esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Watermark Embedding
#### 2.3.4 Insertion in the Least Significant Bit (LSB)  
A digital image is composed of small dots called pixels, and each pixel is a series of zeros and ones that represent a given color. When masking the least significant bits of the color value with message bits, embedding takes place. This process is called the least significant bit (LSB) technique, and it is a widely used method in image manipulation. The more bits used in masking, the more noticeable the distortion in the image. On the other hand, using more bits in the masking process provides more embeddable data. As a result, a compromise should be made to balance the quality of the resulting watermarked image and the amount of data embedded. The process of LSB is repeated in the given manner until the whole message has been embedded. Although it has been used in various applications, such as information hiding, copyright protection, and authentication, LSB has some limitations, especially when implemented in a time domain. For example, the use of advanced steganalysis techniques may result in the whole method becoming corrupted. Therefore, to overcome these limitations, researchers have developed more complex steganography techniques that provide better security and greater capacity for hiding data while maintaining imperceptibility.  
One such technique is the transformation of the image to the frequency domain and the application of LSB on selected coefficients of the transformation. By utilizing other image domains, larger amounts of data can be hidden while still maintaining excellent transparency. As more steganalysis techniques are introduced, the acquisition of more advanced and secure steganography techniques becomes of paramount importance.
## 2 Related Work and Security Landscape in mHealth
### Positioning Our Contribution  
The rise of interest in mHealth environments has generated a wealth of literature surrounding security, but much of this literature is focused on discrete components, such as BLE communication in wearables, permission models in Android applications, or container hardening in cloud-based healthcare infrastructure. This literature helps advance knowledge in their respective areas but is heavily siloed, typically only looking at a single layer of the mHealth architecture.  
Additionally, many assessments are theoretical, simulation-based, or simply rely on abstract threat models without any real-world validation. This means that most assessments lack practical applicability to real-world situations, especially with complex systems integrating different layers of hardware, firmware, and software.  
Some recent work in mHealth has related frameworks for risk monitoring or adaptive threat modelling. However, many are evaluated on modular, testbeds without the contextual complexity of real devices and infrastructure. The inter-layer dependencies, such as how the wearable's BLE data might influence mobile behavior or how a mobile's misconfiguration would be introduced to a backend API, is insufficiently examined in the literature [25; 26].  
This limited perspective is addressed in this study through a full-stack, real-world security audit on one end-to-end (wearable, mobile, and server) mHealth platform (PHGLCOVID) comprised a Galaxy Watch 7 wearable, Samsung Galaxy A55 smartphone, and a Docker backend platform. Each layer was analyzed with a combination of static analysis, dynamic evaluation, and penetration testing. The research examined the extent of some vulnerabilities at just the wearables level, mobile level, and server level, but also at how the vulnerabilities map together across each system layer.  
Unlike previous assessments that focus on simulation investigations, the assessments within this study were validated under operational conditions, using the actual firmware as well as the real BLE traffic and data flows. By utilizing real data collections, we were able to identify compound risks such as insecure BLE characteristics leading to token leakage on mobile devices, and misconfigured server containers that accept mesh-based payloads without validation.  
As a result, we provide a reproducible and transferable audit mechanism that preserves the totality of security threats in mHealth environments as a whole. To our knowledge it is also among the first end-to-end (wearable, mobile, server) security audits of a clinical-inspired mHealth testbed, providing both applicable findings as well as a broader view of where we need to go for future mHealth system creation.
