esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. Analysis of Deep Roadway Floor Grooving: The study determined that central grooving offers the best control effect on surrounding rock stability while side grooving shows weaker results and causes asymmetric stress distribution, which is unfavorable for surrounding rock control. The research is a detailed supplement to the study of deep roadway floor grooving.
2. The study completed a qualitative analysis of underground engineering models and quantified the control indicators for surrounding rock. Stress indicators were quantified using circular measurement lines to assess stress concentration, while the volume of the plastic zone was used to quantify the state of the surrounding rock in three-dimensional space. The study provides a pathway from qualitative to quantitative research for large-scale engineering simulations.
3. The SEGA algorithm was used to optimize machine learning models suitable for small datasets, selecting the optimal objective function model. Multi-objective optimization provided solutions for conflicts in surrounding rock control indicators. Additionally, a solution screening method based on the Pareto front was designed.  
Conceptualization, Y.Q.; methodology, Y.Q. and Y.S.; investigation, G.L., E.C., S.Y. and C.Z.; resources, Y.Q. and C.Z.; data curation, Y.Q., S.Y. and G.L.; formal analysis, Y.Q., C.Z. and J.L.; visualization, G.L., E.C., S.Y. and C.Z.; validation, Y.Q., G.L. and C.Z.; writing--original draft preparation, Y.Q. and J.L.; writing--review and editing, E.C.; supervision, Y.Q. and Y.S.; project administration, Y.Q. and C.Z.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China (52104106, 52474160, U22A20165, 52174089).  
Not applicable.  
The data included in this study are available from the corresponding author upon request.  
**Acknowledgments:** The authors are grateful to Shandong Energy Wanfu Coal Mine for their work support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Optimal Parameters**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**MSE**} \\ \cline{3-8}  & & **Train\_Set** & **Test\_Set** & **Train\_Set** & **Test\_Set** \\ \hline KNN & \(K=4\) & 0.908 & 0.879 & 0.042 & 0.052 \\ RT & \(max\_depth=47\) & 0.974 & 0.903 & 2.365 & 0.040 \\  & \(min\_samples\_leaf=1\) & & & & \\ RF & \(n\_estimators=87\) & 0.937 & 0.919 & 0.033 & 0.0426 \\  & \(min\_samples\_split=2\) & & & & \\  & \(max\_leaf\_nodes=68\) & & & & \\  & \(max\_features=4\) & & & & \\ SVR & \(C=19.74\) & 0.964 & 0.923 & 0.015 & 0.033 \\  & \(Epsilon=0.03\) & & & & \\ MLP & \(hidden\_layers=90\) & 0.926 & 0.902 & 0.042 & 0.051 \\  & \(learning\_rate=0.001\) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Optimized model performance.
# Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
## Implementation Details
### Robust Bacterial Foraging (RBF) Algorithm  
With RBF, the robot takes one step at a time and records these steps in a temporary memory. The target location always attempts to attract the robot, whereas the obstacle always tries to push the robot away. If a robot is stuck in a local minimum, it will stop for a while and save its current position \(p_{vo}\) and the number of iterations \(j\) in the memory. At this position, a virtual obstacle is generated whose information is conveyed to the swarm. Using this information, other robots can continue smooth travel without falling into local minima. The distance between the robot's current location--the same as the position of the virtual obstacle--and each point in its trajectory is then calculated. Thereafter, the robot chooses a point from an available set that has a specific distance (constant distance) and moves back to it with the help of an additional repulsive function. Once the robot returns to a certain point from its path, it again starts traveling towards the target, along which it might encounter another obstacle in the surroundings.  
