esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Overall Classification Performance  
The OFFNN achieves an overall accuracy of 75.43% on the validation set, as shown in the confusion matrix (Figure 8). Table 3 summarizes precision, recall, and F1-scores for each class. Class 1 (normal operation) exhibits the highest performance, with an F1-score of 0.79, benefiting from real-world data samples. Other classes, representing simulated degradation modes, show good generalization with F1-scores between 0.74 and 0.76.  
The results indicate that the hybrid simulation-classification approach is effective in distinguishing between multiple degradation mechanisms using a limited number of physical sensors. The combination of bond graph-based simulation and neural network classification provides a scalable and interpretable method for root-cause identification in PV systems.  
It is also important to note that the synthetic dataset, while comprehensive in its coverage of degradation types and environmental variations, may still lack the stochastic  
Figure 8: Confusion matrix showing classification results across the five classes. Numerical labels are retained for consistency with the original model output. Note: The confusion matrix in the figure uses numerical class indices due to consistency with the classification model’s training labels. For improved clarity, the corresponding degradation types are as follows: Class 1—Normal; Class 3—EVA Discoloration; Class 4—Cracks; Class 5—LID; and Class 6—Corrosion. These mappings are also reflected in Table 3, which presents performance metrics using descriptive labels.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Metric** & **Normal** & **EVA Discoloration** & **Cracks** & **LID** & **Corrosion** \\ \hline Precision & 0.8230 & 0.7326 & 0.7666 & 0.6811 & 0.7339 \\ \hline Recall & 0.7588 & 0.7550 & 0.7609 & 0.6893 & 0.7911 \\ \hline F1-Score & 0.7896 & 0.7436 & 0.7638 & 0.6852 & 0.7614 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics (total: 75,000 samples; accuracy: 75.43%).  
complexity of real-world data. For example, Figure 7 shows high correlations (even near-perfect) between certain features like irradiance and cell voltage. Such relationships, while consistent with physical modeling, may not fully reflect sensor variability, measurement noise, or aging effects in actual deployments.
## 4 Experimental Environment and Results
### Model Training  
Modern anomaly detection models such as Skip-GANomaly [30], and AnoGAN [4] train the model on the majority of the normal-designated dataset and then run inference on the remaining normal data along with the unseen anomaly data. As a result, normal sample images are expected to have a low reconstruction loss, and their latent vectors will have similar characteristics. However, abnormal sample images are expected to fail in both cases since these images are from a class with which the model has not been trained.  
Root Mean Square Propagation (RMSProp) optimization function with \(\gamma=0.9,\eta=0.001\) parameters are used for the model training. Three separate loss functions with specific weighting, shown in Section 3, are applied. Optimal loss weighting is found to be \(\lambda_{context}=50,\lambda_{latent}=1,\lambda_{adv}=5\) after a grid search shown in Figure 2. Model is trained for 50 epochs until the convergence was achieved. The batch size is taken as 128. Implementation of the system is done using TensorFlow [41] (Python 3.6.9, CUDA 10.0, cuDNN 7.3). The training is done on two NVIDIA P5000 GPUs. The anomaly score introduced in [29] gives a proper indication that the input image is considered real or fake, which is given in Equation (5) below:  
\[\mathcal{A}(\hat{x})=(1-\lambda)\cdot R(\hat{x})+\lambda\cdot L(\hat{x}) \tag{5}\]  
Figure 3: Example images from each class in the dataset: (**a**) Adult class, (**b**) Child class, and (**c**) Elderly class. It should be noted that in each class, there are also images of pedestrians moving in different directions (front-facing, back-facing, side-facing) and with different lighting conditions. Variation in the distance of pedestrians against the camera causes many images to have less-than-ideal quality, which should be taken into consideration in real-life applications.  
where \(R(\hat{x})\) is the reconstruction loss calculating the similarity between the real input image and the generated image based on Equation (1), and \(L(\hat{x})\) is the latent representation loss calculating the difference between the real input image and the reconstructed image based on Equation (3). \(\lambda\) is the weighting parameter of the importance of each of the functions. In this study, \(\lambda=0.2\) is employed by default.  
