esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Cross-Correlation Loss  
Most of the current research uses attention mechanisms to capture relevant information from both intramodality and intermodality, but few scholars consider the relationship between these two different attention levels. In order to extract this relationship in dual-attention, we propose a cross-correlation loss to obtain relevant information. By adding it to the objective function, the model is able to accomplish an undirected interaction between two different kinds of attention.  
As shown in Figure 3, we use linear projectors to expand the feature sequence dimensions of the two different attention mechanisms and perform modality-specific matrix multiplication to obtain a set of matrices with a shape of (batch, length, length).  
\[C_{m}=F_{m}^{S}\bigotimes F_{m}^{C} \tag{8}\]  
where \(C_{m}\) represents the cross-correlation matrix of the \(m\) modality's feature sequences in two different attention mechanisms, \(m\in\{t,a,v\}\). The diagonal elements in this matrix represent the correlation between the corresponding positions of the two feature sequences, while the off-diagonal elements represent the redundant information.  
Taking the textual modalities of the samples in the CMU-MOSI dataset as an example, as shown in Figure 4, the model maximizes the diagonal elements in the intercorrelation matrix in order to capture the correlation between the different attentional mechanisms during the training process. At the same time, nondiagonal elements are minimized in order to reduce redundant information in this process.  
\[\mathcal{L}_{Carr}=\frac{1}{M}\cdot\sum_{m}^{M}(\sum_{i=j}^{n}(c_{ij}-1)^{2}+ \sum_{i\
eq j}^{n}c_{ij}^{2}) \tag{9}\]  
As shown in Equation (9). The term \(\sum_{i=j}^{n}(c_{ij}-1)^{2}\) in \(\mathcal{L}_{Carr}\) is the correlation term, which denotes the correlation between the sequence of modality features of \(m\) in different attention mechanisms, and the other term \(\sum_{i\
eq j}^{n}c_{ij}^{2}\) is the redundancy term. Intuitively, the model increases the correlation between different attentional mechanisms by making the  
Figure 4: The cross-correlation matrix.  
Figure 3: The cross-correlation matrix in dual-attention. We perform modality-specific matrix multiplication on the two types of unimodal feature sequences to obtain a cross-correlation matrix, and we use the diagonal elements of the matrix to represent the indirect interaction between these two feature sequences. The deeper the diagonal elements in the matrix \(C_{m}\), the stronger the correlation between the two unimodal feature sequences at the corresponding positions is represented.  
diagonal elements of the cross-correlation matrix close to 1. At the same time, it reduces the redundancy term by making the off-diagonal elements of the cross-correlation matrix close to 0.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 2 Materials and Methods  
This section introduces the process and architecture of preprocessing depth image data for efficient tire defect detection and training. For tire error detection, a depth image having tire height information is acquired, using a 3D camera, and this image is used to detect defects in the tire.  
The tire depth image contains depth information that best describes the error that may occur in the tire. However, (1) the height value is concentrated in a narrow section, and (2) with the same tire error, the shape is often different. For these two reasons, there is a limit to the error detection accuracy when the deep learning model trains using only pure depth images. Although this paper aims to classify the area of vent spew error, it must have the robustness to classify additional errors in the future. In other words, it should not damage the original data, and show maximum classification performance.  
Therefore, this paper generates three images based on the original depth image: (1) the original image, (2) a histogram equalization depth image, and (3) a height information heatmap image. A heatmap is a graphical representation of data in which data values are represented as colors. These images are stacked into one three-channel image and used for deep learning network training. Histogram equalization data can compensate for the phenomenon that height data are concentrated in a narrow section, and heatmap data inform the degree of the anomaly of height values in depth images. By providing these additional data as training information, better results are obtained. Figure 1 compares deep learning, using original depth images and the learning method presented in this paper.  
Figure 1: Comparison of the existing training method and the proposed method.
