esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
## 3 Material and Experimental Methods
### Decision Tree  
DTs are prominent and extensively utilized machine learning methods renowned for their capacity to facilitate easy understanding in predictive and classification solutions. They hold great significance due to their inherent interpretability and ability to generate clear, intuitive rules for decision-making. The process of constructing a DT involves dividing the input data into distinct groups or subsets based on certain features or attributes. Each division represents a node in the tree, and this process continues recursively until a decision or prediction is reached at the leaf nodes. At each node of the DT, a specific attribute is chosen to make a decision. This selection is based on its ability to segregate the data into distinct groups with homogeneous characteristics. The attribute that leads to the most significant reduction in impurity or increase in information gain is selected, thereby enhancing the tree's predictive power. DTs are highly versatile and can be applied to both regression and classification tasks. For classification problems, the tree's leaf nodes correspond to specific class labels, while, for regression problems, they represent predicted continuous values.  
The interpretability of DTs is a key advantage, as the rules governing each split are easily understandable and can be visualized, making them particularly useful in decision-making processes. Moreover, DTs are robust against outliers and can handle both categorical and numerical data effectively. By leveraging the clustering algorithm, DTs repeatedly divide the data into subsets, ultimately leading to decision outputs that aid in making accurate predictions and classifications. This adaptability and ease of interpretation have contributed to DTs' widespread application across various domains, ranging from finance and healthcare to marketing and environmental research [61; 62; 63].  
Figure 5 showcases a DT analysis, providing a visual representation of the decision-making process employed by the DT algorithm. DT analysis is a powerful tool used in various fields, including machine learning, data mining, and decision support systems, to make informed and structured decisions based on input data. The figure presents a tree-like structure, starting with the root node at the top, which represents the initial decision point. Each subsequent node in the tree represents a decision based on a specific attribute or feature from the input data.
## 4 Discussion
Additionally, the DynamicHead detection head adapts to detection tasks of varied sizes and complexities by dynamically adjusting its settings, therefore greatly enhancing the model's capacity to recognize multiscale objects. By dynamically modifying regression weights, the WIoU loss function increases bounding box localization accuracy and fortifies the model's capacity to learn from difficult samples. Finally, the DynamicATSS label assignment approach optimizes sample distribution during training by dynamically altering the selection mechanism for positive and negative samples, considerably increasing the model's generalization capabilities. Despite its excellent performance in detection of maize leaf diseases and pests, the YOLOv11s-RCDWD model has some limitations. The dataset used is limited in size and diversity, affecting the model's generalization ability. Future work should focus on expanding the dataset's scale and diversity to improve the model's robustness in practical applications. Although YOLOv11s-RCDWD demonstrates excellent resource efficiency, there is still room to further reduce computational load and the parameter count. Future research could explore more efficient model compression methods (e.g., knowledge distillation and quantization) and acceleration techniques (e.g., hardware acceleration) to reduce computational costs. Additionally, the current model is primarily designed for detection of maize leaf diseases and pest, and its generalization ability to other crops and scenarios has not been fully validated. Future efforts should extend this method to detection of diseases and pests in different crops, providing more comprehensive technical support for smart agricultural development.
## 6 Conclusions and Future Work  
In this paper, we have proposed and described a novel approach to link real-world objects (with sensor and actuator capabilities) to virtual world information relying on proximity communications. The system combines dynamic AR markers (which can also be used to locate objects and estimate their spatial orientation in video streams), VLC channels and a supporting IoT architecture. VLC has been chosen _versus_ other communication technologies, such as RFID/NFC, Bluetooth and WiFi, due to its more extended hardware support, better accuracy for AR positioning and relatively long range. These components allow one to represent the sensors and actuators in a smart city as services that can be easily accessed via graphical interfaces based on real-world images, without the need for permanent connection with the backbone network. As described, this new approach may help to improve maintenance tasks in smart cities. Furthermore, our architecture can be easily adapted to other use cases. For example, providing AR information to tourists, obtained from different sources of urban information, or providing technical AR information to workers in industrial environments.  
Future extensions of this work will consider mechanisms to reduce the decoding latency of marker VLC messages, implement the finer AR user interfaces we have described, introduce new node actions and apply our technologies to other interesting use cases.
