esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
## 4 Experimental and Simulation Results  
### Laboratory Crane  
To test the robustness of the proposed algorithm, a laboratory crane was constructed at the University of Split, Faculty of Electrical Engineering, Mechanical Engineering, and Naval Architecture in Croatia. It was implemented using the Texas Instruments TMS320F2812 microcontroller (Dallas, TX, USA), which features a C28x core. Spectrum Digital's eZdspF2812 (Sugar Land, TX, USA) is the PCB on which this microcontroller is mounted. It has peripheral expansions and JTAG for programming and debugging.  
The C28x core incorporates a dual Harvard architecture, providing high throughput for instructions and data. It has several specialized instructions essential for digital signal processing tasks in real-time control applications. The memory used to implement the control algorithm is 256 KB of on-chip FLASH. For dynamically allocated variables and the realization of STOG memory, 36 KB of on-chip SRAM was used together with 128 KB of off-chip SRAM.  
The IDE used to implement the proposed control algorithm was Code Composer Studio C2x eZ DSP Kit Tools V2 (C2000). It has all the software components needed to write, build, and debug the algorithm in real time using a JTAG connector over a parallel port.  
The proposed control algorithm uses one incremental encoder sensor to measure the cart's position and one DC motor actuator made of power electronics and a DC motor to drive the cart forward and backwards. To record the cart's position, serial communication is used between the DSP and PC, while the sway is measured using an infrared camera with a marker. The camera calibration is performed using a mechanical angle sway sensor. In Figure 7, the described experimental setup is shown.  
Figure 7: Experimental setup.
## 7 Synchrosqueezed CWT Scalogram Fault Prediction System
### Analysis of Synchrosqueezed Data  
The synchrosqueezed scalograms were counted in each time segment. After counting the number of pulses in different frequency ranges, the frequency range distribution can be drawn. Figure 8a shows the number of pulses in different frequency ranges of each time segment in an abnormal data set. Figure 8b is a normal data set of frequency range distributions in each time segment.  
Figure 7: The synchrosqueezed wavelet transform synchrosqueezed scalograms of the four CWT scalograms shown in Figure 6: (**a**) more concentrated pulse in frequency range 0.4–0.5 Hz, and a new pulse was found around frequency 0.8 Hz, (**b**) more concentrated pulses in various frequency ranges, (**c**) more concentrated pulse in frequency range 0.9–1.0 Hz, (**d**) more concentrated pulses in frequency ranges 0.8–0.9 Hz and 0.9–1.0 Hz, moreover, the time information is explicit compared with Figure 6d.  
There must be some singular pulse distributions in a data set that contains fault information. One hundred more data sets were acquired over the space of a week. Approximately half of these data sets were \"normal\" (i.e., no fault) and the other half were \"abnormal\" (i.e., with some faults induced somewhere in the duration). After analysing these data sets, it was found that the number of pulses in frequency range 0.3-0.4 Hz and 0.7-0.8 Hz were highly simultaneous in both types of data sets. This phenomenon can be observed from the pulse distribution graphs in Figures 9 and 10. There are numerous time segments containing the above-mentioned feature, and we selected eight similar features in different time segments of six data sets (three normal and three abnormal), then demonstrated in Figures 9 and 10.  
Figure 8: Pulse distribution in different time segments of two data sets: (**a**) pulse distribution of frequency ranges in different time segments (Abnormal data set), (**b**) pulse distribution of frequency ranges in different time segments (Normal data set).  
All of the data sets obtained experimentally exhibited similar pattern, including those with fault recorded, with the number of pulses in 0.3-0.4 Hz and 0.7-0.8 Hz reaching a large number simultaneously in both data types. Compared with other frequency ranges, the two frequency ranges were found to be different from the others, because the distribution of pulses in these ranges has the previously mentioned regularity, and it has repetitively appeared in both normal and abnormal data sets. For easier explanation, these two frequency ranges were named as \"dominant frequency ranges\".
# Noise Reduction Using Active Vibration Control Methods in CAD/CAM Dental Milling Machines
## 2 Methodology  
The first step to reducing the noises produced by CAD/CAM milling machines was through an investigation of the causes and sources of noise level through experimentation in a closed environment. After determining the cause and location of the noise source, we identified the main target area for noise reduction. Using the information on the targeted area, a noise control method was proposed. A block diagram description of the method is shown in Figure 1.  
