esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Continuous Water-Softening Stage  
During the excavation of underground rock mass, the water-rock interaction includes: (1) the composition and mechanical properties of the rock mass change under the action of water, (2) the fluid properties and chemical composition of the water itself are also changing. Usually, physical action, chemical action, and mechanical action are complementary and inseparable, especially in the soft rock. As the cementation between diagenetic minerals of soft rock is relatively weak, the gap between mineral particles is large, and the area affected by water is large. Similarly, compared with intact rock, rock mass has more channels conducive to water flow, so rock mass is more influenced by water.  
The physical interaction between water and rock includes lubrication and softening effect. Lubrication reduces the cohesion between diagenetic minerals and the friction between rock masses, thus, reducing the shear strength of rock mass. The softening effect is to change the physical properties of rock fracture fillings, and the physical properties of rock mineral composition. According to the definition of the Engineering Geology Handbook [47], the property that decreases in strength after saturation with water is called softening. The rock softening coefficient is defined as \(K_{R}=\frac{\sigma_{\rm cw}}{\sigma_{\rm c}}\), Where, \(K_{R}\) is the softening coefficient of rock; \(\sigma_{\rm cw}\) is the saturation compressive strength of rock; and \(\sigma_{\rm c}\) is the dry compressive strength of rock.  
The water-rock chemical interactions include ion exchange, dissolution and solubilization, hydration, hydrolysis, and redox [48]. The water-rock mechanical interactions include pore hydrostatic pressure and seepage force. The hydrostatic pressure reduces the strength of rock mass by reducing the effective stress of the rock discontinuities, and seepage force reduce the strength of the rock by producing a hydraulic fracturing effect on the rock.  
Figure 2: Stress-strain curve for typical rock unloading.
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 3 Information Exchange between HEMSs  
The mathematical formulations presented in this section elaborate on the information exchange between the \(k\)th and \(\ell\)th HEMSs at a certain time point. To be specific, we have considered the cases for \(k\),\(\ell\) both odd; \(k\) odd, \(\ell\) even; \(k\) even, \(\ell\) odd; and \(k\), \(\ell\) both even.
# Implementation of Autonomous Mobile Robot in SmartFactory
## 4 Experimental Setup
### Cooperation with SmartFactory
#### 4.2.1 Communication  
The task of the mobile robot was to automatically take over the products from the production line and transport them to the designated place. For this functionality, it was necessary to ensure communication between the line and the mobile robot. Communication was provided by the WISE-4050/LAN module. It is equipped with 4 digital inputs and 4 digital outputs. The module is connected to the line switchboard and communicates directly with the C system, which controls the production line using inputs and outputs. To pair a mobile robot with a WISE module, it is necessary to place them on the same network. After pairing, instructions to read the digital input or change the digital output can be entered into robot missions. Since 8 digital signals were used for communication between the mobile robot and the production line, it was necessary that both the mobile robot side and the line side, and the programmable logic controller (PLC) knew the exact purpose of the signal. Communication was also necessary for the need of shutting down the safety barriers and gates, as the robot would otherwise activate them and set up a disruptive process. For this reason, individual signals were assigned a specific meaning. All signals required for communication are listed below.  
* DO0: The mobile robot sends a signal that it is ready on the line to pick up the product;
* DO1: The mobile robot sends a signal that it is ready on the product collection line (LEGO);
* DO2: The mobile robot sends a signal that it is ready to call from the line;
* DI0: The mobile robot receives a signal to arrive at the line;
* DI1: The mobile robot receives a signal that it is loaded/unloaded;
* DI2: The mobile robot receives a signal that it has finished.
## 5 Experimental Results  
This section presents the different controllers' implementation in Section 4. They have been implemented in a DSP F28M36 from Texas Instruments (headquartered in Dallas, TX, USA) at 30 kHz. The inverter used is a full SiC 20 kVA device.  
