esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Motivation
### Data-Based Methods  
Data-based methods use sampling methods to rebalance the distribution of classes during pre-processing. This involves either oversampling instances of the minority class or undersampling instances of the majority class. Oversampling involves the random duplication of instances from minority classes [15; 16; 17]. Undersampling involves the random removal of instances from majority classes. Schemes that use oversampling in conjunction with undersampling are referred to as hybrid-sampling. These techniques are meant to produce a balanced dataset in which classifiers would tend not to be biased toward one class or another. However, in practical situations, this is not always the case. Oversampling minority classes can lead to overfitting through the duplication of instances drawn from an already small pool. Undersampling majority classes often leads to the exclusion of important instances required to differentiate between two classes. This has led researchers to develop more complex methods referred to as synthetic minority oversampling techniques (SMOTE) [18; 19]. This approach can reduce the risk of data loss and overfitting; however, it is still prone to over-generalization or variance [20; 21].
# Usability Measures in Mobile-Based Augmented Reality Learning Applications: A Systematic Review
## 4 Results and Discussion
### Detailed Information of Selected Studies
#### 4.2.3 Research Contributions  
Referring to RQ1 from literature studies, it is important to highlight types of contribution and novelty each research paper offers. From the 72 collected papers, this research work has categorized all contribution type into five different categories defined as follows:  
(1) Tool  
This type of contribution focuses on producing MAR-based software tools, including systems, applications, learning packages, authoring tools, simulation tools, and prototypes, all of which can be integrated with other frameworks  
(2) Method  
This type of contribution concentrates on procedures and systematic processes supplementing MAR and usability research. Methods categorized here refer to learning methodologies, pedagogies, usability methodologies, and algorithms promoted with the use of MAR.  
(3) Model  
This type of contribution investigates the relationships, comparisons of proposed techniques, existing challenges, or classification among papers [99]. Model categorized here refers to learning models, new approaches, original concept, and innovative usability theories.  
(4) Technique  
This type of contribution focuses on proposing new techniques to add values to MAR research. Techniques categorized here refer to MAR technical approaches that help innovate the technology  
(5) Case Study/Experience Paper  
This type of contribution presents evidence on case studies and user experience involving utilization of MAR technology. Some of the contributions include exploratory findings, experimental results, user requirement studies, and comparative outcomes.  
In answering the final part of RQ1, it can be seen in Table 8 that most research contributions in the area of MAR-based usability studies primarily focus on producing tools for problem solution (41), followed by formulating methods (10), designing models (9), reporting on case studies or experiences (9), and the introduction of new techniques (3).
# Fake Base Station Detection and Link Routing Defense +
## 2 Related Work  
A fake base station assumes a base station's functionality, including its wireless communication capability and its role as a bridge gateway between wireless and wired networking. Therefore, its threats focus on RRC and SIB communications, which distinguishes the fake base station from other threat actors that use injections. In this section, we thus focus on fake base station threats to the RRC and SIB as opposed to generic digital communication injections. While much of the works in the literature focus on threat mechanisms and impacts, our work focuses on defense based on detection and identification to avoid fake base stations and connect to a legitimate base station for availability.
# Two-Layer Matrix Factorization and Multi-Layer Perceptron for Online Service Recommendation
## 3 The TMMNN
### Embedding Layer  
The embedding layer consists of two parts, namely, the embedding table and the linear transformation layer. For the conciseness of description, the following introduction is mainly from the perspective of the user's embedding vector. First, given a user \(id\) and user embedding dimension \(d\), the embedding layer will obtain the corresponding user embedding \(e\) from the embedding table according to the user embedding dimension \(d\). Then, the layer will send the vector \(e\) to the linear transformation layer. The output is a normalized embedding. The process is described in detail as below.  
