esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preliminaries
#### 2.2.3 Model
This formulation adapts the structural representation to incorporate information from different hops, enabling the model to capture nuanced relationships and dependencies within the graph. We calculate the similarity scores for a given target link pair \((i,j)\) based on the computed values \(h_{i}\), \(h_{j}\), \(z_{i}\), and \(z_{j}\). By adjusting the weights of structure and attributes using parameter \(\alpha\), the \(y_{ij}\) is determined as follows:  
\[\hat{y}_{ij}=\alpha\cdot\sigma\Big{(}z_{i}^{T}z_{j}\Big{)}+(1-\alpha)\cdot \sigma\Big{(}s\Big{(}h_{a}^{i},h_{a}^{i}\Big{)}\Big{)} \tag{10}\]  
This equation combines the structural similarity \(\big{(}z_{i}^{T}z_{j}\big{)}\) and attribute-based similarity \(\Big{(}s\Big{(}h_{a}^{i},h_{a}^{i}\Big{)}\Big{)}\) with weights controlled by \(\alpha\). The model is jointly trained using a composite loss function consisting of three standard binary cross-entropy terms:  
\[\mathcal{L}=\sum\
olimits_{(i,j)\in\mathcal{D}}\Big{(}\lambda_{1}BCE\Big{(} \hat{y}_{ij},y_{ij}\Big{)}+\lambda_{2}BCE\Big{(}\sigma\Big{(}z_{i}^{T}z_{j} \Big{)},y_{ij}\Big{)}+\lambda_{3}BCE\Big{(}\sigma\Big{(}s\Big{(}h_{a}^{i},h_{ a}^{j}\Big{)}\Big{)},y_{ij}\Big{)}\Big{)} \tag{11}\]  
Here, \(BCE\) represents the binary cross-entropy loss, \(\sigma\) is the activation function, and \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\) are utilized to adjust the weight of each loss term. This comprehensive loss function guides the model to simultaneously optimize structural, attribute, and combined similarities during training.
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Invertebrates
#### 2.1.3 Flea Inspiring Jumping Robot  
It is reported that fleas can jump over obstacles that are more than 40 times their own size, and they can execute a brilliant jump in less than one millisecond [54]. Although jumping performances vary among species [114], all jumping mechanisms of fleas are essentially very similar to each other [115; 116; 117]. First, fleas lock the two hind legs, slowly deforming their muscles as they were springs in which energy is stored; then a huge amount of power is provided to perform a brilliant jump. During take-off, their legs work as a lever system to transmit forces from the springs to the ground, passing through the tarsus [55].  
[PERSON] et al. [118] developed a flea inspired jumping robot, using three SMA springs to mimic the coordination of different muscles inside the hind legs of flea. With a 2 cm body length and a 1.1 g body weight, it can achieve a jump height around 30 times its body size. Based on this robot, the effect of legs' compliance and take-off angle on jumping efficiency are studied [119; 120]. The team subsequently proposed another flea inspired jumping robot [121; 122; 123] that exploits bistable torque reversal mechanism. This robot can achieve a jump height of about 150 times its own body size with a body mass of 30 mg.  
[PERSON], in collaboration with Boston dynamics, developed jumping robots using explosive energy, such as \"Urban Hopper\" [124] and \"SandFlea\" [125]. \"SandFlea\" can freely adjust the take-off angle, height, and distance. The explosion energy helps \"SandFlea\" achieve a jumping height of 10 m. The jumping height can be adjusted according to the size of the obstacles, and it is possible to execute a maximum of 25 consecutive jumps. However, it is unable to realize aerial righting.
# Predictive Fraud Analysis Applying the Fraud Triangle Theory through Data Mining Techniques
## 4 Results and Discussion
### Detection of Phrases Related to Fraud
#### 4.2.3 Classifier Performance  
To benchmark these different classifiers, choosing a corresponding metric is critical. For this work, we selected AUC since it is very popular and adequate when we care about ranking predictions and not necessarily about obtaining well-calibrated probabilities [79]. Particularly, if classes are balanced, and there is no certainty that the classifier chose the best decision threshold, it is best to select AUC, which is equivalent to the probability that the classifier will assign the highest score to the relevant classes compared to the irrelevant ones [80]. As described in Section 2, ROC is a curve that represents the true positive rate vs. the false positive rate, where the area determines the performance of the model under such a curve. The closer the AUC score is to 1, the better the model distinguishes between classes. On the other hand, if it is closer to 0.5, the model performs just as well as a coin toss.  
