esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. The Cowper-Symonds kinetic hardening model improved using initial damage degree and damage modification coefficient considering rock residual strength, and can be used to simulate the damage evolution process of rock and softening of rock caused by damage growth under compression.
2. Blasting damage range of surrounding rock with \(D\geq 0.5\) very significantly reduces from 1.0 m to 0.3 m as the spacing between the box culvert and the tunnel increases from 1.0 m to 4.0 m.
3. Blasting damage of surrounding rock exists some concentrated phenomena beneath the box culvert when the spacing between the box culvert and the tunnel is less than 2.0 m.
4. Using the proposed model can reasonably determine the safe distance between the tunnel and the existing box culvert in blasting construction, which should be not less than 4.0 m.  
Figure 14: Rock damage degree variation of different monitoring points with \(d\).  
Figure 15: Damage degree variation of surrounding rock beneath the box culvert with \(d\).  
5. This paper lacks comparative analysis and validation with other methods such as the model using rock tension-compression coupling damage algorithm. Further studies are required for these deficiencies as well as the validation of numerical simulation by field damage detection.
6. Notwithstanding its limitation, the simulation using the proposed model conducted in the paper is still very significant as a reference to guiding tunnel drill-blasting construction closely under-passing sewage box culverts.  
J.X.: conceptualization and constitutive model development, writing--review and editing; H.X.: numerical simulation and analysis, writing--original draft preparation; G.R.: data curation, investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Project of China Railway 20 th Bureau Group Co., Ltd., China Postdoctoral Science Foundation, grant number 20060390165.  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflict of interest.
# Damage Behaviour of Quasi-Brittle Composites: Mathematical and Computational Aspects
## 3 A Model Problem
### The First Step: A Viscoelastic Model
 \tag{17}\]Inserting (16) and (17) into (15), we obtain  
\[\left((\tau_{r}^{m},\mathcal{C}^{-1}\tau_{r}^{m}/\alpha)\right)+h \sum_{s=1}^{r}((\tau_{s}^{m}-\tau_{s-1}^{m},\mathcal{C}^{-1}(\tau_{s}^{m}-\tau_{ s-1}^{m})/\alpha)) \tag{18}\] \[+2h\sum_{s=1}^{r}((\tau_{s}^{m},\mathcal{C}^{-1}\tau_{s}^{m}/ \beta))+((\epsilon(u_{r}^{m}),\mathcal{C}\epsilon(u_{r}^{m})))\] \[+\ \sum_{s=1}^{r}((\epsilon(u_{s}^{m}-u_{s-1}^{m}),\mathcal{C} \epsilon(u_{s}^{m}-u_{s-1}^{m})))\] \[+\ \sum_{s=1}^{r}(\eta u_{s-1}^{m}+\eta^{-1}f_{s}^{m},\eta u_{s-1} ^{m}+\eta^{-1}f_{s}^{m})+\sum_{s=1}^{r}(\widetilde{\eta}u_{s-1}^{m}+\widetilde {\eta}^{-1}g_{s}^{m},\widetilde{\eta}u_{s-1}^{m}+\widetilde{\eta}^{-1}g_{s}^ {m})\] \[=\eta h\sum_{s=1}^{r}(u_{s-1}^{m},u_{s-1}^{m})+\widetilde{\eta}h \sum_{s=1}^{r}(u_{s-1}^{m},u_{s-1}^{m})\] \[+\ \eta^{-1}h\sum_{s=1}^{r}(D_{fs}^{m},D_{fs}^{m})+\widetilde{ \eta}^{-1}h\sum_{s=1}^{r}\langle D_{\mathcal{S}^{s}}^{m},D_{\mathcal{S}^{m}} ^{m}\rangle\,.\]  
The second, third, fifth, sixth, and seventh left-hand-side additive terms in (18) can be estimated by zero from below. The third and fourth left-hand-side additive terms are bounded from above by some positive constants, thanks to the interpolation properties of \(\{f^{m}\}_{m=1}^{\infty}\) and \(\{g^{m}\}_{m=1}^{\infty}\) of \(f\) and \(g\). Taking into account (12) for the fourth left-hand-side additive term (the lower estimate for the first one is simple) and (10) and (11) for the first and second right-hand-side additive terms, we again obtain, for a sufficiently large positive constant \(c\), the following:  
\[\|\tau_{r}^{m}\|_{\mathcal{H}}^{2}+\|u_{s}^{m}\|_{\mathcal{V}}^{2}\leq c\Bigg{(} 1+\sum_{s=1}^{r}\|u_{s-1}^{m}\|_{\mathcal{V}}^{2}\Bigg{)}. \tag{19}\]  
