esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Results
### Dataset
#### 4.2.1 Data Definition and Introduction
In this experiment, we skewed the distribution by varying the ratio between labels on each client to address data imbalance issues that may occur in real-world environments. Specifically, Server and Client 1, Client 5, and Client 6 have a balanced data distribution with equal proportions of fire, smoke, and normal data, 1:1:1. Client 2, Client 7, and Client 8, on the other hand, have a heavy normal data setup, with a 1:1:4 distribution of fire, smoke, and normal data. Client 3 and Client 10 have a 4:1:1 ratio of fire, smoke, and normal data, reflecting the dominance of fire data. Finally, Client 4 and Client 9 have a distribution that is heavily dominated by smoke data, so we set the fire, smoke, and normal data to 1:4:1. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fire** & **Smoke** & **Normal** \\ \hline Server & 1000 & 1000 & 1000 \\ Client 1 & 1000 & 1000 & 1000 \\ Client 2 & 500 & 500 & 2000 \\ Client 3 & 2000 & 500 & 500 \\ Client 4 & 500 & 2000 & 500 \\ Client 5 & 1000 & 1000 & 1000 \\ Client 6 & 1000 & 1000 & 1000 \\ Client 7 & 500 & 500 & 2000 \\ Client 8 & 500 & 500 & 2000 \\ Client 9 & 500 & 2000 & 500 \\ Client 10 & 2000 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of label image data by server and clients. Figure 9: Sample images in the experiment dataset corresponding to the following labels: (**a**) normal; (**b**) fire; (**c**) smoke.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 4 Experiment
### Experimental Results
The DFESN model's design, which allows for efficient adaptation through hyperparameter optimization, suggests that it could maintain high performance under different conditions, provided that appropriate training data are available. Although the current results demonstrate promising performance, several avenues for further research remain open. Future studies should assess the generalizability of the proposed DFESN approach across diverse robotic platforms and multi-arm collaborative systems. It is worth noting that this study focuses primarily on fault classification, specifically identifying the component and type of fault (sensor, actuator, or communication). Future research will aim to extend this framework to include fault detection, isolation, and identification (FDII), enabling not only localization but also root cause analysis of faults. Additionally, while our current work provides comparisons between DFESN and standard methods such as ESN, CNN, LSTM, and BP neural networks, subsequent research could further extend these comparisons by exploring performance under more varied operational scenarios, including complex and combined fault conditions. While this study focused primarily on common fault types encountered in remote-controlled robotic arms--such as sensor faults, actuator faults, and communication faults--future investigations should incorporate more challenging fault scenarios, including simultaneous multi-sensor failures, sophisticated actuator degradations, and variable communication delay scenarios, to comprehensively evaluate the robustness of the proposed method. Finally, although real-time performance was evaluated primarily using online diagnosis time metrics, further research is necessary to explicitly examine other critical real-time factors, such as communication delays and tracking latency, thus ensuring more thorough insights into operational reliability under realistic working conditions.
## 3 Polar Codes for the Multi-Way Relaying System
### Polar Decoding of the Multi-Way Relaying System
 \tag{41}\]  
Each terminal \(T_{i}\) knows its own information bit \(m_{i}[k]\); thus, when \(m_{i}[k]=0\),  
\[\ln\frac{p(y^{{}^{\prime}}_{ij}[k]|y^{{}^{\prime}}_{ij}[k-1],x_{i}[k]=0)}{p(y^ {{}^{\prime}}_{ij}[k]|y^{{}^{\prime}}_{ij}[k-1],x_{i}[k]=1)}=\ln\frac{1}{0}=\infty \tag{42}\]  
Similarly when \(m_{i}[k]=1\),  
\[\ln\frac{p(y^{{}^{\prime}}_{ij}[k]|y^{{}^{\prime}}_{ij}[k-1],x_{i}[k]=0)}{p(y^ {{}^{\prime}}_{ij}[k]|y^{{}^{\prime}}_{ij}[k-1],x_{i}[k]=1)}=\ln\frac{0}{1}=-\infty \tag{43}\]  
Therefore, the decoder at \(T_{i}\) sets the channel LLR corresponding to \(m_{i}[k]\) to the following:  
\[L(y^{{}^{\prime}}_{ij}[k])= \begin{cases}\infty&\text{if }m_{i}[k]=0\\ -\infty&\text{if }m_{i}[k]=1\end{cases}  \tag{44}\]  
