esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Ductile Crack Propagation by Fractal Energy Dissipation Rate
## 5 Discussion  
During ductile crack growth, massive plastic flow occurs in the vicinity of the crack tip, and the wake is governed by dislocation-dislocation interactions in crystalline materials. Although the actual cell structures depend on various extrinsic and intrinsic parameters, dislocation patterning and its relation to work hardening are common among various materials. Thus, the dislocation pattern plays a critical role in energy dissipation, which can be directly characterized by transmission electron microscopy [37] and indi  
Figure 2: Fitting results of R(\(\Delta a\))-curve by the proposed theory for (**a**) HY80, (**b**) 20 MnMoNi55 Group A, and (**c**) 20 MnMoNi55 Group B.  
rectly by acoustic emission (AE). Because AE can represent the rapid release of energy from localized sources within materials, e.g., dislocation avalanches and microcrack generation and propagation, AE also provides direct information regarding the existence and location of pressure wave energy emissions [38; 39; 40; 41]. AE results for single ice crystals under plastic deformation showed that the probability density function of signal intensity follows a power-law distribution with an exponent (fractal dimension) in the range of 1.6-2.0 [42]. Dislocation avalanches were also observed spatially with a self-similarity dimension \(D\) = 2.5 \(\pm\) 0.1 [43]. Furthermore, the fractal dimensions \(D\) in Table 1 range from 2.5 to 2.8, which is consistent with the experimental results [42; 43]. Here, the fractal dimension of the plastic energy dissipation is always greater than 2.5. For ductile fracture, it is believed that the energy dissipated is proportional to the area slipped by dislocation loops with different orientations, i.e., the cellular pattern, the fractal dimension of which is generally greater than 2.5 [42]. This is higher than the fracture surface observation by [PERSON] [20; 21; 22; 23] wherein the limit fractal scaling exponents are equal to 0.5. In this study, the defect size distribution was a power law with a negative exponent \(D\) = 2\(b\), which can be interpreted as a fractal dimension of the damage domain. By linking the \(b\)-value to the damage fractal dimension \(D\), the evolution of the power-law exponent is captured immediately. In the initial phase, criticality \(D\) = 3.0, whereas, when final failure is imminent, the exponent tends to 2.0.
# A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams
###### Abstract  
Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents' postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens' discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density.  
GIS; hot spots; cold spots; fuzzy partition; emotional categories; social streams Article
conventional methods have been proven over time and again to be very effective in treating commonly occurring environmental pollutants, these methods are not effective in degrading industrial pollutants of a complex and recalcitrant nature, such as aromatic dyes and heavy metals. Apart from this bottleneck, these conventional methods produce a wide range of secondary pollutants during the process of degradation that are harmful to the environment [9].  
The limitations of traditional wastewater treatment systems, such as the inability to degrade pollutants of a complex nature and the production of environmentally toxic byproducts, have led to the exploration of novel and innovative treatment methods. Most of the newly developed techniques employ biological methods, involving microorganisms because of their natural ability to bioptake and degrade pollutants of a complex nature [10; 14; 15; 16; 17; 18]. Among a wide variety of microorganisms, White Rot Fungi (WRF) have recently gained substantial attention from scientists due to their exceptional ability to produce different extracellular lignin-degrading enzymes, such as lignin peroxidase, manganese peroxidase, and lactose. These extracellular enzymes enable WRF to degrade and mineralize industrial/environmental pollutants of a complex nature into less toxic or non-toxic compounds [19]. The extracellular enzyme-producing capability of WRF makes them an appealing candidate in the field of bioremediation to degrade complex/resistant pollutants emanating from different industries, such as the textile, paper and pulp, pharmaceutical, and/or agrochemical industries, etc. [20].  
In the recent past, researchers have been focusing specifically on WRF's biodegradation potential and its performance optimization, as well as WRF's effective integration in already existing wastewater treatment facilities [21]. Mycoremediation involving White Rot Fungi (WRF) is a growing area of interest for researchers to address challenges associated with industrial wastewater treatment specifically involving complex organic pollutants and heavy metals. WRF fungi can offer effective wastewater treatment solutions at an industrial scale.