Thanks to (19), the discrete Gronwall lemma (see [57], Part 1.6) guarantees the uniform boundedness both of \(\{u^{m}\}_{m=1}^{\infty}\) in \(\mathcal{V}\) and of \(\{\tau^{m}\}_{m=1}^{\infty}\) in \(\mathcal{H}\) for each \(t\in\mathcal{I}\). The Eberlein-Shmul'yan theorem (see [62], Part 3.4) then guarantees, up to appropriate subsequences, the existence of weak limits of \(\{u^{m}\}_{m=1}^{\infty}\) in \(L^{\infty}(\mathcal{I},\mathcal{V})\) and of \(\{\tau^{m}\}_{m=1}^{\infty}\) in \(L^{\infty}(\mathcal{I},\mathcal{M})\), denoted by \(u\) and \(\tau\). Coming back to (7) and (8), this enables us to make the final limit passage to (2) and (8). However, some more detailed convergence results can be derived, e.g., from the compact embeddings mentioned in the text block before (10). In particular, \(\{u^{m}\}_{m=1}^{\infty}\) converges to \(u\) even strongly in \(L^{\infty}(\mathcal{I},\mathcal{H})\); similar results can be repeated for \(\{\psi(u)\}_{m=1}^{\infty}\) and \(\psi(u)\), where \(\psi\) is a continuous function.
## 1 Introduction and Motivation  
The data imbalance commonly encountered in deep network training can have a profound effect on the training process and detection capability of the network [1]. Data imbalance refers to situations where the classes are not represented equally [2]. Specifically, the imbalance can be found in the number of data points pertaining to separate objects in a given point cloud. Furthermore, the number of data points pertaining to the objects differs significantly from the number of data points pertaining to the background. Under these conditions, the network often learns to detect only the background and large surface objects; i.e., it tends to skip smaller objects.  
Growing interest in deep learning has brought the problem of data imbalance to the foreground, particularly in the field of data mining [3], medical diagnosis [4], the detection of fraudulent calls [3], risk management [5; 6; 7], text classification [8], fault diagnosis [9; 10], anomaly detection [11; 12], and face recognition [13]. Conventional machine learning models, i.e., non-deep learning, have been extensively applied in the study of class imbalance; however, there has been relatively little work using deep learning models, despite recent advances in this field [3; 14]. For the imbalanced data problem, there were three main methods: data-based, algorithm-based, and ensemble methods.
## 3 Current Applications
### Ocular Surface System
#### 3.1.2. Chalazion  
Chalazia are benign granulomatous inflammatory lesions affecting the eyelids, primarily attributed to inflammation of the meibomian gland leading to meibum thickening [25]. These lesions are typically self-limiting with a spontaneous resolution rate of 25% after 6 months; however, in the remaining cases they can become recurrent or chronic [26; 27]. In a recent prospective study by [PERSON] and co-workers, the authors found an association between recurrent and multiple chalazia and Demodex infestation, particularly in paediatric patients [28]. Conventional treatments include warm compresses, topical (antibiotic and/or corticosteroid) or systemic medications (e.g., tetracyclines, macrolides), intralesional corticosteroid injections, or surgical procedures like incision and curettage. The efficacy of LLLThas been explored in a retrospective study conducted by [PERSON] and co-authors. In this study, 26 eyes of 22 patients with chalazia unresponsive to previous pharmaceutical or surgical interventions received a 15 min LLLT treatment. A second LLLT session was applied if there was no evidence of resolution after the first one. Overall, 92% of eyes showed therapeutic benefits with LLLT, while surgery was required only in the remaining few cases. Therefore, LLLT appears to be beneficial in this setting reducing the likelihood of surgical intervention. In this case, the LLLT device investigated was part of the Eye-light(r) or Epi-C PLUS system, depending on if the treatment was performed, respectively, outside USA or in the USA. The wavelength was 633 \(\pm\) 10 nanometers with an emission power of 100 mW/cm\({}^{2}\). The total fluence in the treated area was 110 J/cm\({}^{2}\)[29].
