esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Data Quality Results  
After finding the correct distributions of the latency values, we generated the TLA specifications of 10,000 instances. As a result, TLC collected the list of windows and the information as to whether they were dropped or not due to latency, and this list was saved into a file. The file content is a sequence of windows with statuses in the form of Boolean values. This sequence is applied to real executions using the pretrained AI model and the ECG dataset, so AI processes only those windows of the ECG dataset that have not been dropped due to latency. After evaluating the executions, we split the results into two sets. We measured the data quality and collected the anomalies after both the first level of aggregation and the second level of aggregation. At the first aggregation level, data quality loss is determined by the average number of AF episodes and AF windows missed in the simulations. At the second aggregation level, data quality loss shows the proportion of misclassifications in the breakdown of the chosen classification algorithms. Table 5 presents the summary of our results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approximation Method** & **Parameters** & **SSE** \\ \hline \multirow{4}{*}{GA} & \(\gamma=-0.44013026\) & \multirow{4}{*}{15.4543} \\  & \(\delta=32.52262997\) & \\  & \(\xi=0.31010099\) & \\  & \(\lambda=0.17227367\) & \\ \hline \multirow{4}{*}{PSO} & \(\gamma=-0.43741796\) & \multirow{4}{*}{15.4407} \\  & \(\delta=30.00\) & \\  & \(\xi=0.31001392\) & \\  & \(\lambda=0.16483363\) & \\ \hline \multirow{4}{*}{SA} & \(\gamma=-1.23205541\) & \multirow{4}{*}{53.9464} \\  & \(\delta=109.67317707\) & \\ \cline{1-1}  & \(\xi=143.09754355\) & \\ \cline{1-1}  & \(\lambda=771.84080061\) & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of approximation methods in finding the best parameters for Johnson’s Su distribution.  
Figure 9: Histogram of execution times with the fitted Johnson’s Su PDF in case of AF detection algorithm.  
It is seen that, on average, we dropped 15.11% of the windows due to latency in this use case and the average of detected AF windows reduced by 4.25%. AF episodes showed a different decrease of only 2.5%. We found it interesting to see how data quality decreases if we dropped every second window. It was observed that the number of AF windows were reduced by 14.5%, but the number of AF episodes became higher than in our simulation. This is because fewer windows are far apart in time and cannot be considered connected.
## 3 Methods
### Ensemble Methods
#### 3.5.1 Learning a Projection Function  
We train and learn a projection function on the intersection words of the pre-trained embedding models (SBERT and ADA-002) and ConceptNet Numberbatch, denoted by \(\Rightarrow\). The training direction is from SBERT or ADA-002 feature spaces to the ConceptNet Numberbatch space. This is because the pre-trained language models such as SBERT and ADA-002 tend to contain more words than ConceptNet Numberbatch since they are trained on large datasets and use WordPiece tokenization.  
Given \(\{E:e_{1},\ldots,e_{i},\ldots,e_{167321}\}\) as the concept vocabulary of ConceptNet Numberbatch, \(\{Y:y_{1},\ldots,y_{i},\ldots,y_{167321}\}\) is the corresponding ConceptNet Numberbatch embedding vector set, and \(\{W:w_{1},\ldots,w_{i},\ldots,w_{167321}\}\) is the corresponding concept entity embeddings from the pre-trained contextual language models, i.e., SBERT or ADA-002, respectively, in our case. A function \(f\) for \(f(W)\approx Y\) is trained. Here, we represent the function as a multi-layer perceptron (MLP). Starting from initial random weights, MLP minimizes the loss function by repeatedly updating these weights. After computing the loss, a backward pass propagates it from the output layer to the previous layers, providing each weight parameter with an update value meant to decrease the loss. We set the MLP to be two hidden layers and train it with the sum of two loss functions: mean squared error (MSE) + mean cosine similarity (MCS). Other parameters are set: epochs = 50, batch_size = 32, validation_split = 0.2, optimizer = adam. The MLP is trained on the intersecting words of SBERT! ADA-002 embeddings and ConceptNet Numberbatch. After training the MLP, the remaining word embeddings that exist in SBERT! ADA-002 but not ConceptNet Numberbatch are projected into the ConceptNet Numberbatch space. Note that we project from high-dimensional spaces of pre-trained language models to the 300-dimension ConceptNet Numberbatch space, as SBERT and ADA-002 embeddings have 768 and 1536 dimensions, respectively. Figure 2 provides an overview of the proposed method.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.4 Data (D) \(\rightarrow\) Wisdom (W)  
The cognitive subject integrates ethical and value judgments to generate goal-aligned, wise decisions through the semantic analysis of Data. This process requires not only a profound understanding of the Data itself but also the integration of experience and intuition to achieve a comprehensive cognition that transcends specific contexts. The following are several TRIZ principles closely related to this transformation process, along with their specific applications:  
* **Principle 40: Composite Materials** We extend the semantics of materials to transform them into Data, while composites are expanded into multiple channels or methods serving as Data sources. Correspondingly, in Information technology, this process involves integrating various Data sources and analytical models to generate comprehensive, high-quality decisions guided by Wisdom. This approach is exemplified in applications such as financial risk management [57] and the formulation of optimized traffic management strategies [58], where diverse Data inputs and sophisticated analysis converge to produce holistic, informed decisions.
# LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
## 4 Experiment and Analysis
### Experiment Setting
#### 4.1.3 Baselines  
LogBD was compared with the following log anomaly detection methods to evaluate the performance of LogBD:  
* PCA (principal component analysis) is based on a dimensionality reduction anomaly detection method, combined with term weighting technology in the field of data retrieval.
* logCluster, based on a similarity clustering method, can automatically find similar log patterns and aggregate them into clusters to achieve automatic identification of anomalies.
* DeepLog, an anomaly detection model based on log templates, uses LSTM to model log data, automatically learns abnormal behavior patterns, and compares predicted logs with real logs for anomaly detection.
* LogAnomaly integrates the log template by analyzing the synonyms in the log, encodes the log template into a semantic vector, and inputs it into the LSTM model for anomaly detection.
* LogBERT, a BERT-based method LogBERT, constructs two self-supervised training tasks to learn the pattern of normal log sequences and detect anomalies that deviate from the normal model.
* LogTransfer uses transfer learning to achieve cross-system anomaly detection, utilizes labeled normal and abnormal data of two systems to train the model, and shares a fully connected network that classifies logs.
## Appendix A Derivation of the Proposed Cost Terms  
### Feeder Duell User Cost (\(C_{duiF}\))  
The average cost of dwell time (\(C_{duiF}\)) is determined by the demand of route \(k\) multiplied by the passenger boarding and alighting rate. Therefore, the average dwell cost is derived as follows:  
\[C_{duiF}=\mu_{I}\left(Q_{k}\times t_{dF}\right) \tag{1}\]  
Since the time spent on boarding and alighting differs for each bus stop, the number of passengers and the dwell cost will be different. Figure 11 shows the real situation for passenger demand in each feeder bus route connected to the rail station. As can be seen in Figure 11, \(q_{i}\) denotes the demand at bus stop \(i\). Thus, at bus stop \(i+1\), the boarding and alighting time will be subject to the demand of the \(i\)-th bus stop (\(q_{i}\)) and to the demand of previous bus stops (\(q_{1}-q_{i\text{-}1}\)). Accordingly, the dwell time will be increased by increasing demand in subsequent bus stops.  
The algebraic proof for the geometric series, as well as a detailed derivation of the feeder dwell user cost is presented as follows:  
\[C_{duiF}=\mu_{I}\left[(q_{1})+(q_{1}+q_{2})+(q_{1}+q_{2}+q_{3})+........+(q_{ 1}+q_{2}+.....+q_{n})\right](t_{dF}) \tag{2}\]  
To simplify the model, the average demand was assumed for each of the bus stops along the route as shown in the following equation:  
\[\frac{Q_{k}}{n}=\frac{\sum\limits_{i=1}^{n}q_{i}}{n} \tag{3}\]where \(n\) is the number of feeder bus stops in route \(k\). Therefore, the dwell cost is formulated as follows:  
\[C_{duiT}=\mu_{I}\left[\left(\frac{n(n+1)}{2}\times\frac{Q_{k}}{n}\right)\right]( t_{dF})=\mu_{I}\left(\frac{1}{2}(n_{k}+1)\times Q_{k}\times t_{dF}\right) \tag{10}\]
## 1 Introduction  
Dental Class II malocclusions without skeletal discrepancies are often associated with maxillary dentoalveolar protrusion and/or mesial migration of the maxillary first molars [1]. Contributing factors may include premature loss of the second primary molar, interpersonal caries, or congenital tooth agenesis, which can lead to mesial molar displacement, resulting in arch length reduction, space deficiency, and crowding [1, 2].  
