esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
This section compares and discusses the performance of deep and traditional classifiers based on accuracy as commonly used performance metrics. Figure 2 shows the comparison of the top three best performing deep learning and conventional supervised classifiers trained on features extracted with AlexNet and ResNet-18, separately. Deep learning classification outperforms traditional machine learning, with InceptionV3 achieving the highest performance. GoogleNet and MobileNetV2 achieve approximately the same accuracy, i.e., 98.40% and 98.61%, respectively. Among the conventional supervised classifiers, K-NNand SVM share the same best performance of about 95%, similar to LDA in second place with 92%.  
The performance analysis of InceptionV3 and AlexNet can be committed using the confusion matrices shown in Figure 3a,b to show overall performance and performance by class. InceptionV3 achieved an overall highest accuracy of 99.47% among five tested deep classifiers, while AlexNet achieved 97.86%. InceptionV3 perfectly classified all images in the Joy and Anger classes, while AlexNet misclassified several images. In addition to the confusion matrix of the best-performing network, it is interesting to observe the confusion matrix of the deep model with low performance.  
Indeed, from the confusion matrix, we can see which classes were misclassified into other classes, and whether these classes share a common element that can motivate the errors, or if the error is obvious when training the network. In this case, we can observe that AlexNet cannot recognize the emoji pictogram features as easily as other networks. If some errors depend on the examples, such as the emotion Angry being mistaken for Sadness or Disgust, where the associated expressions share a downward direction of the lips, or in other cases, such as Joy being mistaken for Sadness, Fear and Angry, then an error has occurred in training the network. The final result is highly accurate, but the individual errors are more critical than those of InceptionV3. In the latter, Joy, the emotional class that  
Figure 3: Confusion Matrices of AlexNet and InceptionV3 deep models (**a**) on the left, confusion matrix of AlexNet achieved an accuracy 97.86% (**b**) on the right, confusion matrix of best performing deep model (InceptionV3) achieved 99.47% accuracy for emoji classification.  
Figure 2: Comparison of three best performing deep and conventional supervised classifiers (in parentheses the networks used for feature extraction).  
is also easier to detect in facial recognition, has no error. The errors are evident in the Fear class, which are mistaken for Sadness or Disgust since they share similar graphical features in emojis. Sadness is recognized once as anger, surprise once as fear, with the large open mouth being a common element. Only in the case of disgust, which is mistaken for joy, is a training issue evident. The results obtained with the InceptionV3 model are superior to the other tested classifiers in this study.
## 1 Introduction
Hence, we decided to focus on novel mastitis traits in the present genomic study, including specific mastitis pathogens and differential somatic cell fractions comprising lymphocytes (LYM), macrophages (MAC), polymorphonuclear leucocytes (PMN), and their subgroups of PMN segmented neutrophils (sN) and banded neutrophils (bN), and considered these for detailed phenotypic analyses in free walk housing systems [23]. In a previous GWAS without farming interaction effect, dense SNP marker panels were used to infer possible candidate genes playing a role in defense mechanisms against pathogens and mastitis resistance [11]. Possible candidate genes or quantitative trait loci (QTL) and SNPs for clinical mastitis were detected on nearly all chromosomes [11; 12], again raising the question of proper mastitis trait definitions. Nevertheless, in gene annotations, detected genes such as _LY6K, LY6D, LYNX1, LYPD2, SLURP1_, and _PSCA_ are part of the lymphocyte antigen 6 complex and influence the regulation of PMN [12]. Further detected genes, e.g., _LY75, NR4A2_, and _ITGB6_, are involved in regulating immune response or were regulated during the presence of specific pathogens [12]. [PERSON] et al. [22] identified _RASGRP1_ as a potential candidate gene based on differential gene expression studies, considering different levels of pathogen burden. The aim of the present study was to identify significant main and interaction effects for specific mastitis pathogens and differential somatic cell counts in conventional cubic and compost-bedded farming systems. Significant SNP markers were annotated with potential candidate genes with the aim of improving mastitis resistance in specific farming systems, taking GxE into account. In this regard, our own software package, allowing GWAS with SNP by housing system effects, was applied.
