esa-sceva/satcom-chunk-collection huggingface dataset:
# Interclass Interference Suppression in Multi-Class Problems
## 4 Design of Interclass Interference Suppression Algorithms
### Multi-Classifier Unified Collaborative Decision Algorithm  
Algorithms for constructing binary classifiers based on OVA and OVO have been elaborated in Algorithm 1 and Algorithm 2. In order to classify new instances of multi-class problems, it is necessary to design a collaborative decision algorithm for these binary classifiers. Algorithm 3 gives a unified classifier collaborative decision algorithm. In Algorithm 3, the classification decision scoring indexes of each classifier can be classified in terms of support coefficients, certain coefficients and classification decision results. Different scoring indicators are chosen to get different collaborative decision-making strategies. It is necessary to explain that, for the binary classifiers based on OVA, the decision-making algorithm can make decisions based on either the classification decision score of each category or the negative class of each category. Meanwhile, we can still use the unified storage structure and algorithm described in Figure 1 to classify decision; the difference is that the classification decision scoring index value needs to be negative, and the algorithm is based on the absolute minimum principle of categorical decision scoring of negative categories to classify new model instances. The classification decision based on each class of negative classes is usually based on the majority of the class rules, so the rules usually have good statistical properties.  
```
Input: two types of classifiers for M class problems (\(i\)Redu, \(i\)Ruleset and \(iSupp\)) and new instance \(x\) Output: the final classification result of \(x\) Begin for the \(i\) classifier in all binary classifiers do
1. According to the relevant classification decision method, the classifier is used to classify \(x\);
2. According to the classification decision scoring index, the classification decision score of the classifier is stored in the corresponding classification decision of the unified structure of Figure 1 through the accumulative method. end[for] Selecting the largest category of cumulative classification decision score from the structure as the final classification result of \(x\) End
```  
**Algorithm 3** Multi-classifier unified collaborative decision algorithm.  
The binary-classifier-based approach decomposes multi-class problems into several binary classification problems. The OVO strategy needs M(M-1)/2 binary classifiers, and the OVA strategy needs M classifiers. The classification of a new instance is made by voting of these binary classifiers. The OVO strategy has three voting methods, including classification results, certain coefficient and support coefficient of each binary classifier. The OVA method has one method besides the above three methods, namely voting based on the support coefficient of negative classes of each binary classifier. The consumed score of each class is stored in the structure shown in Figure 1 after selecting one voting method. The final classification decision is made in terms of the class with the highest consumed score.
## 2 System Model and Problem Formulation
### System Model
The channel capacities, i.e., the spectrum efficiency of the CU \(m\) and the D2D pair \(k\), can be respectively expressed as  
\[R_{k,m}^{C}(p_{k,m}^{D})=\log_{2}\Bigg{(}1+\frac{p_{m}^{C}h_{m,B}^{C }}{p_{k,m}^{D}h_{k,B}^{D}+\sigma_{0}^{2}}\Bigg{)} \tag{4}\]  
\[R_{k,m}^{D}(p_{k,m}^{D})=\log_{2}\Bigg{(}1+\frac{p_{k,m}^{D}h_{k}^{DD}}{p_{m}^ {C}h_{k,m}^{C}+\sigma_{0}^{2}}\Bigg{)} \tag{5}\]  
where \(p_{m}^{C}\) and \(p_{k,m}^{D}\) are the transmission power of the CU \(m\) and D2D-Tx \(k\) on the \(m\)th subcarrier, respectively. Similarly, the spectrum efficiency of CU \(m\) without resource sharing can be expressed as  
\[R_{m}^{C}=\log_{2}\Bigg{(}1+\frac{p_{m}^{C}h_{m,B}^{CB}}{\sigma_{0}^{2}}\Bigg{)} \tag{6}\]  
By observing Equations (4) and (6), it is found that there is a decrement of the spectrum efficiency at CU \(m\), which is caused by the interference of the transmitter of D2D pair \(k\). Thus, we have the spectrum efficiency decrement of the CU \(m\), i.e.,  
\[\Delta R_{m}^{C}(p_{k,m}^{D})=R_{m}^{C}-R_{k,m}^{C}(p_{k,m}^{D}) \tag{7}\]  
Figure 1: System model of D2D multicast groups and CUs. Therefore, the spectrum efficiency gain after D2D pair \(k\) reuses the subcarrier of CU \(m\) is  