###### Abstract  
The scope of the current study was to monitor if semi-quantitative data of volatile compounds (volatilelome) of citrus honey (ch) produced in different countries could potentially lead to a new index of citrus honey authentication using specific ratios of the identified volatile compounds in combination with machine learning algorithms. In this context, the semi-quantitative data of the volatilome of 38 citrus honey samples from Egypt, Morocco, Greece, and Spain (determined by headspace solid phase microextraction coupled to gas chromatography mass spectrometry (HS-SPME/GC-MS)) was subjected to supervised and unsupervised chemometrics. Results showed that honey samples could be classified according to the geographical origin based on specific volatile compounds. Data were further evaluated with additional nectar honey samples introduced in the multivariate statistical analysis model and the classification results were not affected. Specific volatile compounds contributed to the discrimination of citrus honey in different amounts according to geographical origin. These were lilac aldehyde D, dill ether, 2-methylbutanal, heptane, benzaldehyde, \(\alpha\),4-dimethyl-3-cyclohexene-1-acetaldehyde, and herbicide (isomer II). The numerical data of these volatile compounds was summed up and divided by the total semi-quantitative volatile content (R\({}_{\text{ch}}\), Karabagias-Nayik index) of citrus honey, according to geographical origin. Egyptian citrus honey had a value of R\({}_{\text{ch}}\) = 0.35, Moroccan citrus honey had a value of R\({}_{\text{ch}}\) = 0.29, Greek citrus honey had a value of R\({}_{\text{ch}}\) = 0.04, and Spanish citrus honey had a value of R\({}_{\text{ch}}\) = 0.27, leading to a new hypothesis and a complementary index for the control of citrus honey authentication.  
citrus honey; volatiles; characterization; machine learning; discrimination; new index Article
###### Abstract
[23] focused on the need for timely dynamic anomaly detection in monitoring data streams and proposed a new parameter configuration method for continuous outlier monitoring in sliding window-based data streams using distance-based techniques, validating the approach on both real-world and synthetic datasets. Deep learning-based methods for time series anomaly detection typically include convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and transformer-based approaches. For instance, [PERSON] et al. [24] proposed an automatic anomaly diagnosis method using a multimodal deep neural network to address various abnormal patterns in structural health monitoring data. [PERSON] et al. [25] proposed a multi-feature deep fusion model for unstable log anomaly detection to handle challenges such as concept drift, noise interference, and ambiguous feature relationships, thereby improving detection accuracy in dynamic environments. [PERSON] et al. [26] proposed a multi-channel input autoencoder framework for multivariate time series anomaly detection to overcome limitations of deep learning, including poor generalization capability and the requirement for large labeled datasets. In summary, while traditional outlier detection methods can effectively identify anomalies in monitoring data, they require manual setting of outlier criteria or optimization algorithms to adjust detection parameters. This introduces strong subjectivity or operational complexity. Moreover, such methods are typically limited to detecting a single type of outlier and fail to effectively identify multiple types of anomalies. To address these limitations, this study constructed a novel approach for detecting, identifying, and imputing multi-type anomalous data. Building upon traditional particle filtering, an improved particle filter based on a Bernoulli probability model is constructed to characterize multi-type anomalies in monitoring data [27; 28; 29]. By extending the probability model parameters to the seepage pressure state vector, online detection and identification of multi-type anomalies are achieved. After removing anomalous data, the MissForest method--an iterative imputation algorithm based on random forest--is introduced for data correction. Subsequent case studies and comparative experiments validate the effectiveness of the proposed method. The results further demonstrate that the proposed approach overcomes key shortcomings of traditional methods, such as offline detection, low efficiency, strong subjectivity in evaluation, and inability to identify multi-type anomalies. Additionally, it enhances the capability for seepage pressure data reconstruction and provides reliable data support for dam safety assessment and abnormal seepage pressure monitoring.