## 5 Conclusions
The DAS system in combination with the MA-MD and FDDA filters can be used to train and operate the scheduling algorithm based on learning automation for Internet of Things (SALA-IoT) [89]. We believe that this will speed up training, and at the operational stage, it could simplify the sensor's design. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Event Frequency, kHz** & **Event Type** & **Most Efficient Technique** & **SNR Increase, dB** \\ \hline
10 & Pointwise & MD+FDDA & 5.2 \\ \hline
3 & Pointwise with harmonics & FDDA & 6.6 \\ \hline
2 & Distributed & SA+FDDA & 9.7 \\ \hline
6.5 & Distributed & SA+FDDA & 13.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the considered noise reduction algorithms comparison. **Author Contributions:** A.A.F., D.A.K., I.O.Z. and C.A.L.-M.: setup arrangement, programming of MA and MD methods, discussions, writing; A.T.T. : setup arrangement, programming of FDDA method, paper arrangement, writing, drawings, editing of all parts; Y.A.K. and F.L.B. : idea, conception, project administration, editing of all parts. All authors have read and agreed to the published version of the manuscript. **Funding:** Sections 1 and 4 were performed as a part of state assignment No. 122031100058-3; Section 2.1 is supported by the Ministry of Science and Higher Education of the Russian Federation, grant number 075-15-2021-581 (experiment and data interpretation) and the Russian Science Foundation, grant number 23-79-30017 (master laser design and application); Sections 2.2 and 3 were performed as a part of state assignment No. AAAA-A19-119042590085-2; A.A.F. is supported by the European Union's Horizon 2020 research and innovation program (Individual Fellowship, H2020-MSCA-IF-2020, #101028712). **Data Availability Statement:** Not applicable. **Acknowledgments:** We are grateful to [PERSON] for fruitful discussions. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results  
### Phytochemical Investigation of H. _hirsuta_  
The dried powder of \(H\). _hirsuta_ (4 kg) was extracted with MeOH (10 L \(\times\) 3 times) overnight at room temperature. Evaporation of the solvent under reduced pressure formed a tarry MeOH residue (370 g). The MeOH residue was suspended in H\({}_{2}\)O and successively partitioned with \(n\)-hexane, CH\({}_{2}\)Cl\({}_{2}\), EtOAc to yield an H (220 g), D (37 g), EtOAc (26 g), and water layer (52 g), respectively. Using combined chromatography separation techniques (Silica gel, RP-18, and Sephadex(tm) LH-20), 16 known compounds (1-16), namely 4,5-dihydrolumenol A (1), (+)-springaresinol (2), indole-3-carboxaldehyde (3), syringanesinol-\(\beta\)-D-glucose (4), acariol A2 (5), cucumegastigmane I (6), methyl rosmarinate (7), 2-O-\(\beta\)-D-glucopyranosyl methyl salicylate (8), dihydrohphaseic acid (9), benzyl 2-O-\(\beta\)-D-glucopyranosyl-2,6-dihydroxybenzoate (10), kaempferol-3-\(\beta\)-D-(6-\(O\)-_trans-p_-coumaroyl)glucopyranoside (11), byzantinoside B (12), citrusin C (13), 20-hydroxycdsone (14), siringin (15), and 3,4\({}^{\prime}\),7,8-tetrahydroxyflavone (16) were isolated from the aerial part of the \(H\). _hirsuta_ (the detailed isolation scheme and chemical structures are shown in Scheme 1). Their structures were determined using the modern spectroscopic method (1D and 2D NMR) and by correlating previously reported data. The results demonstrated that the MeOH extract of \(H\). _hirsuta_ contained many bioactive compounds, including alkaloids, steroids, lignans, and phenolics.
## 5 Performance Evaluation
### Performance Results
#### 5.3.1 User-Level Benchmark
Although the performance impact varies depending on the synchronization mechanism, all schemes show the NUMA boundary effect. As shown in Figure 5d-f, when threads are allocated across NUMA nodes 0 and 1, the performance degrades or is saturated. When execution threads are spread across multiple NUMA nodes, the cost of accessing data structures and synchronizing caches increases. Since the chained hash table has a lock for each bucket, the simultaneous operation of different buckets is possible without synchronization. The critical section is relatively short because each bucket has only a few linked nodes. Due to the parallelism of each bucket, the performance of the chained hash table of RCU is better than that of the linked list. When accessing a data structure that shares a lock, the write threads of RCU are executed sequentially. Since all write threads share one spinlock in a linked list, the higher the update ratio, the worse the performance. On the other hand, the chained hash table spreads out the competition for the spinlock because each bucket is synchronized with the spinlock, so the performance degradation is not as significant as with the linked list. Due to the small number of chained nodes per bucket, the hash table's critical section is relatively short. As a result, the load of rcu_synchronize, which is a blocking wait API for releasing memory of RCU, is reduced. In the case of MV-RLU, the version traversal cost required for node traversal is reduced due to the shortened critical section. In the case of RLU, the write operation necessitates a blocking wait for the Grace Period and Write Back, as shown in the Figure 2b. Therefore, RLU shows less or no performance improvement as the number of threads increases, as shown in Figure 5e,f. MV-RLU shows high performance at all update ratios because it improves the write throughput and solves the bottleneck of RLU, the blocking wait for log recycling through multiple version creation. The reason why MV-RLU's performance in the hash table is better than that of RLU is that the critical section is significantly reduced compared to the linked list, so the version traversal cost due to node traversal is greatly reduced.