## 4 Effective Attenuation Factor for Layered Composites
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{\(\begin{array}{c}\boldsymbol{\gamma}\text{-Ray}\\ \text{Energy}\\ \text{[MeV]}\end{array}\)} & \multicolumn{5}{c}{**Linear Attenuation Coefficient [cm\({}^{-1}\)]**} \\ \cline{3-8}  & & **1.9** & **3:7** & **5:5** & **7:3** & **9:1** \\ \hline \multirow{8}{*}{PE/concrete} & 1 & 71.99 & 77.50 & 83.93 & 91.51 & 100.61 \\ \cline{2-8}  & 10 & 103.60 & 109.58 & 116.30 & 123.90 & 132.55 \\ \cline{2-8}  & 30 & 109.91 & 115.93 & 122.65 & 130.20 & 138.73 \\ \cline{2-8}  & 50 & 118.78 & 124.02 & 129.74 & 136.02 & 142.93 \\ \cline{2-8}  & 80 & 138.92 & 141.77 & 144.74 & 147.83 & 151.06 \\ \cline{2-8}  & 100 & 157.23 & 157.05 & 156.87 & 156.69 & 156.51 \\ \hline \multirow{8}{*}{PE/copper} & 1 & 24.94 & 30.05 & 37.78 & 50.86 & _77.82_ \\ \cline{2-8}  & 10 & 37.76 & 45.02 & 55.72 & 73.10 & 106.22 \\ \cline{2-8}  & 30 & 43.33 & 51.28 & 62.81 & 81.03 & 114.13 \\ \cline{2-8}  & 50 & 56.06 & 64.98 & _77.28_ & 95.31 & 124.33 \\ \cline{2-8}  & 80 & 88.09 & 97.24 & 108.50 & 122.72 & 141.22 \\ \cline{2-8}  & 100 & 124.39 & 130.32 & 136.84 & 144.05 & 152.07 \\ \hline \multirow{8}{*}{PE/lead} & 1 & 14.02 & 17.37 & 22.82 & 33.25 & 61.27 \\ \cline{2-8}  & 10 & 21.62 & 26.60 & 34.57 & 49.33 & 86.13 \\ \cline{2-8}  & 30 & 26.75 & 32.65 & 41.89 & 58.44 & 96.60 \\ \cline{2-8}  & 50 & 33.27 & 40.17 & 50.69 & 68.66 & 106.38 \\ \cline{2-8}  & 80 & 44.32 & 52.62 & 64.75 & 84.13 & 120.09 \\ \cline{2-8}  & 100 & 52.65 & 61.76 & 74.67 & 94.40 & 128.32 \\ \hline \multirow{8}{*}{PE/tungsten} & 1 & 9.041 & 11.35 & 15.23 & 23.17 & 48.34 \\ \cline{2-8}  & 10 & 13.73 & 17.17 & 22.89 & 34.33 & 68.67 \\ \cline{1-1} \cline{2-8}  & 30 & 15.81 & 19.70 & 26.15 & 38.85 & 75.60 \\ \cline{1-1} \cline{2-8}  & 50 & 17.95 & 22.30 & 29.43 & 43.27 & 81.63 \\ \cline{1-1} \cline{2-8}  & 80 & 20.76 & 25.69 & 33.70 & 48.97 & 89.51 \\ \cline{1-1} \cline{2-8}  & 100 & 22.47 & 27.76 & 36.28 & 52.37 & 94.10 \\ \hline \end{tabular}
\end{table} 
Table 4: Calculated thickness of laminated composites with 99.99% shielding rate. in the direction perpendicular to the incident radiation, but if the internal structure is more complex, the effect will be greater.
## 3 Results and Discussion
### Motor Angle Trajectory Verification  
This part was to verify the angle error between the motor output angle simulation and the actual motor output. In the first place, the motion sensor was installed on the crank. Then, the motor trajectory measurement of the platform roll, and pitch motion was carried out. Among them, the roll and pitch motion were to let the upper platform to obtain the actual crank output angle, the average value of the trajectory error, and the standard deviation of the measurement. The angle trajectory test of the platform is shown in Figure 23.  
Both of the roll and pitch motions tilted the platform at 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\), 15\({}^{\circ}\), and 20\({}^{\circ}\). When the testers controlled the movement of the upper platform, the angle of the three sets of driving motors was measured by the motion sensor. Figure 24a,b shows the motor motion simulation trajectory of the roll and pitch motion.  
Figure 23: The angle trajectory test.  
Figure 22: (**a**) Angular velocity (**b**) Angular acceleration comparison chart of the pitch motion.  