## 1 Introduction
Since the power source generally has a large moment of inertia, directly engaging the clutch or synchronizer will cause a large friction loss, which will affect the shift quality. Reference [26] regulated the motor rotary velocity, using a proportional-integral-derivative (PID) algorithm to make it synchronize with the second gear gradually. In references [27; 28], an experimental platform for motor active synchronous shifting was built, and the sliding mode control algorithm was used to regulate the rotary velocity of the ring gear. The effectiveness of the control algorithm was verified through a shift experiment. In [29], motor active synchronization technology, based on a PID algorithm, was used to assist in the engagement of the synchronizer. At present, there have been few studies that considered the stability and rapidity of the control algorithm in motor active synchronization technology. To solve the problem of power interruption seen in traditional HEVs, this paper proposes a novel dual-motor hybrid power system that can realize a shift without power interruption. At present, most gearbox technologies that overcome the power interruption of shifting are adding planetary gears or other transmission devices on the basis of traditional gearbox technical solutions. These technical solutions will increase the difficulty of structural arrangement and cost. In addition, since the gear shifting process requires the power to be switched between the two power transmission circuits twice, it is difficult to achieve better gear shift quality. The configuration proposed in this paper uses another motor to output its power to the wheels through another power transmission route, as to realize the uninterrupted transmission of power during the shifting process, and the flexibility of power compensation is better. In addition, for the motor rotary velocity regulation control involved in the shift process, a compound intelligent control algorithm is proposed for the motor rotary velocity control system. At the same time, the characteristics and laws of the control affect when this motor rotary velocity control algorithm was used with different target rotary velocities were explored. Based on this, a coordinated control strategy for the shift process was designed and simulated. The rest of the paper is organized as follows. Section 2 presents the configuration and working principles of the novel HEV. Section 3 introduces the modeling of the powertrainsystem. By analyzing motor rotary velocity regulation control algorithm, the gear shifting control strategy is proposed in Section 4. The performance of the proposed control strategy is analyzed in Section 5, followed by concluding remarks in Section 6.
## 3 Problem Definition
### Problem Parameters, Variables and Constraints  
A set of \(n\) assets is given by the market, \(A=\{a_{1},a_{2},\ldots,a_{n}\}\), where: _(a)_\(\forall i\in\{1,2,\ldots,n\}\), \(a_{i}\) has a known expected return, \(r_{i}\geq 0\); _(b)_\(\forall i,j\in\{1,2,\ldots,n\}\), the pair \((a_{i},a_{j})\) has a known expected covariance, \(\sigma_{ij}=\sigma_{ji}\geq 0\); _(c)_ a user-provided value, \(R>0\), represents the minimum expected return from the investment (expected return constraint); _(d)_ a portfolio is a vector \(X=(x_{1},x_{2},\ldots,x_{n})\) such that each \(x_{i}\) represents the fraction of the total wealth invested in asset \(a_{i}\), i.e., \(0\leq x_{i}\leq 1\) and \(\sum_{i=1}^{n}x_{i}=1\); _(e)_\(\forall i\in\{1,2,\ldots,n\}\), \(z_{i}=1\) if \(x_{i}>0\) (i.e., \(a_{i}\) in portfolio) and \(z_{i}=0\) otherwise; _(f)_ the number of assets in the portfolio, \(\sum_{i=1}^{n}z_{i}\), is bounded by user-defined values, \(k_{min}\) and \(k_{max}\) (cardinality constraints); _(g)_ the user can pre-select certain assets to be included in the portfolio, i.e., \(\forall i\in\{1,2,\ldots,n\}\), \(p_{i}=1\) if \(a_{i}\) is pre-assigned (i.e., \(x_{i}>0\)) and \(p_{i}=0\) otherwise (pre-assignment constraints); and _(h)_ for each asset \(a_{i}\), its associated quantity in the portfolio, \(x_{i}\), is bounded by user-defined values, \(\varepsilon_{i}\) and \(\delta_{i}\) (quantity constraints). The typical objective of this problem is to select the optimal combination of fractions of each asset, \(x_{i}\), so that the overall variance (risk) is minimized while satisfying all the aforementioned constraints.
## 4 Methodology
### The Hybrid Approach  
Using a single supervised classifier to detect saturation attacks leaves SDNs vulnerable to unknown saturation attacks [9]. The supervised classifiers require a large size of training dataset that includes large samples of each type of attack. This is because the supervised classifiers cannot generalize enough to detect any unknown attack. Therefore, the SDN environment will be vulnerable to flooding attacks.  
In this paper, we study the effectiveness of combining supervised and semi-supervised classifiers to detect known and unknown attacks against SDNs. The architecture of the hybrid approach comprises two classification algorithms: First, a supervised classifier to detect the known attacks. In our case, the K-NN classifier is adopted since it provides the highest detection performance for known attacks among the other supervised classifiers evaluated in this work. Second, we adopt the variational autoencoder as a semi-supervised classifier to detect unknown attacks due to it is high performance in detecting saturation attacks, as reported in the experimental results.  