\[ \begin{array}{ll}\Delta R_{k,m}^{D}(p_{k,m}^{D})&=R_{k,m}^{D}(p_{k,m}^{D})- \Delta R_{m}^{C}(p_{k,m}^{D})\\ &=R_{k,m}^{D}(p_{k,m}^{D})+R_{k,m}^{C}(p_{k,m}^{D})-R_{m}^{C}\end{array}  \tag{8}\]  
For the resource sharing between D2D pair \(k\) and CU \(m\), it may bring the network additional benefit in signal-to-interference-plus-noise ratio (SINR); it also causes a loss of cellular system for the interference. We introduce the system positive gain and the system negative gain to express the benefit and the loss as \(G_{k,m}^{P}\) and \(G_{k,m}^{N}\), respectively. Thus, we can have  
\[G_{k,m}^{P}(p_{k,m}^{D})\triangleq\frac{p_{k,m}^{D}h_{k}^{DD}}{p_{m}^{C}h_{k,m }^{CD}+\sigma_{0}^{2}} \tag{9}\]  
\[G_{k,m}^{N}(p_{k,m}^{D})\triangleq\frac{p_{k,m}^{D}h_{k}^{D}+\sigma_{0}^{2}}{ \sigma_{0}^{2}} \tag{10}\]  
Therefore, upon achieving a positive system gain in resource allocation, we can have  
\[G_{k,m}^{P}(p_{k,m}^{D})\geq G_{k,m}^{N}(p_{k,m}^{D}) \tag{11}\]
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### Status Monitor Module  
The _Status Monitor module_ is responsible for monitoring and collecting data such as the network and the workload characteristics status of each VM and sending data to the _VF schedule module_ for scheduling decision during each sampling period. The sampling frequency is set based on minimizing the performance loss caused by frequent read and write files.  
Our goal is to allocate VF to network-intensive VMs. If we only collect the I/O characteristics of VMs, we can only distinguish between I/O-intensive VMs, but we cannot know whether the VM is network I/O-intensive or otherwise I/O-intensive (e.g., disk I/O-intensive). On the contrary, if only the network data of VMs are collected, the network-intensive VMs can only be distinguished, and it is impossible to get whether the VMs are pure network-intensive or hybrid-intensive (for example, both  
Figure 5: Virtual ACPI Hotplug workflow.  
CPU and network-intensive). We tend to assign VF to pure network-intensive VMs, so _Status Monitor module_ needs to acquire the I/O characteristics and network data of VMs.
# Contrast Analysis of Polarization in Three-Beam Interference Lithography
## 2 Contrast in Double-Beam Non-Coplanar Interference Patterns
As shown in Figure 4a,b, when the polarization angle deviation of the two beams reaches \(5^{\circ}\), the greater the incident angle, the greater the loss of contrast. The incident angle, azimuth, and polarization for non-coplanar interference contrast were analyzed in turn. As shown in Figure 5, considering all factors at a time, we obtained the contrast of two-beam non-coplanar interference at different incident angles, azimuth angles, and polarization angle deviations. The Figure shows that the polarization orientation must be adjusted to reduce the contrast loss caused by the increase in the incident angle. The maximum co-directional polarization component mode is not suitable for large-angle incident situations.
# Artificial Intelligence-Based Discontinuous Reception for Energy Saving in 5G Networks
## 5 Conclusions  
In this work, we have suggested an AI-based DRX mechanism for energy saving in multiple beams communications scenario. We have modeled DRX as a ten-state model and suggested AI-DRX algorithm depending on these 10 states. AI-DRX algorithm enables dynamic short and long sleep cycles for energy efficiency of UE in the 5G network. We have trained LSTM network, a popular type of RNN, to extract the packet arrival time pattern from real wireless traffic traces. Later, we have utilized the learned model in AI-DRX algorithm for energy saving in 5G enabled devices. AI-DRX economizes power consumption of a UE by enabling dynamic short and long sleep cycles. Extensive training with selected hyper-parameters achieves the least RMSE of 5 ms on a random test set from trace 1 and 6 ms on a random test set from trace 2, respectively. The energy efficiency obtained with AI-DRX is approximately 60% and 95% for trace 1 and trace 2, respectively. AI-DRX achieves 69% higher energy efficiency on trace 1 and 55% more energy efficiency on trace 2 as compared to LTE-DRX, respectively. We also validated the performance of AI-DRX with traditional Poisson packet arrival model. AI-DRX attains 70% more energy efficiency on trace 2 as compared to Poisson packet arrival rate for \(\lambda=1/20\).  