The next step is calculating the anomaly score for each inference image \(\hat{x}\) in the test set \(D_{te}\) and assign an anomaly score \(A\) such that \(A=\{A_{i}:\mathcal{A}(\hat{x}),x\in D_{te}\}\). Following the method proposed in [10], feature scaling (min-max normalization) is applied to \(A\) to scale the obtained scores within the range \([0,1]\). The updated anomaly score then becomes:  
\[\mathcal{A}^{\prime}(\hat{x})=\frac{\mathcal{A}(\hat{x})-min(A)}{max(A)-min(A)} \tag{6}\]  
After obtaining the \(\mathcal{A}^{\prime}(\hat{x})\), for the entire test set, the obtained vectors are used to plot the anomaly score shown in Figure 4.
## 3 Field Testing
### Data Collection  
A falling weight deflectometer (FWD) was employed to apply impulse load and measure the deflections of concrete pavement slabs. The impulse load was produced by dropping a large weight onto a buffer which shapes the pulse and then transmitted to the pavement through a circular load plate. Then pavement's deflections were measured by nine displacement sensors, as shown in Figure 6a.  
To assess the deflection-based LTE of a joint, the second and third displacement sensors were set symmetrically against the joint, as shown in Figure 6b,c. The deflection-based LTE of the joint could be calculated as:  
\[LTE=\frac{w_{a}}{w_{l}} \tag{26}\]  
where \(w_{a}\), \(w_{l}\) denotes the measured deflections on the adjoining unloaded slab and the loaded slab respectively.  
In the field testing, the impulse load was applied to different joints. For each joint, the weight was dropped from three different heights, which produced impulse loads at three levels (80, 100, and 120 kN). The measurement was repeated three times for each  
Figure 6: Measurement of deflection-based LTE: (**a**) Falling weight deflectometer (FWD); (**b**) Details of FWD; (**c**) Layout of the nine displacement sensors.  
load level, so there were nine measurements of deflections. Meanwhile, the DOVS-based vibration sensing system measured the vibration around joints. The corresponding **RPSD** was calculated according to Equations (17)-(25).  
The above procedures were conducted twice. The first time was in July 2020 (ambient temperature was 31 \({}^{\circ}\)C) and the second time was in November 2020 (ambient temperature was 13.6 \({}^{\circ}\)C). Thus there were two test series to have a better comparison between deflection-based LTE and **RPSD**.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### The Definition of the Technological Model
#### 4.2.2.4. _Haptic Device Selection_  
Once the visual design and selection of the interaction are established, the developers and physics experts should decide which haptic device is better suited for the interaction medium between the simulator and user. Haptic devices are electromechanical tools used to recreate the sense of touch in virtual environments (Figure 4). These can either be impedance-based or admittance-based [32]. In the first case, the user sends the interaction movements to the simulator, and the application is responsible for calculating the feedback force. On the other hand, in admittance-based haptic devices, the user applies a force on the device, which reacts by displacing it and sends a proportional distance to the application to update the visuals in the simulation. In general, impedance-based haptics are preferred due to their lower cost and size. However, it must be noted that admittance-based haptic devices provide freedom in the mechanical design and deliver greater force and stiffness feedback.  
Haptic devices can also be classified by the number of degrees of freedom and degrees of force feedback they provide. Commercial haptic devices usually consider three or six degrees of freedom in the design; however, low-cost devices typically provide three degrees of force feedback. A haptic with six degrees of force feedback is required if torque is also to be modeled. This specification is principally required in visuo-haptic simulations used in the medical field [14].  
Several obstacles must be overcome when selecting appropriate haptic devices. For example, this may be the need to import the devices in some countries where additional customs fees considerably delay the entire design process. Maintenance is not usually provided by dealers or manufacturers in all countries. Additionally, if a certain educational scenario requires more than three degrees of freedom, it is difficult to modify the experimental setting to include additional degrees of freedom.  
A careful selection of the brand and model strongly suggests avoiding unnecessary setbacks in the simulator requirements. One must always first look into academic agreements to reduce the costs. It is advisable to avoid, if possible, customs fees, and it is strongly recommended that maintenance services be provided.  