The start and the target locations for the mobile robot are assumed as \((q_{s},q_{g})\), where \(d_{min}\) is defined as the radius of the field used to determine the location of the goal. The population of bacteria around the robot in each step is represented by \(N_{s}\), where \(l_{p}\) represents the iteration that helps the mobile robot to save its trajectory. When the robot falls in a local minimum, it chooses one of the points in its trajectory that has a specific distance, \(dis\), and moves back to it. The location of the robot is represented by \(q\). The number of virtual obstacles that the robot creates when it falls into a local minimum is denoted by \(l_{o}\) (Algorithm 1).  
```
Input:\(q_{s},q_{g},d_{min},N_{s},dis\) Procedure:  Initialize robot position, \(q\gets\ q_{s}\); \(l_{o}\gets 1;l_{p}\gets 0;i\gets 0;j\gets 0\); \(d\leftarrow\left\|q-q_{g}\right\|\); while\((d\geq d_{min})\) \(l_{p}\gets l_{p}+1\); while\(i\leq N_{s}\)  generate random bacterium \(N_{s}\) around \(q\) in radius \(R\);  use Equation (9) to calculate the next point;  use Equations (10)-(12) to calculate \(J_{o}\), \(J_{g}\), and \(J\);  compute error using Equations (13) and (14); \(i\gets i+1\); endwhile \(i\gets 0\);  sort cost function error in Equation (14);  select best bacterium; \(q\leftarrow\mathit{bestpoint}\);  save the trajectory, \(T(l_{p})\gets q\); if robot falls in local minima then \(p_{\mathit{vo}}\gets q\); \(j\gets l_{p}\); while\(\|q-T(j)\|\leq\mathit{dis}\) \(j\gets j-1\); endwhile \(\mathit{virobstale}(l_{o})\gets p_{\mathit{vo}}\); \(l_{o}\gets l_{o}+1\);  use additional cost function in Equation (15) to reach a specific trajectory  point, \(q\gets T(j)\); endif  update \(d\); endwhile Output: Location of the virtual obstacles and trajectory of the robot.
```  
**Algorithm 1** Pseudocode of the robust bacterial foraging (RBF) algorithm.
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 3 Results
### Using the Filtered SCADA Signals to Estimate Power Curves: Test Data  
Using the same anomaly filtering and estimation method, in this section, we estimate the same curves from the last quarter of available data that we reserved for the test. Once estimated, we compare them with the curves obtained in the training part. We provide the results in Figure 13.  
Figure 12: Power curves of each WT estimated from the wind and power data reported by the SCADA system after removing the anomalies. The black line is the power curve provided by the manufacturer.  
Figure 13: Comparison between the power curves estimated with the training and the test data for each of the WTs of the WF.  
It is interesting to check that the estimated power curves of each machine in the test part fit very well with those obtained using the training data. The most notable difference is the WT84 machine, which suffered a major breakdown, causing it to be stopped for a long time. In this case, we see that the differences between the curves are observed in the part of the initial start-up of the WT when little power is still generated.
## 2 The Overall Strategy of D-PMU Placement
### Analysis of the Observability  
A D-PMU on a bus can provide the voltage phasor of the bus and the current phasors of associated branches. When the voltage phasor of a bus can be measured or calculated, the bus is observable. If all buses are observable, the network is fully observable. Supposing a D-PMU has enough channels, based on the Kirchhoff law and D-PMU characteristics, the observability rules are as follows:  
1. If a D-PMU is placed on a bus, this bus and its neighbors are observable, because for a bus with a D-PMU, the voltage phasor and current phasor of the associated branches are known, and the voltage phasors of its neighbors can be calculated by the Kirchhoff laws;
2. If a ZIB is observable, and if only one neighbor's observability is unknown, the voltage phasor of this unknown neighbor can be calculated by the Kirchhoff laws. In other words, this neighbor is identified as observable;