Conceptualization, M.L.M. and M.K.M.; methodology, A.R.; investigation, M.L.M. and M.K.M.; resources, M.L.M.; data curation, M.L.M.; writing--original draft preparation, M.L.M. and M.K.M.; writing--review and editing, N.S. and A.R.; supervision, N.S., A.R. and D.R.S.; project administration, D.R.S.; funding acquisition, D.R.S. This research received no external funding. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B03935633).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Qos Class Identifier** & **Packet Loss Rate** & **Packet Delay Budget** & **Examples** \\ \hline GBR & 1 & \(10^{-2}\) & \(100\) ms & Voice services \\ \hline GBR & 2 & \(10^{-3}\) & \(150\) ms & Live streaming services \\ \hline GBR & 3 & \(10^{-3}\) & \(50\) ms & Real time gaming services \\ \hline GBR & 4 & \(10^{-6}\) & \(300\) ms & Buffered streaming services \\ \hline Non-GBR & 5 & \(10^{-6}\) & \(100\) ms & IMS Signaling services \\ \hline Non-GBR & 6 & \(10^{-6}\) & \(300\) ms & TCP based application services \\ \hline Non-GBR & 7 & \(10^{-3}\) & \(100\) ms & Interactive gaming services \\ \hline Non-GBR & 8 & \(10^{-3}\) & \(300\) ms & TCP based video services \\ \hline Non-GBR & 9 & \(10^{-6}\) & \(300\) ms & TCP based video services \\ \hline \hline \end{tabular}
\end{table}
Table 2: Standardized QoS class identifier (QCI) characteristics in LTE/LTE-A [6; 30; 31].
## 1 Introduction
The tolerance ranges of some BAs have been suggested: histamine, 50-100 mg/kg; tyramine, 100-800 mg/kg; and 2-phenylethylamine, 30 mg/kg** **[**27; 28**]****. Presently, for putrescine and cadaverine, no toxic concentrations were available** **[**29**]****. However, spermine and spermidine increases the toxicity of histamine and tyramine** **[**30**]****. For this reason, analysis of the BAs of the prepared sourdoughs in this study was included. **In addition, the fatty acid (FA) profile of the prepared sourdoughs was analyzed. It was reported that hydroxy FAs produced by sourdough LAB exhibit antifungal activity [31], which is related to the increase in the fungal membrane permeability [31]. Also, when sourdough is prepared from cereal bran and/or WW, both the antifungal and mycotoxin degradation properties of the used LAB starters for sourdough preparation are very important. Despite the fact that aflat B1, B2, G1, and G2 are the most important known carcinogenic mycotoxins [16], in addition to regulated mycotoxins, emerging and masking mycotoxins should be controlled. Emerging toxins are a group of mycotoxins for which no regulations exist at the present [32]. The clinical observations in animals in some cases of mycotoxicosis did not correlate with the low mycotoxin content determined in the corresponding feed [33]. The unexpectedly high toxicity has been attributed to undetected conjugated forms of mycotoxins that were possibly hydrolyzed in vivo [32]. Our previous studies showed that cereal pre-treatment by using fermentation with selected LAB strains could lead to the better detoxication of some mycotoxins in vivo [34]. Finally, biocontrol of mycotoxins by LAB could be an important factor to reduce their risks during food fermentation. The aim of this study was to analyze and compare the acidity, microbiological, and chromaticity parameters; fatty acid (FA) and volatile compound (VC) profiles; and biogenic amine (BA), macro- and microelement, and mycotoxin concentrations in nontreated and fermented (with antimicrobial/antifungal properties possessing LAB strains, isolated from spontaneous sourdough: _Pediococcus acidilactici_-LUHS29, _Liquorilactobacillus vacuum_-LUHS245, _Lactiplantiobacillus plantarum_-LUHS122) 'Ada', 'Sarta', and new breed blue (DS8472-5) and purple (DS8526-2) wheat lines WW. Also, this study will contribute to the creation of the database of the grain chemical composition of the new breed wheat lines.