## 4 Discussion
### Spatiotemporal Parameters
Research has shown that eccentric contractions disturb both the muscle spindle and Golgi tendon organs' proprioception [54; 55], potentially disrupting body balance, as both sensory inputs are believed to contribute to joint stability by modulating the stiffness of muscles controlling the joint [56]. Other studies have also demonstrated that a 30-min downhill walking [11] and as low as 20 reciprocal isokinetic maximum concentric and eccentric contractions at an angular velocity of 180 degrees per second [57] can deteriorate knee joint-position sense, indicating that both prolonged eccentric low load activity and local loading might compromise joint proprioception, thus ultimately affecting joint stability. Moreover, widening the step during downward walking may have served to prevent overstriding, enabling the impact of each step to be more uniformly distributed over a larger surface area. This could potentially relieve stress on joints and muscles, particularly in the lower extremities, thus reducing the risk of injury (e.g., falls) [58; 59]. There is a consensus among the present and other published studies regarding temporal characteristics such as step, stride, stance, and swing time, with all of them showing an increase during uphill and a decrease in downhill walking [15; 17; 47]. Consistent were also our findings with findings reported elsewhere regarding the decreased cadence during uphill and increased cadence during downhill walking, albeit not always significant compared to level walking [16; 47]. Given that all participants in the present study were students of similar age, musculoskeletal health, and fitness level, and considering that they walked without shoes and had the same period of adaptation to the treadmill, any changes in temporal parameters are expected to have resulted from the variation in speed selection for walking on the different slopes. The progressively lower speeds determined for walking at progressively increasing slopes resulted in gradually increasing step time and gait cycle duration, ultimately decreasing cadence. [PERSON] et al. [60], after conducting an extensive and systematic review of the existing literature, reported that lower cadence and greater stance duration should be expected at slower speeds during level walking. Muscle activation that increases during uphill walking [7; 18] is likely to have further contributed to the observed changes in the temporal parameters studied, as the reduced walking speed may enable the production of the force necessary to propel the body. During downhill walking, despite the speed being the same or slower than the level walking speed, participants exhibited shorter step times and gait cycle durations, leading to an increase in cadence. This adjustment may have been necessary for participants to synchronize their pace with the moving treadmill belt as it passed beneath their feet. These responses are likely attributed to gravity-assisted propulsion, combined with a reduced necessity for muscle activation [7; 18].
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 4 Internal Clustering Validation Index: CUBAGE
### Upper and Lower Bounds of AGE  
By the property of information gain, the lower bound of \(AGE(P)\) is zero:  
\[AGE(P)\geq 0. \tag{43}\]  
We will discuss the upper bound respectively:  
1. When the number of clusters \(k\leq 2\): \[AGE(P)=\frac{1}{k}\frac{k}{l}[H(V)-E(P_{l})]=H(V)-E(P),\quad k\leq 2.\] (44)  
This indicates that for a given dataset, maximizing \(AGE(P)\) is equivalent to minimizing \(E(P)\) when the size of the partition is no greater than 2. This is because that the whole partition is affirmatory when one of the clusters is learned. Moreover, when the objects are not separated at all, i.e., \(k=1\), the value of \(AGE(P)\) is 0;
2. When the number of clusters \(k>2\), due to Theorem 1, we can establish that: \[AGE(P)=\frac{1}{k}\frac{k}{l}[H(V)-E(P_{l})]\leq\frac{1}{k}\frac{k}{l}[H(V)-E( P)]=H(V)-E(P),\quad k>2.\] (45)  
The equality of Inequality (45) holds if and only if the attribute value distributions in each cluster are all the same, under which circumstances the value of \(E(P)\) would be equal to \(H(V)\); therefore, \(AGE(P)=0\).  
To sum up, the upper bound of \(AGE(P)\) is \(H(V)-E(P)\) and the lower bound is 0. The value of the upper bound becomes equal with the lower bound 0 when the objects are not separated at all, or the attribute value distributions in each cluster are all the same; this means that the \(AGE(P)\) yields the minimum possible value when the objects are least separated.