It can be seen in Figure 14. The experimental parameters are the same as in Table 2. The experimental plots' data have been captured with a DL9040 Yokogawa oscilloscope (headquartered in Tokyo, Japan) at 1.25 MHz and depicted by Matlab v.2024a (headquartered in Natick, MA, USA). Two different scenarios are considered, being consistent with the study cases in Section 4.5: open-circuit configuration and operation under non-linear load. For implementation purposes, the system proposed in (1) is discretised by the zero-order hold method. Then, each controller approximation obtained by [PERSON] in Section 3 is moved to the discrete-time domain using the Tustin transformation, as suggested in [16].
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.1 Optimization Problems
The comparison results suggest that LSTM can achieve better performance than other DL based approaches. CNN and LSTM are categorized as supervised learning methods, but they can be used in an unsupervised learning approach with satisfactory results. CNN is mostly supervised ML approach, but can be also used in an unsupervised way depending on the problem at hand [73]. The authors in [74] propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks. The LSTM network could be trained in an unsupervised manner, by using a branched structure where one branch learns the regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. Furthermore, the authors in [75] investigated anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms with significant performance gains. The authors in [76] propose a new architecture for extracting features from images in an unsupervised manner, which is based on CNN. The model, namely Unsupervised Convolutional Siamese Network (UCSN), is trained to embed a set of images in a vector space, in a way that the local distance structure in the image space is preserved.The results indicate that the UCSN produces representations that are suitable for classification purposes. So LSTM and CNN are mainly used as supervised ML approaches, they can also be used in an unsupervised manner and as an unsupervised learning paradigm.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 4 The Proposed Combination Algorithms
### Modified Combined Algorithm  
Similar to Algorithm 1, every CH has to forward its SNR, then FC computes the threshold \(\gamma_{c}\). Based on the threshold, each CH determines its fusion rule and the probability of detection. However, we modify Algorithm 1 by adding another threshold, i.e., eligibility threshold, to the simple combined algorithm. The eligibility threshold is added to improve the detection performance by involving only the clusters with high probability of detection in a final decision making at the CRN level. In other words, the clusters with low probability of detection will be excluded from decision making at the CRN level, therefore, reporting overhead will be significantly reduced.  
The eligibility threshold is determined as the minimum accepted probability of detection, \(Q_{D}=0.5\). The eligible clusters have probability of detection, \(Q_{d}\), greater than the eligibility threshold.  
Then, eligible clusters will forward their observations to the FC, while others refrain from forwarding their observations. Note that in this algorithm, we employ two fixed thresholds. Algorithm 2 illustrates the modified combined fusion algorithm.  
```
1:Find the estimation of SNR of each cluster (\(\gamma_{c}(i)\)) and compute (\(\tilde{\gamma}_{c}\))
2:Specify\(Q_{D}\).
3:for\(i=1:C\),
4:if\(\gamma_{c}(i)>\tilde{\gamma}_{c}\)
5:compute\(Q_{d}(i)\) using Equation (6)
6:elseif
7:compute\(Q_{d}(i)\) using Equation (11)
8:endfor
9:if\(Q_{d}(i)>Q_{D}\)
10: The \(i^{th}\) CH forwards its decision to the FC
11:else
12:continue
13:endif
14: The global decision is made at the FC using Equation (6) for the eligible clusters.
```  
**Algorithm 2** Modified Combined Algorithm
## 3 Simulation Results
### Numerical Robustness Experiment  
In this experiment, we showed the proposed algorithm to be numerically more robust than CR-RMC in the finite-precision environments. We performed channel estimation with finite precision by quantization to show the numerical robustness [28]. The round-off error from the quantization with finite bits was accumulated and propagated through the inverse matrix operation of \(\mathbf{\Phi}(n)\), and, finally, explosive divergence occurred [15; 28]. To illustrate this, we repeated numerical stability experiments, decreasing the quantization bit from 32 bits by 1 bit to find the quantization bits that started numerical instability in each algorithm while comparing and verifying the performance for the case of S = 4 and S = 16. In addition, the rest of the setup for the experiment was the same as Experiment 3.1.  