Suppose the user embedding has \(n\) candidate dimensions \(D=\{d_{1},d_{2},\ldots,d_{n}\}\). Suppose there is a user \(u\), and the user embedding \(e\) with dimension \(d\) is obtained through the embedding adjustment layer and the embedding table. Since the size of the embedding received by the recommendation layer is fixed, the embedding layer needs to send \(e\) into the linear transformation layer to obtain an embedding with uniform size. The specific process of linear transformation is shown in Equation (2):  
\[\begin{split} e_{2}&=W_{1\to 2}e_{1}+b_{1\to 2}\\ e_{3}&=W_{2\to 3}e_{2}+b_{2\to 3}\\ &\ldots\ldots\\ e_{n}&=W_{n-1\to n}e_{n-1}+b_{n-1\to n}\end{split} \tag{2}\]  
\(e_{n}\) refers to the embedding vector of the \(n\) layer. \(W_{n-1\to n}\) and \(b_{n-1\to n}\) refers to the transition of trainable weight and bias parameters from \(n\) to \(n+1\) layers, respectively.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 4 Estimation of Forwarder Search Delays
### Conclusions on k-First Forwarder Selection  
These results mean that the general strategy of forwarding to the first node which replies is a viable default one:  
* if transmission and then beacon initiation (forwarder search procedure) is considered to have zero delay (negligible).
* if the next-hop has itself as many (or more) potential next-hops as the current node.  
Indeed the rationale behind that is: instead of having a node \(A\) waiting for a second reply from node \(C\), it can forward to the first node \(B\) that replies. Then, the node \(B\) that replied can send a beacon to find a next-hop of itself. On average the node \(B\) will get a first reply from a node \(D\) at least as fast as the node \(A\) will get the reply from node \(C\). Furthermore, on average (as the scheduling is independent from positions), \(D\) will be closer to the destination than \(C\).
## 2 Design and Optimization  
### Numerical Model  
The spur gear discussed in this article has a 2.5 mm module, 34 teeth, a pressure angle of 200, and a gear width of 10 mm. A gear hub diameter of 25 mm was taken into account. Accordingly, the nominal torque can be determined at around 200 Nm. A 3D geometric model of the gear with an involute tooth profile was created in KISSsoft [33], while the numerical model was prepared in Abaqus/CAE [34]. The modulus of elasticity for Ti-6 Al-4V was E = 206,000 MPa, and the Poisson ratio was \(\
u\) = 0.3. The gear was partitioned and meshed with 2,918,412 linear hexahedral elements. These elements are of the C3D8 type and are 8-node linear brick elements with first-order shape functions. The base element size was 1 mm, with local mesh refinement applied in the gear-tooth region to capture higher stress gradients and geometric detail. A relatively fine mesh was employed overall to support subsequent topology optimization. Hexahedral elements were selected for their inherent geometric symmetries, which positively influence the symmetry and structural quality of the optimized design.  
Since the gear has 34 teeth, the pressure load was applied to both flanks of each tooth, resulting in 68 load cases--one for each side of each tooth. As seen in Figure 1, the first pressure load was applied on the right side of the tooth flank in the first load case; this pressure load was only active in this load case. The second pressure load was then applied to the left side of the same tooth. The process was repeated for all 34 teeth, influencing the final topology, as optimization is carried out for each load case.  
Figure 1: Spur gear with fixing hub, partitions, and applied load.  
A pressure load of 618 MPa was applied to a tooth flank, as shown in Figure 1. This pressure was applied to both sides of the tooth flank due to the planned topology optimization. Additionally, the hub surface was fixed in all free directions, as shown in Figure 1.
