esa-sceva/satcom-chunk-collection huggingface dataset:
It is recognized that linear methods possess certain limitations, especially in ascertaining the complexity of motion and the temporal structure inherent in a time series [22]. To overcome these constraints, a nonlinear approach, such as entropy measures, can be utilized [19; 23]. In MV analysis, entropy stands out as one of the most promising metrics for evaluating the complexity of biological signals [24]. Within this context, sample entropy (SampEn) analysis has emerged as a widely accepted method for quantifying system complexity. This approach has been effectively employed to characterize alterations resulting from various interventions in resistance training [19; 25] and to assess running performance [26]. However, entropy analysis has been limited in its application to evaluate the amount of complexity induced by vibration environments [16]. Therefore, using tools like entropy, the functional variability of athletes during movement performance must be considered a key element in identifying the extent to which constraints cause perturbation [27]. The increase in MV during WBV exercise could be considered as a promoter of environmental adaptability, which is a crucial aspect of enhancing athletic performance [28]. The integration of WBV into training exercises provides a promising avenue for eliciting adaptations at various levels of the neuromuscular system, thereby approaching the reality faced by athletes [29]. Additionally, the increased stretch-shortening cycle of skeletal muscles, a characteristic of WBV training, can elicit sufficient muscular activity to elevate whole-body oxygen consumption (VO\({}_{2}\)) [30; 31]. By building upon these premises, we hypothesize that combining SB training with WBV could induce profound alterations in physiological parameters and MV. Therefore, this study aimed to evaluate the impact of WBV on MV and physiological parameters during an incremental SB skating test.
## 5 Discussion
### Assessment of Dual-Branch Structure  
Our proposed HDCDF is an end-to-end dual-branch structure, which combines two joint feature extraction-independent branches, which are the upper-branch network based on 2D-DCAtention and the lower-branch network based on hybrid residual modules. Theoretically speaking, the information obtained by the single-branch network is very limited, while the features extracted by the two-branch network with different methods are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Training (s)** & **Testing (s)** & **Parameter (K)** \\ \hline
3 DCNN & 96.69 & 8.49 & 1481.6 \\ DBMA & 82.60 & 11.17 & 609.0 \\ SpectralFormer & 73.06 & 9.85 & 351.2 \\ ExViT & 71.64 & 9.18 & 346.4 \\ LSSMA & 61.72 & 10.41 & 8.7 \\ Proposed & 70.11 & 9.35 & 22.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Complexity analysis of different models and our method on the IP dataset using 5% training samples in 16 classes.  
Figure 17: Classification maps of the different models for the SV dataset using 0.5% training samples: (**a**) false-color image; (**b**) ground-truth map; (**c-h**) maps with different models.  
usually richer and more comprehensive than those extracted by the single-branch network, so the two-branch network can obtain better results in the classification task. To verify the conjecture, we split the HDCDF network into an attention branch and residual branches and conducted experiments on three datasets. As can be seen from Table 8, the performance of our proposed two-branch-structure HDCDF on the three datasets is superior to each single branch of HDCDF; the OA is at least 1.2% higher than that of a single branch. Compared with the residual branch, the attention branch obtains higher classification accuracy through three layers of 3D depth-wise separable convolution that further strengthens the features in the spatial dimension and spectral dimension. Compared with the attention branch, the residual module retains the original classification features and also achieves relatively good classification results.  
In short, the two branches in the double-branch structure can complement each other to obtain more abundant features. In order to obtain a better classification effect, it is necessary to choose the double-branch structure over the single-branch structure.
## 5 Testing Environment and Configuration
### Energy Measurements Methodology  
We use the Otii Arc Pro Application Programming Interface (API) to configure, start and stop the measurements, extract the values, etc. The measurements are started as soon as a packet is sent by the application, generating the DLMS payloads, and stopped once the echo of the packet is received. For the DTLS handshake, the measurements are started at the initiation of the handshake and stopped once the handshake is completed. Each scenario is run at least 20 times, and the measurements are then averaged over these 20 samples.  
Figure 8: Impact of deterioration of radio condition on the transmission.  
Figure 9: Application setups. Red color represents the case of SCHC, while black color represents the case without SCHC.