For our work, we use the ROC curve to depict the performance of different machine learning models when classifying documents as being related or unrelated to fraud. The results can be seen in Figure 6, but are also presented in Table 6.  
These results show that random forest and gradient boosting obtain the best performance with a mean AUC of 0.81. Interestingly, \(k\)-nearest neighbors, GNB, and SVM also perform well with a mean AUC higher than 0.75. These results suggest that our approach to detecting activity related to fraud, based on identifying topics with LDA, might be feasible in practice when building machine learning models.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Classification Method’s**} & \multicolumn{4}{c}{**Predictive Accuracy**} & \multirow{2}{*}{**Mean**} \\ \cline{2-2} \cline{4-5}  & **T1** & **T2** & **T3** & **T4** & \\ \hline Logistic Regression: AUC & 0.83 & 0.64 & 0.68 & 0.65 & 0.70 \\ Random Forest: AUC & 0.88 & 0.77 & 0.80 & 0.79 & **0.81** \\ GNB: AUC & 0.86 & 0.70 & 0.74 & 0.73 & 0.76 \\ Gradient Boosting: AUC & 0.89 & 0.77 & 0.79 & 0.79 & **0.81** \\ \(k\)-NN: AUC & 0.86 & 0.72 & 0.76 & 0.74 & 0.77 \\ Decision Tree: AUC & 0.80 & 0.71 & 0.73 & 0.75 & 0.74 \\ SVM: AUC & 0.86 & 0.70 & 0.75 & 0.74 & 0.76 \\ \hline \end{tabular}
\end{table}
Table 6: Performance, measured with AUC, of different machine learning models when classifying a document as related or unrelated to fraud. T1, T2, T3, and T4, correspond to each data set, where a topic learned from LDA is dominant.
## 2 Materials and Methods
### Emission Factor (EF) and Potential Ozone Forming Index (POCI)  
The emission rates (ER) represent the amount of compound species emitted for each terpene into the atmosphere per unit of leaf mass and time. The ER was calculated as:  
\[\text{ER}=\text{C}\times\text{Q}/\text{M} \tag{1}\]  
ER: emission rate (\(\mu\text{g}\) h\({}^{-1}\)), C: compound concentration (\(\mu\text{g}\) m\({}^{-3}\)), Q: measured airflow (m\({}^{3}\) h\({}^{-1}\)) and M: leaf dry mass (g\({}_{\text{ldw-1}}\)).  
For comparison purposes, ER were standardized at 30 \({}^{\circ}\)C and 1000 \(\mu\text{mol}\) m\({}^{2}\)s\({}^{2}\) photosynthetically active radiation (PAR), according to the protocols provided by [PERSON] et al. (1993) [20], being called henceforth emission factor (EF). Environmental variables (i.e., relative  
Figure 2: Measurements procedure for sampling BVOCs (green circles), CO\({}_{2}\) and methane (blue circles); with a common step to both procedures in a yellow circle.  
humidity, air temperature and PAR) were automatically recorded using the corresponding sensors connected to an EM50 Data logger, which stores the data every minute, allowing for an average value for the variables required during sampling to be obtained.  
To quantify the contributions of BVOCs to the formation of tropospheric ozone, the potential ozone-forming index (POCI) was used as proposed by [PERSON] et al. (2013) [17]:  
\[\text{POCI}=\Sigma\text{EF}_{\text{i}}\times\text{POC}_{\text{i}} \tag{2}\]  
where \(\text{EF}_{\text{i}}\) is the emission factor obtained experimentally for each chemical species of BVOC; and \(\text{POC}_{\text{i}}\) is the Photochemical Ozone Creation Potential calculated by [PERSON] et al. (2007) [21] for each chemical species.
## 1 Introduction
It has been observed that so far in OFDM-based CR system, a GSC has not been used to tackle the OOB radiation problem. In the proposed technique, the signal is passed through two branches of a GSC. The upper branch consists of the weight vector designed by multiple constraints to preserve the desired portion of the signal, while the lower branch consists of a blocking matrix followed by a weight vector. The blocking matrix blocks the desired portion and preserves the undesired portions (sidelobes). The weight vector adjusts the undesired portion (sidelobes) in such a way that when subtracted from the signal of the upper branch, it results in significant cancellation of the sidelobes of the OFDM signal. We have compared the performance of the proposed technique with already existing techniques via simulations. The proposed technique achieves better suppression of the sidelobes as compared to the existing methods. The remaining paper is arranged as follows: Section 2 contains the data model and Section 3 gives the proposed methodology. In Section 4, simulation results are carried out, while Section 5 concludes and gives future work recommendations.