After that, comparing the motor output angle of actual and simulation of roll motion, the error of the output angle of drive motor 1 was 2.95%, motor 2 was 8.5%, and motor 3 was 6.5%. The overall motion error was 7.5%. The comparison chart is shown in Figure 25. Motor 1 during the roll motion was almost static. Therefore, the error of motor 1 was relatively smaller than motor 2 and motor 3. Overall errors might come from mechanism machining accuracy and errors at the moment of the motor start.  
In the pitch motion, the error of the output angle of drive motor 1 was 4%, motor 2 was 3.5%, and motor 3 was 3.5%. The overall motion error was 5.5%. The comparison chart is shown in Figure 26. The motion angle during the pitch motion was small. Overall errors were the same as roll motion. This might also come from mechanism machining accuracy and errors at the moment of the motor start.  
Figure 24: The (**a**) roll (**b**) pitch motor motion input trajectory.  
Figure 25: Comparison chart of actual and simulation of roll motion.
## 4 Methods
### Data Preparation  
To maintain consistency and comparability with our previous work [2], the data preparation steps largely follow the same procedure. The UltraSuite CLEFT dataset [3] provides synchronized ultrasound video sequences and audio recordings of speech. As in our prior study, each ultrasound video sequence is segmented into \(K\) chunks. The number of chunks, \(K\), is determined empirically to balance the need to capture relevant articulatory movements with computational efficiency. Each of these chunks represents a short, distinct segment of the ultrasound video. For the audio data, which are time-aligned with the video, we generate spectrograms using the short-time Fourier transform (STFT). A spectrogram provides a visual representation of the frequencies present in the audio signal as they change over time. This conversion of the 1D audio signal into a 2D spectrogram image allows us to treat the acoustic information as an image, making it compatible with image-based processing techniques. This process is mathematically represented as follows:  
\[\text{Spectrogram}(S)=|\text{STFT}(s(t))|^{2} \tag{1}\]  
where \(s(t)\) is the original speech signal in the time domain, and STFT is the short-time Fourier transform. By treating the resulting spectrograms as images, we can leverage the image processing capabilities of SigLIP 2, enabling a unified feature extraction approach for both the visual (ultrasound) and acoustic (spectrogram) data, thus facilitating multimodal analysis.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 4 Proposed System for Massive Uplink Scenario
### EDCA Parameters for Multiple STA Operation  
The MU transmission procedure reduces the end-to-end delay of the entire transmission, but it also increases the collision probability as the number of STAs in the MU OFDMA procedure increases. As depicted in Figure 12, each STA may have an uplink packet arrival, and the contention procedure is operated when there is an uplink packet in the STA during the wake-up delay. Because in WLAN transmissions procedure with EDCA, the STA may start to transmit the data when the channel is idle for the arbitration inter-frame spacing (AIFS) period, there is a high probability that there will be collisions.  
Collisions in the wake-up receiver procedure cause severe degradation in terms of power efficiency because the AP should then restart the wake-up frame transmission. In particular, if the TF encounters a collision, STAs in the MU transmission procedure remain in an idle state and wait for the wake-up frame and subsequent TF for the long wake-up delay.  
To prevent collisions, a shorter AIFS can be applied to the TF, which gives high priority to the transmission of TF. By implementing the short AIFS for the TF, STAs that have uplink data defer their transmissions, which reduces the probability of collisions. The implementation of a short AIFS parameter in the MU TF is possible using the EDCA parameters determined by the AP because the IEEE 802.11 ax standard enables the AP to decide the EDCA parameter of each access category (AC), and the trigger frame can use any access category to access the channel.
