esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Calculation of Force Frequency Components of the Electro-Mechanical System
The relationships between the operating frequencies of the bearings are as follows:  
\[f_{ep}=n_{b}f_{c} \tag{6}\]  
\[f_{ip}=n_{b}(f_{s}-f_{c}) \tag{7}\]  
where:  
\(f_{ep}\), frequency of rolling of the rolling elements on the outer raceway;  
\(f_{ip}\), frequency of rolling of the rolling elements on the inner raceway;  
\(n_{b}\), number of bearing rolling elements;  
\(f_{c}\), bearing cage frequency;  
\(f_{s}\), the relative rotational frequency of the inner and outer race equal to the rotational frequency of the shaft. Damage to a given bearing element causes the appearance or increase of the amplitude of vibrations of the appropriate frequency. Analytically calculated vibration frequency components are used to create a reference pattern of an FF image. It has to be emphasized that analytical calculations determine the frequency of potential frequency components. The individual elements of the electro-mechanical system are characterized by strictly defined natural frequencies depending on the shape and material parameters. Resonant vibrations can occur when exciting forces of appropriate frequencies appear. Usually, the frequencies of the forces of electromagnetic origin (3) and (5) are sufficiently distant from the natural frequencies to induce significant resonant vibrations. Mechanical shocks are the most common forces forcing resonant vibrations. In the analyzed electro-mechanical system, shocks are caused by damage to a given bearing element (local defect or disturbance of its continuity).
## 5 Conclusions  
This paper presents a comparative study on the performance of various machine learning algorithms for fault detection in rotating machines, using multi-modal data collected from real-world conditions in a typical manufacturing industry. The dataset comprises approximately one-year-long organic data, acquired from a coupled suction fan. Our results have shown that the Random Forest classifier achieved the highest accuracy of 77.1%, followed by the CatBoost and Gradient Boosting with 76.9% and 76.3% respectively.  
Hyperparameter optimization has resulted in a slight improvement, as the labels used in this study require derived features from the frequency-domain transformation of the raw vibration and ultrasound signals. The original feature space, consisting mostly of time-domain features, is not powerful enough to discriminate the cases, and even the optimized models may perform poorly. Additionally, correlated features may introduce noise rather than contribute representative information. To account for this, alternatives such as iterative feature elimination, mutual information-based selection, or dimensionality reduction (e.g., PCA) can be used to retain only informative features. A possible future study will focus on estimating the Fourier representation of the collected observations with different approaches and inferring the data to re-evaluate the optimization schemes, which are expected to yield better performance. For the sake of clarity and simplicity of the presentation on the available data, only time-domain features in vibration analysis, the highest frequency range of the ultrasound RMS level, revolution speed, and temperature  
Figure 9: Confusion matrix showing the optimized Random Forest model performance across fault categories.  
were used in this study. Nevertheless, it has been shown that even without time-dependent and band-selective frequency power (simply short-time Fourier transform) information, the classification algorithms proved a relatively satisfactory performance given the simple raw data indexes from acceleration and ultrasound signals.  
Notably, although the observed improvement in classification accuracy through hyperparameter tuning was statistically significant (paired \(t\)-test, \(p=0.0197\)), the magnitude of this improvement remains limited. This outcome highlights the importance of more informative and discriminative feature sets to fully leverage the benefits of model optimization. Despite this limitation, the classification system was still able to detect underlying patterns associated with mechanical faults.  
In particular, the aforementioned imbalance and its indirect effect of bearing wear were successfully identified, yielding a system that could automatically detect mechanical failures. The importance list of features in Figure 7 proves the fact that the fault-related parameters, velocity, and ultrasound contribute to the classification at a considerably higher level, in line with the observations and expectations.
## 2 Data Collection
### Millimeter-Wave Radar and Camera Joint Calibration  
The joint calibration is a preparatory condition for multimodal fusion. As the millimeter-wave radar and the camera have different sampling frequencies and coordinate systems, the sensor coordinates must be adjusted to the same coordinate system and time has to be  
Figure 1: Framework of vehicle-detection algorithm.  
aligned before fusion. Furthermore, the spatial calibration of the millimeter-wave radar and the camera requires the concurrent point cloud and the corresponding pixel points in the image.  