The use of haptic devices has considerably increased in the last decade in education, gaming, and training (automotive, aviation medicine, robotics, etc.). The Novint Falcon devices have the benefit of being low-cost devices with good expansion capabilities. Their design is based on the high-end OMEGA haptic device, developed by Force Dimension. However, Novint Technologies has been out of business since 2010; therefore, Force Dimension, which has continued to provide software support to these devices and their stores, such as haptic houses, has continued the commercialization of these types of devices [33].
###### Abstract  
In order to study the interactive relationship between urban economic and ecological environment, taking Wuhan as an example, Landsat and MODIS remote sensing satellite data and social and economic data were fused with multisource data, and multidimensional indicators were selected to construct the comprehensive evaluation index system of urban economic and ecological environment. The weights were determined by combining subjective and objective methods. Then, the decoupling elasticity coefficient method and spatial autocorrelation model were used to evaluate the dynamic relationship and spatial relationship between economic development and ecological environment in Wuhan from 2014 to 2020. The results showed that there was an interaction between the urban economic and the ecological environment in Wuhan. The ecological level index had a spatial effect, the adjustment of industrial structure had a positive effect on the improvement of the ecological level, and the improvement of the ecological level was also helpful to promote economic development. The typical districts of Huangpi District, Xinzhou District, Jiangxia District, Hannan District, Caidian District, and Hongshan District had superior location and ecological advantages, as well as high development potential. Lastly, on the basis of the empirical analysis results, policy suggestions are made from four aspects: regional differentiated construction, green development, energy consumption, and wetland construction.  
ecological environment; multisource data fusion; decoupling elasticity coefficient method; spatial autocorrelation model +
Footnote †: journal: Journal of Computational Research and Public Health
# Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
## 2 Related Work  
Guaranteeing the fair distribution of resources can have a notable influence on the effectiveness of scheduling algorithms. In the realm of scheduling problems, fairness can be defined in various ways. There exists a wealth of literature dedicated to defining fairness concepts and designing efficient algorithms with fair constraints [21]. [PERSON] et al. [22] addressed the fair scheduling problem of multicloud workflow tasks and proposed a reinforcement learning-based algorithm. In response to cache contention issues in on-chip multiprocessors, a thread cooperative scheduling technique considering fairness was proposed by [PERSON] et al. [23]. It was based on non-cooperative game theory. They wanted to ensure equitable thread scheduling in order to improve the performance of the entire system. On heterogeneous processors with multiple cores, [PERSON] et al. [24] suggested an energy-efficient framework for addressing fairness-aware schedules. This framework simultaneously addressed fairness and efficiency issues in multicore processors. For multiprocess contexts, [PERSON] et al. [25] developed a fair resource distribution method that aimed to maximize the overall system utility and fairness. This technique enabled the concurrent execution of multiple scalable processes even under CPU load constraints. [PERSON] et al. [26] presented a multiprocessor-system fair scheduling algorithm based on task satisfaction metrics, which achieved a high proportion of fairness even under highly skewed weight distributions. Their algorithm quantified and evaluated fairness using service-time errors. A review of pertinent research on fair scheduling is given in Table 1.  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  & To optimize the scheduling order for multiple workflow tasks, they designed a reinforcement learning-based fair scheduling algorithm for multiworkflow tasks. & The authors created an evolving priority-driven method to avoid service level agreement violations through dynamic scheduling. Additionally, they implemented load balancing between virtual machines using a reinforcement learning algorithm. \\ \hline  & They proposed a fairness-aware thread collaborative scheduling algorithm based on uncooperative game theory, and the on-chip multiprocessor cache congestion problem was addressed. & The authors aimed to enhance the overall system performance by fairly scheduling threads. They employed an uncooperative game approach to address the thread collaborative schedule problem and introduced an iterative algorithm for finding the Nash equilibrium in non-cooperative games. This allowed them to obtain a collaborative scheduling solution for all threads. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research on fair scheduling in the relevant literature.