## 4 Discussion
### Correlation between the Soil-Sediment pH and Nitrogen  
It can be seen from the correlation analysis table (Table 7) that, overall, pH was negatively correlated with soil-sediment total nitrogen, organic nitrogen, total inorganic nitrogen and nitrate, but the correlations were not strong. Ammonium and nitrite showed a weak positive correlation, and the effect of pH on nitrogen forms in sediments was not obvious. The specific pattern (Tables 3-7) showed that inland soil pH and all nitrogen forms were negatively correlated, but not significantly. Lagoon sediment pH was positively correlated with total nitrogen, organic nitrogen, inorganic nitrogen, nitrate and nitrite and negatively correlated with ammonium, but not significantly. The soil pH in the sand bar was weakly, positively correlated with total nitrogen and organic nitrogen and negatively correlated with inorganic nitrogen. Offshore sediment pH was negatively correlated with all nitrogen species, but not significantly. This may be because pH is related to whether nitrogen compounds can undergo redox changes, and in soil with a high pH, clay has a stronger ability to fix nitrogen [21]. The pH of the study area ranges from weakly acidic to weakly alkaline from west to east, the suitable pH range for denitrification is 6.50-7.50 and the suitable pH range for nitrification is 7.50-8.50 [26]. However, in the normal pH range, the change in pH will not have an important effect on the mineralization, nitrification and denitrification of organic nitrogen [27]. This indicates that pH is not the main factor influencing soil-sediment nitrogen.
# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
## 3 The Proposed Method
### Improved EEMD  
As mentioned above, the decomposition result of EEMD was directly affected by the sampling frequency. Too high of a sampling frequency did little to help increase the accuracy. On the contrary, it decreased the computational efficiency. Therefore, a proper resampling frequency should be selected according to the frequency characteristic of the vibration signal. In this paper, we show how EEMD was improved by replacing the raw vibration signal with a resample signal. The procedures of the improved EEMD are as follows:  
1. In order to ascertain the frequency components of the vibration signal, Fourier transform was first performed to obtain its frequency spectrum.
2. Twelve-times the maximum frequency was subtracted from the sampling frequency: \[f_{e}=f_{\mathrm{s}}-f_{r}=f_{\mathrm{s}}-12\ \times\ f_{\mathrm{max}}\] (16) where \(f_{\mathrm{s}}\) was the sampling frequency of vibration, \(f_{r}\) was the resampling frequency calculated by the resampling frequency criterion, \(f_{\mathrm{max}}\) was the maximum frequency of the vibration signal and \(f_{e}\) was the frequency to determine whether or not to execute the resample algorithm. If \(f_{e}\geq f_{\mathrm{max}}\), we resampled the raw signal with the resampling frequency \(f_{r}\) and output the resample signal; otherwise, we output the raw signal directly.
3. Perform EEMD on the signal obtained in Step 2.
## 5 Use Case
### Routing Problem in Bipartite Circulant Graphs
Against the background of the total resource consumption of the router, according to the experience of previous studies [48], such a complication should not affect the resource consumption of the router. This statement needs to be verified in the future. **Example 3**. : _Consider how proposed rule works based on the example of graph \(C_{8,4}\)._  
Suppose there is a situation of a cycle of vertices \(0->4->2->7->0\), and according to the rules of the routing algorithm (it is static, not adaptive [46]), the packets move clockwise along this cycle. We need to transfer the data from node 7 to node 4 (packet A), from 4 to 2 (packet B), from 2 to 0 (packet C), and from 0 to 2 (packet D). As a result, a deadlock situation arises, because packet A will block packet C, which (in turn) will block packet B, and through it packet D, which interferes with A. Using the proposed rule, the deadlock will be eliminated, since packets C and D (on the second hop) will cross the boundaries of the subgraph (Figure 8) and switch to virtual channel 2 with no path blocking. A few more simple rules to help resolve deadlocks can be proposed. The most important is the fact that the proposed plane tessellation methods allow you to mark up a graph into a set of simple subgraphs without cycles, based on which it is possible to build deadlock-free routing algorithms.
# A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes
## 5 Results and Discussion
### Performance Comparison of WECALM with Other Algorithm
#### 5.1.1 Performance on NewMIPS Complexes  
We compared the robustness of our proposed WECALM method to other existing methods for detecting protein complexes. We considered the evaluation matrices described in Section 4.2. Based on the NewMIPS complex using the BioGRID dataset (see Figure 3A), we found that WECALM performed better in terms of recall (0.7701), F-measure (0.7252), coverage rate (0.6743), and maximum matching ratio (0.3975). In terms of precision score (0.7131), ProRank+ outperformed all other methods. On the NewMIPS complex using the DIP dataset (see Figure 3B), again we observe that WECALM performed better in terms of recall (0.7141), F-measure (0.5889) and maximum matching ratio (0.3531). ProRank+ (0.6657) and CMC (0.5736) performed best in terms of precision and coverage rate respectively. Though, ProRank+ and CMC in terms of precision and coverage rate WECALM performed best in the overall composite score. Table A1 in Appendix A provides supplementary results for performance comparison of WECALM and another algorithm on the NewMIPS dataset using the BioGRID and DIP complexes.  