Based on the assumption that the object point \(P\) in the vehicle coordinate system is (\(x_{1}\), \(y_{1}\), \(z_{1}\)) and the image point Q in the image coordinate system is (\(x,y\)), the pixel-level fusion equation \(Z_{C}\) is:  
\[Z_{C}(x,y,1)^{T}=K\Big{(}\mathbf{R}_{c}(x_{1},y_{1},z_{1})^{T}+\mathbf{T}_{c}\Big{)}. \tag{1}\]  
where the coordinate of object point \(P\) is in the \(Z\)-axis direction in the camera coordinate system, \(\mathbf{K}\) is the internal camera parameter matrix and \(\mathbf{R}_{c}\) and \(\mathbf{T}_{c}\) are the rotation matrix and translation vector of the external camera, respectively.  
Millimeter-wave radar presents three-dimensional information as two-dimensional information in a polar coordinate system. If the radial distance between the millimeter-wave radar and the target is \(R\), the angle to the center is \(\alpha\), the plane in which the coordinate system is located is parallel to the environmental coordinate system and the distance between them is \(H_{0}\), then the interconversion relationship of the coordinates of the object point P between the environmental coordinate system (\(x_{n},y_{n},z_{n}\)) and the millimeter-wave radar coordinate system (\(R,\alpha\)) can be expressed as:  
\[\left\{\begin{array}{c}x_{n}=R\sin\alpha\\ y_{n}=-H_{0}\\ z_{n}=-R\cos\alpha_{0}\end{array}\right. \tag{2}\]  
Combining Equations (1) and (2) yields the transformation between the millimeter-wave radar coordinate system and the image pixel coordinate system as:  
\[zc\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{c}f_{x}\ 0\ u_{0}\\ 0\ fy\ v_{0}\\ 0\ 0\ 1\end{array}\right]\left(\begin{array}{c}R_{c}^{*}\\ -H_{0}\\ -R\cos\alpha\end{array}\right]+\mathbf{T}_{c}^{*}\right). \tag{3}\]  
The transformation relationship between the spatially calibrated car body coordinate system and the pixel coordinate system is shown in Figure 3.  
In this study, time alignment consisted of total data acquisition, millimeter-wave radar data acquisition and image acquisition, where each frame of data was given a system time. The data were transmitted to the buffer queue with a tag. The millimeter-wave radar data acquisition and image acquisition were initiated in the total data acquisition, with low sampling frequency. The image acquisition was triggered for every frame of millimeter-wave radar data acquired, and the image data with the same time tag was selected from  
Figure 2: Diagram of sensor installation.  
the buffer queue to achieve synchronous data acquisition and storage, the process of which is shown in Figure 4.
# Optimizing Foreign Language Learning in Virtual Reality:
## 5 Case Study
### Comparative Analysis of VR Language Learning Platforms
#### 5.3.2 Comparative Discussion of Selected Platforms with Rosetta Stone VR and Duolingo VR  
VRchat, although not a platform dedicated solely to language learning, offers an eclectic milieu where linguistic skills can be honed through organic interaction with a global community. Unlike Rosetta Stone VR and Duolingo VR, which are structured around curriculum-based learning, VRchat's open-world concept allows learners to engage in spontaneous conversations, providing a less formal yet potentially more diverse language practice environment. Here, the social dynamics of communication come to the forefront, reflecting the complexity of real-life language use that structured lessons may not fully encapsulate.  
Another noteworthy application is Speak to Go with Google, which, while not explicitly designed for language education, enables users to virtually travel to different locationsby simply using their voice. This novel approach to exploration encourages incidental language learning, contrasting with the more guided and targeted experiences offered by Rosetta Stone VR and Duolingo VR. The potential for language acquisition in these serendipitous interactions is significant, providing learners with a sense of autonomy and adventure that may lead to higher levels of engagement and motivation.
## 3 Results  
### Test-1  
Figure 4 shows the results of the experimental measurements perfomed in Test-1. In particular, each \(K_{R}\) bar was determined as the arithmetic mean of the six \(K_{R}\) values of the corresponding six acquisitions available for each combination of sensor and measurement conditions. The corresponding standard deviation (\(\sigma_{R}\)) is also reported through vertical segments indicating \(\pm\sigma_{R}\) intervals.  
For each sensor, measurements associated with the non-leaking conditions were characterized by similar values of \(K_{R}\). Therefore, no significant variations in the background noise were observed.  
Leak detection based on \(K_{R}\) does not seem achievable through the Ard sensor, since similar values are observed for all the conditions.  
