esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
As a local improved technology, the local search is a practical tool and a common technique looking for the near-optimal solutions for combinatorial optimization problems [4]. In order to obtain high-quality solutions, it was applied to many metaheuristic algorithms, such as simulating annealing, genetic algorithm, memetic algorithm and swarm intelligent algorithms, in many cases.  
The local search is a common tool looking for near-optimal solutions in reasonable time for combinatorial optimization problems. Usually, the current solution is iteratively replaced by an improved solution from its neighborhood, until no better solution can be obtained. Then, the current solution is called _locally optimal_.  
The basic local search starts with a feasible solution, \(x\), and repeatedly replaces \(x\) with an improved one, \(x^{\prime}\), which is selected from the neighborhood of \(x\). If no better neighbor solutions can be found in its neighborhood, the local search immediately stops and returns as the final best solution found during the search [4].  
Since the basic local search is usually unable to obtain a good-quality solution and often far from the optimal solution, various improved local searches were proposed, e.g., tabu search [5; 6; 7], variable depth search [8; 9], variable neighborhood search [10; 11; 12], reactive local search [13; 14; 15; 16], \(k\)-opt local search (KLS) [4; 17], iterated local search [18], iterated \(k\)-opt local search [17] and phased local search [19].  
In many cases, the local search can be applied into heuristic algorithms, such as simulated annealing, ant colony and particle swarm optimizations. However, normally, it is hard to obtain the best solution, or even an approximate solution with high quality, for a certain combinatorial optimization problem. Therefore, various local search methods, such as phased local search [19] and variable depth local search, were proposed. The variable depth local search was initially used to solve the Graph Partitioning Problem (GPP) [9] and the Traveling Salesman Problem (TSP) [8]. Then, it was applied to other heuristic algorithms [20; 21; 22; 23]. In [4], it was applied to solve the maximum clique problem and successfully obtained good solutions.  
Different from the previous studies, the proposed local search algorithms are based on KLS and are combined with a depth variable search, which can improve the performance and efficiency.
# Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms
## Appendix B Model Architectures and Hyperparameters Used in the Experiments  
In the main paper, we compared the performances of TGN-Attn and DyRep with heuristic baselines. We used the implementation provided in the examples of the open-source TGB library [[https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred/tgbl-wiki](https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred/tgbl-wiki)]([https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred/tgbl-wiki](https://github.com/shenyangHuang/TGB/tree/main/examples/linkproppred/tgbl-wiki)), (accessed on 15 April 2024).  
The architecture of the TGN model is as follows:  
1. The _message function_ is the identification function (the same as in the original paper [21]).
2. The _aggregation function_ is the last message aggregator (we keep for each node only the last message received from the batch).
3. The _memory updater_ is a GRU.
4. The _embedding_ module is a temporal graph attention layer. Its purpose is to integrate both the network's connectivity information and temporal data, ensuring that embeddings remain current by combining the memory state with the most recent network information.
5. The _edge-level decoder_ (i.e., Link Predictor) is a two-layer MLP with 100 hidden units and ReLU activation.  
The DyRep model implemented in the library is very similar to the TGN architecture, with two main differences:  
* The _memory updater_ is a simple RNN.
* The _embedding_ module is the identity: the memory is used directly for prediction. Note that this makes the model vulnerable to the memory staleness problem.
* However, the _messages function_ is calculated using a graph attention module on the destination node.  
**Hyperparameters**. As the goal of our study is not to maximize the performance but to compare it across negative sampling strategies, we used the default hyperparameter values for both TGN-Attn and DyRep. Thus, the memory, time encoding, and embedding dimensions are all set to 100. We use the Adam Optimizer [45] with a learning rate of \(1\times 10^{-4}\) and a weight decay of \(1\times 10^{-4}\). To prevent overfitting, we use early stopping on the validation of AUC: we stop the training if there has been no improvement in the AUC of more than \(1\times 10^{-3}\) in the last 20 epochs.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 6 Impact of Quantum Money on the Banking System  
The current financial system relies heavily on the ability of commercial banks to create money through debt [44; 45]. When a bank issues a loan, it does not lend out pre-existing funds; instead, it creates a new deposit, effectively generating money through accounting entries. This process results in a majority of money existing only as digital records, rather than being backed by physical assets. Consequently, commercial banks exert significant influence over the money supply, often surpassing the central bank's control.  