Figure 3: A comparison of the performance of WECALM and other existing algorithms on NewMIPS complexes. (**A**): The BioGRID dataset and (**B**): The DIP dataset. Evaluation matrices include; Recall, Precision, F-measure, coverage rate (CR), and the Maximum Matching Ratio (MMR). The overall composite score is determined by the length of the bar. The longer the bar, the better an algorithm’s overall performance is.
# Unbalance Detection in Induction Motors through Vibration Signals Using Texture Features
## 2 Methodology
### Texture Feature Extraction
The training-validation subset was formed for 4000 samples (2000 samples representing the unbalance fault and the rest representing the healthy class), which was 80% of the final dataset, where 800 samples (400 samples representing the unbalance class and the rest representing the healthy class) were used to tune the hyperparameter in the SVM. The testing subset was formed by 1000 samples where 500 samples represented the unbalance class and the rest represented the healthy class. The test set was used to evaluate the tuned model. In order to follow the same evaluation setup as [PERSON] et al. (2014), the accuracy metric was used to evaluate the performance of the model. This metric is defined as how close a given set of measurements (observations or readings) are to their true value and is represented by Equation (16):  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN}, \tag{16}\]  
where \(TP\) represents the healthy class correctly assigned, \(TN\) corresponds to the unbalance instance correctly classified, \(FP\) represents the healthy instance classified as the unbalance class, and \(FN\) corresponds to the unbalance class classified as the healthy instance.
# Flow Control of Radial Inlet Chamber and Downstream Effects on a Centrifugal Compressor Stage
## 2 Methodology
### Numerical Method  
#### 2.2.1 CFD Code  
The governing equations used to model the internal flow of the radial inlet chamber and the centrifugal compressor are the three-dimensional steady and compressible Reynolds-averaged Navier-Stokes equations. The general expression of the governing equations in a Cartesian frame can be written as  
\[\frac{\partial}{\partial t}\underset{\Omega}{\int}\underset{\Omega}{\int} \underset{\Omega}{Ud\Omega}+\underset{S}{\int}\overset{\rightarrow}{F}_{I} \cdot d\overset{\rightarrow}{S}+\underset{S}{\int}\overset{\rightarrow}{F}_{V }\cdot d\overset{\rightarrow}{S}=\underset{\Omega}{\int}S_{T}d\Omega \tag{1}\]  
where \(\Omega\) and \(S\) represent the volume and the surface, respectively, \(U\) is the vector of the conservative variables and \(\overset{\rightarrow}{F}_{I}\) and \(\overset{\rightarrow}{F}_{V}\) are the inviscid and viscous flux vectors, respectively. Assuming that the flow inside the centrifugal compressor is adiabatic, the source term vector \(S_{T}\) contains the contributions of the Coriolis and centrifugal forces, which is expressed as [30]  
\[S_{T}=\left[\begin{array}{l}0\\ (-\overline{\rho})\Big{[}2\overset{\rightarrow}{\omega}\times\overset{ \rightarrow}{w}+\Big{(}\overset{\rightarrow}{\omega}\times\Big{(}\overset {\rightarrow}{\omega}\times\overset{\rightarrow}{r}\Big{)}\Big{)}\Big{]}\\ \overset{\rightarrow}{\overline{\rho}}\overset{\rightarrow}{w}\cdot \
abla\big{(}0.5\omega^{2}r^{2}\big{)}\end{array}\right] \tag{2}\]  
The flow solver EURANUS [35] of the commercial software NUMECA FINE/Turbo, Brussels, Belgium was employed for the numerical simulations in the present study; it performs a finite volume scheme for the spatial discretization and adopts a multistage Runge-Kutta scheme for the temporal discretization. In addition, a multi-grid strategy, local time stepping and implicit residual smoothing technology were applied in the CFD code to accelerate the calculation convergence. In order to make the equation system closed, it was necessary to adopt the corresponding fluid model and choose a proper turbulence model to specify the relationship between the pulsation value and the time-averaged value of the turbulent flow. In this paper, the working fluid was air, which was treated as a perfect gas. The Spalart-Allmaras model was chosen for the turbulence closure, which has been proven to ensure sufficient accuracy in predicting the flow conditions inside compressors with radial inlet chambers [30; 31].  