Sensors Aax and Hyd appear very sensitive to leaks L1 and L2, as the corresponding signals exhibit a sizeable increase in \(K_{R}\), with respect to the non-leaking conditions. In particular, for the closest leak, such an increment is extremely significant. Hence, leaks L1 and L2 can be clearly identified through sensor Aax. The detection of such leaks seems viable by using the Hyd signals as well, although with lower robustness, due to the high values of the standard deviation. Conversely, distinguishing leak L3 from the non-leaking conditions by means of the proposed method could not be attained with either of the two sensors.
## 3 Optimization of Parallelization
### Optimization A: Improving the Parallelization Scheme  
We optimized the parallelization scheme of LICOM, as shown in Figure 4. \(N\) grid points were to be allocated to \(np\) processors. If \(N\) was divisible by \(np\), the best decomposition was to allocate n, which equalled \(N\) divided by \(np\), to each processor. However, in most cases, \(N\) was not divisible by \(np\). Thus, in the original scheme, \(n+1\) grid points were allocated to each processor from processor No. 0. Here, we set \(n\) to \(\lfloor N/np\rfloor\). For the last processor, \(N-(n+1)*(np-1)\) grid points were allocated. In the optimized scheme, \(n\) grid points were allocated to each processor, while the rest of the \(N-n*np\) grid points were evenly allocated to some processors. In this way, the scalability of the model was considerably improved.  
We redesigned the software structure of LICOM to improve the modernization of the software. Thus, LICOM was more convenient to use. For example, we replaced an alterable array with a fixed array, used structured data, and extracted important parameters. Therefore, there is no need to recompile or alter the code for different experiments or processor distributions.
## 4 Synthesis and Summary
### Future Directions
#### 4.4.1 Enhanced Intelligence and Autonomy  
The intelligent waterway has been increasingly heated in recent years, spurring the technology of intelligence and autonomy. As the development of the Yangtze River's smart waterway infrastructure progresses, the deployment of USVs in its inland channels has seen a significant rise. For the autonomous navigation of the \"Jinghai-l\" USV within these inland waterways, a composite route determination technique has been formulated. This technique merges an enhanced A* algorithm with a refined model predictive control algorithm, demonstrating proficient autonomy and intelligent navigational capabilities [72]. To improve the autonomy level, a method combining the use of sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC), while implementing a feedback strategy that navigates through sparsely connected areas free of obstacles using a sequence of MPC policies, was proposed by [PERSON] [73]. New technology with intelligence was also used in the health management of USVs, exemplified by a hybrid neural network (HNN) prediction model, which integrates Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) and Attention mechanisms, designed specifically for the prediction of Exhaust Gas Temperature (EGT) in marine diesel engines. This approach provides a new way of thinking for the research of fault early warning and the health management of marine diesel engines [74]. To speed up autonomy, the Unmanned Maritime Systems Program Office (PMS 406) is enhancing autonomy in unmanned maritime vehicles through the Unmanned Maritime Autonomy Architecture (UMAA) for software standards and the Rapid Autonomy Integration Lab (RAIL) for developing new capabilities [75]. Intelligence and autonomy likewise show their advantage in risk analysis [76].  
In the future, it is predicted that USVs will become more intelligent and autonomous through the integration of AI, deep learning and machine learning technologies [77]. This will enable them to better perceive their environment, make autonomous decisions, and execute tasks. This advancement will encompass state-of-the-art sensing and obstacle avoidance systems, as well as robust decision support systems. Additionally, the level of autonomy in USVs will continue to rise, including autonomous path planning, autonomous cruising and autonomous maintenance. This will alleviate the burden on operators, enhance efficiency and reduce costs.
# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 2 Degradation Modeling and Prognostic Hybrid  
This paper develops a hybrid degradation modeling and prognostic method for the multi-modal system to characterize the underlying degradation process accurately. Considering that different combinations of modes have different effects on the degradation process, the method predicts remaining useful life from two aspects: the long-term cumulative degradation assessment based on a cumulative dynamic differential health indicator, and the latest stage degradation assessment based on local features.  