## 6 Simulation Results
### Experimental Configuration and Parameters
The trajectories shown here represent connected line segments with position points marked along their paths in a 2D coordinate system measured in meters, demonstrating potential spatial relationships between UAVs in the operational area. The successful three-point localization of the anomalous radiation source by UAVs is demonstrated as shown in Figure 5d. When three UAVs simultaneously detect the anomalous radiation source, the three-point localization function is triggered to precisely determine its position. In the diagram, the three green UAVs represent the red team UAVs, serving as the centers of dashed circles. The blue point indicates the location of the anomalous radiation source. Red lines connect the green UAVs to the blue point, representing the distance relationships calculated by the UAVs using Equation (11) based on the perceived RSSI, which also serve as the radio of the dashed circles. The intersection point of the dashed circles corresponds to the position of the blue point, demonstrating the localization result of the anomalous radiation source. Figure 5: Environment and information for intelligent spectrum warfare platform: (**a**) global location information; (**b**) global sensing information; (**c**) UAVs’ movement trajectory; (**d**) schematic diagram of three-point localization.
## 4 Discussion
### Tree Species Composition and Mortality  
Our findings indicate that bald cypress (_Taxodium distichum_), sweepqum (_Liquidambar styraciflua_), and American holly (_Ilex opaca_) demonstrated relatively low mortality rates. Among these species, bald cypress is particularly well known for its resilience to flooding. As a native deciduous conifer of the southeastern United States, it often dominates coastal freshwater wetland ecosystems [54]. While this study did not directly investigate adaptive traits, previous research has shown that bald cypress seedlings can tolerate flood conditions for up to 45 days; however, prolonged submergence, especially of the canopy, significantly increases mortality risk [51]. Moreover, bald cypress has been observed to persist long after its habitat is altered by saltwater intrusion, suggesting a high tolerance for environmental stressors [3; 69; 70]. These traits may help explain the relatively low mortality of bald cypress in our study. Further investigation into the physiological basis of bald cypress's tolerance mechanisms is warranted.  
In contrast, loblolly pine (_Pinus taeda_) exhibited the highest mortality of the species studied. We suggest that this elevated mortality was likely driven by prolonged hydrologic stress, compounded by infestations from wood-boring insects such as the southern pine beetle (_Dendroctonus frontalis_), black turpentine beetle (_Dendroctonus treebrans_), and Ips engraver beetles (_Ips calligraphus_, _Ips grandicollis_, and _Ips avulsus_). These beetles typically target older, weakened trees whose defenses have been compromised by stress [58]. Although loblolly pine is considered moderately flood-tolerant, its capacity to survive is generally limited to a single growing season under inundated conditions [54]. It is likely that the persistently rising groundwater table observed at our site (Figure 3) decreased tree vigor, predisposing the pines to beetle attack and increased mortality risk. Southern pine beetle is native to the southeastern U.S. [59; 60] and is particularly problematic in waterlogged forests, where hydrologic stress undermines tree resistance [61]. Flooding is known to impose physiological stress by reducing oxygen availability [4] and impairing secondary metabolic functions [63], thereby creating favorable conditions for beetle infestation and mortality events [61].
# On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio
## 2 System Model
### Cognitive Radio System  
Figure 1 illustrates a scenario for CR system with cooperative sensing in wireless networks. A SU can temporally and spatially utilize the licensed or unlicensed spectrum band of the PU. To avoid possible interference to the PU, the CR system requires SUs to simultaneously perform in-band sensing, which means SUs periodically sense the operating spectrum band to verify whether the PU appears or not. In addition to in-band sensing, out-of-band sensing implies that SUs sense the non-operating bands and maintain a list of available bands. Even though our work is applicable to both in-band and out-of-band sensing purposes, hereafter we consider in-band sensing. When SUs finish sensing, they send their own sensing results to a fusion center so that it combines all local results and makes a final decision about the presence of PUs. We assume that SUs honestly report their local decisions to the fusion center via common control channels [11; 12; 13; 14; 15; 16; 17], e.g., an individual reporting channel is reliable and error-free for simplicity [19; 20; 21; 22; 23; 24].  