## 7 Results  
### Effect of Correction  
Figure 6 shows the plots for the measured data (actual data) and predicted values. Figure 6a is the result graph without using the update, whereas Figure 6b is the result after using the update. The former demonstrates a larger prediction error compared with the latter case where the update is employed. The bottom plot shows that the predicted values are closer to the measurement data. The results demonstrate that the update is effective in reducing the error.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 2 Edge Intelligent with IoT
### Cloud-Edge Intelligent Architecture  
Cloud is a centralised infrastructure system that stores data, executes business models, and performs data analytics activities at a long distance away from data sources and end-users [18]. However, with large-scale traffic and a proliferation of connected devices, it is neither feasible nor feasible to transmit all data to the cloud for processing. Edge  
Figure 1: Data analytics using edge computing.  
computing has arisen as a means of addressing these issues by shifting processing to the network's and data sources' edges [19; 20]. Edge computing and fog computing share similar characteristics. Sometimes, the terms \"fog\" and \"edge\" are commonly used to describe the same thing [21]; but the difference is that edge computing is primarily focused on nodes that are closer to IoT devices, while fog computing may encompass any resource that is situated anywhere between the end device and cloud [22]. In this work, we use the word \"edge\" to refer to compute nodes that are near the water network edge, such as edge servers [23]. Even though edge computing reduces traffic, the computer resources accessible on the edge do not have the same capabilities as those available in the cloud. As a result, computationally intensive processes like machine learning are not well suited to edge devices. However, the edge can enhance cloud computing by performing a portion of the computation, lowering network traffic and latency [24].  
As illustrated in Figure 2, the edge-cloud learning framework is made up of three primary components: end-user devices, edge learning servers, and remote cloud deep learning clusters [25]. End-user devices, such as mobile phones, cameras, and internet-of-things sensors, send data to the edge server, which can be noisy and highly redundant [26]. Edge computing servers collect enormous amounts of raw data from end-users and utilize pre-processing and basic learning algorithms to filter out the noise and extract significant features from the raw data. Deep learning tasks, such as convolutional neural networks (CNN) and artificial neural networks (ANN), are carried out by the deep learning cluster (cloud), which is outfitted with scalable and robust GPU resources [27].
# Discriminating Pattern Mining for Diagnosing Reading Disorders +
## 2 Preliminaries
### Tachistoscope  
Tachistoscopes are widely employed devices useful in many scenarios [1; 20]. Roughly speaking, these instruments display a word for some milliseconds (on the basis of the wanted difficulty for the exercise) and next the patient is asked for writing down the word. The results shed lights on reading speed, reading comprehension and are generally helpful to highlight many kinds of reading disorders. Such devices provide the therapist with a set of correctly written words and a set of erroneously written words. Tachistoscopic training, also known as Flash Recognition Training (FRT) in some environments, requires that an individual be able to acquire visual information and remember them later on in an optimal way. A second phase requires that the individual express the derived information through verbal, written, or keyboard means. The tachistoscope is therefore used by the speech therapist to evaluate the visual reaction time, speed and recognition interval, automation and visual sequencing.  
In a session, the user is provided with a sequence of trials each having a word associated with it. The trial consists of two phases: _visualization phase_ and _guessing phase_. During the visualization phase, a word is shown for some milliseconds (typically ranging from 15 to 1500). To this phase, the guessing phase follows. During this phase, the word disappears and the user has to guess the word by typing it. After that, the word disappears; it could be substituted by a set of '#' to increase the difficulty since the user loses the visual memory. In such a case, the word is said \"masked\".  