# On Wind-Induced Fatigue of Curtain Wall Supporting Structure of a High-Rise Building
## 3 Analysis of Results
### Hypothesis Test for Fatigue Life Distribution
According to the theorem, for a given significance level \(a\), critical value \(\chi^{2}_{a}\) is obtained by looking up the distribution table:  
\[P\left(\chi^{2}>\chi^{2}_{a}\right)=\alpha \tag{17}\]  
Obtain rejection region:  
\[\chi^{2}>\chi^{2}_{a}(l-k-1) \tag{18}\]  
If the measured value of statistic \(\chi^{2}\) calculated according to the given sample value \(x_{1},x_{2},\ldots,x_{n}\) falls into the rejection region, the null hypothesis is rejected. Otherwise, the difference is considered insignificant and the null hypothesis is accepted. Propose a hypothesis for statistical data of wind-induced fatigue of curtain wall: fatigue life data obeys normal distribution, generalized extreme value distribution, and kernel distribution. The default intervals are 10, 2 unknown parameters of the normal distribution, 3 unknown parameters of GEV distribution, and 1 unknown parameter of kernel distribution. According to them, the critical value \(\chi^{2}_{a}\) corresponding to the corresponding significance level \(a\) can be found on the quantile table on the upper side of the chi-square distribution. Table 7 lists the chi-square test parameter calculation and search results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{\begin{tabular}{c} **Actual** \\ **Frequency** \\ \end{tabular} } & \multicolumn{3}{c}{**Theoretical Frequency**} \\ \cline{3-5}  & & **Normal Distribution** & \
\begin{tabular}{c} **Generalized Extreme** \\ **Value Distribution** \\ \end{tabular} &
\begin{tabular}{c} **Kernel Density** \\ **Estimation** \\ \end{tabular} \\ \hline
1 & 12, 11 & 12.7, 10.3 & 12.3, 10.7 & 11.9, 11.1 \\ \hline
2 & 6, 8 & 7.0, 7.0 & 7.6, 6.4 & 7.4, 6.6 \\ \hline
3 & 6, 9 & 7.0, 8.0 & 7.6, 7.4 & 7.4, 7.6 \\ \hline
4 & 7, 7 & 8.7, 5.3 & 5.7, 8.3 & 5.6, 8.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Hypothesis test frequency for life distribution. It shows from the table, \(\chi^{2}\) values of four selected nodes are all much smaller than \(\chi^{2}_{a}\), indicating that the null hypothesis is established, and fatigue life data obey the above three distributions. Among them, the calculated value of the chi-square distance of node 1 for three distributions is small, meaning the degree of conformity is good. On the whole, the minimum value is found in the calculation of Node 1 to the kernel distribution, which is consistent with the statistics of life probability distribution in the previous part. The maximum value appears in the calculation of Node 4 to a normal distribution, less than 1, indicating that fatigue life data are in good agreement with the three distributions.
# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Data Preprocessing
#### 4.1.4 Creation of Derived or Interaction-Based Features to Enhance Predictive Power  
To enrich the feature space and improve the model's ability to capture complex patterns, a variety of derived and interaction-based features were engineered in addition to the raw attributes. This transformation aimed to encode domain-specific knowledge into structured numerical indicators, thus enhancing the predictive power of the learning algorithms.  
For instance, Car Age (Years) was computed from service and registration dates to represent vehicle tenure. Mileage per Day was calculated by dividing cumulative mileage by vehicle age in days, standardizing customer usage patterns. Similarly, Service Visits per Year normalized service behavior over time, and Z-score (User and Car) quantified temporal deviation from a user's typical service rhythm, highlighting anomalous visit timings. The DSG Ratio, on the other hand, reflected the historical failure rate of DSG gearboxes within similar model clusters, thereby embedding contextual mechanical risk.  
These features were carefully selected and constructed through iterative analysis and consultation with domain experts. Notably, Table 11 in Appendix A provides a comprehensive overview of all raw and derived variables along with their definitions and modeling rationale, ensuring transparency and interpretability of the engineered feature set.  
Together, these enhancements allowed the model to access latent relationships not readily visible in the raw data, leading to measurable gains in performance and generalization.