## 2 Materials and Methods
### Statistical Analysis  
The data were analyzed using GraphPad Prism version 8.0 software for Windows (GraphPad Software, San Diego, CA, USA) and were presented as means \(\pm\) SEM for three to six experiments (samples from independent donors). All the results were normalized against cell numbers in the corresponding cell culture dishes. Statistical differences between the means were determined by a Mann-Whitney U test, where \(p<0.05\) was considered to be statistically significant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Forward (5’ \(\rightarrow\) 3’)** & **Reverse (5’ \(\rightarrow\) 3’)** \\ \hline _Nd1_ (mtDNA) & CGCCATAAAACTCTCACCAAAG & GGGTTCATAGTAGAAAGAGCGATGG \\ \hline _Globulin_ (nDNA) & GTGCACCTGACTCCTGAGGAGA & CCTTGATACCCAACCTGCCCAG \\ \hline _\(\beta\)-actin_ & ACCGAGCCGCTACAGC & CTCATTGCCCAATGTGAT \\ \hline _Pink-1_ & GGGGATATGAGCAGTCAC & CATCAGGGTAGTCGACCAGG \\ \hline _Parkin_ & TACGTGCACAGACGTCAGAG & GACAGCCAGCCCACACAAGGC \\ \hline _PGC-1a_ & GTCACACCCAAATCCAT & ATCTACTGCCTGAGACCTT \\ \hline _Drp1_ & AGGTTGCCCGTGACAATGA & ATCAGCAAAGTGGGGGTGTTT \\ \hline _Fis1_ & GTCCAAGAGCACGCAGTTTG & ATGCTTACGATGTCATCATT \\ \hline _Mfn1_ & ATGACTGTGTTAGTAGACAGT & AGACATCAGCATTAGGCAAAAC \\ \hline _Mfn2_ & TGATGGGTACAATGACCAG & AGCTTCTCCTGCTGCATGC \\ \hline _Ppif_ & GACAGACTGGCTAGGTGCAA & CTTCCCACTTTTTGACCCA \\ \hline _Opa1_ & AGCCTCCCAATTTTTG & AGCCGATCCATGATG \\ \hline _Caspase3_ & GTAGATGTTTGAGCCCTG & CCAGTGCGTATGAGAATG \\ \hline _JNK_ & TCCCCCAGCTATCTATATGCAAT & TCACAGCACATGCCACTTGA \\ \hline _SOD1_ & TTGAACAAAGAATCC & AGCCAATGACACCACAAGCAG \\ \hline _SOD2_ & CAGGCAGCTGGTCCGCTT & TGCAGTGGATCCTGATTTGG \\ \hline _GPX1_ & TCCAGACCATTGCACATCGAG & ACTGGGATCAACAGGACCAG \\ \hline \hline \end{tabular}
\end{table}
Table 1: Human-specific primer sequences for quantitative real-time polymerase chain reaction (qRT-PCR).
No adjustments of the harvester have been made during the studies (sampling) [17]. The analysis of the KKU-2 harvester study results [17] made it possible to present the share of work elements and drops in tuber damage as a pie chart (Figure 2). The figure shows that separating work elements account for 63.2% of damage instances out of the total number of potato tuber damage. Figure 2 shows the structure (by damage type) of potato tuber damage during collection by a harvester. The diagram (Figure 3) shows that internal damages (darkening of flesh) occurring under shock stress by the separating work elements, and drops account for 69.4% of the total number of damages. Figure 3: The share of work elements in the damage of potato tubers (percentage of the total damage number by weight). Figure 2: General view of the harvesting root crops machine. To make the presentation of potato tuber damageability at work elements and drops of potato harvesters more intuitive, Figure 4 shows the number of damaged tubers as a stepped diagram. The X-axis of the diagram represents the location of harvester work elements within the sequence of the potato heap travel along them, while the Y-axis represents the number of damages as percentage at a certain work element of the harvester, as a running total. Under the X-axis, the harvester's work elements are shown schematically to conveniently appraise the degree of influence of a specific work element on potato damage, especially during the transition of the heap from one work element to another. Based on the analysis of the stepped diagram (Figure 5), the pie diagram in Figure 6 was constructed. The diagram shows the relative share of drops and separating work elements in the total amount of potato tuber damage during harvester-based pickup. Figure 4: The structure of tuber damage during harvester potato pickup as percentage of the total number of damages. Figure 5: Damage to potato tubers on the working bodies of the combine. The diagram shows that the transition of potato heap from a work element to another (drops), accounts for 67.5% of the total number of damaged tubers, while drops and separating work elements together account for 95% of tuber damage. The range of existing methods for the evaluation of small batches of tubers mainly focuses on the discovery of individual factors. In addition, the response of varieties and hybrids is different depending on the testing method of mechanical action resistance used. This means that tests should be carried out using the methods that would closely simulate the conditions occurring during a standard harvester-based pickup. For this reason, procedures and technical means are required that would make it possible to evaluate seed stock in terms of resistance to mechanical damage on a limited number of tubers available. Therefore, the objective of this study is the quantitative and structural evaluation of damage to potato varieties and hybrids resulting from the device simulating the operation of harvester separating elements depending on the machine type and harvesting conditions.