# One-Dimensional Consolidation Properties of Soft Clay under Multi-Stage Loading
## 3 Test Results and Discussion
### Variation Law of Consolidation Coefficient
In the context of one-dimensional seepage consolidation theory for saturated soil, the consolidation coefficient is defined as follows:  
\[\frac{\partial e}{\partial t}=\frac{k(1+e_{0})}{\gamma_{w}}\frac{\partial^{2}u }{\partial z^{2}} \tag{3}\]  
where \(k\) is the permeability coefficient; \(e_{0}\) is the initial pore ratio; \(\gamma_{w}\) is soil bulk density. From the compression curve \(e\)-\(\log\sigma\) of the soil,  
\[e=e_{0}-C_{c}\lg\left(\frac{\sigma^{\prime}}{\sigma_{0}^{\prime}}\right) \tag{4}\]  
where \(C_{c}\) is the compression index; \(\sigma_{0}^{\prime}\) is the initial consolidation stress; \(\lg\) means logarithm to base 10. According to the effective stress principle,  
\[\sigma^{\prime}=\sigma-u \tag{5}\]  
Substituting Equation (5) into Equation (4) and taking the derivative of \(u\), we can obtain  
\[\frac{\partial e}{\partial u}=\frac{C_{c}}{2.33(\sigma-u)} \tag{6}\]  
where  
\[\frac{\partial e}{\partial t}=\frac{\partial e}{\partial u}\frac{\partial u }{\partial t} \tag{7}\]  
Substituting Equation (6) and Equation (7) into Equation (3), we can obtain  
\[\frac{\partial e}{\partial t}=\frac{2.33k(1+e_{0})\sigma^{\prime}}{\gamma_{w} C_{c}}\frac{\partial^{2}u}{\partial z^{2}} \tag{8}\]  
Figure 3: Variation in \(C_{v}\) with \(P\) for multi-stage loading. According to [PERSON]'s consolidation equation,  
\[C_{v}=\frac{2.33k(1+\epsilon_{0})}{\gamma_{w}C_{c}}\sigma^{\prime} \tag{9}\]  
The permeability coefficient of soft soils varies widely during the consolidation process [30] and has a large impact on the consolidation process. According to the results of a large number of tests, for the variation characteristics of \(k-\sigma^{\prime}\), the relationship between the permeability coefficient and the stress level is as follows:  
Figure 3.0  
Substituting Equation (10) into Equation (9), we can obtain  
Figure 3.1  
It can be seen from Equation (11) that the consolidation coefficient, \(C_{v}\), is related to the consolidation stress. With an increase in the load, the permeability coefficient decreases, so the consolidation coefficient also decreases.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 2 Smart Ships and Navigation Technology
### Navigation Technology
#### 2.2.3 Motion Control Technology  
Uncertainties in ship dynamics, random environmental perturbations and inaccuracies in measurement information are the main problems facing ship motion control [33]. The types of ship motion control are shown in Figure 6. The last three categories concern the control of heading, path, speed and stability, with the focus on which control strategy to choose. Different types of ship motion control correspond to different control algorithms. The basic algorithms used for ship heading control are proportional-integral-differential (PID) control [34], fuzzy logic control [35], predictive control [36], sliding mode control [37], autoresistant control (ADRC) [38] and artificial intelligence (AI) [39] algorithms, among others. On this basis, other algorithms are used to improve the effectiveness of heading control, such as optimizing PID controllers using Particle Swarm Optimization (PSO) [40],  
Figure 5: The communication architecture of a smart ship.  
applying Genetic Algorithms (GA) to the optimization of the parameters of fuzzy controllers, and so on. For the study of ship stability control, the basic algorithms used are PID control [41], fuzzy control [42], predictive control [43], sliding mode control [44], ADRC [45], etc. Optimization algorithms are combined with basic algorithms on this basis, such as the combination of predictive algorithms with fuzzy control [46] and PID algorithms [47], to ensure the stability of ship navigation. For path tracking, the basic algorithms used include PID control [48], fuzzy logic control [49], predictive control [50], sliding mode control [51], ADRC [52], etc. The basic algorithms can be used in combination, with adaptivity being progressively improved as artificial intelligence is applied to path tracking. For ship trajectory tracking, the basic algorithms used include PID control [53], ADRC [54] and artificial intelligence algorithms [55]. Optimization algorithms, such as fuzzy logic control combined with PID control [56], PSO combined with ADRC [57], and neural networks combined with predictive control [58], are combined with basic algorithms, which can be used to achieve trajectory tracking and ship collision avoidance in different situations.  