Figure 1: Noise improvement process.  
Dental CAD/CAM milling machines can be categorized as drying or wetting type depending on the use of water during the milling process. A more suitable method is determined by considering the type of material and milling process used. The wet type does not require a dust collector because it uses water. On the other hand, the drying type requires a dust collector because it produces dust, but the dust collector causes noise. Therefore, the process conditions were classified as follows and the investigation methods of milling machine noise were selected accordingly (Table 1).
## 4 Methodology
### Feature Engineering  
The set of features that were developed for both the activity detection and emotion detection were purely statistical in nature and the set of features that were developed for both the routines, namely, activity detection and emotion detection, were the same. The features were calculated based on a certain number of samples that differed for both the routines. For the activity detection routine the complete dataset for a particular subject was folded by having 33 (data worth 1 s) samples in each fold and maintaining a step distance of 16 samples. This means the 16 samples at the end of each fold were further used as the samples in the subsequent fold, sequentially. The usage of this particular method was further supported by having optimum variance in the features. For emotion detection, a similar approach was followed, but the size of the fold was increased to have 66 samples (data worth 2 s) and the step distance was increased to 32 samples. The features for the activity detection were calculated using the raw accelerometric and gyroscopic data of the neck and the raw accelerometric and gyroscopic data of the tail, whereas for the emotion detection, the features were calculated based on the raw accelerometric and gyroscopic data from the tail-wearable device only. The set of features that were calculated were basically of two types; namely, pure statistical features and peak-based features. The calculation procedure was implemented in such a way that the features were calculated for all the samples of a subject for a particular data fold of 33 samples to maintain optimum variances between the feature's values. Table 3 depicts all the statistical features that were used for the detection of activity and emotion. The use of statistical features in the study provided us with the ability to identify some discriminant features even if they lacked obvious interpretability but were still important ones for determiningintrinsic patters for the decision making [33; 34]. Table 4, on the other hand, plots some peak-based features which have been generated for all the axis of the triaxial accelerometer and triaxial gyroscope for both neck and tail devices. The activities for which data was fetched tended to show a wide range of physical movements; therefore, features based on the peakedness of the signal were hoped to provide us with subtle differences in the movement patterns [23; 24].
## 5 Indirect Cooperation via a Third-Party Entity
### Adjustment of LTE and Wi-Fi Transmission Based on Requirements and History
Similar to the method described in Section 5.2, such a cooperation mechanism requires changes for both LTE and Wi-Fi, so they can inform the CCE about their requirements. Additionally, extensions are required to eNB and AP, so they can receive and use the information transmitted by the CCE. Finally, the CCE itself has extra complexity compared to the previous method, as it needs to keep track of the transmission for all of the participating networks and it has to decide about the sharing of the channel based on complex criteria.
## 4 Results and Discussion
The proposed approach, leveraging an improved DenseNet201 model enhanced with multi-scale convolutions and the Mish activation function, outperformed most compared methods, achieving the highest accuracy of 0.9642 and an F1 score of 0.9640. Its ability to deliver superior performance without requiring ensemble complexities highlights its efficiency. However, like other pre-trained architectures, its reliance on transfer learning may limit domain-specific interpretability compared to custom CNNs tailored exclusively for the task. By utilizing a deep convolutional architecture, the model significantly reduces computational demands compared to ensemble-based methodologies, making it suitable for deployment in resource-constrained environments. This efficiency enables integration into point-of-care diagnostic tools, such as portable X-ray devices and mobile applications, thereby broadening access to accurate and timely diagnoses in low-resource and remote clinical settings. Additionally, the model has the potential to integrate seamlessly into telemedicine workflows, where portable X-ray systems combined with cloud-based analysis could provide rapid diagnostic support for patients in underserved areas. The model's resilience to class imbalance, in conjunction with training on publicly available datasets, significantly enhances its generalizability across varied patient populations. This model is also engineered to accommodate cloud-based deployment strategies, which yield several notable advantages. For instance, it offers scalability to manage substantial volumes of radiological data originating from multiple healthcare facilities. However, it also enhances accessibility, allowing clinicians in under-resourced areas to upload and analyze images in real time. Despite these strengths, limitations include the reliance on publicly available datasets, which may not fully capture the variability in image quality or geographic and demographic diversity. Plans for external validation using diverse real-world datasets are essential to further establish the model's reliability and applicability. Although it establishes a framework for continuous model refinement through iterative updates and retraining,this ensures alignment with evolving diagnostic standards and expanding datasets. A possible solution that incorporates cloud-based infrastructure could facilitate centralized data processing, all while preserving the model's clinical relevance over time, making it an adaptable and sustainable solution for medical image analysis.