In the simulations, the positive infinity and negative infinity in (44) are set to \(10^{12}\) and \(10^{-12}\), respectively. Next, we consider the channel LLRs for the silent relays. In our system, if a relay detects a wrong symbol and does not transmit, the terminals will receive only noise; hence, this sample should not be taken into account in the decoding process. This idea is encapsulated by the notion of the erasure channel. The basic binary erasure channel (BEC) model [23] is shown in Figure 3. The transition probabilities of BEC are given by \(W(0|0)=W(1|1)=1-p,W(e|0)=W(e|1)=p\), where \(\varepsilon\) is the erasure symbol. In general, if the received signal is unreliable and hence cannot be demodulated properly,then the receiver declares an erasure denoted by a specific symbol, \(\epsilon\). In our system, an erasure corresponds to the event that a relay remains silent and hence the terminal receives only noise. Since \(p(y_{ij}[k]=\epsilon|y_{ij}[k-1],x_{i}[k]=0)=p(y_{ij}[k]=\epsilon|y_{ij}[k-1],x_{ i}[k]=1)\), the corresponding channel LLR is set to the following:  
\[L(y^{{}^{\prime}}_{ij}[k])=\ln\frac{p(y^{{}^{\prime}}_{ij}[k]=\epsilon|y^{{}^{ \prime}}_{ij}[k-1],x_{i}[k]=0)}{p(y^{{}^{\prime}}_{ij}[k]=\epsilon|y^{{}^{ \prime}}_{ij}[k-1],x_{i}[k]=1)}=\ln 1=0 \tag{45}\]  
Hence, such a sample does not influence the decoding process. The polar decoding process for the multi-way relaying system is summarized as follows [57]:  
* Reversely permute the received signal vector \(\mathbf{y}\) into \(\mathbf{y}^{{}^{\prime}}\) based on \(\pi^{-1}\). * Calculate channel LLRs \(L(y^{{}^{\prime}}_{ij}[k])\) according to (40) and (45). * Perform SC or SCL decoding of polar codes to recover \(\mathbf{\hat{m}}_{A}\). * Obtain \(\mathbf{\hat{m}}_{T}\) from \(\mathbf{\hat{m}}_{A}\) using (36) implemented recursively through polar encoding.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 3 Brazil's Energy Landscape and Electricity Mix: An Overview of 2023
### Evolution of Sugarcane Biomass in Brazil's Energy Systems  
Sugarcane biomass has played a dynamic role in Brazil's energy systems, showcasing both its historical significance and its potential to address contemporary energy challenges. As highlighted in Figure 9, the contribution of bioelectricity--largely derived from sugarcane bagasse (63.3%) and black liquor (26.1%)--to electricity generation has remained consistently high, with a peak of 58,742 GWh in 2020 and sustained production at 57,825 GWh in 2023 [65]. This trend underscores the resilience of sugarcane-derived bioelectricity in meeting energy demands and reflects its strategic importance in Brazil's broader transition to renewable energy. However, the temporal variability of sugarcane biomass as a source of electricity highlights its adaptability to policy and market shifts, particularly in response to hydropower fluctuations during periods of climatic stress.  
Figure 10 provides a broader perspective on the time variability of Brazil's primary energy production, emphasizing the growing role of sugarcane-derived energy in relation  
Figure 8: Evolution of the share of renewable energy in the internal electricity supply over the last 20 years. Adapted from [65].  
Figure 9: Historical growth of bioelectricity production in Brazil (1970–2023). Adapted from [65].  
to other sources over recent decades. While hydropower and petroleum have historically dominated the energy matrix, sugarcane-based bioelectricity has demonstrated consistent growth, solidifying its position as a critical renewable resource. This consistent contribution not only stabilizes the energy supply but also offers potential synergies with emerging technologies, such as hydrogen production.  
In conclusion, the evolution of sugarcane biomass in Brazil's energy systems demonstrates its significant contribution to electricity generation and its strategic role in diversifying the energy matrix. While hydropower continues to dominate, sugarcane-derived energy has proven to be a reliable and adaptable resource, particularly in mitigating the impacts of hydrological variability. Its stable contribution over recent decades underscores its importance as both a renewable energy source and a foundation for future innovations, such as hydrogen production. Moving forward, integrating sugarcane biomass with emerging technologies could further enhance its value, positioning Brazil as a global leader in sustainable energy practices.