Figure 1: A scenario for cooperative sensing of cognitive radio system in wireless networks; there is a primary user, cooperative secondary users, and a fusion center. SU: secondary user.
## 4 Defect Recognition Algorithm  
The defect recognition algorithm is the core of the surface quality inspection system. Generally, defect recognition algorithm consists of image preprocessing, image feature extraction, and image classification. In order to obtain more comprehensive information of the surface images of continuous casting slabs, we do not carry out image preprocess. Figure 3 is the schematic diagram of the defect recognition algorithm. The details are as follows.  
1. Extract DNST features. All sample images are decomposed into multiple subbands at different scales and different directions by DNST, and the coefficients matrices of subbands of horizontal and vertical cones are obtained. Then calculate the mean and variance statistics of each subband for all samples. The mean reflects the average value of the subband coefficient matrix, and the variance reflects the deviation between the subband coefficient and the average value of the subband coefficients. The DNST feature of each sample image is a one-dimensional long vector.
2. Extract texture features. The gray level of the original image is compressed to reduce the computational complexity. The directions choose \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\). The texture descriptors energy, contrast, entropy, homogeneity, and correlation are calculated for four directions,respectively. Then, the mean and variance of each texture descriptor are calculated. Thus, each sample image can get a 10-dimensional GLCM texture feature vector.
3. Feature combination. The DNST and GLCM feature vector of each sample are spliced into a new one-dimensional long vector. Then the feature vector of each sample are arranged from top to bottom to form the feature matrix. Finally, the feature matrix is normalized to [\(-\)1, 1].
4. Dimensionality reduction. Since the extracted features are high-dimensional and redundant, which is not conducive to subsequent classification. Therefore, we first use the KSR to reduce the dimensionality of the feature matrix of the training set and use the projection matrix obtained from the training set to reduce the dimensionality of the feature matrix of the test set. The high-dimensional feature matrix reduced to c - 1 dimensional subspace, where c is the number of classes.
5. Defect classification. First, the low-dimensional feature matrix is normalized to [\(-\)1, 1]. Then, the low dimensional features and labels data are input into the SVM [26] for training and classification. Finally, the surface defects of the continuous casting slabs are recognized.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 3 System Model
### System Architecture  
The system architecture is introduced by considering edge computing and WBAN techniques, as shown in Figure 1. Unlike the work in References [27; 28], we present the concept of C-MEC instead of the coordinator, as this offers computation resources to execute the medical tasks and forward the results to medical professionals in a timely manner. As can be seen from Figure 1a, an implanted sensor transmits the computing task \(C_{i}\) to the on-body relay via the intra-body region. Moreover, since the implanted sensor broadcasts short information to the relays, one should note that relay selection should be considered, as shown in Figure 1b. Once the relay is selected, different task offloading schemes are given in Figure 1c. The task \(C_{i}\) can be executed by the corresponding relay, by other relays, or by the C-MEC. One should note that we encourage tasks from intra-body devices to be executed locally by relays so that user-friendly interaction can provide timely patient warnings under emergency conditions and avoid transmitting tasks to the C-MEC, which is typically far away from the patient. In this paper, the Cartesian coordinate system in which each implanted sensor is fixed is \(\mathbf{w}_{k}=[x_{k},y_{k}]^{T}\in\mathbb{R}^{2\times 1},k\in\mathcal{K}\) with the transmitted data packet \(C_{i}\) to the corresponding relay. The coordinates of the relay \(i\) are expressed as \(\mathbf{w}_{i}=[x_{i},y_{i}]^{T}\in\mathbb{R}^{2\times 1},i\in\mathcal{N}\) and those of the C-MEC are expressed as \(\mathbf{w}_{c}=[x_{c},y_{c}]^{T}\in\mathbb{R}^{2\times 1}\). The data transmission energy consumption for the implanted sensor \(k\in\mathcal{K}\) to transmit \(C_{i}\) to the corresponding relay \(i\) can be expressed as:  
\[E_{k}^{trans}=C_{i}\bigg{(}E_{k}^{\epsilon}+nE_{amp}\sqrt{\left|\left|\mathbf{w}_{ k}-\mathbf{w}_{i}\right|\right|^{2}}\bigg{)}, \tag{1}\]  
where \(E_{k}^{\epsilon}\) and \(E_{amp}\) represent the energy consumption to activate the implanted sensor circuit per bit and the radio amplifier, respectively. The PL exponent is represented by \(n\), which primarily depends on the transmission environment. The energy consumption for relay \(i\) to receive \(C_{i}\) can be expressed as:  
\[E_{i}^{rec}=C_{i}E_{i}^{elec},i\in\mathcal{N}, \tag{2}\]  
where \(E_{i}^{elec}\) is the essential energy consumption to activate the relay electronic circuit per bit.