## 4 Experiments
### Experiment Settings
#### 4.2.3 KolektorSDD Dataset  
Since the heights of the images in the KolektorSDD dataset are inconsistent, we first scale the images to 1024 \(\times\) 256 to ensure that the images and labels are scaled with the same regularity. The dataset is divided into a training set (containing 256 non-defective and 36 defective) and a test set (containing 91 non-defective and 16 defective). After that, a random window method is used to extract 64 \(\times\) 64 patches from the defective and non-defective images. Since the defect-free data are relatively small, the data enhancement methods of up-down, left-right, and inversion are adopted. A total of 798 defect-free patches and 684 defect patches were extracted, and all patches were 64 \(\times\) 64 pixels in size. In the defect detection stage, we set the sliding window step size to 32 pixels and the judgment threshold to 0. As long as there is a patch abnormality, it will be judged as defective.
## 6 Conclusions  
We developed a semi-empirical method--a filtered Markov process--to convert 10 min rain rate time series into 1 min rain rate time series, the latter to be used as input in the synthetic storm technique to calculate rain attenuation time series in satellite communication systems in hydrology and to estimate runoff, erosion, and pollutant transport. The rationale for the need for such a method lies in the fact that now, several meteorological institutes provide time series of the quantity of water collected every 10 min, compared to the past, when the minimum collecting interval was 1 h. To develop and validate the method, we used a very large data bank of 1 min rain rate time series collected in sites of different climatic conditions, which we converted into 10 min time series to simulate the alleged data provided by meteorological institutes. From them, we obtained an estimate of the original 1 min rain rate time series. The simulated 1 min rain rate time series are very similar to the original ones; therefore, we think that the method can be reliably used in the fields mentioned above.  
In conclusion, we have reached two important results:  
1. The conversion of \(R_{10}(t)\) into \(R(t)\) is very accurate, even using the rain rate conversion parameters of Spino d'Adda.
2. \(P(A)\) obtained by using the rain rate parameters of Spino d'Adda as the SST input is indistinguishable from that simulated with the local conversion parameters, in a large probability range useful for practical applications in satellite communications.  
Conceptualization, E.M.; methodology, E.M.; software, E.M. and C.R.; validation, E.M. and C.R.; investigation, E.M. and C.R.; data curation, E.M. and C.R.; writing--original draft preparation, E.M. and C.R.; writing--review and editing, E.M. and C.R.; visualization, E.M. and C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 26: **Vancouver.** Average annual probability distribution \(P(A)\) that the rain attenuation \(A\) (dB) in abscissa is exceeded, estimated with the SST; blue line: experimental \(R_{1}(t)\), black line: simulated \(R(t)\). **Left panel**: \(P(A)\) is obtained by using local values of the conditional rain rate PDFs. **Right panel**: \(P(A)\) is obtained by using Spino d’Adda conditional PDFs (Table 2).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** We wish to thank [PERSON], at NASA years ago, for providing the rain rate data for Tampa, White Sands, and Vancouver; [PERSON], Institute of Atmospheric Physics in Prague, for providing the rain rate data for Prague; and [PERSON], Universidad Politecnica de Madrid, for providing the rain rate data for Madrid.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Methodology
Consider the column vectors \(\mathbf{p}_{1}\equiv\left[HbO_{1},Hb_{1},Sa_{1},Sp_{1}\right]\), and \(\mathbf{p}_{2}\equiv\left[HbO_{2},Hb_{2},Sa_{2},Sp_{2},L\right]\), denoting the parameter sets for the two step parameter recovery with the corresponding measurement data given by the column vectors \(\mathbf{y}_{1}\equiv I(\rho,\;\lambda)\) at \(\rho=10\) mm and \(\lambda=650\) to 850 nm in steps of 10 nm, and \(\mathbf{y}_{2}\equiv I(\rho,\;\lambda)\) at \(\rho=10\) to 50 mm in steps of 10 mm, and \(\lambda=650\) to 850 nm in steps of 10 nm. Let \(\mathbf{v}_{m}\) be the \(m\)-th entry in the column vector \(\mathbf{v}\), then the element \(\mathbf{j}_{1 mn}\) (of \(m\)-th row and \(n\)-th column) of the Jacobian (or sensitivity) matrix \(\mathbf{J}_{1}\) corresponding to recovery step-1 is given by,  