Figure 2 shows the results of comparing the performance of the proposed algorithm and CR-RMC in terms of MSD with different numbers of quantization bits. Figure 2a,b shows the results when quantized to 32 bits. In this case, we can observe that the proposed algorithm as well as CR-RMC converges normally as Figure 1a,b. Figure 2 shows the results of comparing the performance of the proposed algorithm and CR-RMC in terms of MSD with different numbers of quantization bits. Figure 2a,b shows the results when quantized to 32 bits. In this case, we can observe that the proposed algorithm as well as CR-RMC converges normally as Figure 1a,b. Figure 2c,d shows the quantization results for 16 bits. In 16 bits, CR-RMC started numerical instability. Compared with the results of Figure 2a,b, it can be observed that quantized CR-RMC diverges due to the cumula  
Figure 1: Steady state MSD for S = 4, 8, 16, 32 (->-: the proposed algorithm, ->-: convex regularized recursive maximum correntropy (CR-RMC), ->-: maximum correntropy criterion (MCC)- recursive least squares (RLS), solid line: conventional RLS without considering impulsive noise and sparsity): (**a**) S = 4, (**b**) S = 8, (**c**) S = 16, (**d**) S = 32.  
of quantization noise. Figure 2e,f shows the quantization results for 11 bits. In 11 bits, the proposed algorithm also started numerical instability. If we consider the level of quantization error with signal to quantization noise ratio (SQNR), SQNR (dB) = 1.76 + 6.02 \(\times\) bits [29], CR-RMC is stable at above 98.08 dB in SQNR. The proposed algorithm is stable at above 67.98 dB in SQNR. In other word, the proposed algorithm has 30.1 dB gain in numerical stability compared to CR-RMC.  
The experimental results confirm that the proposed algorithm is numerically more robust than CR-RMC.
## 5 Conclusions  
In conclusion, we are the first to propose a novel data augmentation technique, named random slices mixing data augmentation (RSMDA), to deal with feature loss problems in single-image data augmentation techniques. RSMDA mixes two images uniquely in a sliced way. Furthermore, we proposed and investigated three different strategies of RSMDA, namely, RSMDA row-wise (horizontally), RSMDA column-wise (vertically), and RSMDA row-column-wise based on binary randomness. Among these strategies, RSMDA row-wise showed a significant performance in terms of accuracy and robustness. We also found the best parameters of the proposed approach: slice size and probability. Using different datasets and models, overall, RSMDA has shown better performance than single- and multi-image data augmentation methods. We also checked the robustness of RSMDA in detail, and it improves robustness over the baseline and random erasing. Finally, we drew CAM to analyze the model focus, and it was found that the model focuses on tiny and quite helpful features. These tiny features are very important in object recognition, so RSMDA has its importance in object recognition. In the future, we will explore different slice shapes, such as triangular, circular, and elliptical, rather than only rectangular. Additionally, we may explore the same approach for only salient parts of the images.  
Conceptualization, T.K. and M.B.; methodology, T.K.; software, T.K. and A.M.; validation, T.K. and M.B.; formal analysis, T.K. and R.B.; investigation, T.K., M.B. and A.M.; resources, R.B. and M.B.; writing--original draft preparation, T.K. and M.B.; writing--review and editing, T.K., A.M., R.B. and M.B.; visualization, T.K. and A.M.; supervision, M.B., R.B. and A.M.; project administration, A.M. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Science Foundation Ireland under grant numbers 18/CRT/6223 (SFI Centre for Research Training in Artificial intelligence), SFI/12/RC/2289/P_2 (Insight SFI Research Centre for Data Analytics), 13/RC/2094/P_2 (Lero SFI Centre for Software) and 13/RC/2106/P_2 (ADAPT SFI Research Centre for AI-Driven Digital Content Technology). For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.  