## 4 Results and Discussion  
### Synthetic Topology Dataset Generation  
To investigate the dependence of wavelength usage on network topological parameters and conduct statistical analysis, it is necessary to generate a large set of synthetic network topologies. This can be achieved using random graphs designed to model the characteristics of real-world optical networks. In this work, we use the \(k\)-neighbors model described in [9] for this purpose. In a simplified way, this model works by randomly placing \(N\) network nodes in a 2D space with area \(A_{k}\) and minimum distance \(d_{min}\) between them. The nodes are then interconnecting with their nearest neighbors based on Euclidian distance. First, each node connects to \(k_{0}\) nearest neighbors to form an initial graph. A minimum spanning tree is then created to ensure all nodes are connected. Next, the model continues to add links randomly between the \(k\)-nearest neighbors of each node until the maximum average node degree \(<\delta>_{max}\) is reached. During the interconnection process, it must also be ensured that the resulting graph is biconnected to provide a minimum degree of resilience to failures. Specifically, the networks were generated in a 2D space with area \(A_{k}=N\times 10^{4}\) km\({}^{2}\), with \(N\) ranging from 7 to 50, and \(<\delta>\) varying from 2 to 5. The \(k\)-neighbors model had the following parameters: \(k_{0}=4\) and \(k=8\), and \(d_{min}\) was equal to 25 km.
## 3 Selected MPC Algorithms
### Improved Model Predictive Control Algorithm (Impc)  
Using the conventional CF in (15) results in satisfactory performance in the case of the first-order system. Moreover, the controlled variable can be directly regulated by the control input, allowing an instantaneous change of its derivative at a particular sampling instant. For second-order systems, the coupling between the state variables makes the performance somehow unstable and introduces a high amount of ripples in the controlled variable. Capacitor voltage, as shown in Figure 3a, which is used in this work, can only be regulated indirectly through the inductor current. Therefore, by involving only the capacitor voltage error as given in conventional CF, which is described in (15), will result in a voltage trajectory \(V^{p}\) pointing significantly away from the reference trajectory \(V^{*}\). High voltage deviations increase the error between the predicted and the reference voltages resulting in a high THDv. In order to improve the capacitor voltage quality of the 2L-VSC, the regulator should track both the voltage reference and its derivative as described mathematically in the improved CF in (16). Further details can be found in [14]. The first two terms already accomplish this the conventional way in Equation (16). Noting that, the term \(g_{I}\) ensures that the voltage derivative tracks the reference and the objective importance is controlled by \(\lambda_{d}\). Hence, the considered CF, as described in (16), can predict the voltage and control the current resulting in lower THDv.  
\[g=(V^{*}_{c/\alpha}-V^{p}_{c/\alpha}(k+1))^{2}+(V^{*}_{c/\beta}-V^{p}_{c/ \beta}(k+1))^{2}+(\lambda_{d}*g_{I}) \tag{16}\]  
\[g_{I}=(i_{L/\alpha}-i_{\alpha\alpha}+C_{f}\omega_{ref}(V^{*}_{c/\beta}))^{2}+( i_{L/\beta}-i_{\beta\beta}+C_{f}\omega_{ref}(V^{*}_{c/\alpha}))^{2}. \tag{17}\]
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 3 Propagation Models
### 3 GPP Indoor Hotspot (InH) Model
#### 3.3.2 Mixed Indoor  
\[Pr_{LoS}=\begin{cases}1&d_{2D}\leq 1.2m\\ e^{-\frac{d_{3D}-12}{4D}}&1.2m\geq d_{2D}\leq 6.5m\\ 0.32e^{-\frac{d_{2D}-65}{32.6}}&d_{2D}>6.5m\end{cases} \tag{13}\]  
Thus, according to [52], at any given distance, the path loss along with shadowing in an indoor environment \(PL_{InH,dB}\) under 3 GPP InH model is given by Equation (14)  
\[PL_{InH,dB}=Pr_{LoS}\cdot PL_{LoS,AB}+(1-Pr_{LoS})\cdot PL_{NLoS,AB} \tag{14}\]  
Figure 3 shows the path loss that is acquired through 3 GPP InH model at 500 MHz in an open and mixed type indoor environment, and the path loss that is acquired through free space path loss (FSPL) model is also shown in Figure 3 for reference. The pseudocode of the ITU outdoor D2D propagation model is given as Algorithm 3.  