The structure of ship motion control is shown in Figure 7:
###### Abstract  
This paper concerns the experimental validation of optimized near-field (NF) spherical spiral scannings employing a minimum number of samples, when an offset-mounted elongated antenna under test (AUT), i.e., with its center shifted with respect to that of the measurement sphere, is considered. In order to perform the standard NF/far-field transformation (NF/FFT) technique, a non-centered AUT would generally require the collection of a significantly increased amount of voltage data if compared to the onset scenario. This issue is addressed here by using the non-redundant (NR) sampling representations of electromagnetic (EM) fields. These representations, by leveraging the analytical properties of the EM field, allow one to perform the NR NF/FFIs for offset-mounted AUTs by using only a minimum number of (offset acquired) samples, equal to that required by the NR approaches for the onset case (over 85% fewer samples compared to the standard NF spherical scanning). In particular, these NR NF/FFIs are obtained by modeling the AUT with a prolate spheroid or a rounded cylinder and their effectiveness is fully assessed by the reported experimental results.  
antenna measurements; non-redundant sampling representations of the electromagnetic field; NF/FF transformations; offset mounting; spherical spiral scanning Article
# Application of Tooth Gear Impact-Echo System for Repeated and Rapid Data Acquisition
## 3 Tooth Gear Testing System
### Tooth Gear IE System Design and Data Acquisition  
Based on the background information of the contact mechanism, a tooth gear IE system was designed as shown in Figure 4. For the receiver, a microelectromechanical system (MEMS, SPU0410 LR5H-QB, Knowles) was used with a circuit amplified 2000 times, able to measure acoustic pressure with a sensitivity of \(-\)38 dBV/Pa. The location of the MEMS was in the same line as the impact source, 65 mm away from the tooth gear. The holder was manufactured using a three-dimensional (3-D) printer, and the system was manually controlled using a handle. The total length of the system was 200 mm so that inspectors could easily use the system in the field. While the system moves backwards and forwards, numerous signal data were measured. For data acquisition, the signals were digitized using the NI 6366, at a sampling rate of 200 kHz, and 2000 samples were collected. The total length of a signal was 10 ms. As shown in Figure 5, the testing equipment (including the laptop, data acquisition, and IE system) was placed on the concrete slab. Based on the test results, more than 100 data signals were recorded within one minute, while the system was moving.  
Figure 4: Designed IE system including tooth gear and microelectromechanical systems (MEMS).  
Figure 5: Data acquisition and experimental set-up for the IE system.
# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
## 2 Coupling Coefficient Data
In this paper, a one-quarter wavelength monopole antenna with an operating frequency of 2 GHz (the main operating frequency of the 5G-R antenna) is used as the roof  
Figure 2: A simplified model for the coupling coefficient of pantograph arcing and roof antenna: (**a**) front view of model; (**b**) top view of model; (**c**) probes of electric field simulation; and (**d**) installation position of antennae for Table 3.  
antenna for simulation, and the single simulation time is about 40 min. The simulation model established in this paper is based on the electromagnetic simulation software CST 2019. The numerical calculation method is using a time-domain solver, in which the mesh type is set to Hexahdral, the accuracy is set to \(-40\) dB, and the adaptive mesh optimization function is used. The simulation computing platform used in this paper is an Intel(r)17 12600 KF(r) CPU with a WIN10 64-bit processing system and 16 GB of memory. In order to further prove the validity of the simulation model, the coupling coefficient of the monopole antenna (operating frequency of 1.268 GHz), calculated by using the similar simulation model in reference [12], was compared. As shown in Table 1, the change rules of the two coupling coefficients are the same; that is, as the linear distance between the roof antenna and the pantograph arcing increases, the coupling coefficient naturally decreases, which is caused by the attenuation of electromagnetic wave propagation in free space. However, since the roof in the simulation model of reference [12] is only \(22\) m \(\times\) 2.82 m, the spacing between the roof antenna and the pantograph arcing under the same coordinates is smaller. In addition, the lower working frequency of the antenna and the different material settings in the simulation model will cause the inconsistency between the two coupling coefficients, but it is enough to prove that the simulation model in this paper can effectively construct the original dataset of coupling coefficients. When constructing the dataset, considering the actual installation requirements of the roof antenna and the size of the antenna, the distance of the horizontal co-ordinate X was increased from 1 m to 25 m at an interval of 1 m, and the distance of the vertical co-ordinate Y was increased from 0 m to 1 m at an equal interval of 0.1 m, so that a total of 275 co-ordinates were included. In order to further verify the generalization of the prediction model in this paper, the coupling coefficients of two roof monopole antennae for the GSM-R system and the satellite navigation system are simulated at the same time, and their working frequencies are 0.9 GHz and 1.5 GHz, respectively. Therefore, 825 sets of different coupling coefficients can be obtained, and 80% of the data (660 sets) are randomly sampled as the training set, and the remaining 165 sets of data are used as the test set. Part of the simulation data (four sets) is shown in Table 2.