## 4 Results and Discussion
### SM and LST Time-Series: Coupling/Decoupling and Critical SM Estimation
This can be explained by the fact that MODIS LST Aqua day observations have also a higher dynamic range than in situ observations, in agreement with results shown in Figures 7 and 10. Considering the LST variability in both datasets (std LST \(\approx\) 4.4 \({}^{\circ}\)C to 5.9 \({}^{\circ}\)C) the minimum LST value is similar (12.4 \({}^{\circ}\)C _vs._ 8.4 \({}^{\circ}\)C). Other regions with different climates should be analyzed in further research studies. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**In Situ Data**} & \multicolumn{2}{c}{**Satellite Data**} \\ \hline  & **SM** & \(T_{max}\) & **SMOS SM** & **MODIS LST Aqua Day** \\  & **(m\({}^{3}\)/m\({}^{3}\))** & **(\({}^{\circ}\)C)** & **(m\({}^{3}\)/m\({}^{3}\))** & **(\({}^{\circ}\)C)** \\ \hline minimum & 0.10 & 12.4 & 0.08 & 8.4 \\ maximum & 0.16 & 22.6 & 0.22 & 32.7 \\ mean & 0.12 & 16.4 & 0.12 & 15.2 \\ std & 0.06 & 5.9 & 0.05 & 4.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics obtained from SM–LST coupling/decoupling transitions along the study period. Figure 11: Time-series of in situ SM at the SMOS morning passes and \(T_{max}\) (**top**; blue and red solid lines, respectively), SMOS SM at the morning passes and MODIS LST Aqua day (**top**; cyan and magenta dashed lines, respectively), and daily mean precipitation of the REMEDHUS network (**bottom**). Winter seasons (defined as December–January–February, DFJ) are shaded in light green color. Similar behavior is obtained at the afternoon passes and/or using Terra day (not shown).
## 2 Estimation of the Welding Parameters  
It is well known that the shape and temperature distribution of the weld pool depend on the type of welding process, the welding parameters (welding speed, wire diameter, wire feed rate, tension, current density, workpiece inclination, etc.) and the welding materials (heat transfer, specific heat, latent heat); therefore, a systematic sensitivity analysis has been performed to identify the critical parameters that are required to predict the most realistic grain orientation distribution. The first step of a welding simulation is the definition of the welding parameters, which are essential for numerical analysis. For this purpose, a welding specimen was prepared. This study is based on a V-veld manufactured for the ADVISE project at the University of Stuttgart (MPA). It was an originally circumferential weld joining two 39 mm thick pipes, which was then sectioned and used for ultrasonic evaluation and examination. The pipes with a nominal outside diameter of 517 mm and made of 1.4550 steel were joined using ESAB OK 61.30 filler material and manual shielded metal arc welding (SMAW) technique. Figure 1 shows the resulting weld seam and sectioning. The threads were laid out from right to left, and their arrangement and the metallographic image after sectioning are shown in Figure 2. For experimental convenience, the curved surfaces of several sections were flattened, and this modification has been adopted in the present study. The chemical compositions of the materials are listed in Table 1. Using these data, the temperature-dependent material properties were derived using the JMatPro software [19; 20; 21]. These properties include Young's modulus (\(E\)), yield strength (\(\sigma_{y}\)), specific heat capacity (\(c\)), thermal expansion coefficient (\(\upalpha\)), and thermal conductivity (\(\lambda\)). The thermo-mechanical properties at 20 \({}^{\circ}\)C are listed in Table 2.  
Figure 1: Schematic diagram and a photograph of the considered sample.  
Figure 2: Layout of passes from the welder’s notebook and metallography image.  
According to the received welding report, it is composed of 12 layers with different numbers of welds on each layer:  
* layers 1-4: one pass;
* layers 5-7: two passes;
* layers 8-10: three passes;
* layers 11-12: four passes;
* layers 13: back weld.  
The welding consists of a total of 28 weld passes, where the first is the root pass, the 2 nd to 23 rd are the filler passes, the 24 th to 27 th are the cover passes, and, finally, the 28 th is the back weld.  
The detailed welding report is shown in Table 3; it includes the welding position, the fillers, and a few welding parameters. Unfortunately, these are insufficient for developing welding simulations, and determining other parameters is necessary. The desired parameters include the nugget areas, the welding velocity and the welding power; the following sections outline the origin of the finally adopted values.