## 4 Experiment Result  
### Answer to RQ1--Performance on Different Failure Types  
The experimental results are shown in Table 4. We can see that the control condition modification fault type is better than the other types in the eight fault localization methods, and the average index is 23.72%. The fault type modified by the expression statement is better than the other types among the three fault localization methods, and the average index value is 21.05%. The fault type of missing expression statement is better than other types only in two fault localization methods, and the average value of the index is 15.85.  
We analyze the result and find that even if more test cases are generated, the missing fault statement will not be covered due to the missing type of expression statement. Therefore, this method has the least improvement in iterative fault localization. However, both the control condition modification fault type and the expression statement modification fault type will improve the positioning effect because there are more fault-related test cases.  
We can conclude that the method in this paper improves the fault localization effect of two types of expression statement modification and control condition modification. However, the fault localization for the type of missing expression statement needs to be improved.
# Mixed-Degree Cubature \(H_{\infty}\) Information Filter-Based Visual-Inertial Odometry
## 5 Conclusions  
In this paper, we presented our visual-inertial navigation system called MC\(H_{\infty}\)IF-VIO, which uses a raw intensity-based measurement model. By analyzing the integrated navigation system, we constructed a numerically stable mixed-degree cubature information filter scheme for the state estimation problem. The \(H_{\infty}\) filter was combined for the non-Gaussian noises in the intensity measurements. The proposed VIO system was suitable for the RGBD camera-IMU system. The system was evaluated on the TUM RGBD dataset and compared with the RGBDSLAM and LVT systems.  
Given the raw visual measurement model with intensities directly used as measurements in frame-to-frame alignment, the proposed MC\(H_{\infty}\)IF-VIO system with consideration of the system nonlinearity and non-Gaussian measurement noises was shown to achieve good performance in our tests. Though the method prefers short durations, the implementation with a patch size of two outperformed the RGBDSLAM and LVT in the long-duration test. With the aid of adjustable patch size, the proposed method could be tuned from an accurate-dense algorithm to a fast-sparse algorithm.  
This paper aimed to build a robust state-estimation framework according to the fundamental characteristics of the visual-inertial navigation system. The camera measurements were used in a raw and direct way without heuristics in selecting the feature descriptors or tracking a sliding window. Other well-designed intensity-based and feature-based measurement models can be applied in this hybrid filter-based framework.  
Figure 6: Test results on the sequence _ft1_xyz_: (**a**) ATE; (**b**) RPE.  
Figure 7: Test results on the sequence _ft3_office_: (**a**) ATE; (**b**) RPE.  
**Author Contributions:** Investigation, C.S. and X.W.; Methodology, C.S.; Project Administration, X.W.; Resources, N.C.; Software, C.S.; Supervision, N.C.; Writing - Original Draft, C.S.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Blockchain Applications in Education: A Systematic Literature Review
## 4 Discussion
### Research Question 2: What Are the Features of Blockchain That Could Benefit Education?
#### 4.2.1 Distributed Consensus  
This feature allows a blockchain-based system, made up of tens or even thousands of participants, to agree upon a single version of the information stored throughout the network, without the need for a central authority.  
In the field of education, grades, competences, portfolios, certificates and in general, students' information is presently stored in each academic institution, and it is partially shared with official educational bodies. It is difficult to make accessible these records among other stakeholders or third parts. The distributed consensus could allow organizations to collaborate and store in a single platform the same information, and, as a consequence, make it easily available to others.  