Figure 3: Path loss with FSPL and 3 GPP InH model at 500 MHz.  
```
Input:\(d_{3D,m}\), \(d_{2D}\), \(f_{c,GHz}\), \(\xi_{LoS,dB}\), \(\xi_{NLoS,dB}\), Environment type Output:\(PL_{InH,dB}\)
1:procedureComputation of path loss
2 Compute path loss in LoS condition \((PL_{LoS,dB})\) with given shadowing factor \((\xi_{LoS,dB})\) using Equation (10)
3 Compute path loss in NLoS condition \((PL_{NLoS,dB})\) with given shadowing factor \((\xi_{NLoS,dB})\) using Equation (11)
4 ifEnvironment type is \"Open indoor\"then
5 Compute LoS probability \((Pr_{LoS})\) as a function of 2D distance \((d_{2D})\) using Equation (12)
6 elseifEnvironment type is \"Mixed indoor\"then
7 Compute LoS probability \((Pr_{LoS})\) as a function of 2D distance \((d_{2D})\) using Equation (13)
8 Finally, the path loss \((PL_{InH,dB})\) is computed using Equation (14)
9 return\(PL_{InH,dB}\)
```  
**Algorithm 3**3 GPP Indoor Hotspot (InH) propagation model
# New Candidate Multicomponent Chalcogenide Glasses for Supercontinuum Generation
###### Abstract  
Broadband supercontinuum (SC) generation requires host material attributes defined by both optical and physical properties and the material's manufacturability. We review and define the trade-offs in these attributes as applied to fiber or planar film applications based on homogeneous glass property data, and provide a series of examples of how one might optimize such attributes through material compositional and morphology design. As an example, we highlight the role of varying composition, microstructure, and linear/nonlinear optical properties, such as transmittance, refractive index, and the multiphoton absorption coefficient, for a series of novel multicomponent chalcogenide glasses within a model GeSe\({}_{2}\)-As\({}_{2}\)Se\({}_{3}\)-PbSe (GAP-Se) system. We report key optical property variation as a function of composition and form, and discuss how such glasses, suitable for both fiber and planar film processing, could lend themselves as candidates for use in SC generation. We demonstrate the impact of starting glass composition and morphology and illustrate how tailoring composition and form (bulk versus film) leads to significant variation in linear, nonlinear, and dispersive optical property behavior within this system that enables design options that are attractive to optimization of desirable SC performance, based on optical composites.  
Keywords:photonics; supercontinuum generation; nonlinear optics; infrared optical materials; chalcogenide glass science +
Footnote †: journal: Applied Physics Letters
## 4 Results and Discussion
### Future Directions of ML and MM Approaches in Predicting GWL Changes  
Although the use of machine learning and mathematical model techniques has expanded since 2016, machine learning accounts for the majority of primary publications. Between 2000 and 2009, the majority of the MM techniques were employed to forecast groundwater level fluctuations. Global climate change has already had visible consequences on the environment, and groundwater, which serves as a supply of drinking water for humans, remains a key worry. Challenges and issues of climate change continue to evolve. Today, the world strives for technology, which has a massive impact. Machine learning and mathematical model techniques are making life easier since they learn from previous events and forecast future ones. Researchers employed machine learning algorithms more frequently than mathematical models in the primary publications included in this systematic review. Figure 8 shows that ML accounted for 53 of the 117 articles, whereas MM accounted for 46. It has been discovered that MM approaches for predicting groundwater level changes have been used less frequently in recent years. Machine learning has surpassed mathematical models, and the random forest (RF) algorithm is the most commonly used algorithm for predicting groundwater level fluctuations, according to Table 3. Some algorithms in Table 3 were classed as \"other\", and they appeared 20 times in the sampled data. These algorithms were found to be utilized in fewer than three articles. Decision tree (DT) and multiple linear regression (MLR) are examples of the \"other\" algorithms. To predict groundwater level changes, researchers are turning to newer, maybe more inventive methodologies. As a result, future research may employ unique methodologies such as hybrid and ensemble models. Furthermore, researchers may again use hybrid mathematical models, combining new models with old mathematical models. Nonetheless, it should be noted that academics are worried about the requirement for models to minimize computational complexity, to be computationally efficient, and increase performance accuracy. Furthermore, based on some findings in our main research, increasing activism and education on the effects of climate change are essential.