This section explores the hypothesis:  
_If non-replicable quantum money replaces digital deposits, banks would stop creating money through debt, and central banks would have full control over money supply._  
Our approach to quantum money assigns to the central bank the role of the sole trusted institution authorized to create money. This raises the issue commonly known as the trusted institution paradox. On one hand, quantum money aims to decentralize financial transactions (e.g., through quantum teleportation) and prevent unauthorized duplication through the fundamental laws of quantum mechanics. On the other hand, the system inherently depends on a single trusted authority (the central bank) to certify and issue valid quantum currency. Rather than viewing this dependence as a weakness, our approach highlights that quantum money actually reinforces and strengthens the central bank's critical function in monetary control. The paradox is thus resolved by recognizing that quantum money enhances the central bank's role as the trusted issuer, while eliminating the ability of commercial banks or other entities to expand the money supply arbitrarily through money creation. This unique balance preserves necessary trust in the national currency but harnesses quantum mechanics to secure and limit money creation strictly to authorized institutions.
## 4 Experimental Results
### Evaluation of Proposed Method  
To prove the performance of our proposed dehazing method, we show in this section a detailed comparison with one powerful dehazing assumption, DCP [14], and four recently proposed haze removal methods, including three learning-based methods: Cai [28], Ren [26], Li [40], and Salazar-Colores [35]. Figures 13-15 show some examples of haze removal results of these methods, including ours. For an efficient evaluation, the proposed method was tested on three different hazy images datasets that are publicly available, namely FRIDA [45], D-HAZY (Middlebury) [46], and RESIDE (SOTS) [47], and a set of hazy images that were generated by us.  
We carried out quantitative and qualitative comparisons analysis on hazy real-world and synthetic images, as detailed in the next subsections. The comparative examples shown in figures are named as room, cable, jade, teddy, river, park, street, mountain1, tree, forest, and mountain2.
# Analyzing Volatile Compounds of Young and Mature _Docynia delavyi_ Fruit by HS-SPME-GC-MS and rOAV
## 3 Results
### rOAV Analysis of Key Volatile Compounds in D. delavyi Fruit of Varying Maturities
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**NO**} & \multirow{2}{*}{**Compound Name**} & \multirow{2}{*}{**T\({}_{1}\) (mg/kg) A**} & \multicolumn{2}{c}{**rOAV**} & \multirow{2}{*}{**Odor Description B**} \\ \cline{4-4}  & & & **DY** & & **DM** \\ \hline C11 & 1-Octanol & 0.022 & 1.04 \(\pm\) 0.07 & 1.15 \(\pm\) 0.01 & green, citrus, floral with a sweet \\ C13 & Hexanal & 0.005 & 0.27 \(\pm\) 0.01 & 1.69 \(\pm\) 0.20 & green, grass, fruity \\ C14 & (_E_)-2-Hexenal & 0.0031 & 1.52 \(\pm\) 0.09 & 13.18 \(\pm\) 1.51 & green \\ C17 & Octanal & 0.17 & \(<\)0.1 & \(<\)0.1 & orange, green, herbal \\ C18 & Benzeneacetaldehyde & 0.0017 & 0.20 \(\pm\) 0.15 & 2.40 \(\pm\) 0.46 & sweet, honey, floral \\ C19 & (_E_)-2-Octenal & 0.0027 & 0.28 \(\pm\) 0.02 & \(<\)0.1 & green, herbal, banana \\ C23 & 2,6-Dimethyl-4-heptanone & 9.3 & \(<\)0.1 & \(<\)0.1 & green, fruity, pineapple, banana \\ C26 & Butly acetate & 0.01 & \(<\)0.1 & \(<\)0.1 & fruity, banana \\ C28 & Ethly heptanoate & 0.24 & 0.52 \(\pm\) 0.06 & 0.56 \(\pm\) 0.04 & fruity, sweet, banana \\ C31 & Ethly caprylate & 0.04 & 6.22 \(\pm\) 1.01 & 6.27 \(\pm\) 0.52 & pineapple, fruity \\ C32 & Phenethyl acetate & 0.0067 & 73.99 \(\pm\) 4.30 & 100 & floral rosy, sweet, honey \\ C36 & Dihydro-\(\beta\)-ionone & 0.0017 & 0.96 \(\pm\) 0.06 & 11.35 \(\pm\) 1.51 & cedar aroma, woody mahagony aroma \\ C37 & Geranyl acetone & 0.006 & \(<\)0.1 & 0.28 \(\pm\) 0.02 & rose floral, green, fruity \\ C38 & \(\beta\)-Ionone & 0.00012 & 100 & 62.78 \(\pm\) 6.23 & woody, floral, sweet, fruity \\ \hline \hline \end{tabular}  
* A: Ti, odor threshold: the data from compilations of odor threshold values in air, water, and other media (second enlarged and revised edition). Beijing; Science Press. 2018. B: Odor descriptions adapted from [[http://www.thegoodscentscompany.com](http://www.thegoodscentscompany.com)]([http://www.thegoodscentscompany.com](http://www.thegoodscentscompany.com)) (accessed on 2 November 2022).  