## 3 Quantification of Uncertain Information
### Motion Intention-Switching by Identifying the Actual Intention of the User  
#### 3.3.1 Rationale behind the Evaluation of Gestures Accompanied with User Instructions  
The two example scenarios given in Figure 3 are considered for investigating the limitations of the system proposed in [20]. In scenario (a), the user issues the command, \"move far forward\". In this situation the maximum quantified output of the system proposed in [20] will be less than \(D_{r}\) since the perceptive distance is limited to \(D_{r}\). Therefore, the robot will move to position 'B'. However, there are situations where the intention of the user is to move the robot to a position similar to location 'A' since the user expects that the robot can see beyond the obstacle. In situation (b), the user issues the command, \"move right\". In this situation the quantified output of the system proposed in [20] will result in a movement of the robot to location 'B'. However, there are situations where the intention of the user is to move the robot to a location similar to the location 'A' since the user expects that the robot can consider the nearby obstruction for adapting the perception. Therefore, the system proposed in [20] is not capable of understanding the intention of the user effectively.  
Typically, humans combine pointing gestures with voice instructions in order to convey the idea or the intention more clearly to the peers [21; 22; 24]. Therefore, the information conveyed from pointing gestures is analyzed by the intention identifier module (IIM) in order to identify the intention of the user effectively. The two example scenarios given in Figure 4 are considered for the explanation of the gesture-based user intention identification process that can be used in order to overcome the above-mentioned limitations. In case (a), the user is pointing to a location that is well beyond the default perceptive distance (i.e., \(D_{r}\)). Therefore, if the gesture is being pointed towards a location well beyond the default perceptive distance it can be concluded that the intention of the user is to navigate the robot beyond \(D_{r}\) (i.e., location 'A' instead of 'B' in Figure 3a). Similarly, in case (b), if the user is pointing to a location that is well within the default perceptive distance (\(D_{r}\)), then it can be concluded that the intention of the user is to move the robot to an alternative position 'A' instead of position 'B' in Figure 3b.
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 2 Background
### Problem Definition  
Let \(X\in\mathbb{R}^{d}\) be the the configuration space of the path planning problem,where \(d\in\mathbb{N}\), \(d\geq 2\). Let \(X_{obs}\in X\) be the obstacle space, which is an impassable space, and denotes the obstacle-free space as \(X_{free}=X\setminus X_{obs}\), which is a passable space. \(x_{init}\in X_{free}\) and \(x_{goal}\in X_{free}\) are the initial configuration and the goal configuration, respectively, and the goal region is defined as a circle with radius \(r\): \(X_{goal}=\left\{x\in X\middle|\middle|\middle|x-x_{goal}\middle|\middle|<r \right\}\). A path is defined by continuous function \(\sigma:[0,T]\to X_{free}\), and \(\sigma(0)=x_{init}\), \(\sigma(T)=x_{goal}\). If the path is feasible, then \(\sigma(\tau)\in X_{free}\) for all \(\tau\in[0,T]\).  
The path planning problem is to find a feasible path. Problem 1 defines the feasibility problem of path planning.  
**Problem 1 (Feasible Path Planning)** Given a configuration space of planning problem \(X\in\mathbb{R}^{d}\), a free space \(X_{free}\), an initial state \(x_{init}\in X_{free}\), and a goal region \(X_{goal}\in X_{free}\), find a path \(\sigma:[0:T]\to X_{free}\) if one exists. If no such feasible path exists, return failure.  
The cost from the initial configuration to the goal region is not necessarily the same each time planning or on each path. Let \(c(\cdot)\) be the cost function. The optimization problem of path planning is to find the path with minimum (non-negative) path cost. Its formal expression is given by Problem 2.  