As shown in Figure 1, the proposed method works in an offline stage and an online stage. In the offline stage, the degradation model is learned from history monitoring data. The learned model is used to predict the remaining useful life in real-time in the online stage. \(S\) is defined as the input dataset. \(s_{i,k}(t)\) is the data sequence of \(k\)th sensor data of the \(i\)th sample (system) to represent the change in data as the number of actions in time \(t\) changes. Then, the remaining useful life estimation process is as follows.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 6 Discussion and Prospect
### Implicit Flow and Explicit Flow  
The problem of implicit flow is an open problem in DIFT design. The authors in [31] pointed it out that identifying all implicit flows is a very complex problem. As shown in Listing 1, the explicit information flow cannot track nor determine whether the variable \(y\) is influenced by the branch statement when \(x\) is not equal to _true_. In order to determine all implicit flows, the statements influenced by control-flow related instructions need to be identified. The common methods for dealing with implicit flow include: (i) static analysis in [45] to find all implicit flows and assign tag operations to the corresponding instructions and data; and (ii) transforming the implicit flow into explicit flow through the static and dynamic method in [31]. However, the above methods work at the cost of performance overheads and precision. Therefore, how to identify all implicit flows and track the implicit flow at a reasonable cost is still a challenge for DIFT designs.  
```
if(x==true) y=true; else y=false;
```  
Listing 1: An example of implicit flow.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### System Performance Characteristics  
The experimental results demonstrate several key performance characteristics:1. Modulation Fidelity: Both time-domain and frequency-domain analysis confirm accurate signal modulation and recovery across different signal types.
2. Implementation Flexibility: Three distinct demodulation approaches validate system compatibility with various receiver architectures.
3. Noise Tolerance: Quantitative analysis demonstrates acceptable performance degradation characteristics under realistic noise conditions.
4. Circuit Simplicity: Hardware implementation using standard components (op-amps, VCO IC4046, monostable 74 LS123) demonstrates practical feasibility without requiring specialized or expensive components.
5. Design Simplification Rationale Compared to typical carrierless PPM systems, the proposed architecture adopts a simplified structure in several specific aspects. First, it reduces the number of active components by not requiring phase-locked loops, carrier recovery circuits, or digital synchronization elements such as flip-flops. Second, the design relies on RC-defined time constants and VCO bias control for modulation and demodulation, which can reduce tuning complexity compared to systems involving loop filter design or digital clock recovery. Third, the implementation uses analog building blocks and does not depend on digital platforms or FPGA-based logic, which may offer practical benefits in scenarios where a compact circuit layout, ease of implementation, or minimal digital infrastructure is preferred.
## 4 Discussion
### Accessibility Issues from Personas
#### 4.2.2 Persona-Specific Issues
He is simply a novice user; the appliances must be more intuitive and self-explanatory. The persona-specific issue under the feedback/status-check is assigned to Ms. [PERSON], the cochlear-implemented persona. A hearing-impaired person with a cochlear implementation does rely on auditory information similar to how a low-vision person also dependent on visual information. The cochlear implant is optimized for the vocal domain frequency range, which is narrower than that of music or melody [70; 71; 72]. Many home appliances implement non-verbal auditory-feedback, which have a frequency domain beyond the optimized domain of a cochlear implant, resulting in an incompatible frequency range for Ms. [PERSON]. Therefore, home appliances with auditory feedback should allow such users to customize the frequencies of feedback alarms at their preference, in other words, at their perceivable range. For the reaching/posture and grasping/control issues, both the spinal-cord-impaired personas have dedicated clearance issues. These two personas require two different design approaches, though both the issues point toward the clearance issues. Therefore, they are sorted as persona-specific issues. The closed-fist persona (Ms. [PERSON]) has difficulties in pushing through a button that is flushed to the surface of an appliance when the button clearance is narrower than the size of her fists. In this case, [PERSON] cannot fully push the button to activate an operable part. Furthermore, most of the handles--even the protruding bar type--do not provide enough clearance for her fist. Therefore, [PERSON]--the closed-fist type--requires a clearance design based on her fist circumference unless she can hook her wrist on an operable without her fist interfering. On the other hand, for the opened-palm persona (Mr. [PERSON]), it may seem more accessible to achieve operation in comparison to [PERSON]'s case. The clearance design for him is based on his fingers or hand blade size, which are comparably smaller or thinner than the fist circumferences. However, his fingers can be unintentionally hooked into a groove, which eventually hinders his intended movement or action. The fingers of [PERSON] will prevent doors from opening like a hinge lock. The gap between the two handles should be wider than his finger length or hand length. Lastly, there is a safety issue for the low-vision persona. A previous study [73] and the observation we conducted demonstrated that the lower-vision users have shorter viewing distances than those of normally sighted users, meaning they have a closer distance to interact with devices. Under the home appliance context, this means they are in close contact with possible risks such as heat and abruptly moving parts--especially when considering unintentional activation is one of the other issues they have.