [7; 8; 9]. A 3D in-silicon magnetic core toroidal inductor is presented in [7], in which the magnetic powder-based core is embedded into an air-core inductor using a casting method. A 3D solenoid inductor that is embedded in the substrate and integrated an iron core is reported in [8]. However, the introduction of magnetic material not only causes a core saturation issue but also increases the design and fabrication complexity.  
In this paper, a fully integrated air-core power inductor is proposed, which is developed for a high-density silicon-interposer based on 2.5D and 3D integrated circuits, and a compact analytical model has been derived to capture the total inductance of the proposed inductor, where the screw pitch and cross-sectional sharp are considered. Comparted to traditional circular coil method [10; 11] and the model developed for coils with a circular section-area [12], the proposed model can offer a better accuracy and is more suitable for the inductance prediction of power inductance fabricated with 2D planar technology.
## 5 Discussion  
Once the parametric model was obtained and used to obtain the 1D model (resistivity-depth) of all TDEM survey points, three profiles were elaborated to visualize the structure and thickness variation of the saline-evaporitic Barbastro Formation (Figure 2): Profile A, with NW-SE orientation, and Profiles B and C, with NW-SE orientation. Profile A's path follows the supposed maximum slope and strike of the Barbastro-Balaguer Anticline in the survey area, deduced from the geological cartography (Figure 2), while Profiles B and C were placed perpendicular to Profile A. All TDEMs reached a minimum penetration over 3000 m, enough to reach the hanging wall and footwall of the Basbastro Formation and reach the Mesozoic local basement. TDEM is very sensitive to the conductive layers [45; 46], helping to distinguish the saline-evaporitic formation from the upper and lower geological formations due to its higher resistivity.  
Profile A (Figure 8) follows the deduced maximum slope of the saline-evaporitic Barbastro Formation. The hanging wall is located at greater depths as we move to the SW, ranging from 1000 m to 1433 m depth. The footwall shows a subhorizontal morphology with slight depth variations, being located along the profile around 2050 m depth. As a result of the defined structure, saline-evaporitic materials thickness ranges along the profile from 1077 m to 601 m.  
Profiles B and C follows the same direction as the Barbastro-Balaguer anticline, but at different distances from the axis, so a thickness decrease of the saline-evaporitic Barbastro Formation is observed towards Southwest, but with a constant value along the profile. Profile B defines a 900 m thickness of the saline-evaporitic formation with the hanging wall and footwall placed at 1125 and 2030 m depth, respectively (Figure 9B). Profile C, located further to the SW, shows the thinning of the Barbastro Formation as it is placed away from the core of the anticline, showing a formation thickness close to 670 m, with the hanging wall lying at 1375 m depth, while the footwall is located at 2050 m depth (Figure 10B).  
Figure 7: TDEM resistivity—time curves.
## 2 Digital Beamforming Theory Derivation
After applying a scaling transformation, the \(x\)-axis is scaled by a factor of \(i\), the \(y\)-axis by a factor of \(j\), and the \(z\)-axis by a factor of \(k\). Then, the coordinates are mapped to the conformal spherical cap array coordinates via a spherical mapping matrix. The relationship between the processed coordinates \((q_{x_{k}},q_{y_{k}},q_{z_{k}})\) and the initial coordinates is given by  
\[ \begin{bmatrix}q_{x_{k}}\\ q_{y_{k}}\\ q_{z_{k}}\end{bmatrix} = \begin{bmatrix}\sin\theta\cos\varphi&0&0\\ 0&\sin\theta\sin\varphi&0\\ 0&0&\cos\theta\end{bmatrix}\begin{bmatrix}i&0&0\\ 0&j&0\\ 0&0&k\end{bmatrix}\begin{bmatrix}r_{x_{k}}\\ r_{y_{k}}\\ r_{z_{k}}\end{bmatrix}  \tag{21}\]  
Let the spherical mapping matrix be \(\mathbf{A}= \begin{bmatrix}\sin\theta\cos\varphi&0&0\\ 0&\sin\theta\sin\varphi&0\\ 0&0&\cos\theta\end{bmatrix} \). The scaling matrix \(\mathbf{B}= \begin{bmatrix}i&0&0\\ 0&j&0\\ 0&0&k\end{bmatrix} \), then  
\[ \begin{bmatrix}q_{x_{k}}\\ q_{y_{k}}\\ q_{z_{k}}\end{bmatrix} =\mathbf{AB} \begin{bmatrix}r_{x_{k}}\\ r_{y_{k}}\\ r_{z_{k}}\end{bmatrix}  \tag{22}\]  
That is  
\[ \begin{bmatrix}r_{x_{k}}\\ r_{y_{k}}\\ r_{z_{k}}\end{bmatrix} =(\mathbf{AB})^{-1} \begin{bmatrix}q_{x_{k}}\\ q_{y_{k}}\\ q_{z_{k}}\end{bmatrix}  \tag{23}\]  
Figure 8: Schematic diagram of conformal spherical cap array coordinates. Then the steering vector of the antenna array can be expressed as  
\[\mathbf{\alpha}_{k}(u,v,w)=\exp\left\{\left(j\frac{2\pi}{\lambda} \begin{bmatrix}r_{x_{ k}}\\ r_{y_{k}}\\ r_{z_{k}}\end{bmatrix} ^{-1} \begin{bmatrix}u\\ v\\ w\end{bmatrix} \right)\right\}=\exp\left\{\left(j\frac{2\pi}{\lambda} \begin{bmatrix} r_{x_{k}}^{\prime}\\ r_{y_{k}}^{\prime}\\ r_{z_{k}}^{\prime}\end{bmatrix} ^{-1} \begin{bmatrix}u^{\prime}\\ v^{\prime}\\ w^{\prime}\end{bmatrix} \right)\right\} \tag{24}\]  
where \(\left[u^{\prime},v^{\prime},w^{\prime}\right]^{\mathsf{T}}=\left(\left(\mathbf{ AB}\right)^{-1}\right)^{T}\left[u,v,w\right]^{\mathsf{T}}\). After the elements of the regular cubic array undergo scaling and mapping transformations, the element spacings \(d_{x}^{\prime}\), \(d_{y}^{\prime}\), and \(d_{z}^{\prime}\) of the conformal spherical cap array antenna satisfy the following relationships with the original coordinate system's element spacings \(d_{x}\), \(d_{y}\), and \(d_{z}\):  
\[ \begin{bmatrix}d_{x}^{\prime}\\ d_{y}^{\prime}\\ d_{z}^{\prime}\end{bmatrix} =\mathbf{AB} \begin{bmatrix}d_{x}\\ d_{y}\\ d_{z}\end{bmatrix}  \tag{25}\]