**Problem 2 (Optimal Path Planning)** Given a configuration space of planning problem \(X\in\mathbb{R}^{d}\), a free space \(X_{free}\), an initial state \(x_{init}\in X_{free}\), a goal region \(X_{goal}\in X_{free}\) and cost function \(c\). Find a feasible path \(\sigma^{*}\), such that \(c(\sigma^{*})=min\Big{\{}c(\sigma):\sigma\in\sum_{feasible}\Big{\}}\).  
The path planning algorithm usually takes some time to plan the path. Let \(t\in\mathbb{R}\) be the set of times it takes to find a set of paths. Problem 3 defines finding the best path in Problem 2 in the least amount of time possible.  
**Problem 3 (Fast Path Planning)** Minimum time \(t\in\mathbb{R}\) required to plan the Optimal Path.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 2 Network Model and Spectral Efficiency
### Uplink Pilot Transmission and Channel Estimation  
When the \(\sqrt{\tau_{p}}\mathbf{\varphi}_{k}\in\mathbb{C}^{\tau_{p}\times 1}\) is defined, the \(\tau_{p}\) is a pilot transmission length of coherence block \(\tau_{c}\), and \(||\mathbf{\varphi}_{k}||^{2}=1\) is a pilot sequence for user \(k\). Then, the received pilot signal from the AP \(l\) to user \(k\) is given by  
\[y_{p,l}=\sqrt{\tau_{p}\rho_{p}}\sum_{k=1}^{K}h_{ll}\mathbf{\varphi}_{k}+\mathbf{ \omega}_{p,l}, \tag{2}\]  
where \(\rho_{p}\) is the power of the pilot sequence, and \(\mathbf{\omega}_{p,l}\) is a vector of additive noise at the AP \(l\) which has i.i.d. \(\mathcal{N}_{\mathbb{C}}(0,1)\). Then, the AP \(l\) estimates the channel \(h_{kl}\) locally based on the received pilot signal (2) by using the projection [8]. The equation obtained by conducting the projection is as follows,  
\[\begin{split}\ddot{g}_{p,lk}&=\mathbf{\varphi}_{k}^{H} y_{p,l}\\ &=\sqrt{\tau_{p}\rho_{p}}h_{lk}+\sqrt{\tau_{p}\rho_{p}}\sum_{k^{ \prime}\
eq k}^{K}h_{ll}\mathbf{\varphi}_{k}^{H}\mathbf{\varphi}_{k^{\prime}}\mathbf{ \omega}_{p,l}.\end{split} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(\begin{matrix}(\cdot)^{T},(\cdot)^{H}\\ \text{E}\{\cdot\}\\ (\cdot)^{*}\end{matrix}\) & \begin{matrix}\text{Transpose and conjugate transpose}\\ \text{Expectation value}\\ \text{The complex conjugate operation}\\ \text{Frobenius norms}\\ \mathcal{N}_{\mathbb{C}}(\cdot,\cdot)\end{matrix}\) & \begin{matrix}\text{Transpose and conjugate transpose}\\ \text{Expectation value}\\ \text{The complex conjugate operation}\\ \text{Frobenius norms}\\ \mathcal{N}_{\mathbb{C}}(\cdot,\cdot)\end{matrix}\) & \begin{matrix}\text{Expectation value}\\ \text{The complex conjugate operation}\\ \text{Frobenius norms}\\ \text{Circularly symmetric complex Gaussian distribution}\end{matrix}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and Symbols.  
As mentioned in Section 1, the MMSE estimator is adopted as the channel estimation technique. The MMSE estimator is given by  
\[\hat{h}_{lk}=\frac{\mathrm{E}\Big{\{}\tilde{y}_{p,lk}^{*}\eta_{lk}\Big{\}}}{ \mathrm{E}\Big{\{}|\tilde{y}_{p,lk}|^{2}\Big{\}}}\tilde{y}_{p,lk}=t_{lk}\tilde{y }_{p,lk}, \tag{4}\]  
where  
\[t_{lk}\triangleq\frac{\sqrt{\tau_{p}\rho_{p}}\beta_{lk}}{\tau_{p}\rho_{p}\sum_ {k=1}^{K}\beta_{lk}\big{|}\boldsymbol{\varphi}_{k}^{\mathrm{H}}\boldsymbol{ \varphi}_{k}\big{|}^{2}+1}. \tag{5}\]
## 4 Simulation Analysis
### Scenario 1  
In this scenario, the landing procedure is simulated under no wind disturbance. Figure 7(a) compares the actual trajectory and desired trajectory. Additionally, Figure 7(b) shows the errors between actual trajectory and desired trajectory. It is clearly seen that, when compared with the LQR and RAC, the actual trajectory more closely follows the desired trajectory in glide slope and flare segment while using SIRAC. Additionally, the trajectory error of SIRAC is less about 3.5 m and 3 m than the LQR and RAC, respectively. The velocity of the SIRAC is also more close to the desired velocity, which is 67.4 m/s, see Figure 7(c). Based on this result, it is also expected that the descent rate is slow. As seen from Figure 7(d), the descent rates of three controllers are both approximately \(-3.5\) m/s in the glide slope and reduce to zero in the flare segment. However, at the transition, the descent rate of SIARC has a larger variation, which will enable the aircraft to track the trajectory more quickly and closely. Figure 7(e) shows the response of pitch angle and Figure 7(f) shows the response of pitch rate. It can be seen that the pitch angle and pitch rate of three controllers are smoother in the glide slope. Similarly, the variation of SIRAC is larger in the transition, as it more closely tracks the trajectory.  
