esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 2 Materials and Methods
### Model Validation  
The performance of the model was evaluated by comparing the state inference to the labelled accelerometer data provided by [PERSON] et al. [7]. Firstly, the total weekly time spent in each activity was calculated. [PERSON]'s correlation coefficients were calculated for each activity, and the agreement between the two methods was assessed with a Bland-Altman analysis [22]. In order to enable such comparison, the inferred states were mapped to the classified activities (sedentary behaviour, moderate-to-vigorous physical activity, walking, light tasks and sleep) based on the Euclidean acceleration. Additionally, the usefulness of the HSMM-inferred activity for epidemiological research was validated through a regression analysis by comparing the coefficients estimated using the HSMM-inferred states and the activities classified by [PERSON] et al. [7]. Two known associations between physical behaviours and anthropometric measures were chosen, namely BMI and WC. Data from the UK Biobank were used for this purpose.  
The accelerometer data from the UK Biobank were used in the association analyses following a compositional paradigm [23]. In a compositional framework, the time spent in different activities is considered as a relative proportion of the overall time budget (24 h), such that the vector \(x=[x_{1},x_{2},\ldots,x_{D}]\in R^{D}\), with D being the number of activities and with \(\sum_{i=1}^{D}x_{i}=C\), is constrained by the closure constant \(C=24\) h. The closure constant implies multicollinearity among the activities and thus conventional statistical methods cannot be employed with compositional data [24]. The isometric log-ratio (ILR) transformation maps the data from the constrained simplex space to the unconstrained real space, which allows for the application of regression. Therefore, the transformation \(z=ILR(x)\) was applied to the accelerometer data as follows:  
\[z_{i}=\sqrt{\frac{D-1-1}{D-i+1}}ln\frac{x_{i}}{\prod_{j=i+1}^{d}x_{j}}\;\;with \;\;i=1,\ldots,D-1 \tag{2}\]
## 3 Methodology
### Joint Optimization Framework  
#### 3.3.1 Loss Components and Formulation  
To simultaneously optimize text detection, character recognition, and glyph restoration, we design a multi-task joint loss function that integrates region localization, category prediction, and structural reconstruction into a unified optimization framework. The joint loss consists of three components, corresponding to text detection loss, character classification loss, and glyph restoration loss. By jointly optimizing these objectives during training, the model aims to accurately localize text regions, recognize character categories, and recover detailed glyph structures under complex backgrounds. The overall loss function is defined as follows:  
\[\mathcal{L}_{\text{total}}=\mathcal{L}_{\text{det}}+\mathcal{L}_{\text{cls}}+ \mathcal{L}_{\text{res}} \tag{4}\]  
The text detection loss, denoted as \(\mathcal{L}_{\text{det}}\) includes two components. The first is a region segmentation loss, where Dice Loss [17] measures the overlap between the predicted text areas and the corresponding ground truth regions. The second is a geometric regression loss, where Smooth L1 Loss [18] is used to optimize the geometric parameters of text boxes, such as the distances to the top, bottom, left, and right boundaries, as well as the rotation angles. This combination is designed to improve both the segmentation quality and the precision of geometric regression.  
\(\mathcal{L}_{\text{cls}}\) is the character classification loss, which enhances the model's ability to distinguish character categories at the pixel level. We adopt a cross-entropy-based strategy that selects character stroke regions as the supervision area, focusing on the foreground to suppress background interference and improve classification performance. The detailed formulation of this loss is provided in Section 3.3.2.  
The glyph restoration loss, denoted as \(\mathcal{L}_{\text{res}}\), aims to recover character details from degraded or noisy input images, thereby improving the quality of character reconstruction. This loss consists of three components. The first uses mean squared error (MSE) [19] to perform pixel-wise regression between the restored output and the target image, enhancing the accuracy of grayscale reconstruction. The second introduces binary cross-entropy with logits (BCEWithLogitsLoss) [19] to guide the segmentation of foreground regions and ensure the structural completeness of the characters. The third employs the soft-clDice [20] loss to preserve the topological structure of character skeletons, which helps maintain the connectivity of elongated strokes and improves the recovery of fine-grained glyph details.