3. If all neighbors of a ZIB are observable, the voltage phasor of the ZIB can be calculated by the Kirchhoff laws.
## 4 Experiment  
### Experiments Setting  
We evaluate our method on the dataset from the 2019 IEEE GRSS Data Fusion Context [18; 19]. This dataset provides 26 WorldView-3 images at 0.3 m resolution collected in Jacksonville, Florida, between 2014 and 2016. For comparison with other methods, we followed the settings of [16] and selected scenes numbered 004, 068, 214, and 260, covering vegetation, urban buildings, water bodies, etc., as shown in Figure 7. Following [16], the original 2048 \(\times\) 2048 image was cropped according to the DSM geographical area of each scene, approximately 800 \(\times\) 800 pixels, with a resolution of 0.3 m, so each AOI area covers 256 \(\times\) 256 m, approximately 60,000 m\({}^{2}\). Table 2 displays the comprehensive details for each scene within the dataset.  
**Metric.** For quantitative comparison, the Peak Signal-to-Noise Ratio (PSNR), which computes the mean square error (MSE) in a logarithmic scale, and the Structural Similarity Index Measure (SSIM), designed to reflect human visual perception, are employed. The calculation of SSIM metrics is described in Section 3.5. The PSNR metric is calculated as follows:  
\[PSNR(I)=10\cdot\log_{10}\left(\frac{MAX(I)^{2}}{MSE(I)}\right) \tag{25}\]  
where \(MAX(I)\) represents the maximum potential value of a pixel in the image (an eight-bit integer is 255), and \(MSE(I)\) is the pixel mean square error calculated on all color channels.  
**Baseline.** We compare our method with the original NeRF [15] method, as well as S-NeRF [11] and Sat-NeRF [16], which are specifically designed for remote sensing scenes. The same framework and data loading is used in all NeRF variants.  
**Implementation details.** Our approach builds on NerfAcc [43]. NerfAcc is a plug-and-play toolbox that provides efficient sampling for volume rendering and is universally applicable to various radiation fields. For the training process, we employ the Adam optimizer and select a batch size of 1024 rays. The computational environment for our experiments is detailed as follows: our models are trained on a machine equipped with an NVIDIA RTX 3090 GPU. The CPU is an Intel Core i9-10900K, complemented by 64 GB RAM. The system runs on Ubuntu 20.04 LTS, and we use Python 3.8 for our implementations. Each model converges within a timeframe of 5-10 min, showcasing the efficiency of our setup.
## 3 Example Forgetting in Federated Learning
### Example Forgetting in Non-IID  
In considering federated learning, one can never dismiss non-IID. As mentioned in Section 3.2, non-IID implemented is a cluster-level label size imbalance. The clients are split into two clusters. Clients of a cluster have the same classes, and each cluster has different classes. Each client has the same amount of data in total. However, the number of clients in each cluster are disproportionate so that certain knowledge would be harder to acquire. Note that the nodes, both server and clients, do not know which cluster each client is in or even if such clusters exist because they do not have privilege to access another node's data. The dataset used is CIFAR-10. CIFAR-10 is an RGB-colored 32 \(\times\) 32 image dataset of 10 classes. Each cluster is given two randomly chosen classes of data. Each client contains 1200 non-overlapping samples distributed via uniform random sampling.  
A shallower variant of ResNet [26] is implemented as the backbone model for FedAvg. It has four residual blocks followed by a global average pooling layer and a single fully connected layer. Each residual block is consisted of two sets of a 3 \(\times\) 3 convolutional layer and a batch normalization layer. There are three shortcut connections made with a \(1\times 1\) convolutional layer and a batch normalization layer. Training proceeds normally as FedAvg would as in Section 3.1, except iterating 100 rounds instead. At the end of each round, the number of forgettable examples are recorded.  