\[\mathbf{j}_{1 mn}=\frac{\partial\mathbf{y}_{1m}}{\partial\mathbf{p}_{1}n}. \tag{11}\]Similarly, the element \(\mathbf{j}_{2 mn}\) of the Jacobian matrix \(\mathbf{J}_{2}\) corresponding to recovery step-2 is given by,  
\[\mathbf{j}_{2 mn}=\frac{\partial\mathbf{y}_{2m}}{\partial\mathbf{p}_{2n}}. \tag{12}\]  
The inverse problem at each step is then implemented by an iterative regularized least-square minimization, where the data is modelled corresponding to the estimated parameter set at each iteration (beginning with assumed initial values). Based on the mismatch between modelled and measured data the following parameter update equation is calculated at the end of each iteration, [18]  
\[\Delta\mathbf{p}_{\mathbf{i}}=\left(\mathbf{J}_{\mathbf{i}}^{\mathrm{T}} \mathbf{J}_{\mathbf{i}}+\beta\mathbf{I}\right)^{-1}\mathbf{J}_{\mathbf{i}}^{ \mathrm{T}}\,\Delta\mathbf{y}_{\mathbf{i}}. \tag{13}\]  
Here, 'i' denotes the recovery step-1 or 2, \(\Delta\mathbf{p}_{\mathbf{i}}\) represents the update for the parameter set \(\mathbf{p}_{\mathbf{i}}\), \(\Delta\mathbf{y}_{\mathbf{i}}\) is the data-model misfit at the end of iteration, and \(\mathbf{J}_{\mathbf{i}}\) is the corresponding Jacobian matrix. To further aid the stability, the inverse problem is guided with a regularization parameter given by \(\beta\), which is considered as 0.01 times the maximum value of the Hessian \(\mathbf{J}_{\mathbf{i}}^{\mathrm{T}}\mathbf{J}_{\mathbf{i}}\). A relative change of less than 2% in the error corresponding to data-model misfit over two successive iterations is considered as the stopping criteria for the inverse problem. The simulation study is split into three parts. Part-(1) deals with data generated on a two-layered semi-infinite model and recovered using a two-layered semi-infinite model as well. To consider a more practical scenario, in part-(2) the data is generated on a two-layered head model but parameter recovery is implemented using two-layered semi-infinite model. Part-(3) deals with data generated on head model and recovered using a head model as well. Part-(2) and (3) are compared to see if the semi-infinite model-based recovery is close enough to head-model-based recovery.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 4 Experiments
### Ablation Study
#### 4.6.5 Choice of Optimizer  
In addition, the optimizer is one of the crucial components in deep learning. Using different optimizers to execute deep learning tasks can lead to vastly different outcomes. In order to explore ways to improve model performance, we chose four classic optimizers and conducted ablation experiments on RHPNet. The experimental results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **Location** & **AP** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** \\ \hline RHPNet & Running Human & & 95.7 & 92.5 & 95.9 \\ \hline +PSA [28] & Running Human & Basic Block (A) & 95.3(\(-\)0.4) & 90.3 & 95.6 \\ +PSA [28] & Running Human & Skip Connect (B) & 95.1(\(-\)0.6) & 91.5 & 95.2 \\ +PSA [28] & Running Human & RFSP (C) & 95.5(\(-\)0.2) & 91.2 & 95.7 \\ +CA [35] & Running Human & RFSP (C) & 95.0(\(-\)0.7) & 90.9 & 95.3 \\ +CBAM [36] & Running Human & RFSP (C) & 94.9(\(-\)0.8) & 89.4 & 95.1 \\ \hline
**Method** & **Dataset** & **Location** & **Mean** & **[EMAIL_ADDRESS]** \\ \hline RHPNet & MPII & & 92.0 & 44.3 \\ \hline +PSA [28] & MPII & Basic Block (A) & 91.7(\(-\)0.3) & 43.8 \\ +PSA [28] & MPII & Skip Connect (B) & 91.6(\(-\)0.4) & 43.2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study incorporating different attention modules at different locations in the RHPNet.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **AP** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** \\ \hline SGD & 84.4 & 73.9 & 85.2 \\ Adadelta & 82.2 & 76.9 & 82.7 \\ AdamW & 95.5 & 91.3 & 95.9 \\ Adam & 95.7 & 92.5 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study with different optimizers applied to the Running Human val dataset.  
Figure 9: The attention module in different locations on the RHPNet: (A) Basic Block; (B) Skip Connect; (C) RFSP.