## 2 Transmitarray Antenna Design
### Feed Antenna Design
It is obvious that the symmetric aperture fields are generated at the horn's cross section which lead to the symmetric radiation patterns regardless of the azimuthal angles. Figure 1: The cross-sectional view of the Potter horn antenna as the feed source. Figure 2: The radiation pattern of the proposed antenna. (**a**) Comparison of cos\({}^{\rm q}\) (\(\theta\)) curve and the radiation pattern of the horn in E-plane and H-plane. (**b**) The 3D radiation pattern at 28 GHz.
# Terahertz Photoconductive Antenna Based on a Topological Insulator Nanofilm
## 1 Introduction
A significant improvement in the characteristics of these devices was achieved with the help of such nanotechnological tools as plasmonic contact gratings, arrays of optical nanoantennas, optical nanocavities [22; 23], and non-standard orientation of the substrates [24; 25]. Another important area is the development and optimization of couplers at the output surfaces of photoconductive antennas. The performance of planar terahertz antennas can be improved by using hemispherical dielectric lenses. However, this increases the weight and volume of the antenna. Artificially created composite metamaterials have recently attracted considerable attention from both the scientific and engineering communities due to their unconventional properties [26; 27; 28; 29; 30]. It is also of interest to use various other photoconductive materials for their application in antennas pumped by telecom wavelength lasers. [PERSON] et al. [31] presented a significantly ultra-broadband (extending up to 70 THz) THz emission from an Au-implanted Ge emitter that was compatible with mode-locked fiber lasers operating at wavelengths of 1.1 and 1.55 \(\upmu\)m.  
The development of new photoconductive nanomaterials for THz PCAs still remains a relevant and urgent task. In our previous work [32], we demonstrated amplification of the THz radiation power by applying a weak external electric field to a TI. In this work, we study the generation of a photoconductive antenna under the application of a strong bias external field.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background
### Learning Rate Policies  
The learning rate is one of the most critical hyperparameters in training neural networks. This parameter determines the step size at which the neural network adjusts its weights in each iteration of the training process to minimize the network's loss function. Selecting an appropriate learning rate is crucial for the model's fast and efficient convergence to the minimum of the loss function. A high learning rate can cause the model to take excessively large steps, potentially leading to instability and overshooting the minimum. Conversely, a very low learning rate makes convergence slow due to the small updates to the network's weights, increasing the risk of the model getting stuck in local minima.  
The choice of an appropriate learning rate policy is essential for the efficient training of neural networks, as the learning rate directly impacts the speed and stability of the model's convergence. Three popular policies that have been extensively used are the fixed learning rate, decaying learning rate, and cyclic learning rate [31].  
* Fixed Learning Rate (Fixed LR): This is the simplest learning rate policy, where a fixed value is used throughout the entire training process. While easy to implement, this approach often proves less effective in more complex models, where the demands for weight adjustments change as the network learns.
* Decaying Learning Rate (Decaying LR): This popular strategy involves gradually decreasing the learning rate as training progresses. The gradual reduction helps better approximate the minimum of the loss function by avoiding drastic weight changes that could lead to instability. Depending on the predefined method for adjusting the learning rate, common decaying strategies include time-based decay, step decay, and exponential decay.
* Cyclic Learning Rate (Cyclic LR): This is an innovative approach where the learning rate oscillates between a minimum and a maximum threshold on a cyclic basis. This method allows the network to explore the parameter space more effectively and avoid local minima, improving the overall performance and stability of the learning process [4].
# Usability and Affects Study of a Virtual Reality System Toward Scorpion Phobia Exposure Therapy
## 3 Results
### Biofeedback Findings  
Biofeedback is used in this study as a noninvasive monitoring of the physiological signal of the heart rate to determine the progress of the intervention, as well as as a stop criterion. In normal resting conditions, as in the experiment, the heart rate of adults ranges from 60 to 100 beats per minute (ppm). During the experimental test, the participant uses a smartband to measure heart rate through the HypeRate app. To illustrate the behavior of the signal during the test, Figure 10 shows a real heart rate signal for a man and a woman who participated in the intervention at the three levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Affect** & **Mean** & **SD** \\ \hline PA & 28.18 & 8.33 \\ NA & 13.67 & 4.25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Means and standard deviations for the PANAS at the time approach, where PA: Positive Affects and NA: Negative Affects.  
Figure 10: Real heart rate signals of a man and a woman during the test on the three levels of the VRET system.  