The therapist, other than the visualization time, can impose several settings about the word--in particular, (_i_) the _frequency_ of the word, which represents how much the word is in current use; (_ii_) the _length_ of the word, which represents how long the word is; (_iii_) the _easiness_ of the word, which represents how difficult reading and writing the word is (for example, each consonant is followed by a vowel); (_iv_) the _existence_ of the word, which represents the existence of the word in the dictionary. As for the existence of the word, the tachistoscope is able to show both existing words and non-existing words which are random sequences of letters with the constraints that (i) each syllable has to appear in at least one existing word and (ii) each pair of adjacent letters has to appear in at least one existing word. The constraints aim at generating readable sequences of letters.
## 2 Study Area and Data Preparation
### Data Preparation  
#### 2.2.1 Landslide Dataset  
This study utilized the 2021 National Geological Hazard Census Database to gather geological disaster data within the study area. Employing optical remote sensing imagery, the raw data underwent a screening process to exclude instances of collapse, debris flow, and landslides with less distinct features. This procedure resulted in the acquisition of 462 landslide data points. These points were then analyzed using optical remote sensing images. Due to the temporal gaps and resolution limitations inherent in optical remote sensing imagery, this research successfully interpreted 174 instances of landslide hazard points. The remaining 288 landslide hazard points that could not be successfully interpreted, along with the 174 successfully interpreted landslide areas, together constitute the landslide hazard database. Typical interpretation results are shown in Figure 2.  
Figure 1: Map of the study area and the distribution of landslide points. (**a**) The geographical location of the study area; (**b**) Elevation map of the city of Lishui with landslide locations.
# An Optical Encoder Chip with Area Compensation
## 4 Testing and Analysis
The parts marked by dotted lines in Figure 9b are the square wave signals generated by another pair of light and dark stripes in channels A1, A6, and A11. After calculation, the duty cycles of the square  
Figure 8: (**a**) Signals of A1, A6 and A11 channels of M-code after TIA and comparator processing. (**b**) Signals of B1, B6 and B11 channels of M-code after TIA and comparator processing. Figure 7: (**a**) Schematic diagram of test circuit. (**b**) Photo of test system. wave signals in the three channels are 63.3%, 60.2%, and 61.7% respectively. It can be seen that when the LED working current changes by \(\pm\)2.85 mA, the square wave duty cycle error of the three channels is within 3.1%. Therefore, even if the LED's luminous intensity changes, the M-code channels' output signals still have good consistency. At the same time, the phase relationship between M-code photodiode arrays M1 and M2 is verified. Taking A6 and B6 closest to the light source and A11 and B11 farthest from the light source as examples, the test results are shown in Figure 10. It can be seen from the dotted lines in Figure 10 that the phase difference between the output signals of A6 and B6 channels is half the period of the incremental signal INC1, and the phase difference between the output signals of A11 and B11 channels is also half the period of the incremental signal INC1. This is consistent with the previous theoretical analysis, and the phase relationship of the photodiode arrays M1 and M2 meets the design requirements.
# Passively Q-Switched KTA Cascaded Raman Laser with 234 and 671 cm\({}^{-1}\) Shifts
## 2 Experimental Setup  
KTA is an anisotropic optical crystal of the orthotropic crystal system. According to the polarization-dependent spontaneous Raman scattering spectra of x-axis cut KTA crystals, strong Raman scattering with two main Raman shifts at 234 cm\({}^{-1}\) and 671 cm\({}^{-1}\) can be realized from the A1 (ZZ) geometry. Therefore, an x-axis cut KTA crystal of 4 \(\times\) 4 \(\times\) 20 mm\({}^{3}\) dimension was employed as the Raman medium to satisfy the X(ZZ)X Raman configuration, according to Porto notations. The KTA crystal was placed in a compact plane-concave cavity and driven by the passively Q-switched Nd:YAG laser at 1064 nm. Figure 1 shows the diagrammatic sketch of the passively Q-switched KTA cascaded Raman laser experimental setup. The Nd:YAG crystal composited with Cr\({}^{4+}\):YAG crystal in order to make the system more compact and easy to dissipate heat, and these two crystals were used to serve as the laser gain medium and the saturable absorber for passively Q-switching, respectively. The Nd:YAG crystal with the size of 3 \(\times\) 3 \(\times\) 7 mm\({}^{3}\) is 1.0 at.% doped, and the Cr\({}^{4+}\):YAG crystal with size of 3 \(\times\) 3 \(\times\) 3 mm\({}^{3}\) has an initial transmission of about 85%. While the optics was closed set with each other, the total cavity length was about 50 mm.  