Among various space-gaining strategies, maxillary molar distalization remains a widely used and effective approach that improves both molar relationships and arch length [3; 4; 5; 6]. In recent years, clear aligners have gained popularity as an alternative to traditional appliances for molar distalization due to their improved aesthetics, comfort, and enhanced patient compliance [7]. However, the biomechanics involved in achieving effective distalization with aligners remain a subject of ongoing investigation.  
Studies have demonstrated that upper molar distalization with aligners is feasible and effective, especially when combined with composite attachments or auxiliary devices like buttons to improve movement control [7; 8; 9; 10]. However, such distalization may also induce unwanted vertical and transverse displacements, compromising treatment outcomes [11]. While various biomechanical strategies have been developed to counteract these effects in fixed appliances, limited research has explored similar approaches in aligner therapy [7; 8; 9].  
Finite element analysis (FEA) is a computational technique employed to evaluate stress distribution and deformation in complex structures. FEA has been extensively applied in orthodontic research to analyze the biomechanics of clear aligners, particularly in assessing the effects of varying aligner thicknesses and attachment configurations [12; 13; 14; 15]. Despite its broad application, limited studies have utilized FEA to examine upper molar distalization with clear aligners, and most existing studies have focused on a narrow set of attachment types and trimline designs [10; 16; 17]. To date, no study has systematically evaluated the combined effects of varying aligner trimline lengths and attachment designs on upper molar distalization.  
Previous research suggests that increasing aligner trimline length enhances tooth movement control and improves aligner retention [10; 16; 17]. Moreover, studies analyzing tooth movement with clear aligners highlight the crucial role of attachments in force distribution and movement predictability [18; 19; 20].  
Thus, this study aims to investigate the biomechanical effects of different attachment designs and aligner trimline lengths on upper molar distalization, using finite element analysis.
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results
### Experimental Results and Analysis
#### 4.3.2 Effectiveness of the Attention Module  
This section compares the model size and number of parameters when the attention module was added. We added the attention module to the rear end of the residual blocks. As mentioned, the number of parameters in the attention module was not large. Table 2 shows that there was no significant difference compared to the existing model.  
The table shows a slight difference in the forward/backward size and estimated total size, but the total number of parameters was almost the same.
## 3 Results and Discussion
### Heavy Metals  
Table 3 shows the concentration of heavy metals, especially cadmium (Cd) and arsenic (As), according to the type of sample and treatment. The results indicate that Cd concentrations in all samples analyzed, including brown and white rice grains from chemically and biologically controlled plots, remained below 0.30 mg/kg; similarly, arsenic concentrations in all samples were also found to be below 0.55 mg/kg. This demonstrates that both cadmium and arsenic do not exceed the maximum limits recommended for human consumption and that, in the context of our study, there were no significant differences between the two sources of variation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **TPC** & **TDF** & \(\gamma\)**-Oryzanol** & **PA** & **AA** \\ \hline TPC & 1 & & & & \\ TDF & 0.955 *** & 1 & & & \\ \(\gamma\)-oryzanol & 0.97 *** & 0.956 *** & 1 & & \\ PA & 0.846 * & 0.843 * & 0.851 * & 1 & \\ AA & 0.973 *** & 0.941 * & 0.979 *** & 0.876 * & 1 \\ \hline \end{tabular} Note. *, \(p<0.05\); **, \(p<0.01\); ***, \(p<0.001\).  
\end{table}
Table 2: Correlation matrix.  
Rice, like other crops, can absorb heavy metals such as cadmium, lead, arsenic, and mercury from the soil and irrigation water, with levels varying based on location, planting conditions, and processing methods [5]. Brown rice typically contains higher levels of contaminants compared to white rice due to the outer layers of the grain, which retain most of the heavy metals and are removed during processing [23,38]. However, the contaminant load significantly depends on environmental and anthropogenic factors, such as the pollution of cultivation areas and the techniques used for rice processing and storage [5,39].
## 6 Performance Evaluation  
In this section, we take the sound signal consisting of 30 different frequencies (K = 30, much larger than the temperature's sparsity [8]) as the original signal needed to be transmitted. The acoustical signal is sparse in the Fourier domain, which is fundamental for using compressed sensing. We use ns3 to simulate the random data packet loss procedure, where wireless links with different qualities are obtained through changing the distance between the sender node and the receiver node. The default backoff exponent of the IEEE 802.15.4 standard is adopted, and Channel 11 of the standard is used considering the difference between different channels.  