## 6 Conclusions and Outlooks
**Nomenclature**  
C equivalent damping matrix  
\(K\) equivalent stiffness matrix  
\(F\) equivalent force vector  
\(M\) number of DOFs  
\(O_{l}(i=1\dots 12)\) geometric centers of the discs  
\(m_{l}(i=1\dots 12)\) equivalent lumped masses  
\(c_{i}(i=1\dots 12)\) equivalent damping coefficients  
\(k_{l}(i=1\dots 11)\) equivalent stiffness  
\(\omega\) rotating speed  
\(\alpha\) depth of crack  
\(\beta\) intersection angle between unbalance and crack normal vector  
\(k\) stiffness of cracked rotating shaft  
\(\Delta k_{\xi,\Delta}k_{\eta}\) stiffness variations of cracked normal and tangential vectors  
\(r\) radial displacement  
\(r_{0}\) clearance between rotor and stator  
\(k_{b}\) rub-impact stiffness  
\(\mu_{0}\) frictional coefficient  
\(P_{x}\), \(p_{y}\)  
rub-impact forces along x and y directions  
\(X_{ij}\), \(Y_{i}(i=1,\dots 12)\) vibration displacement of x and y directions  
\(f_{x}\), \(f_{y}\) dimensionless non-linear oil-film force  
\(c\) bearing clearance  
s Sommerfeld number  
\(\mu\) lubricating oil viscosity  
\(L\) bearing length  
\(R\) radius of the bearing  
\(p\) loading  
\(\tau\) dimensionless time  
**Appendix A**  
\(m_{1}=m_{12}=50\) kg, \(m_{2}=m_{11}=47\) kg, \(m_{3}=m_{10}=44\) kg, \(m_{4}=m_{9}=41\) kg, \(m_{5}=m_{8}=38\) kg, \(m_{6}=m_{7}=35\) kg, \(R=60\) mm,  
\(c_{1}=c_{22}=1800\) N - s/m, \(c_{2}=c_{11}=1700\) N - s/m, \(c_{3}=c_{10}=1600\) N - s/m, \(c=0.11\) mm, \(c_{4}=c_{9}=1500\) N - s/m,  
\(c_{5}=c_{8}=1400\) N - s/m, \(c_{6}=c_{7}=1300\) N - s/m, \(k_{b}=2.5\times 10^{8}\) N/(\(i=1,\cdots,11\)), \(L=30\) mm, \(\beta_{3}=\frac{2}{2}\), \(E_{3}=0.18\) mm,  
\(\mu=0.018\) Pa - s, \(E_{i}=0(i=1,\cdots,12,i\
eq 3)\), \(\mu_{0}=0.15\), \(\beta_{i}=0(i=1,\cdots,12,i\
eq 3)\), \(b_{b}=1\times 10^{3}\) N/m, \(r=0.18\) nm  
**Appendix B**  
\(\left\{ \begin{array}{l}f_{x}\\ f_{y}\end{array} \right\}=-\frac{\left[(x-2y)^{2}\right]^{2}+\left(y+2i\dot{x} \right)^{2}\left|1/2\right. }{1-x^{2}-y^{2}}\times\left\{ \begin{array}{l}3 xV( x,y,a)-\sin\alpha G(x,y,a)-2\cos\alpha S(x,y,a)\\ 3 yV(x,y,a)+\cos\alpha G(x,y,a)-2\sin\alpha S(x,y,a)\end{array} \right\}\)  
\(\alpha=\arctan\left(\frac{y+2i}{x-2y}\right)-\frac{\pi}{2}sign\left(y+2i\right)\)  
\(G(x,y,a)=\frac{2}{(1-x^{2}-y^{2})^{1/2}}\left[\frac{\pi}{2}+\arctan\frac{y \cos\alpha-x\sin\alpha}{(1-x^{2})^{1/2}}\right]\)  
\(V(x,y,a)=\frac{2+(y\cos\alpha-x\sin\alpha)G(x,y,a)}{1-x^{2}-y^{2}}\), \(S(x,y,a)=\frac{\cos\alpha+y\sin\alpha}{1-(\cos\alpha+y\sin\alpha)^{2}}\)
## 3 Experimental Results and Discussion
### Quasi-Static Response and Residual Static Capacity  
The quasi-static test results are presented in Figure 15. In (a), the force and vertical mid-point displacement measured directly from the MTS machine are presented. Beams A1, E, and D1 presented brittle responses, and thus sustained shear failure modes. Beams F, B1, and C1 showed ductile flexural failure modes indicated by the horizontal plateau corresponding to the yielding of the tensile reinforcement. Beams B1 and E contained the same shear reinforcement amount, which implies that the initial cracks from the initial impact test for Beam B1 resulted in a favourable response as the failure transitioned from brittle to ductile. Beams C1 and F were equivalent in reinforcement, but a slightly larger capacity of Beam C1 was observed. It can be seen that Beam D1 failed by shear, as shown by the brittle response, although it contained the largest amount of shear reinforcement.  