## 5 Results
### Discussion  
In summary, we present the results obtained by our proposal in the KITTI data set and in the NYU_V2 data set in Tables 10 and 11, respectively.  
In Table 10, in the second row, we show the results obtained by our proposal in the KITTI data set. We obtained an \(MSE=1.1269\) in the KITTI data set, outperforming our previous published version of the model in [1] (\(MSE=1.1397\)) and other similar contemporaneous models [13]. In Table 11, in the NYU_V2 data set, we obtained \(MSE\)s of 8.31, 9.35, and 17.56 with an upsampling factor of \(4\times\), \(8\times\), and \(16\times\), respectively. Our results outperform many contemporaneous models, such as [28] and [10].  
Last but not least, we would like to mention the main limitations of our model and our evaluation:  
1. The model struggled to accurately fill in objects with weak edges in the reference image. In the case of objects with weak edges, the model failed to accurately propagate the depth information correctly, causing data from different objects to merge. A possible solution to tackle this problem could be to use a semantic segmentation of the reference color image and use superpixel boundaries in order to stop diffusion.
2. The model faced challenges when dealing with transparent and reflective objects. We will tackle this problem in our future work.
3. Performance was evaluated using only two data sets. We consider that having only two data sets is insufficient to generalize our conclusions. Therefore, we plan to extend the evaluation to more data sets. In any case, the preliminary results are promising, particularly in using this technique in real-world scenarios and applications such as 3D reconstruction of an urban scene. An example of the depth completion for an urban scene is shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MSE** & **MAE** \\ \hline Anisotropic Metric & 1.1269 & 0.3107 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Best results obtained in KITTI data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **MSE** & **MSE** \\  & \(\times\)**4** & \(\times\)8 & \(\times\)**16** \\ \hline Anisotropic Metric & 8.31 & 9.38 & 17.56 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Best results obtained in NYU_V2 data.
## 3 Results
### Artificial Ageing Protocol (AAP)
#### 3.1.2 Fourier Transform Infrared Spectroscopy (FTIR)
A reduction in the aforementioned functional groups can be attributed to the partial evaporation of the organic medium, resulting from the decomposition of triglycerides of unsaturated fatty acids and the formation of volatile compounds induced by UV irradiation [36]. In other samples, an increase is observed in the 1750-1700 cm\({}^{-1}\) region, as seen in P1 aCa(C), P2 vCa(B), P2 vCa(C), and P1 Ga(C). This may be attributed to the formation of carbonyl groups, specifically ketones, esters, and carboxylic acids, during oxidative polymerization. The formation of ester bonds, in particular, plays a crucial role in the oxidative polymerization processes of lipid-based materials such as linseed oil [42]. In addition, new functional groups related to the hydroxyl group have been identified, 3550 cm\({}^{-1}\) and 3600 cm  
Figure 7: Comparative ageing of polychrome plaster samples with Arabic gum and different pigments. (**a**) (P1 aGa(A) and P1 aGa(C) before and after ageing; (**b**) P1 aGa(B) before and after ageing compared with azurite pigment; (**c**) P2 vGa(A), P2 vGa(B), and P2 vGa(C) before ageing; and P2 vGa(A), P2 vGa(B), and P2 vGa(C) after ageing. (P1 aCa(C) aged, 3600 cm\({}^{-1}\) (P1 aGa(C), 3620 cm\({}^{-1}\) (P2v Ga(C), 3490.1 cm\({}^{-1}\) (P1 aGa(C), and 3423 cm\({}^{-1}\) (P1 aGa(B), arising as a result of the oxidation and polymerization processes characteristic of linseed oil ageing (Figures 6 and 7) [36; 43]. These chemical transformations were particularly pronounced in samples containing ultramarine blue and titanium white, whereas ochre-based samples demonstrated greater stability under the same ageing conditions. These groups of the Arabic gum binder and animal glue binder have been identified in a real polychrome of plasterworks, with examples such as those shown in Figure 8 from the Oratory of the Madraza. In the case of the Arabic gum binder, the components identified are the hydroxyl group at 3432 cm\({}^{-1}\), attributed to C=C conjugated double bond stretching at 1419.76 cm\({}^{-1}\), and the carbonyl group at 1031 cm\({}^{-1}\) at 988 cm\({}^{-1}\) (Figure 8a). An animal glue binder is possible to identify the characteristic groups of amide I at 1618.54 cm\({}^{-1}\) and amide II at 1543.90 cm\({}^{-1}\) (Figure 8b). The last binder analyzed, linseed oil, is also possible to identify in a real repolychrome of plasterworks from the Oratory of the Madraza and the Courtyard of the Madiens at the Royal Alcazar of Seville, mixed with artificial ultramarine blue pigment. As seen in the studied cases, the hydroxyl group has been identified due to oxidation and polymerization at 3522.84 cm\({}^{-1}\) and 3400.35 cm\({}^{-1}\), the characteristic band of oil referent to the carbonyl band at 1683 cm\({}^{-1}\) (Figure 8c).