# Secure Physical Layer Network Coding versus Secure Network Coding +
## 4 Network with Three Source Nodes
### Secure NC
#### 4.1.1 Security (S3)  
The following code satisfies Security (S3) when \(q\) is not a power of 2. This code uses \(1/2\), which cannot be allowed in finite field \(\mathbb{F}_{q}\) of a power \(q\) of 2. Notice that the matrix \(\left(\begin{array}{ccc}0&1&1\\ 1&0&1\\ 1&1&0\end{array}\right)\) is invertible because \(\left(\begin{array}{ccc}-1/2&1/2&1/2\\ 1/2&-1/2\\ 1/2&1/2&-1/2\end{array}\right)\) is the inverse matrix. Source node \(S_{i}\) sends \(M_{i}\) in each edge. Each intermediate node sends the sum of the received vector. Finally, applying the inverse matrix \(\left(\begin{array}{ccc}-1/2&1/2&1/2\\ 1/2&-1/2&1/2\\ 1/2&1/2&-1/2\end{array}\right)\) to the received vector, the node \(D\) recovers all messages. In this code, each of the messages \(M_{1}\oplus M_{2}\), \(M_{2}\oplus M_{3}\), and \(M_{3}\oplus M_{1}\) are independent of anyone of \(M_{1}\), \(M_{2}\), and \(M_{3}\). Hence, Security (S3) is satisfied. This protocol achieves the optimum transmission rate even when the secrecy condition is not imposed.  
As the next step, let us proceed to the case when \(q\geq 4\) is a power of 2. We choose an element \(e\in\mathbb{F}_{q}\) such that \(e^{2}\oplus e\
eq 0\), which implies that \(\left(\begin{array}{ccc}0&1&1\\ 1&0&e\\ e&e&0\end{array}\right)\) is invertible because its determinant is \(e^{2}\oplus e\
eq 0\). For example, when \(q=4\), since \(e^{2}=e\oplus 1\), the inverse matrix is: \(\left(\begin{array}{ccc}e\oplus 1&e&e\\ e\oplus 1&e&1\\ e&e&1\end{array}\right)\). Then, the following code is secure. Source node \(S_{i}\) sends \(M_{i}\) in each edge. The intermediate nodes \(I_{1}\), \(I_{2}\), and \(I_{3}\) send the received information \(Z_{1}:=M_{2}\oplus M_{3}\), \(Z_{2}:=M_{1}\oplus eM_{3}\), and \(Z_{3}:=eM_{1}\oplus eM_{2}\), respectively. Finally, applying the inverse matrix of \(\left(\begin{array}{ccc}0&1&1\\ 1&0&e\\ e&e&0\end{array}\right)\) to the received vector \(\left(\begin{array}{c}Z_{1}\\ Z_{2}\\ Z_{3}\end{array}\right)\), the node \(D\) recovers all messages. In this code, each of the information symbols \(eM_{1}\oplus eM_{2}\), \(M_{2}\oplus M_{3}\), and \(eM_{3}\oplus M_{1}\) are independent of anyone of \(M_{1}\), \(M_{2}\), and \(M_{3}\). Hence, Security (S3) is satisfied.
## 4 Numerical Study
### Simulation Results
#### 4.3.2 Detailed Analysis of the Scenario Without Obstacles  
Presented in Figures 7-9, all simulations can return a planned shipping path with no obstacles settled, demonstrating the feasibility of the proposed LH algorithm.  
With the incremental map sizes, all of the metrics that evaluate the computation cost, e.g., the average and standard deviation of the 'Searching Time' and the 'Number of Traversed Nodes', increase for all four algorithms. But, the proposed method always returns a lower average value and a smaller standard deviation in comparison to the methods of A-star (\(l_{1}\)) and BFS (\(b_{w}^{*}=0.7\)), indicating better performance than these two methods. RRT-star (\(\eta=3\)), benefiting from its optimal parent node selection and rewiring mechanisms, exhibits the shortest average searching time \(\overline{t}_{\text{search}}\) in all three scale maps, shown in Figures 7a, 8a, and 9a, with a lower maroon curve. But, because this algorithm needs to check different nodes in the nearby neighborhood whenever a new node is generated, it returns a much larger value of \(\text{std}_{\text{search}}\), \(\overline{n}_{\text{search}}\), and \(\text{std}_{\text{node}}\) or poorer performance stability.  