Figure 1: Schematic diagram of the radial inlet chamber and the compressor stage.
## 2 System Architecture and Methods  
### Experimental Setup: An Overview  
Our robot system's experimental setup enables dynamic interactions with children, as Figure 1 shows. A robot, central to this setup, operates within a clearly defined workspace marked by four QR codes. These codes identify the area where the system detects and manipulates objects. While the workspace serves as the main stage for object detection and interaction, a key feature of our system enables the robot to move objects beyond this area into designated color boxes. These color boxes act as destinations for the objects children need to relocate as part of their tasks during the system's experimental evaluation.  
The robot's interaction with children follows a structured sequence of steps, as depicted in Figure 2. The process begins with a child using a wake-up word that is easy to pronounce and recognizable by the voice recognition system. This activation triggers the robot to expect a new instruction. Children then have six seconds to issue voice commands for tasks such as moving to a specific object, moving the robot arm down or up, and grabbing or dropping an object. A speech-to-text system converts these voice commands into written text.  
Next, an LLM analyzes the written text to determine the child's desired action (semantic analysis). An object detection system then detects and localizes objects within the scene and relays this information to a robot action module. This module combines the action information from the LLM system with the object localization data to execute the given function.  
If a command is unclear or the action is not feasible, the robot responds, _I do not understand the instruction; try again_, prompting the child to rephrase their request more clearly. This structured interaction ensures the robot responds appropriately to children's commands and facilitates effective communication and debugging.
# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 1 Introduction
The OCC algorithm builds a decision boundary using only the target class, corresponding to normal vibration signals, and the decision boundary determines whether a newly collected signal belongs to the target class or not. Up to now, numerous OCC algorithms have been proposed, and most of them are based on the data description techniques, including \(k\)-nearest neighborhood (k-NN)-based approaches, clustering analysis, and support vector-based models, and thus, more sophisticated boundaries can be obtained. In the fault detection model, the newly collected signal falling outside the boundary is rejected as an anomaly corresponding to a fault signal, whereas those located within the boundary are accepted as normal ones. Among various OCC algorithms, in this study, we considered the following: [PERSON]'s T\({}^{2}\) statistics, mixture of Gaussian (MoG; [18]), Parzen window density estimation (PWDE; [19]), one-class support vector machine (OCSVM; [20]), isolation forest (IsoForest; [21]), and local outlier factor (LOF; [22]), which have shown decent performance in many fault detection problems. The main contributions of the proposed fault detection framework can be summarized as follows:  
* Owing to short-time Fourier transformation (STFT)-based spectrograms, information on time-varying patterns of frequency components can be revealed. By doing so, intrinsic properties of noisy and nonstationary signals can be successfully utilized for fault detection on rotating machinery. * Because the convolutional blocks in CAE are specialized to generate features from the image-formatted data, latent feature vectors obtained by CAE can extract more informative features from two-dimensional spectrograms. * The OCC algorithm helps to build elaborate decision boundaries under the situations wherein counter classes (i.e., fault signals) are not available, which is frequently encountered in many real industrial fields. The rest of this paper comprises the following: Section 2 summarizes related works on FDD for rotating machinery with vibration signals. Section 3 presents a detailed process of the proposed fault detection framework for the rotating machinery. Section 4 describes an experimental study to demonstrate the effectiveness of the proposed fault detection framework. Finally, the concluding remarks on this study are presented in Section 5.