This feature is utilized in all of the articles reviewed, according to different approaches to registering information. For example, [PERSON] and [PERSON] [26] specifically design and build a framework to issue, manage and share educational records among educational organizations using blockchain and off-chain storage servers that reduce transaction costs and improves efficiency. Initiatives like this facilitate learners' mobility [13] and minimize the possibility of mistakes, while also helping to solve the cold-start challenge in learning analytics systems [31] and the loss of records [32].
## Appendix A  
To determine the stress-strain behavior of the rock mass, we numerically solved the system of solid mechanics equations. The system includes: the laws of mass and momentum conservation. The geometrical relations are used to determine the strain increments. The relations of the hypo-elastic medium are used as the constitutive equations to determine separately the increment of the stress tensor volumetric and deviatoric parts. For further details, the reader is referred to [24].  
We use the Drucker-Prager model in Equation (A1) [27] to describe the inelastic deformation of host rocks.  
\[f(\sigma_{ij},D)=-\alpha P+\sqrt{J_{2}}-Y\] (A1)  
\[Y=Y_{0}(1-D)\] (A2)  
\(\alpha\) and \(Y\) are calculated from rock cohesion \(C\) and internal friction angle \(\varphi\) of the Mohr-Coulomb criterion [27].  
The non-associated flow rule in Equation (A3) is used to calculate the increments of the inelastic strain tensor components in Equation (A4)  
\[g(\sigma_{ij})=\tau-\Lambda P+const\] (A3)  
\[d\epsilon_{ij}^{p}=d\lambda\frac{\partial g(\sigma_{ij})}{\partial\sigma_{ij}}\] (A4)  
The increments of the inelastic strain tensor components are calculated as follows in Equation (A5):  
\[d\epsilon_{ij}^{p}=d\lambda(\frac{S_{ij}}{2\tau}+\frac{\Lambda}{3}\delta_{ij})\] (A5)  
The details of the stress relaxation procedure are discussed elsewhere [28].  
The choice of the dilatancy factor of the rocks is described in [24]. Let us discuss the new modification of the kinetic equation for the damage measure time derivative \(D(\sigma_{C},t)\) in Equation (A6)  
\[\frac{dD}{dt}=\frac{\sigma_{C}^{2}}{t_{*}},\] (A6)  
wherein \(\sigma_{C}=\frac{\tau}{\tau+\alpha B}\) is the Coulomb stress and \(t_{*}\) is the model parameter controlling the rate of damage accumulation; \(t_{*}\) has the physical meaning of the characteristic time of a fracture incubation process. The new modification allows for a reduction in the number of constants to be validated in contrast to the previous version [24].  
We use a piece-wise linear yield strength envelope of rocks with two segments. Figure 11 schematically illustrates the proposed piece-wise linear function of the yield/damage surface and possible loading paths of a single continuum point (No. 1 and 2).  
A continuum point is assumed to be fractured when the damage parameter \(D\) reaches unity. After fracture, the continuum point no longer resists tensile stresses (all stress tensor components are nullified when hydrostatic pressure \(P\) is negative) but still resists shear and compressive stresses with a local strength of \(\tau=\alpha P\) (when hydrostatic pressure \(P\) is positive).
# Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer
## 5 Sliding Mode Control-Based Tracking System
### Attitude and Altitude Control
The altitude control input \(u_{4}\) can also be obtained by SMC following the design procedures for the attitude control inputs. The sliding surface \((\hat{\sigma}_{4})\) of the altitude control does not include the estimated states produced by SMO. In Section 3, we estimate only the quadrotor's angle and angular velocity by SMO. Following the design procedures for the attitude control inputs, the control input \(u_{4}\) can be derived as  
\[ \begin{split} u_{4}&=-\frac{m}{\cos{\xi_{1}}\cos{ \xi_{3}}}\Big{[}g+\dot{x}_{8d}-\frac{1}{\lambda_{4}}(x_{8}-x_{8d})\ -\alpha_{4}q_{4}\big{(}\ddot{\hat{e}}_{4}+\lambda_{4}\dot{\hat{e}}_{4}\big{)}\\ &\quad\times\big{\{}j_{4}(\sigma_{4})-j_{4}^{2}(\sigma_{4})\big{\}} +\mu_{7}sat(\hat{\sigma}_{4})+\mu_{8}\hat{\sigma}_{4}\big{]}.\end{split}  \tag{18}\]