Therefore, the proposed LH algorithm can guarantee finding a conflict-free valid path and outperforms the three benchmark algorithms in the scenario without obstacles.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Scrambling  
The first module of the NPUSCH, following the channel coding section, is scrambling. Apart from the fact that it is a separate module, it is also used within the other sections, e.g., during the DMRS pilots generation. In general, scrambling is based on the pseudorandom Gold sequence to provide the randomness within transmitted information [4]. The scrambling sequence generator shall be initialized with the following \(c_{init}\):  
\[c_{init}=n_{RNTI}\cdot 2^{14}+n_{f}\bmod 2\cdot 2^{13}+\lfloor n_{s}/2\rfloor \cdot 2^{9}+N_{ID}^{Ncell}, \tag{7}\]  
where \(n_{RNTI}\) denotes predetermined radio network temporary identifier, \(N_{ID}^{Ncell}\) represents narrowband physical layer cell identity, \(n_{f}\) constitutes system frame number, and \(n_{s}\) indicates slot number within a radio frame. Furthermore, \(c_{init}\) should be reinitialized after every \(M_{identical}^{NPUSCH}\) that may be defined as follows:  
\[M_{identical}^{NPUSCH}=\begin{cases}\min\Big{(}\lceil M_{rep}^{NPUSCH}/2\rceil, 4\Big{)}&N_{sc}^{RUI}>1\\ 1&N_{sc}^{RUI}=1,\end{cases} \tag{8}\]  
where \(M_{rep}^{NPUSCH}\) denotes scheduled number of repetitions of the NPUSCH transmission [2]. The output vector of the scrambling module is the product of the XOR operation between the input bits and the calculated pseudorandom sequence. It is important to mention that the scrambling module operation is time-dependent in the context of continuous transmission, as the initialization factor \(c_{init}\) is based on, e.g., the frame number that varies over time.
## 4 Research Directions for System Designs and Optimization Concepts
### Optimization in PHY Security: Current Status and Main Issues
#### 4.2.4 Zero-Forcing (ZF) Precoding Techniques  
Precoding is another signal processing method that is used to deliver signals to the null space of wiretap channels. In ZF precoding schemes, the source Alice that uses multiple transmit antennae is based on the cancelation of unwanted signals at the destination node in multi-user or multi-stream data transmission [128]. Some authors provided an iterative algorithm to control the interference signals that are leaked to unauthorized users. The design of secure precoding that is based on ZF beamforming was discussed in [129] and requires both the complete CSI of the main transmission and wiretap channels at the transmitter and that the number of intended user antennae is greater than that of the eavesdropper. To be more specific, the security performance of ZF precoding becomes very poor when knowledge of the CSI of the eavesdropper at the transmitter is limited, which makes it less applicable.  
In summary, the concepts of optimization objectives, constrained conditions and corresponding performance parameters were discussed based on four basic signal processing techniques in order to easily understand design and optimization problems. Although optimization problems that use convex optimization achieve better secrecy performance than the conventional secure beamforming and zero-forcing precoding techniques, convex optimization is more complex and expensive to apply to different services within practical scenarios. To tackle these network complexity and computational costs, service providers should develop solutions to ensure reliability and provide secure resource management to their customers in a robust, intelligent and reliable way. In the next section, we discuss the use of AI and ML technologies as key enablers for achieving the above-mentioned system performance metrics.
###### Abstract  
Environmental micro-vibration has a significant impact on the proper functioning of semiconductor production and testing equipment such as the Czochralski growth furnace, polishing machine, photoetching machine, scanning electron microscope, etc. Low-frequency micro vibration has a significant influence on the normal operation of high-precision machining and testing equipment, and even causes irreversible damage to the equipment. Therefore, the micro-vibration test has important theoretical significance and engineering value for guiding the vibration isolation design of an electronic industrial workshop and ensuring the stable operation of various precision equipment in the workshop. As the observed acceleration signal is affected by noise introduced by the acceleration sensor itself, the signal processing circuit, the external power supply and interference from environmental factors, direct integration operations can lead to problems such as baseline drift and signal distortion in the calculation results. Aiming at the problem of noise interference in the micro-vibration measurement process, this paper proposed a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The observed acceleration signal is decomposed into several orders of finite bandwidth intrinsic mode function components. The asymmetric Gaussian mixture model is used to complete the fitting of the spectral curves of each order of intrinsic mode function components. We can obtain the relevant parameters of the asymmetric Gaussian mixture model curves to complete the division of the effective information frequency band and finally achieve the denoising of the observed acceleration signal. Finally, the algorithm of this paper is compared with traditional denoising algorithms through numerical simulation examples and comparative experiments on denoising effects. The results show that the proposed algorithm has higher accuracy and anti-noise ability.  
eurmental micro-vibration; variational mode decomposition; spectral morphology fitting; asymmetric Gaussian mixture model; signal denoising Article