The hybrid approach is a two-stage process: the training stage and the detection stage. In the training stage, a pre-made datasets should be used to train the two classifiers. The training dataset of the supervised classifiers should include labeled observations for known attacks and normal traffic. In this work, the training dataset of the semi-supervised classifier includes observations of normal OpenFlow traffic. In the detection stage, if any of the classifiers or both of them raise an alert, an attack is detected. Our offline experiments show that the hybrid approach provides a higher detection performance for known and unknown attacks than using a single supervised or semi-supervised classifier.
## 5 Conclusions  
The article shows that determining the tolerant control (TC), even for uncertain static systems described by the simplest form of the interval equation \([\underline{a},\overline{a}]X=[\underline{b},\overline{b}]\), is not an easy task, because in many cases it is impossible to find such a value of control (decision) \(x\), which will ensure a reliable hit of the \(ax\) value in tolerance corridor \([\underline{b},\overline{b}]\). However, even in the most difficult tasks it is possible to define partly robust control and it is possible to find its optimal value \(x_{opt}\), which with the maximum possible robustness will give a chance to achieve TC. The reason for the inability to obtain fully robust TC is the presence of disturbance uncertainty generators (DUGs), i.e., various types of uncertain data. As their number increases, the possibility of implementing of fully robust TC decreases quickly. It is rather consistent with the intuition of engineers and scientists. In practice, the conditions for perfect implementation of the control objectives are rare. The paper explains the new approach to determining TC and indicates that it is a starting point for solving more complicated forms of static control, which are described by systems of uncertain, interval and fuzzy linear, quadratic, etc. equations. This opens the way for interesting research. The article also explains the hitherto unexplained Lodwick's anomaly concerning the interval equations.  
In conclusion, the key issues presented in the paper are as follows:  
* the new method based on MIA for solving the basic linear interval equation;
* the method of determination of the solution robustness;
* the method of determination of the solution that is optimal in terms of control robustness for the uncertainty of system parameters; and
* the method of determination of the optimal tolerance control for the equation \([\underline{a},0,\overline{a}]X=[\underline{b},\overline{b}]\) in which the interval uncertainty on the left side of the equation contains zero.  
Conceptualization, A.P.; Formal analysis, A.P.; Investigation, A.P. and M.P.; Methodology, A.P. Software, M.P.; Visualization, M.P.; Writing--original draft, A.P.; Writing--review & editing, M.P. 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.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IA & interval arithmetic \\ SIA & standard interval arithmetic \\ UBM & unnatural behavior in modeling \\ LIEA & [PERSON]'s interval equation anomaly \\ MIA & multidimensional interval arithmetic \\ TSS & tolerance solution set \\ LTP & linear tolerance problem \\ ILS & interval linear system \\ \(n\)V-ILS & \(n\)-variable interval linear system \\ PTS & probability of tolerance satisfaction \\ DUG & disturbance uncertainty generator \\ TO & tolerance output \\ TC & tolerance control \\ PrTC & probabilistic tolerance control \\ IE & interval equation \\ FA & fuzzy arithmetic \\ \end{tabular}
## 4 Case Study--Safety Assessment of the Rockfill Specim in Triaxial Test
### Monitoring Data  
The monitoring data incorporated in this example correspond precisely to the stress-strain state of the reference specimen captured at the moment the triaxial test is halted, before the material reaches its peak strength. At the selected stopping point in the shearing phase, the confining stress is \(\sigma^{\prime}{}_{3}=2000\) kPa, while the axial stress has increased to \(\sigma^{\prime}{}_{1}\approx 5860\) kPa (Figure 8). At this point, the safety of the specimen is assessed. Before stopping the test, the specimen behaves as shown in Figure 9, with a final observed axial strain \(\varepsilon_{1}=2.45\%\).  
The defined analysis scenario places the reference specimen in the high-confinement domain (\(\sigma^{\prime}{}_{3}=2000\) kPa), which is a random event and could not be anticipated when the investigation testing campaign was set for a broad range of possible stress states. Material parameters calibrated from the 12 test results cannot easily reproduce the actual response in one specific portion of the pre-defined broader range. Hence, in this scenario, the monitoring data play a crucial role: they steer the baseline safety analysis, which spans a broader spectrum of possible behaviors, toward the zone that reflects the actual material response.