## 3 Results
### Existing Solutions
#### 3.3.3 Advanced Simulation Models: Enhancing Predictive Accuracy  
Recent research has focused on employing advanced simulation models to predict and optimize the way evacuations are carried out. These models take into consideration multiple criteria, namely ship motion, crowd dynamics, environmental considerations, and passenger behaviors, to generate predictions of evacuation results with exceptional accuracy. [PERSON] et al. [44] highlight the effectiveness of the Simulation System for Passenger Evacuation (SIMPEV), a sophisticated software designed to simulate evacuations on passenger ships. By integrating velocity-based modeling with advanced algorithms for group behavior, leader-following, and counter-flow avoidance, SIMPEV accurately replicates real-world evacuation scenarios. Moreover, it complies with IMO's standards and demonstrates strong compatibility with other simulation programs. Further investigation into the role of simulations, particularly emergency scenarios, has unveiled a model for accounting for path capacity constraints and evacuee risk assessments that determine the optimal route. That model was tested by using PyroSim and Python, as well as considering certain factors of the surrounding environment (such as carbon monoxide levels, visibility map, temperature, etc.), which lead to a significant decrease in Required Safe Egress Time (RSET), which is the time it takes for passengers to detect the emergency, respond to it, and evacuate to safety. These findings demonstrate the ability of the model in improving safety in a high-risk area, and provide specific disaster management strategies for crew during emergencies [41]. Furthermore, another major contribution to simulating human behavior during an evacuation event was developed by [PERSON] et al. [45] in the form of a cell-based model that accounts for human behavior under emergency conditions. This model splits the space into a homogeneous grid to simulate behaviors at the individual level along with crowd dynamics and counter flow avoidance. The accuracy of this model was validated against the mainly used commercial evacuation programs (e.g., EVI, maritimeEXODUS, ODIGO) and demonstrated its utility in real-world evacuation planning, while underscoring the important contribution of a sophisticated simulation models within complex evacuating plans. The findings of these research efforts underscore the need of incorporating sophisticated simulation models into evacuation planning. These models enable the anticipation of possible bottlenecks and optimizations before the evacuation begins, making evacuation actions more reliable. With this approach, the maritime industry cannot only model and simulate efficient/safer evacuations, but can also refine those models by using new data and better integrate them into the planning processes, therefore improving safety even under complex emergency conditions.
# Research Progress on Synergistic Technologies of Agricultural Multi-Robots
## 3 Research Progress on Synergistic Technologies of Agricultural Multi-Robots
### Environment Perception  
Environmental perception is the premise of the cooperative operation of multiple robots in agriculture. That is, the mobile carrier can use the sensors carried by itself (these sensors include internal sensors and external sensors [31], where the internal sensors include odometers, magnetic compasses, inertial navigation, and global positioning systems to determine the speed, position, and direction of the robot in the environment; external sensors include ultrasound, infrared, laser and vision, used to sense surrounding information [26]) to obtain the information of the surrounding environment, extract the effective feature information within the environment for processing and analysis, and finally establish the environment model [32]. This technology mainly involves collaborative positioning, data fusion, and environmental construction.
# Performance Analysis of MIMO System with Single RF Link Based on Switched Parasitic Antenna
## 4 Results
### Spectrum Characteristics  
In addition to the main standard of the above system, the demodulated signal constellation is shown in Figure 10. For an ideal communication system without any noise or interference factors, the demodulated constellation should be fixed at \([1,0]\) and \([-1,0]\), as is shown in Figure 10b. However, in Figure 10a, the demodulation constellation points are uniformly scattered around \([1,0]\) and \([-1,0]\), which can be explained by the additive Gauss white noise, non-idealization of antennas and other components in the system.  
Figure 10: _Cont._  
Figure 9: The channel capacity of the system varies with the signal to noise ratio.  
The spectrum broadening problem caused by switching is verified by the following method. The stronger the randomness of input signal, the greater the degree of spectrum broadening. In order to ensure the first input signal is the fixed symbol pattern, we only change the second input symbols: one is the symbol with the fixed form, the other is the pseudo-random code. The states of the two input signals in the time domain are shown in Figure 11a,b, respectively.  