Not applicable  
The authors declare no conflict of interest.
## 5 Results
### Ndvi  
#### 5.1.1 Seasonal Courses  
The seasonal NDVI courses of the five sites are presented in Figure 6, calculated from red and NIR measurements at solar noon, and corrected for the dark current drift bias inferred from midnight measurements. NDVI from 8-day composite MODIS surface reflectance from the Terra and Aquaplatforms (original MOD09A1 and MYD09A1 data for the tower site pixels) is plotted for comparison. The seasonal variations in NDVI are large for each of our sites due to alternation of snow cover and vegetation cover. The effects of snow during the spring and autumn generally hide the subtle variations in NDVI due to vegetation phenology, such as onset and end of the season. In order to highlight these transition stages we have plotted data with varying y-axis scale to accentuate the subtle changes during the growing season (_ca._ DOY 120-280). This was particularly necessary for the evergreen coniferous forests of Norunda and Hyytiala, and also for sites with semi-evergreen vegetation like the Fajemyr bog and Stordalen mite.  
All the curves in Figure 6 expose a short pause or \"shoulder\" after the abrupt increase of NDVI due to ending of the snow season, just before the beginning of the growing season. This short \"shoulder\" lasted for 39 days at the Fajemyr bog (DOY 95-134), 26 days at the Norunda forest (DOY 106-132), 25 days at Abisko forest (DOY 135-160), and 6 days at the Stordalen mite (DOY 136-142). Moreover, the shoulder at the Fajemyr bog appeared to be decreasing slightly, from 0.59 on DOY 95 to 0.57 on DOY 134. The shoulder value of at the Hyytiala forest dropped even more, from 0.61 on DOY 83 to 0.39 on DOY 101. The shoulder period appears to be a special characteristic of the onset of the growing season in high-latitude regions, as observed at our sites.  
We observed large increases in NDVI during the spring period of the five sites (Abisko forest DOY 103-135, Stordalen mite DOY 128-136, Fajemyr bog DOY 80-95, Norunda forest DOY 100-106,  
Figure 6: NDVI courses of five sites in 2010. Tower-based NDVI are calculated from measurements at solar noon. Terra and Aqua MODIS-derived NDVI were computed from 8-day composite reflectance. The scales are varied along the y-axes, in order to amplify subtle NDVI variations during the growing seasons. The tower-based NDVI curves expose a “shoulder” at the start of the growing season (dotted circle).  
and Hyytiala DOY 58-83). By combining information from phenology camera images and weather station records we know that these are the snow melting periods.  
It can be seen that satellite-based and ground-based measurements agree with each other very well (R\({}^{2}\) = 0.68\(\sim\)0.78, sample size N = 51\(\sim\)70). However, MODIS data tend to display larger scatter during the growing season, probably due to cloud contamination that may affect satellite measurements more than the tower measurements. Tower NDVI from the Hyytiala forest display a relatively larger scatter than the other sites, for unknown reasons. The \"shoulder\" is less visible in MODIS data due to the coarser temporal resolution.
# A Novel Approach to Incremental Diffusion for Continuous Dataset Updates in Image Retrieval
## 3 Preliminary Knowledge  
In the context of image retrieval, the concept of diffusion has gained considerable attention due to its potential to improve the representational learning of image features in complex datasets [19]. Diffusion, in its essence, refers to the process of information spread across interconnected data points, enabling the propagation of relevant features and the preservation of the underlying relationships between data instances [20].  
For image retrieval systems, particularly those operating on continuous or dynamically evolving datasets, diffusion models are integral in addressing challenges related to both scalability and accuracy [21]. When incorporating diffusion over region manifolds, the process becomes a powerful tool for maintaining the structure of the feature space while facilitating robust image matching. This is especially important when the dataset is continuously updated, as it enables the system to adapt to new image data without requiring a complete retraining.  