## 5 Discussion
This suggests that AR technology's benefits extend beyond mere kinematic improvements, potentially fostering a more positive training experience that could encourage greater patient engagement and adherence to rehabilitation protocols. The immersive qualities and real-time feedback inherent in AR may clarify a patient's understanding of their performance during what can often be repetitive rehabilitation exercises, thereby promoting more active participation and potentially maximizing the therapeutic benefits [2; 3]. This observed enhancement in subjective satisfaction aligns with findings from a study on a low-cost, mobile-phone-based AR system (CARS) for stroke patients, which showed significant effects on improving upper limb motor and cognitive functions, thus expanding the potential for home-based rehabilitation. It also resonates with research integrating VR/AR and gamification into robot-assisted therapy, contributing to increased patient motivation [28; 29]. These findings resonate with the positive outcomes observed in prior AR rehabilitation studies targeting stroke patients and the elderly, which have reported improvements in lower limb function and balance [11; 12; 13; 14; 15; 16; 17]. Specifically, in the context of prosthetic training for lower limb amputees, where weight-bearing exercises are critical, AR-based real-time visual feedback has the potential to facilitate a patient's intuitive understanding and control of their weight distribution and balance [7; 9]. Furthermore, our results, when considered alongside the enhanced balance and gait performance reported in VR-based rehabilitation studies for this population [19; 20], indicate that AR technology represents another promising tool for the rehabilitation of lower limb amputees. Notably, the enhanced real-world interaction offered by AR, in contrast to VR, may provide a more natural and safer training environment for patients. Furthermore, AR holds great potential in alleviating phantom limb pain (PLP) and reversing cortical reorganization by providing patients with distorted phantom limbs a visual representation of an intact limb [23]. The expandability of AR/MR technology is also demonstrated by the fact that MR systems, when combined with sEMG analysis, can contribute to increased muscle activation and training efficacy through real-time synchronization with virtual prosthetics [27]. The significance of this study lies in its empirical validation of the positive potential of AR system application in the rehabilitation training of individuals with lower limb amputations. The observed improvements in movement consistency coupled with high levels of subjective satisfaction provide valuable foundational data for the future development of tailored AR-based rehabilitation programs for this population. For instance, an AR environment could offer varied difficulty levels of training content customized to an individual's abilities and goals, coupled with real-time visual feedback on movement accuracy and balance, thereby potentially maximizing training effectiveness and fostering greater patient engagement.
# Study on the Position Deviation between the Iron Roughneck's Spin-Rollers and the Drilling Tool
## 1 Introduction
Based on the above problems: a force transmission mechanism is designed in this paper, which is driven by a single oil cylinder and combined with a wedge plate to realize the clamping of the drilling tool; the relationship between the position deviation of the spin-rollers and the drilling tool is studied, and the measures to reduce the position deviation are put forward; this paper introduces the structure and working principle of the spinner mechanism, and the contact mode between the spin-rollers and the drilling tool and the state of offset are analyzed; the deviation is theoretically analyzed and solved by simulation software, and the corresponding relationship of the position deviation between the spin-rollers and the drilling tool is obtained; the layout of the spin-rollers is improved; the performance of two types of spinner mechanism with different roller arrangements is compared and analyzed through experiments, and the experiments show that the improved structure can reduce the position deviation of drilling tool and improve the working stability of spinner mechanism. The novelty of this paper lies in the following three aspects: (1) Due to the problem concerning the fact that the upper and lower drilling tools are not concentric and affect the spinning process, it is proposed that the lateral position deviation is the main factor affecting the spin performance; (2) The relationship between the position deviation of the spin-roller and the lateral position deviation of the drilling tool is analyzed and obtained. It is concluded that the occurrence of the lateral position deviation of the drilling tool will significantly increase the position deviation of the spinning roller; (3) It is verified that the symmetrically arranged double follow-up rollers can effectively reduce the lateral position deviation of the drilling tool, which can better meet the centering performance requirements of the spinner mechanism and ensure the stability of the spin action.
## 4 Experiment and Result Analysis
### Implementation Details
#### 4.2.1 Different Working Conditions  
Table 2 shows the working conditions of each battery in the Stanford-MIT-Toyota dataset. To ensure that there are no overlapping working conditions between the source and target domains, the g2c32, g3c2, and g5c31 battery data each become the source domains, and some data in g1c38, g2c48, and g7c47 batteries are used as target domains. The data except for the target domains in g1c38, g2c48, and g7c47 are selected as the testing sets, respectively. The proportion of data volume is shown in Table 4.
# Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm
## 2 Construction of the Bounding Volume Hierarchy
### Optimization of the Traditional Method of Constructing an OBB Bounding Box  
The traditional method of constructing an OBB bounding box is based on the triangular patch information of the geometric model, using the statistics of the mean and the unitized covariance matrix. The specific steps are as follows:  
Figure 1: Construction process of the bounding volume hierarchy.  
1. The vertex coordinates of the triangular patch are added and divided by the number to obtain the mean value, and then the covariance of the vertex's coordinate vector is calculated, the mathematical model is derived from [PERSON]'s article [11]. \[m=\frac{\sum_{i=0}^{n}\left(p^{i}+q^{i}+r^{i}\right)}{3n}\] (1) \[C_{jk}=\frac{\sum_{i=0}^{n}\left(\overline{p_{j}^{i}\cdot\overline{p_{k}^{i}}+ \overline{q_{j}^{i}}\cdot\overline{q_{k}^{i}}+\overline{r_{j}^{i}}\cdot\overline {r_{k}^{i}}}\right)}{3n}\] (2) \[\overline{p^{i}}=p^{i}-m\] \[\overline{q^{i}}=q^{i}-m\] \[\overline{r^{i}}=r^{i}-m\]
2. Three eigenvectors of the covariance matrix \(C\) are obtained, which are orthogonal to each other. In order to determine the three axial coordinates of the OBB bounding box, the feature vector is unitized. The difference between the maximum and minimum projection values on the coordinate axis is the size of the OBB bounding box. Because m in Equations (1) and (2) is only a simple average of the coordinates, the coordinate's position will shift when the model's structure is not uniform. Therefore, this study used the centroid to determine the center of the OBB bounding box. The specific steps are as follows: Firstly, the local coordinate system of the triangle is established, where the static moment of the triangle on the \(X\) axis and the \(Y\) axis is: \[S_{x_{i}}=\int_{A_{i}}ydA\] \[S_{y_{i}}=\int_{A_{i}}xdA\] \[A_{i}:\text{the area of the $i$ th}\text{ triangle. }dA:\text{the microarea at this point. }\] The centroid position of the triangle is: \[x_{i}=\frac{S_{y_{i}}}{A_{i}},y_{i}=\frac{S_{x_{i}}}{A_{i}}\]
3. The coordinates obtained above are in the local coordinate system and need to be converted into global coordinates. Finally, the combined centroid coordinates are \[X_{c}=\frac{\sum_{i=1}^{n}A_{i}\cdot X_{c_{i}}}{\sum_{i=1}^{n}A_{i}}\] \[Y_{c}=\frac{\sum_{i=1}^{n}A_{i}\cdot y_{c_{i}}}{\sum_{i=1}^{n}A_{i}}\] (3) \[Z_{c}=\frac{\sum_{i=1}^{n}A_{i}\cdot Z_{c_{i}}}{\sum_{i=1}^{n}A_{i}}\] By including Equation (3) into Equation (2), the improved covariance matrix can be obtained.
###### Abstract  
DC arc faults are dangerous to photovoltaic (PV) systems and can cause serious electric fire hazards and property damage. Because the PV inverter works in a high-frequency pulse width modulation (PWM) control mode, the arc fault detection is prone to nuisance tripping due to PV inverter noises. An arc fault detection method based on the autoregressive (AR) model is proposed. A test platform collects the database of this research according to the UL1699B standard, in which three different types of PV inverters are taken into consideration to make it more generalized. The arc current can be considered a nonstationary random signal while the noise of the PV inverter is not. According to the difference in randomness features between an arc and the noise, a detection method based on the AR model is proposed. The Burg algorithm is used to determine model coefficients, while the Akaike Information Criterion (AIC) is applied to explore the best order of the proposed model. The correlation coefficient difference of the model coefficients plays a role as a criterion to identify if there is an arc fault. Moreover, a prototype circuit based on the TMS320F28033 MCU is built for algorithm verification. Test results show that the proposed algorithm can identify an arc fault without a false positive under different PV inverter conditions. The fault clearing time is between 60 ms to 80 ms, which can meet the requirement of 200 ms specified by the standard.  