The results in terms of force measured from the MTS machine and displacement determined using DIC are presented in Figure 15b. The results indicate a similar stiffness for the beams. Beams B1 and C1 showed a more ductile response where the displacement capacity was not reached, as no decline of the horizontal plateau was initiated.  
The damage to Beams A1-D1 after the first impact test and the residual quasi-static test are presented in Figure 16. Beams E and F tested for static reference behaviour are also presented. In (a), Beam A1 failed during the quasi-static test by the initial global shear crack on the west side, which had less inclination than the steep diagonal crack on the east side. The same phenomenon was observed for A2 during the initial impact test and C2 during sequential impact testing. Beam B1 responded as shown in (b) by opening the diagonal and wide flexural cracks. As for the dynamic case, type B showed a hybrid response which showed to be ductile. Beam C1 responded by opening mainly wide flexural cracks. However, the shear crack started propagat  
Figure 15: (**a**) Force–mid-displacement measured using the MTS machine for the beams; and (**b**) force measured using the MTS machine and mid-displacement measured using DIC for the beams.  
indicating a small residual shear capacity. The hybrid response of Beam C1 was ductile, as shown by the force-displacement curve in Figure 15. Beam D1 showed a brittle failure mode (Figure 15), which is explained by the opening of a wide steep diagonal crack; see Figure 16d. Steep initial diagonal cracks may thus be severe for the residual capacity of the beam. By comparing Beams B1 and E, which had identical reinforcement amounts in (b) and (e), the already opened flexural cracks after the initial impact test had a favourable effect as the response became ductile.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
###### Abstract  
The problem of unmanned supervision of maritime areas has attracted the interest of researchers for the last few years, mainly thanks to the advances in vessel monitoring that the Automatic Identification System (AIS) has brought. Several frameworks and algorithms have been proposed for the management of vessel trajectory data, which focus on data compression, data clustering, classification and visualization, offering a wide variety of solutions from vessel monitoring to automatic detection of complex events. This work builds on our previous work in the topic of automatic detection of Search and Rescue (SAR) missions, by developing and evaluating a methodology for classifying the trajectories of vessels that possibly participate in such missions. The proposed solution takes advantage of a synthetic trajectory generator and a classifier that combines a genetic algorithm (GENDIS) for the extraction of informative shapelets from training data and a transformation to the shapelets' feature space. Using the generator and several SAR patterns that are formally described in naval operations bibliography, it generates a synthetic dataset that is used to train the classifier. Evaluation on both synthetic and real data has very promising results and helped us to identify vessel SAR maneuvers without putting any effort into manual annotation.  
shapelets; SAR maneuvers; trajectory generator; classification; pattern mining +
Footnote †: journal: Pattern Pattern
## 3 Numerical Simulation of EMP Propagation
To enhance the calculation accuracy, the grid was made denser, and the cell size with respect to the both coordinates was equal to 0.125 cm. The geometry of the calculation area is shown in Figure 7. When the influence of the plasma on the EMP propagation was simulated, the tapered transitions were modeled only partially. To simplify the calculation procedure, the coaxial line with the plasma was excited by a vacuum coaxial line with an external diameter of 45.5 cm, in which the EMP was specified as a traveling TEM mode having the necessary waveform. Figure 6: Detailed numerically simulated pattern of the distribution of the modulus of the electric field in the case of propagation of a unipolar EMP with a duration of 200 ns FWHM in the coaxial line with the slit geometry of the outer conductor: longitudinal metal plates 7.5 cm wide are installed with gaps 5 cm wide. The time instant is 18 ns. Figures 8 and 9 present examples of the results obtained by simulating the propagation of a bipolar EMP with an initial duration of 1 ns in the line filled with uniform plasma with the electron density \(n_{e}\) = 7.7 \(\times\) 10\({}^{8}\) cm\({}^{-3}\), the electron temperature \(T_{e}\) = 0.2 eV, and the neutral nitrogen density \(n_{n}\) = 3 \(\times\) 10\({}^{13}\) cm\({}^{-3}\) corresponding to pressure p = 10\({}^{-3}\) Torr. A dispersion transformation of the pulse waveform, which is accompanied by an increase in its duration up to three times, and a decrease in amplitude, is shown in Figures 8 and 9. The cutoff frequency of the electromagnetic radiation for a present density is equal to 250 MHz. The maximum of the pulse spectrum corresponds to a frequency of 500 MHz, and the top boundary of the EMP spectrum is close to 2 GHz. Therefore, the fraction of the EMP energy, which is reflected from the plasma and corresponds to the spectral components with \(f\) < 250 MHz, is small enough. Above the cutoff, the spectrum of the signal transmitted through the plasma was retained (Figure 9c), which confirmed the correctness of the performed calculations and indicated that no significant losses of the EMP energy occurred in the plasma. Figure 8: Calculated structure of the electric field in the line (\(E_{r}\) component) with uniform plasma filling of the central section (electron density \(n_{e}\) = 7.7 \(\times\) 10\({}^{8}\) cm\({}^{-3}\)) at six sequential time instants. Figure 7: Geometry of the calculation area in the case of simulation of a plasma-filled coaxial line. Note that the results with uniform plasma are presented for demonstration purposes. Indeed, we performed simulations with various versions of more realistic bell-shaped plasma density profiles \(n_{e}\)(r,z), both across the line (between the central and outer conductors), and along it. For the same maximum density values, the results of these simulations gave a slightly weaker dispersion effect, but did not differ qualitatively.
## 4 Discussion
### PVAS Devices in Tunonal Vertebral Compression Fractures  
In recent years, some studies have evaluated the outcomes of using PVAS devices in vertebral compression fractures caused by malignant pathologies, such as multiple myeloma (MM). MM is a clonal disorder of B-cells characterized by the proliferation and accumulation of B-lymphocytes and plasma cells within bone marrow. The spine is particularly affected, because vertebrae, with their high hematopoietic marrow content, are a preferred site for plasma cell neoplasms to infiltrate and grow [32]. Osteoclast activation disrupts bone remodeling, leading to localized osteoporosis and increasing the risk of vertebral compression fractures which are further exacerbated by high-dose steroids [33,34]. For MM vertebral fractures, pharmacological options consist of bisphosphonates, denosumab, teripartide, and estrogen therapy. Non-invasive approaches to vertebral fractures involve the use of bracing, physical therapy, and pain management strategies. Interventional radiology is crucial in managing this condition, particularly for alleviating pain caused by secondary vertebral fractures. The primary techniques used are percutaneous kyrophoplasty and VP, as recommended by the 2017 CIRSE guidelines [35,36]. Vertebral deformity and adjacent level fractures are significant long-term complications associated with verteboplasty and kyrophoplasty in osteoporotic patients. While VP effectively reduces pain in the short and long term, it often fails to fully restore vertebral height or correct the kyphotic angle, leading to deformities and an increased risk of adjacent fractures. To address these issues, various percutaneous implant techniques were developed. These techniques aim to minimize the secondary loss of vertebral height after balloon deflation during kyrophoplasty and to maintain vertebral height and kyphotic angle restoration. SpineJack(r) use in patients with multiple myeloma was reported by [PERSON] et al., who retrospectively evaluated its feasibility and effectiveness for treating and stabilizing painful vertebral compression fractures. In their study, 39 patients with 49 fractures underwent verteboplasty with SpineJack(r) implants. Results showed a 100% technical success rate, with no major complications. Pain significantly decreased by 96.3% (VAS score), and functional mobility improved by 47.8% over a 6-month follow-up. The procedure proved safe and effective, offering long-term pain relief and vertebral height restoration without major complications or new fractures [37]. They also noted that the combined CT-Fluuroscopy technique is especially beneficial for patients with significant pre-existing spinal deformities. This approach, in fact, allowed for more accurate implant positioning and provided easier access to the verteb through CT guidance.