## 3 Selection of Finite Element Model (FEM) and Selection of Optimization Objectives
### Validation of FEM  
As required by the validation of FEM simulation accuracy, apart from the control of triangle quantity, it is also necessary to ensure energy conservation throughout the crash simulation process. LS-DYNA software applies Single-Point Gauss Integral method in the calculation of FEM, as this method is very suitable for the structural simulation calculation of whole-vehicle crash in which severe deformation occurs. However, this method may produce an hourglass pattern during the calculation.  
Hourglass deformation will cause the failure of the simulation result, so we should try to protect against it. However, as hourglass deformation is unavoidable, what we need do is to control it within a certain range (10%). Unless this target value is achieved, the accuracy of the simulation model cannot be ensured. Figure 10 shows the energy change  
Figure 8: The establishment process of whole vehicle FEM.  
Figure 9: FEM of whole-vehicle-side crash.  
curve during the crash of the model described in this paper. Hourglass contributes 5.5% of total energy; within the required range, this means that, from the perspective energy, the model is validated.  
To save computation time, element density is adjusted automatically in simulation by controlling mass scaling. However, the change of element density will increase the mass of the model, therefore affecting computation accuracy. Only by ensuring that the mass increase is within a certain range while reducing computation time can we ensure the effectiveness of the model. According to experience and the literature, we should control the mass increase within 5%. As shown in Figure 11, the mass increase is 195 kg, contributes 3.6% of total, so it meets the requirement.  
To sum up, the FEM established under this paper is effective.
# Domain Knowledge Graph Question Answering Based on Semantic Analysis and Data Augmentation
## 5 Experiment and Analysis
### Question Intention Classification Experiment
#### 5.2.3 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Train Dataset** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{2}{*}{Class 1} & unaugmented & 0.9516 & 0.9043 & 0.9271 \\  & augmented & 0.9974 & 0.9893 & 0.9813 \\ \hline \multirow{2}{*}{Class 2} & unaugmented & 0.7953 & 0.8761 & 0.8327 \\  & augmented & 0.9902 & 0.9576 & 0.9271 \\ \hline \multirow{2}{*}{Class 3} & unaugmented & 0.8735 & 0.9858 & 0.9244 \\  & augmented & 0.9911 & 0.9904 & 0.9897 \\ \hline \multirow{2}{*}{Class 4} & unaugmented & 0.9439 & 0.9293 & 0.9354 \\  & augmented & 0.9986 & 0.9973 & 0.9961 \\ \hline \multirow{2}{*}{Class 5} & unaugmented & 0.954 & 0.9537 & 0.9537 \\  & augmented & 0.9981 & 0.9952 & 0.9924 \\ \hline \multirow{2}{*}{Class 6} & unaugmented & 0.8738 & 0.8953 & 0.8843 \\  & augmented & 0.9921 & 0.9837 & 0.9754 \\ \hline \multirow{2}{*}{Class 7} & unaugmented & 0.9089 & 0.8372 & 0.8709 \\  & augmented & 0.9965 & 0.989 & 0.9816 \\ \hline \multirow{2}{*}{Average} & unaugmented & 0.8998 & 0.8965 & 0.8969 \\  & augmented & 0.9949 & 0.9615 & 0.9774 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Train dataset comparison experimental results. head of household?' These samples make it difficult for the model to learn, resulting in weak prediction performance for this intention category. As shown in the evaluation metric average curve comparison in Figure 10, when the model is trained on the \(augmented\_train\_set\), the model's fitting speed increases, and all evaluation metrics improve significantly, resulting in a marked improvement in prediction performance. Similarly, Figure 11 demonstrates that the model's convergence speed accelerates, and the degree of overfitting is reduced, resulting in improved generalization ability.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 4 Wireless Communication System Using Chaotic Filter  
Applying the proposed chaotic filter as a pulse-shaping filter and matched filter, a novel chaotic baseband wireless communication system is constructed. The effects of different wireless channels on the system are discussed. The BER performance and computational complexity of different methods are compared.