Table 2 has two clients for the first minor cluster and eight clients for the second majority cluster. The records of forgettable examples, data samples which forgetting event occurred, demonstrate that FedAvg highly favors the majority cluster. The reason for this is that the classes in the majority cluster have a greater chance of being represented. It is easily  
Figure 1: ResNet-based network architecture used in experiment. An illustrative example of label-size imbalanced non-IID. The clients in the first cluster A only has airplane and car data. The clients in the second cluster B only has bird and cat data. The number of each class samples may vary for each client, however, the total number samples are the same.  
assumable that such a tendency would be mitigated once the clusters are more balanced. However, the following result begs to differ. Table 3 consists of four minority clients and six majority clients. The records show that the overall tendency of learning is almost the same as the 2-to-8 situation in Table 2. According to the results, the majority clients will always be favored. While they achieve near zero forgettable examples, the minority clients have almost no advance.
# Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
## 1 Introduction
No work providing analytical formulations or extended algorithms to estimate the system equilibria for deeper architectures appear to have been published. The method proposed in this paper is designed to have generalisability as a main objective. A multilayer architecture is chosen for this work to illustrate the application in different system identification case scenarios, but any other NN architectures can used in place of the presented one. This paper shows how to assess the stability of a multilayer LSTM network through a black-box analysis, aimed at identifying the system poles. To achieve this, a four step process is detailed, comprising the design of a dataset aimed at exciting different system dynamics, the training and testing of an NN and the analysis of the input/output time series. To eventually assess the stability of the network, standard concepts from the control theory literature are exploited, such as the identification of the poles and their location in the complex plane (i.e., checking whether they lie in the left-hand side plane for the case of continuous dynamic systems). This paper shows how this procedure renders the guarantee and benchmark of the stability of LSTMs possible in both a linear and a nonlinear case. Afterwards, this approach is applied to a case of an unstable dynamic system to show the method's ability to detect and quantify system instability in addition to stability. This work is organised as follows: Section 2 contains the definition of the dynamic models of the benchmark systems, while Section 3 details the system identification method and the software implementation of the framework. Results and discussions are presented in Section 4, alongside the details of the dynamic model and the DNN parameters. Conclusions and future work are reported in Section 5.
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 2 Materials and Methods
### Experimental Set-Up  
The sliders were pulled along the ice tracks using a mechanism driven by a gear motor (Figure 1a). The motor was mounted on a piece of plywood fixed on the ice sheet. The sliding velocity was altered within a rough range using pulleys with different diameters and then adjusted accurately using a frequency converter. A grooved wooden stick was fixed into the ice sheet in front of the motor to restrict the movement of the pulling cable and reduce the swing of the sliders. Figure 1b shows an ice block sliding on its bottom side along the Top track. The pulling cable was a 20 m-long steel wire, and a stainless steel frame was used to contain the slider and ballast. Between the steel cable and slider, a load cell (2.0 \(\times\) 10\({}^{3}\)\(\pm\) 1.0 N) was attached to the frame in the horizontal position to measure the pulling force. Figure 1c shows the iron blocks with a weight of approximately 10 kg each, which were used as ballast to add the normal load of the sliders. The SS plate (silver) and the CS plate (blue) were fixed to the bottom of the frame to change the contact material. Figure 1d shows the SS plate sliding on the Top track with two iron blocks inside.  
Figure 1: Field ice friction experiment: (**a**) gear motor; (**b**) ice block sliding on its bottom along the Top track; (**c**) iron blocks, coated steel (CS) plate (blue), and stainless steel (SS) plate (silver); (**d**) SS plate sliding along the Top track with two iron blocks inside.