Figure 11: Two input signals for spectrum broadening verification. (**a**) Fixed form symbol; (**b**) Pseudo random code.  
Figure 10: Constellation diagram after system demodulation. (**a**) Non-ideal system; (**b**) Ideal system.  
The two input signals are used as the second input symbols of the system, and the spectrum of the output of the SPDT switch is measured, as is shown in Figure 12.  
When the second input symbols and the first input symbols are completely the same, the XOR results will be zero and there are only one far field radiation pattern with the help of spectrum shaping filter. At the 2.4 GHz center frequency, there is only one peak and the spectrum components are clean. When the second input symbols are converted into the random symbols, the XOR results of the two signals are random variable, which results in the change in the antenna far-field radiation pattern. Besides, the spectrum will also show obvious band clutters, which produces the spectral broadening. In addition, when the switched parasitic antenna switches the state, the peak value of the power in the spectrum will also decrease from \(-10\) dBm to \(-14\) dBm.
# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
###### Abstract  
This research demonstrates a practical approach for controlling industrial robotic arms using smartwatches. The system captures gestures and orientation through integrated sensors and connects to robots using Python 3.12.2 APIs. It is particularly useful in environments that require mobility, minimal training, or quick deployment--such as education, maintenance, or small-scale production. This method supports Industry 4.0 and 5.0 goals by lowering the learning curve and promoting human-robot collaboration.  
This paper presents a novel interface that leverages a smartwatch for controlling industrial robotic arms. By harnessing the gyroscope and advanced gesture recognition capabilities of the smartwatch, our solution facilitates intuitive, real-time manipulation that caters to users ranging from novices to seasoned professionals. A dedicated application is implemented to aggregate sensor data via an open-source library, providing a streamlined alternative to conventional control systems. The experimental setup consists of a smartwatch equipped with a data collection application, a robotic arm, and a communication module programmed in Python. Our aim is to evaluate the practicality and effectiveness of smartwatch-based control in a real-world industrial context. The experimental results indicate that this approach significantly enhances accessibility while concurrently minimizing the complexity typically associated with automation systems.  
wearable technology; smartwatch control; human-robot interaction; gesture recognition; user-friendly interfaces Article
## 4 The System Framework
### Data Extraction and Analysis Module  
The Data Extraction and Analysis Module comprises five automated Dynamo scripts, featuring a workflow designed to automatically read, categorize, filter, verify, and list the statistics and failed elements, determine the quality score for each QC category, and transfer these scores to the database for recording and visualization purposes. The module was structured for five disciplines: architectural, structural, mechanical, piping, and electrical. The workflow varies by each discipline because the cases and parameters that require verification differ. Figure 7 presents a schematic flowchart depicting the data flow within the developed Data Extraction and Analysis Module.  
In developing the automated scripts for each discipline, Dynamo leveraged its capabilities in using code blocks. These blocks facilitate the running of compact scripts written in text-based programming languages like C++ and Python, introducing complex  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Discipline** & & & & **Parameters** & & & \\ \hline MECH & Family Name & Element Phase & Element Workset & \begin{tabular}{c} Insulated Elements with \\ Insulation Thickness \\ (Specified Categories) \\ \end{tabular} & \begin{tabular}{c} PIPe Elements \\ Material \\ \end{tabular} & Element Size & Material Name \\ \hline PIP & Family Name & Element Phase & Element Workset & \
\begin{tabular}{c} Insulated Elements \\ (Specified Categories) \\ \end{tabular} &
\begin{tabular}{c} PIPe Elements \\ Material \\ \end{tabular} & Element Size & Material Name \\ \hline ELC & Family Name & Element Phase & Element Workset & N/A & N/A & Element Size & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Listing of parameters for MEP BIM model’s QC process.  
functionalities beyond the scope of standard nodes. Thus, the scripts comprise numerous nodes, connections, and code blocks.  
Figure 6: A picture of the case study BIM models of different disciplines.