Figure 7: Cont.
## 3 Results  
### Variation of PRI at Different Observation Angles and the Result of BRDF Model  
PRI changed with observation angles; it was correlated with vegetation canopy structures and different environmental factors, produced under different observation angles. Figure 3a shows spectral observations at 10:30-10:45 on 13 July 2014, when the solar azimuth angle was 111\({}^{\circ}\) and the solar zenith angle was about 21\({}^{\circ}\). Figure 3b shows spectral observations at 10:30-10:45 on 13 November 2014, when the solar azimuth angle was about 160\({}^{\circ}\) and the solar zenith angle was about 51\({}^{\circ}\). Figure 3a shows that the PRI did not change significantly at different observation angles, which was almost close to each other. Figure 3b shows the variation of PRI from different observation angles. The PRI values in the backward scattering direction were generally higher than the forward scattering direction. When the VAA is about 135\({}^{\circ}\), it is close to the solar incident azimuth angle (called backward scattering direction), and the value of PRI is greater than the VAA of 315\({}^{\circ}\) (forward scattering direction at this time).  
Figure 3: Observed PRI shown in a polar coordinate system (overhead view) obtained during 10:30–10:45 on 13 July 2014 (**a**) and on 13 November 2014 (**b**), respectively. The yellow signs represent the solar position in the hemisphere, which is described by the solar azimuth angle (SAA) and solar zenith angle (SZA). The PRI was obtained at different viewing zenith angles (VZA: ranged from 0–90\({}^{\circ}\)) with varied viewing azimuth angles (VAA: ranged from 0–360\({}^{\circ}\)).  
Figure 4 shows the comparisons of the relationships between the EC-based LUE and PRI with (R\({}^{2}\) = 0.60, \(p\) < 0.01) and without (R\({}^{2}\) = 0.46, \(p\) < 0.01) the correction of the BRDF model at a half-hourly time scale. The red lines represent the linearly fitted functions, and the green lines represent the exponentially fitted functions. The exponential fitting does not differ significantly from the linear adjustment.
## 3 Results and Discussion
### Effect of Steam-to-Biomass Ratio
The ignorance of the effect of tar and some reactions causes the differences between simulation results and experimental data. In the presence of steam in the gasifier, the steam reforming reaction increases the decomposition of tar components at high temperatures. Generally, the model predictions and their error are capable of simulating the gasification performance under various steam-to-biomass ratios. In summary, the study has developed a kinetic model which incorporates both reactor hydrodynamics and reaction rate kinetics to simulate biomass gasification in a bubbling fluidized bed reactor. The model is validated against experimental data at the same operating parameter range in terms of gasification temperature and steam-to-biomass ratio. It is noted that high agreement has been found with experimental results in most cases with the mean error ranging from approximately 0.033 to 0.329 (when fractions and yields of gas components and carbon conversion efficiency are considered). The trends observed in both simulation and experimental investigations are similar. There are slight deviations during the comparisons due to model assumptions. Additionally, the equilibrium model assumes that the reactions can reach a complete equilibrium, while the experimental conditions deviate from the ideal operating conditions, resulting in those errors. Finally, the model is capable of predicting the performance of biomass gasification in a bubbling fluidized bed and is sufficient to provide a good understanding of the phenomena occurring inside the gasifier.