DC series arc fault; arc fault detection; autoregressive model; photovoltaic systems +
Footnote †: journal: Applied Sciences
## 3 Experiments
### Experiment Results
#### 3.4.3 Prediction Results in the Xiamen BRT  
The testing dataset includes Tomb Sweeping Day on 5 April 2019 in the Xiamen BRT, which is Friday in the fifth week. As shown in Figure 7, the inflow and outflow on the holiday included in the three-dotted red boxes are quite different from that on normal days. The inflow in the morning and evening peaks is slightly less than that on normal days, and the inflow in other hours is slightly more than that in normal times. The outflow on that day and even the day before is much more than usual. It is mainly because there is a ferry terminal near 1 st Wharf Station, and a famous place of interest, Gulangyu island, is on the opposite side. Therefore, people who travel to it will choose a more convenient public transport, i.e., the BRT, which causes a sharp increase in outbound flow.  
The performance of prediction results in different time intervals in the Xiamen BRT is summarized in Table 9. The numbers in bold refer the better results between prediction with station classification, and prediction without station classification. The prediction results of MAE, RMSE, and WMAPE with classification in 10 min and 15 min time intervals are better than the prediction results without classification. Only MAE and WMAPE in 30 min time interval presents a slightly poorer performance. This is mainly because there is less abnormal flow included on this holiday in 10 min and 15 min time intervals, so a classified prediction can show a better performance. Under the 30 min time granularity, more irregular flow data are included, and the irregular flow cannot be well extracted through classification, which leads to an unsatisfactory prediction performance. In the end, in the most scenarios, classification can still improve the prediction result for suddenly increasing flows on holiday. The smaller the time interval is, the better the improvement effects are in the most scenarios.
# A Feasibility Study for a Hand-Held Acoustic Imaging Camera
## 5 Array Optimization Methodology
### Problem Parameterization
Optimizing over the full band [f\({}_{\text{min}}\), f\({}_{\text{max}}\)] through integration over f.
4. Robustness. Averaging over microphone imperfections by modelling gain and phase as random variables \(A_{n}\). The overall form is a weighted combination of these terms: \[J(\mathbf{w},\mathbf{r})= \alpha\iiint\ |B(\theta,\phi,f)-B_{0}(\theta,\phi,f)|^{2}d\theta d \phi df\ +(1-\alpha)\iiint\ |B(\theta,\phi,f)|^{2}d\theta d\phi df\]  
where \(\alpha\in[0,1]\) controls the trade-off between directional focus and artefact suppression. To minimize \(J\), the filter coefficients \(\mathbf{w}(f)\) are first optimized analytically for a fixed array layout by extracting them into a quadratic form with closed-form solution \(\mathbf{w}_{\text{opt}}(f)\). The microphone locations \(\mathbf{r}_{n}\) are then optimized stochastically using simulated annealing to avoid poor local minima:  
* Iterative random perturbations \(\Delta\mathbf{r}_{n}\) are applied to the microphone locations. * New locations are accepted probabilistically based on the cost \(J\). * Acceptance probability is higher at higher initial \"temperatures\" and cooled over iterations. * After sufficient iterations, \(\mathbf{r}_{n}\) converges to a geometry minimizing \(J\). This joint optimization determines array layouts and filters tailored for directional imaging over the specified band with artefact suppression and robustness. In the simulation of the beampattern of a planar array (\(z=0\)) of microphones we have two angles of arrival \(\theta\) and \(\phi\), two steering angles \(\theta_{0}\) and \(\phi_{0}\) (Figure 10) and two coordinates for the microphones \(x_{n}\) and \(y_{n}\). The mathematical expression of the ideal superdirective beampattern \(B\) in far-field is:  
\[B(\theta,\phi,\theta_{0},\phi_{0},f)=\sum_{n=1}^{N}w_{n}(f)e^{-2\pi f\cdot \left[x_{n}\frac{\sin(\theta)-\sin(\theta_{0})}{c}+y_{n}\frac{\sin(\phi)-\sin (\varphi_{0})}{c}\right]} \tag{4}\]  
where \(N\) is the number of microphones, \(c=340\) m/s is the speed of the acoustic waves into the medium (\(\lambda=c/f\)), and \(w_{n}(f)\) is the frequency response of the \(n\)-th filter:  
\[w_{n}(f)=\sum_{k=1}^{K}w_{n,k}\cdot e^{-j2\pi f\cdot kT_{c}} \tag{5}\]