Moreover, diffusion techniques in image retrieval are particularly effective in addressing the problem of high-dimensional feature spaces [22], where traditional methods may struggle to capture the complex relationships between images. The diffusion process helps refine these high-dimensional representations by ensuring that similar images are mapped closer together in the feature space, thus improving retrieval precision.  
Compared with previous offline diffusion [5] and online diffusion [4], the proposed incremental diffusion method can handle continuous dataset updates at affordable computational cost while ensuring run-time efficiency. For this characteristic, we name our proposed method as incremental diffusion.  
Incremental diffusion allows for the gradual updating of the diffusion process as new image data points are introduced. Our methodology ensures that the system remains efficient and responsive to changes in the dataset, making it suitable for real-time image retrieval applications. By leveraging incremental updates, the model can diffuse new information across the feature manifold, seamlessly integrating new images while maintaining the coherence of previously learned representations.  
In this paper, we focus on the application of incremental diffusion for continuous dataset updates in image retrieval. We investigate how the diffusion process can be efficiently implemented to improve retrieval accuracy while accommodating the ongoing introduction of new data. Through incremental diffusion, our method ensures that the evolving dataset remains consistent and optimized for high-quality retrieval results.
# Radiation from a Cavity-Backed Circular Aperture Array Antenna Enclosed by an FSS Radome
###### Abstract  
Radiation from a cavity-backed circular aperture array antenna enclosed by a frequency selective surface (FSS) radome is studied using the hybrid analysis method, by combining the mode matching method, the ray tracing technique, and [PERSON]'s principle. The equivalent magnetic surface currents on the apertures are derived from the aperture electromagnetic fields, which are calculated based on the mode matching method. The rays are generated from the equivalent magnetic surface currents and used to analyze the FSS radome based on the ray tracing technique. After being obtained from both the mode matching method and the ray tracing technique, electromagnetic fields on an outermost radome are transformed into the equivalent electric and magnetic surface currents using [PERSON]'s principle. The radiated fields are computed from the equivalent surface currents and compared with the measured data.  
Keywords:frequency selective surface radome; cavity-backed circular aperture array antenna; mode matching method; ray tracing technique +
Footnote †: journal: Applied Sciences
# High-Performance Speed Control of PMSM Using Fuzzy Sliding Mode with Load Torque Observer
###### Abstract
In [18], a model predictive control is designed to estimate the load torque, but the estimation process needs to assume that the flux linkage is constant. In [19], an adaptive robust control method based on a sliding mode control mechanism is designed to address several issues related to state errors, sensor noise, and chattering. The sliding surface of this controller is adjusted dynamically by a fuzzy technique, which is formed by two inputs and one output. However, the conflict between rapidity and buffeting in the traditional exponential approach law has not been resolved. In the paper, an exponential approach law sliding mode controller is adopted on the basis of current feedback decoupling, and the parameters of the exponential reaching term are determined automatically online in the form of piecewise functions; the parameters of equal speed switching term are adjusted automatically online according to fuzzy rules. In order to solve the symbolic function switch discontinuity, an improved smoothing function is proposed to improve the symmetry of the control signal switching and reduce the chattering. On this basis, a simple torque observer is designed to compensate the load and improve the system immunity. Finally, the proposed control strategy in this paper is compared with SMC + LO, PID + LO, and ADRC. The simulation and experimental results show that the proposed control method can quickly reach the sliding mode surface from the initial state and make the control system with strong robustness; the chattering phenomenon is effectively improved. The main innovations of this paper are as follows: (1) Aiming at the chattering problem of the sliding mode with the exponential reaching law, a method of segmentally tuning the parameters of the exponential approaching term and fuzzily tuning the parameters of the constant-speed approaching term is proposed, which improves the response speed and reduces the chattering. (2) In view of the jump problem of the signum function in the exponential reaching law, a function whose curve shape can be flexibly adjusted is designed. (3) A two-degree-of-freedom strategy combining a torque observer with sliding mode control is proposed to improve the tracking performance and anti-disturbance ability of the system.