The pumping source is a fiber-coupled laser diode array operating at 808 nm, which has a fiber core diameter of 200 \(\upmu\)m and a numerical aperture of 0.22. The pump beam was re-imaged by two 808 nm anti-reflection (AR) coated achromatic lenses with focal lengths  
Figure 1: Experimental setup of the passively Q-switched KTA cascaded Raman laser.  
of 50 and 80 mm, obtaining a beam waist of about 320 \(\upmu\)m in the Nd:YAG crystal. Both the Nd:YAG/Cr\({}^{4+}\):YAG composite crystal and the KTA crystal were wrapped with indium foil and mounted in a thermoelectric-cooled copper block with the temperature kept at about 20 \({}^{\circ}\)C throughout the experiments. Both end-faces of the composite crystal and the KTA crystal were AR coated for the fundamental and Stokes wavelengths from 1000 to 1200 nm, and the pump light incident face of the Nd:YAG crystal was AR coated at 808 nm.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
###### Abstract  
The first step in the appropriate packaging design of food, agricultural and industrial products is to conduct an accurate simulation of the vehicle transport environment, in which a power spectral density (PSD) profile is applied. Although several researchers have mentioned the limitations of PSD-based simulation, it is still widely used because accelerated test conditions can be easily generated from the PSD acquired from a particular transport section. In this study, three representative trucks and transport test routes of domestic freight transport were selected to develop a simulation protocol for a truck transport environment in Korea. These studies are needed to compare domestic transport vibration levels with those presented by the International Standards (ASTM: West Conshohocken, USA, ISTA: Chicago, USA) and to simulate damage to packaged products by domestic transport environments. The composite PSD profile for the truck transport environment was established by dividing it into high-and low-level composite PSD profiles representing the top 30% and lower 70%, respectively, of the measured vibration events based on the root-mean-square acceleration (rms G) of the measured vibration events. Also, the effects of these variables on the truck vibration level were analyzed by extracting data corresponding to the truck's pre-planned travel speed and road conditions in the vibration records measured on the test route. Moreover, kurtosis, skewness, and normal quantile-quantity (Q-Q) analyses were conducted to understand the statistical characteristics of Korea's truck transport environment. Statistical analysis showed that the measured vibration events had a heavy-tailed distribution and skewed to the right, causing dissymmetry. The overall rms G of the developed high-level and low-level composite PSD profiles in the range from 1 to 250 Hz were 0.47 and 0.32 for leaf-spring trucks and 0.30 and 0.14 for air-ride trucks, respectively.  
truck transport; truck vibration level; power spectral density; suspension; Korea +
Footnote †: journal: Applied Sciences
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
###### Abstract
To demonstrate the efficacy of the proposed design methods described in this article, a 13 th order power line filter was developed utilizing custom-made inductors. The designed filter exhibits high insertion loss characteristics below 150 kHz. It achieves a remarkable  
Figure 1: Summary of the most used EMC standards. attenuation of \(-130\) dB at 50 kHz while maintaining a low power consumption of 0.6 W with an input and output impedance of 3.6 \(\Omega\). Through these findings, the article highlights the potential and advantages of employing the described methods for designing NB-PLC filters and offering improved performance in terms of insertion loss, attenuation, power consumption, and impedance characteristics. The rest of the article is structured as follows:  
Section 2 provides a comprehensive overview of the integration of power line filters into the power grid, highlighting the key services present in the grid and the two main types of power line filter topologies. Section 3 presents the performance tests that ought to be conducted as an integral part of power line filter benchmarking. The relationship between testing methods and the actual conditions found in the power grid is also presented. By doing so, we aim to provide a better understanding of the factors that affect filter performance in real-world settings. Section 4, in addition to Section 3, outlines the safety and immunity tests that should be conducted as part of benchmarking as well as the passive filter standard [28]. Section 5 presents the design, simulation, and evaluation of the proposed passive power line filter as well as a comparison with three commercially available filters. Our approach is pragmatic and uses S-parameters to facilitate the filter design process. Section 6 presents the conclusions of our research activities presented in the article and proposes future work in this field of expertise.