## 5 Conclusions  
The LN\({}_{2}\) fracturing method was proposed as an alternative to conventional hydraulic fracturing, pushing for eco-acceptable solutions. This research investigated the efficacy of LN\({}_{2}\) in coal fracturing, focusing on two different processes: freezing time (FT) and freezing-thawing cycle (FTC), using image processing methods to characterize treatment effects. An image processing workflow was generated to identify and characterize existing, new, and augmented fractures. The findings of the study revealed the following:  
* The highest change in total crack area, \(\delta_{\text{TCA}}\), was achieved for the freezing-thawing process with four or five cycles of treatment, FTC4, and FTC5, while the maximum total crack area was observed with a freezing time of 150 min, FT150.
* For FT processes, the highest total number of cracks, TNC, after LN\({}_{2}\) treatment was achieved for FT60 (173 cracks), while the lowest was for FT90 (7 cracks). The highest TNC was achieved in FTC2 (61 cracks) for FTC processes, while the lowest was for FTC4 (17 cracks).
* The highest value of crack density, CD, was achieved in FT60 (300 m\({}^{-1}\)), while the lowest value of CD was found in FT90 (31.25 m\({}^{-1}\)) after LN\({}_{2}\) treatment.
* The process FTC4 increased the number of cracks by 50%, whereas FTC5 tripled the number of small cracks and formed several cracks of length. This fact leads to the creation of a fracture network, which is the desired outcome.
* The change in Total Crack Area (\(\delta_{\text{TCA}}\)) increased progressively with both freezing time and the number of cycles.  
* While crack density was strongly affected by all treatments, the change in crack density (\(\delta_{\text{CD}}\)), generally, was not dependent on freezing time, but it increased dramatically after several freezing-thawing cycles. Overall, cracks elongate along with an overall increase in density.
* Prolonged freezing time shows benefits over shorter freezing time cycles until a threshold number of cycles is achieved when the physical changes in samples dramatically alter. The studies in crack characteristics infer permeability enhancement, yet this must be confirmed in follow-on tests on effectiveness.  
Conceptualization, S.N.L.; methodology, S.N.L.; software, A.B. and P.S.; validation, S.N.L. and R.H.; formal analysis, S.N.L.; investigation, S.N.L.; resources, S.N.L.; data curation, S.N.L.; writing--original draft preparation, S.N.L.; writing--review and editing, P.S., A.H.A. and R.H.; visualization, S.N.L.; supervision, S.N.L. and R.H.; project administration, R.H.; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Nazarbayev University, grant number \"021220 CRP2022\", and the APC was funded by Nazarbayev University.  
Not applicable.  
The authors are thankful to Nazarbayev University, Kazakhstan, for financial support in the form of a Collaborative Research Program grant: 021220 CRP2022.  
The authors declare no conflict of interest.
## 3 Results
### Comparison of Models
#### 3.1.4 Ablation Study  
To better understand the impact of each module of the proposed model, an ablation study was performed, wherein the model was systematically retrained with one module disabled for each of the modules incorporated in the model. The testing performance of the model with each module removed is shown in Table 10, using the Sentinel-1 images.  
From these results it can be observed that the deep supervision and weighting had the most modest impact on performance, with the model achieving an IOU of 0.5714 and 0.5706, respectively, where the proposed model achieved 0.5862. Conversely, the spatial pyramid pooling and dense skip connections had the most significant influence on the performance of the model, resulting in a reduction of the IOU to 0.5291 and 0.5361, respectively. The atrous convolution had a moderate impact on the performance, where its removal resulted in an IOU of 0.5480.