The main parameters used in NS-3 simulation are listed in Table 2. Our CS-based methods do not use the data retransmission scheme, even if data loss happens during wireless transmission, while the traditional ARQ interpolation methods adopt a maximum of three-times the transmission scheme. There are four typical interpolation methods adopted in ARQ interpolation methods, which are nearest neighbor (NN), cubic polynomial interpolation, spline interpolation and linear interpolation. Upon data packet reception, we use the orthogonal matching pursuit algorithm or L1-Magic to reconstruct the original signal, comparing to the traditional interpolation method.
## 4 Conclusions  
In this paper, two low-cost piezoelectric sensors, piezoelectric diaphragms (PZT) and microfiber composites (MFC), were fully investigated to find their effectiveness in PD detection. The MFC sensors' cost is around 10 times higher than the cost of PZT sensors. but MFC sensors have better sensitivity and built quality and can last longer in harsh environments. The data collected from both sensors were analyzed by time-domain, frequency-domain, and time-frequency and through statistical analysis. RMS and energy criterion were used to compare the sensors' sensitivity. Increased values of some parameters, such as power spectrum, impulse factor, energy, and kurtosis meant that there was a fault in the system; thus, in online monitoring, these parameters can be set as  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Statistical** & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**RMS**} & \multirow{2}{*}{**STD**} & \multicolumn{2}{c}{**Impulse**} & \multirow{2}{*}{**Energy**} & \multirow{2}{*}{**Kurtosis**} & \multirow{2}{*}{**Skewness**} \\
**Parameter** & & & & & & & & & \\ \hline PZT & 0.2573 & \(-\)0.2698 & \(-\)0.0029 & 0.0477 & 0.0476 & 93.5123 & 9.3092 & 12.0076 & \(-\)0.1379 \\ MFC & 0.3263 & \(-\)0.4204 & \(-\)0.0172 & 0.0745 & 0.0725 & 24.4610 & 22.7376 & 13.5322 & \(-\)0.2543 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical Parameter for Captured Signals by Low-cost AE Sensors.  
a threshold for the system to react. The result showed that using these different modes of analysis together could prevent wrong conclusions made by analyzing signals solely in one or two modes. Throughout this paper, the PD phenomenon was investigated, and several aspects of the PD acoustic signal were studied using different types of analysis. The obtained results indicate that the low-cost monitoring of PDs inside power transformers is feasible.  
Conceptualization, H.B. and S.H.; methodology, H.B.; supervision, S.H. and E.H.-F.; writing--original draft, S.H., E.H.-F. and H.H.A.; writing--review and editing, S.H., E.H.-F., P.S. and H.H.A.; 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.
# Vehicle Control Strategy Evaluation Based on the Driving Stability Region
## Nomenclature  
\begin{tabular}{l l} DYC & Direct yaw moment control \\
4 WS & Four-wheel steering \\
5 DOF & Five-degree-of-freedom \\ GA & Genetic algorithm \\ SQP & Sequential quadratic programming \\ \(M\) & direct yaw moment \\ \(\omega\) & actual body yaw rate \\ \(\omega_{e}\) & expected body yaw rate \\ \(m\) & vehicle mass \\ \(l_{f},l_{r}\) & distance from the front and rear wheels to the center of mass \\ \(L\) & sum of the front and rear wheelbases \\ \(K\) & vehicle stability factor \\ \(k_{f},k_{r}\) & cornering stiffness of the front and rear wheels \\ \(\mathrm{B}_{f},\mathrm{B}_{r}\) & stiffness factor of the front and rear wheels \\ \(\mathrm{C}_{f},\mathrm{C}_{r}\) & shape factor of the front and rear wheels \\ \(\mathrm{D}_{f},\mathrm{D}_{r}\) & peak factor of the front and rear wheels \\ \(v_{y},v_{x}\) & lateral and longitudinal velocity of the vehicle \\ \(\omega_{f},\omega_{r}\) & angular velocity of the front and rear wheels \\ \(T_{bf},T_{br}\) & braking torque of the front and rear wheels \\ \(F_{lf},f_{lr}\) & longitudinal tire force of the front and rear wheels \\ \(F_{sf},F_{sr}\) & lateral tire force of the front and rear wheels \\ \(R_{e}\) & wheel rolling radius \\ \(J_{\omega}\) & moment of inertia of the wheel \\ \(I_{z}\) & moment of inertia of the vehicle around the \(Z\) axis \\ \(\delta_{f},\delta_{r}\) & steering angle of the front and rear wheels \\ \(\mathrm{C}_{\mathrm{air}_{x}},\mathrm{C}_{\mathrm{air}_{y}}\) & longitudinal and lateral air drag coefficient \\ \(A_{\mathrm{L}_{x}},A_{\mathrm{L}_{y}}\) & longitudinal and lateral windward area of the vehicle \\ \(\rho\) & air density \\ \(T_{df},T_{dr}\) & driving torque of the front and rear wheels \\ \(F\) & steady longitudinal force or lateral force of the tire \\ \(x\) & longitudinal slip rate or sideslip angle \\ \(\mathrm{B},\mathrm{C},\mathrm{D},\mathrm{E}\) & stiffness factor, shape factor, peak factor, curvature factor \\ \(\alpha\) & tire sideslip angle \\ \(k\) & longitudinal slip \\ \(G_{x},G_{y}\) & tire force combined slip correction parameters \\ \(F_{fj0},F_{1r0},F_{5f0},F_{5f0}\) & longitudinal force and lateral force of the front and rear tires in steady state \\ \(r_{x,1},r_{x,2},r_{y,1},r_{y,2}\) & tire combined slip correction coefficients \\ \(\omega_{w}\) & wheel rotation angular velocity \\ \(v_{wx}\) & longitudinal velocity at the wheel center in the tire coordinate system \\ \(\alpha_{f},\alpha_{r}\) & sideslip angle of the front and rear wheels \\ \(v_{xf},v_{xr}\) & longitudinal velocity of the front and rear wheels in the tire coordinate system \\ \(v_{yf},v_{yr}\) & lateral velocity of the front and rear wheels in the tire coordinate system \\ \(K_{D}\) & system parameter \\ \(v_{x0},v_{y0}\) & initial longitudinal and lateral velocity of the vehicle \\ \end{tabular}
###### Abstract
Most of the above-mentioned methods have pre-computed forcing terms, causing the streamlined penetration at the solid surface, which indicates that the no-slip boundary condition is not accurately satisfied [29]. To solve this drawback, the multi-direct forcing IBM [18] or extra iterations [30] are applied. On the contrary, a velocity correction IBM is developed without the pre-determined forcing term, which is an implicit method [31; 32; 33; 34; 35]. The forcing term is replaced with the velocity correction, which can accurately enforce the boundary condition imposed on the solid surface. This method is highlighted in this review due to its vigorous applications in simulations of biological flows, which are being continuously improved and developed. All of the aforementioned IBMs need a Dirac delta function to realize information transfer between fluid-solid domains. This function often spans over several grids near the interface, which neglects the sharpness of the interface. These methods belong to the diffuse interface IBM. To eliminate the numerically undesirable feature, a series of sharp interface IBMs has been developed, to name a few, the cut-cell method [36; 37; 38], immersed interface method [39; 40; 41], and the hybrid Cartesian-IBM [42; 43; 44; 45]. Among these methods, the hybrid Cartesian-IBM has been widely applied to the simulations of bio-locomotion. Due to this, only the hybrid Cartesian-IBM [43] is introduced in this review. Some published reviews can assist readers in fully comprehending IBM [46; 47; 48; 49; 50; 51]. Consequently, the flourishing development of the IBM has made it full of potential in the field of bio-fluid dynamics. IBM has become an efficient tool for bionic simulations. Rich species that live in fluid environments have sophisticated motions and deformations. Researchers have created various mathematical models to determine the bionic dynamics for different study purposes. These models usually include active/passive motions and deformations involving both rigid and flexible structures. The inherent simplicity of the IBM to couple with these models has promoted the wide applications of bionic simulations. This review is organized as follows: Section 2 presents the governing equations of bio-fluid dynamics, includingthe incompressible viscous NS equations for fluids and some typical descriptions for active/passive motions and deformations of bionic structures. Section 3 introduces the development of the IBM, where more attention is paid to the diffuse interface IBM. The sharp interface IBM is briefly introduced, as well as some flow solvers combined with the IBM. Section 4 presents applications of the IBM for bio-locomotion involving both flying and swimming behaviors. Section 5 discusses some future issues of IBMs in the area of bio-fluid dynamics. Conclusions for this review are given in Section 6.