## 2 Methodology  
### Task Description and Overview of Method  
The entity and entity relation extraction task from a Chinese EMR is to label all medical entity mentions \(E=\{e_{1},e_{2},\ldots,e_{n}\}\) and classify their types \(\{C(e_{1}),C(e_{2}),\ldots,C(e_{n})\}\) in the EMR text, where \(e_{i}(1\leq i\leq n)\) is in the form of character subsequence of a sentence and \(C(e_{i})\in\{\textit{Disease},\textit{Symptom and sign, Test, Abnormal test result, Treatment, Drug}\}\), and recognize all entity relations \(\Pi=\{\pi=(h,r,t)|h,t\in E,r\in R\}\) between the entities given relation class set \(R\), the definition of which is different in different datasets.  
The architecture of our proposed model is shown in Figure 1. We use the Transformer [27] to encode the input sentence and extract the sentence context features. The GAT is used to learn the correlation between each relation, combined with the sentence context features encoded by the transformer, in order to fully capture the semantic information, and to extract the relation. Then, we use the feature vector based on the specific relation to construct the particular sentence representation under each relation, and perform sequence labeling to extract the entity corresponding to the particular relation. Given a Chinese medical sentence \(S=\{c_{1},c_{2},\cdots,\ c_{n}\}\), and a predefined relation set \(R\), the task of joint entity and relation extraction is to extract relation triplets \(\{\pi=(h,r,t)|h,t\in E,r\in R\}\) from \(S\). The extracted triplets may share the same entity or relation, that is, the problem of overlapping triplets, where \(E\) and \(R\) represent the medical entity set and entity relation set, respectively, and \(h\), \(t\) represent the head entity and the tail entity, respectively.
## 4 Conclusions  
This study analyzed the factors that have a big effect on NOx generation based on a lab-scale DTF test. Based on our results, we developed a formula by which to predict NOx generation, applied it to an actual powerplant model, and compared the predicted and actual amounts of the generated NOx concentrations. Accordingly, we suggest the following.  
1. The moisture content in coal has a negative correlation with generated NOx concentration and a positive correlation to fixed carbon. In general, the moisture content and fixed carbon are inversely proportional to each other. It is believed that the reasons for this are the delay in  
Figure 5: Relation between NOx concentration at the DTF exit and calculated NOx concentration.  
Figure 6: Relation between real-scale boiler NOx concentration and calculated NOx concentration.  
ignition caused by combustion hindrance from the moisture when the coals are reacted in the DTF heating furnace.
2. CR and NOx conversion factor to fuel-N show an exponential correlation to FR. Therefore, we expressed this using the logarithm function in NOx in the prediction empirical formula to display a linear relation.
3. The suggested formula for NOx concentration was based on three factors--moisture, fixed carbon, and FR correlations. Having compared the calculated amount of NOx generation from the actual powerplant using four system indicator factors (coefficients) while considering the design and operating conditions of each boiler, except for the abnormal values that were much higher than the average NOx concentration values, the trends in the generation of NOx were determined.  
Conceptualization, G.-M.K., J.-W.J., D.-Y.K., S.-M.K., and C.-H.J. Data curation, G.-M.K., J.-W.J., J.-S.J., D.-Y.K., and S.-M.K. Formal analysis, G.-M.K. and J.-W.J. Investigation, G.-M.K., [PERSON]-S.J., D.-Y.K., and C.-H.J. Methodology, G.-M.K., J.-W.J., S.-M.K., and C.-H.J. Supervision, C.-H.J. Validation, G.-M.K. and J.-W.J. Visualization, G.-M.K. Writing--original draft, [PERSON]-M.K., J.-W.J., J.-S.J., and C.-H.J. Writing--review & editing.  
The authors declare no conflict of interest. Abbreviations  
NOx Oxides of nitrogen SOx Oxides of sulfur DTF Drop Tube Furnace GHGs Greenhouse Gases CO Carbon monoxide FR Fuel ratio HHV Higher Heating Value LHV Lower Heating Value VM Volatile Matter FC Fixed Carbon FN Fuel Nitrogen CR Conversion factor of Fuel-N to NOx CNOx NOx concentration Vdry Flow rate of dry air