In Figure 10, the domain of the function corresponds to the timeline of the virtual world, at the beginning and when the scorpion appears, hides, and reappears, while the codomain is related to the bpm. In level 1, in general, the woman presents a higher HR level than the man, and in contradiction the appearance of the scorpion provokes the decrement in the HR of the woman and increment in that of the man. Level 2 implies that the scorpion approaches at 3 or 4 m, so the distance is still considerable, and in that case there is no difference in the average HR between the female and the male. However, at level 3, the scorpion approaches between one and two meters close to the bed, making the exposure more perceptive to the alertness of the senses and increasing the HR. In this way, there is a different behavior, since when the scorpion hides for the second time the male HR increases and the female HR decreases. Although we only present the signals of two participants, a more comprehensive study could be conducted with all participants. Table 3 summarizes the mean and standard deviation of the heart rate for the three levels for the participants.  
In general, in the three levels the HR remained at 84 bpm, approximately within the normal range, with greater dispersion at level 1. However, in levels 2 and 3, women had a higher average HR than men. VR technology can provide instant feedback on physiological signals of the patient and proprioception, The biofeedback from therapists and test subjects is very positive regarding the current concept and realization.  
Finally, in the VRET framework, the information generated in the three evaluations (usability, affects, and biofeedback) of the virtual reality environment is fed back into the continuous improvement of the next version of the system (Figure 1).
## 4 Experimental Setup
### Wind Turbine  
The experimental setup employs a Rutland FM910-4 small-scale horizontal-axis wind turbine (MARLEC, Corby, UK), designed specifically for low-power applications, shown in Figure 6. The turbine is mounted indoors, where it is subjected to wind generated by a variable-speed centrifugal fan. The fan's speed is controlled through a frequency inverter, allowing precise adjustments to the wind velocity and enabling the simulation of different environmental conditions for repeatable testing.  
The wind turbine is equipped with a PMSG and a fixed-pitch rotor. Under favorable wind conditions, the turbine can reach up to 200 W of electrical output. The cut-in speed of the turbine is around 3 m/s, and the cut-out speed is 20 m/s, which aligns with typical values for small wind turbine systems. Mechanical energy from the rotor is converted into AC electrical power, which is subsequently processed by the rectification and power conversion stages described in the following sections.
## 2 Experimental Details
### Shear Tests  
#### 2.4.1 Experimental Setup and Specim Assembly  
According to the Chinese standard, JGJ82-2011 [19], the value of the torque coefficient \(k\) of the high-strength bolts ranged from 0.11 to 0.15. The torque coefficient \(k\) of the stainless-steel A4-80 high strength bolts was 0.15 according to [PERSON] et al. [20]. The diameter of the high strength bolt was 6 mm. According to ISO3506-1:2009 [21], the torque limit of M6 stainless-steel A4-80 high-strength bolts was 15 N m. However, due to the low stiffness and strength of the GFRP plates, a prevailing torque higher than 15 N\(\cdot\)m may lead to the excessive pre-tensioning of the bolts, subsequently damaging the GFRP plate during the bolt tensioning process [22]. The value recommended by the authors in [23] was used to pre-tension the bolts, and the prevailing torque \(T\) was 7 N\(\cdot\)m, insuring a level of shear resistance and avoiding initial damage to the components. The values of the bolt preloading force and prevailing torque were listed in Table 4.  
A UTM5305 microcomputer-controlled electro-hydraulic servo universal testing machine was chosen to perform the shear tests, as the machine could firmly clamp the specimen with hydraulic fixtures and the maximum output load could reach 300 kN. The specimens were assembled first, and then clamped vertically into the fixture of the electronic universal testing machine, and the axis of the specimen was strictly aligned with the fixture center, as shown in Figure 4. The test specimens had tension applied monotonically with a speed of 2 mm/min. The tests stopped once the applied tension sharply increased. The load and displacement were recorded synchronously with a data acquisition system.
# An Interoperable UMLS Terminology Service Using FHIR
## 3 Background
### Comparing UMLS Knowledge Structure and FHIR ConceptMap
#### 3.3.1 Similarities  
* UMLS concept and atoms (Figure 3) can be represented by SourceElement and TargetElement, respectively, in the ConceptMap (Figure 4);
* The equivalence attribute of TargetElement in ConceptMap is bound to ConceptMapEquivalence valueset, i.e., the equivalence attribute will only accept values defined in this ConceptMapEquivalence valueset. The ConceptMapEquivalence valueset defines ten values (e.g., equal, specializes, subsumes, wider, etc.), and these values describe the nature of the mapping between SourceElement and TargetElement. For example, when the equivalence attribute takes the value \"equal\", the code captured by TargetElement is equal to the code captured by SourceElement;
* The URI and version attributes of the source standard (Figure 3) map to targetURI and targetVersion in TargetElement, and the URI and version of UMLS maps to sourceURI and sourceVersion in the Group of ConceptMap (Figure 4).