# Application of the Improved Entry and Exit Method in Slope Reliability Analysis
## 5 Conclusions  
This paper proposes an improved entry and exit approach to decide the critical sliding surface and the minimum factor of security. The improved entry and exit method is combined with the Monte Carlo simulation to calculate the failure probability of slope considering spatially variable soils. The outcomes of the analysis lead to the following conclusions:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & & & & & \\
**Trial Slip** & **21 \(\times\) 21 \(\times\) 11** & **41 \(\times\) 41 \(\times\) 21** & **61 \(\times\) 61 \(\times\) 31** & **81 \(\times\) 81 \(\times\) 41** & **101 \(\times\) 101 \(\times\) 51** \\
**Surfaces** & & & & & \\ \hline \hline Example 1 (\(P_{\beta}\)) & 0.0784 & 0.0905 & 0.0877 & 0.0917 & 0.0902 \\ Example 2 (\(P_{\beta}\)) & 0.0181 & 0.0193 & 0.0199 & 0.0201 & 0.0203 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The probability of failure when applying the traditional entry and exit approach in this paper.  
Figure 9: The impact of the number of trial slip surfaces on the probability of failure.  
The improved entry and exit method, compared with the traditional entry and exit method, can achieve high calculation efficiency because the searching range is significantly reduced according to the location of the potential critical slip surface.  
The failure probability increases with the increasing reduced searching range. When reduced searching range is equal to unilateral \(2D\), the improved entry and exit method can obtain reasonable results in the slope reliability analysis.  
The calculation precision of the safety factor has an effect on the failure probability of slopes. The failure probabilities of Example 1 and Example 2 obtained by analyzing 81 \(\times\) 81 \(\times\) 41 trial slip surfaces are 16.9% and 11.0% higher than those obtained by analyzing 21 \(\times\) 21 \(\times\) 11 trial slip surfaces, respectively.  
Conceptualization, R.Y. and Y.W.; methodology, R.Y.; software, B.S.; validation, R.Y., Y.W. and X.G.; formal analysis, Y.W.; resources, B.S.; data curation, X.G.; writing--original draft preparation, R.Y.; writing--review and editing, R.Y.; supervision, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by grants of the Natural Science Foundation of Ningxia (No. 2020 BEB04004 and 2023 AAC03036) and the First Class Discipline Construction in Ningxia (No. NXYLXX2021A03).  
Not applicable.  
The authors solemnly declare and confirm that informed consent was provided by all subjects involved in the study.  
If required, the data supporting the conclusions of this research can be obtained from the corresponding author, [PERSON].  
The authors declare no conflict of interest.
## 4 Data Representation: Runtime Events and Dataset
This allows the analysis process to detect patterns and execution behavior in more detail than the first level; see Table 2. Finally, the third level (or \(L_{3}\)) captures low-level fine-grained execution events; at this level, the tracing language consists of 68 unique execution events that are captured at the virtual machine level or within the runtime system. These three levels provide test engineers with the flexibility to select the level of detail needed for their test suite. Each of these levels generates a string of execution events with different alphabet sizes and consequently different levels of detail about the actual code executed by the test case or set of test cases. Table 2 presents the three levels of abstract representations used by AbstractTrace. It shows _four_ classes of events in \(L_{1}\), _sixteen_ classes of events in \(L_{2}\), and finally _sixty-eight_ types of events in \(L_{3}\). Additionally, in all three levels, line number and major syntactic structures (two more events: _E_Line & _E_Syntax_) are used to introduce spaces in between the collected events' codes; _this makes the resulting trace strings consist of tokens based on source-code statements and blocks_--see Figure 3. Moreover, Table 2 shows the unique letters (_event encoding_) used in each level that represent these events in the captured execution trace, in which each level incorporates a specific level of detail. The following three sub-sections give more insights into each of the levels.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 3 Preliminaries
### Threat Assumption Model  
This paper adopts the Dolev-Yao security model [24; 25; 26] and the Canetti and Krawczyk security model [27; 28; 29] as threat models for the proposed protocol. The capabilities of an adversary, \(A\), are summarized as follows:  
* \(A\) can intercept, modify, eavesdrop, and replay messages on public communication channels.  
Figure 1: Fog-based VANET architecture.  
* \(A\) and vehicles know the identities of all fog nodes. \(A\) may be a legitimate vehicle user or a privileged insider on the cloud server.
* \(A\) can obtain the secret values of the smart card through power analysis attacks [30; 31].
* \(A\) can obtain long- or short-term keys from the network and attempt to compute the session key. The long-term keys are the private keys of the network entities, and the short-term keys are the random values generated during the authentication process [32].  
In a real VANET environment, the cloud server and fog nodes are securely connected, making it difficult for attackers to compromise them. However, vehicles can be captured or stolen by attackers, making them vulnerable targets. Therefore, we considered vehicles as insecure entities and assumed a threat model.