# Digital Transformation of Production Planning and Control in Manufacturing SMEs-The Mold Shop Case
## 1 Introduction  
Planning and monitoring of manufacturing systems to achieve desired performance targets in terms of cost, quality, time and flexibility, is of paramount importance and has received significant attention from researchers [1]. Under this perspective, industrial processes could greatly benefit from the integration of Industry 4.0 digital solutions [2]. Industry 4.0 transition and the underlying Digital Transformation (DT) are believed to provide manufacturing SMEs with valuable advantages vital to their future competitiveness and survival, such as manufacturing productivity, reduced operating costs, improved product quality and product innovation [3]. Research results show that SMEs do not exploit all the resources for implementing Industry 4.0 and often limit themselves to the adoption of Cloud Computing and the Internet of Things (IoT). SMEs find it difficult to implement Industry 4.0 technologies due to a lack of economies of scale and their position in the value chain. Many SMEs are mid-tier suppliers, they usually have limited impact on the value chain and benefit from the digitalization of end products or implementation of'manufacturing as a service concept. Hence the benefits of Industry 4.0 tend to accrue to OEMs [4]. SMEs seem to have adopted Industry 4.0 concepts only for monitoring industrial processes and there is still an absence of real applications in the field of production planning [5]. Significant advantages in the domains of ubiquitous connectivity, intelligent machines, IoT, adaptive user interfaces and intelligent user support have been achieved in recent years. Smart factory technology envisages factories that are adaptive and able to quickly react to changes either coming from internal factors (e.g., machine failure) or external factors (e.g., an urgent high-priority customer order). Modern Cyber-Physical Production Systems (CPPS) rely on the collaboration between production equipment andparts to be produced. Parts, sub-assemblies and products will be produced by loosely defined production schedules that can be efficiently adjusted to the dynamic changes in the shop floor. In many cases, the production schedule needs to be adapted due to unpredictable situations such as machine break-downs, unplanned production orders, missing materials, human errors and so forth [6].  
This work proposes an Industry 4.0 solution for DT and investigates the digital transformation of an SME that manufactures molds to ease the burden of planning and monitoring. In chapter 2 the literature review is presented with a focus on Industry 4.0 and Digital Transformation technologies. Chapter 3 describes a system for digitalizing production planning and monitoring. Chapter 4 presents the SME's DT pilot case and discusses the expected benefits as well as lessons learnt. Finally, chapter 5 concludes with a discussion of the key findings and the way ahead.
## 2 The Proposed Method
### Time-Frequency Compression Fusion
#### 2.1.2 Multisynchrosqueezing Transform  
Unlike TMSST, MSST redistributes the coefficients of time-frequency points to the frequency position indicated by the instantaneous frequency estimation value to complete asynchronous compression transformation and obtains a new time-frequency plane. The frequency redistribution operation is repeated. The instantaneous frequency estimation of the signal can be expressed as:  
\[\hat{\omega}(\tau,v)=\frac{\partial\tau MSMFT(\tau,v)}{jMSTFT(\tau,v)} \tag{5}\]  
when the signal does not satisfy the complex sine model, there is some error between the instantaneous frequency estimate and the actual frequency calculated by Equation (5). Fortunately, literature [11] has demonstrated that this error can be reduced by obtaining a new instantaneous frequency estimate from the original \(\hat{\omega}(\tau,v)\) into \(v\).  
\[\left|\hat{\omega}(\tau,\hat{\omega}(\tau,v))-\omega\right|<\left|\hat{\omega} (\tau,v)-\omega\right| \tag{6}\]  
where \(\omega\) indicates the actual frequency, and the processing of \(\hat{\omega}(\tau,\hat{\omega}(\tau,v))\) can be performed multiple times. As the number of iterations increases, the instantaneous frequency is estimated to be closer to the real frequency. The iterative process is similar to TMSST.  
After the new instantaneous frequency estimation is obtained, the time-frequency coefficient of the improved STFT can be redistributed. The process can be expressed as follows:  
\[MSST^{[N]}(\tau,v)=\int_{-\infty}^{+\infty}MSTFT(\tau,\omega)\delta(v-\widehat{ \omega}^{[N]}(\tau,v))d\omega \tag{7}\]  
where \(MSTFT(\tau,\omega)\) is the improved short-time Fourier transform of the model signal. \(MSST^{[N]}(\tau,v)\) is the time-frequency representation of the final frequency redistribution N resynchronization compression transformation. The larger N is, the more compression times and the better energy concentration of the time-frequency image.