\end{table} 
Table 3: rOAV for volatile aroma compounds in _D. delaxayi_ fruit of varying maturities. Figure 6: Chart showing flavor differences between young and mature fruit. The size of each ellipse, only indicates the strength of each type of aroma. Main flavors in (**A**) DY and (**B**) DM.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
###### Abstract  
As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.  
wireless cellular networks; microwave links; rainfall measurement; machine learning; remote sensing 2022  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks. _Sensors_**2022**, 22, 4395. [[https://doi.org/10.3390/s22124395](https://doi.org/10.3390/s22124395)]([https://doi.org/10.3390/s22124395](https://doi.org/10.3390/s22124395))  
18 May 2022  
1
## 2 Materials and Methods
### Evaluation of Classification Performance
#### 2.10.1 Comparison against NASA-GFZ GRACE-FO  
An Amazon Basin-wide comparison was made between RadWet-L classified total wetted area for each ALOS-2 PALSAR-2 orbit cycle and the NASA-GFZ Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) Gravity Mass Anomaly Dataset [57]. Changes in gravity anomaly are strongly controlled by changes in terrestrial water storage, measured as water equivalent thickness [57]. Although these data are provided at a coarse spatial scale (native resolution of 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\)), mass anomaly measurements and water equivalent thickness are calculated as a monthly product for the entire basin region.  
To make a comparison between the GRACE-FO mass anomaly measurements and classified inundation extent, the total area of inundated forest and open water was compared to a single value of the total wetted area for each classified orbit cycle. As the ALOS-2 PALSAR-2 data acquisitions for the basin as a whole occur over a period of 14 days, the mid-date of the start and end of the acquisition is calculated. Where this date is within 10 days of the GRACE-FO measurement, a comparison of mass anomaly and classified total wetted area is performed.
# Dissimilar Rotary Friction Welding of Inconel 718 to F22 Using Inconel 625 Interlayer
###### Abstract  
Dissimilar metal joining has always been a challenging task because of the metallurgical incompatibility and difference in melting points of alloys being joined. Diffusion and mixing of alloying elements from dissimilar base metals at the weld often cause unwanted metallurgical changes resulting in unsuccessful welds or underperformance of the weldment. Solid-state dissimilar friction welds of Inconel 718 and F22 were prepared in this study with an Inconel 625 interlayer to address the carbon enrichment of Inconel 718 during the welding. Defect-free rotary friction welds were produced in this study. Microstructural and mechanical properties investigation of the weldments and base metals was carried out, and results were analysed. Intermixing zone was observed at the weld interface due to the softening of the metal at the interface and rotatory motion during the welding. The high temperatures and the plastic deformation of the intermixing zone and thermo-mechanically affected zone (TMAZ) resulted in the grain refinement of the weld region. The highest hardness was observed at the Inconel 718/F22 weld interface due to the plastic strain and the carbon diffusion. The tensile specimens failed in the F22 base metal for the weld prepared with and without the Inconel 625 interlayer. Inconel 718/F22 welds exhibited lower toughness values compared to the Inconel 718/F22 welds prepared with Inconel 625 interlayer.  
transition joints; nickel alloys; friction welding; interlayers; mechanical properties +
Footnote †: journal: Applied
## 3 Proposed Methodology
### Features Selection Using Chi-Square  
Selection of meaningful and most significant features increases the precision value and reduces the training and testing time with the reduction in overfitting problems as well. Therefore, the features ranking using chi-square is applied, where how much features should be selected is interpreted manually by giving 16 features as the final selected features. The manual selection is also based upon performance, as the further increase of features does not increase the classification models' performance, where reducing the features by more than16 reduces their performance.Therefore, the most valuable 16 features are selected based upon their ranking. The operational functionality of the chi-square method is shown in Equation (5):  
\[SF_{c}^{2}=\sum\frac{(O_{i}-E_{i})^{2}}{E_{i}} \tag{5}\]  
The selected features (SF) are calculated as the degree of freedom (c) assigned while applying the chi-square method, where O and E are the observed and expected values. The \(i\) is calculated over each feature. The top 16 features are shown in Figure 3 on UNSW-NB15 binary classification. The y-axis of the figure shows the feature names selected from all 42 features of the UNSW-NB15 dataset, where the x-axis shows the scores achieved by each feature in order to predict the normal and attacked instances of the dataset.  
Figure 3: The most valuable 16 features for binary classification using chi-square features selection.  
There are five main steps involved in the chi-square method of feature selection. The hypothesis-based operation is performed, which is based upon contradicted behaviors. The contingency table is built upon values of given data. The expected value is calculated, and then the chi-square value is calculated according to Equation (5). The acceptance of the hypothesis is performed based on the chi-square value, where the rule is to select the higher values that show a greater tendency toward the output classes. The chi-square selected 16 features are given to the ML classifiers. The features selection shows different features on binary and multi-class classification. It means the different features show varying levels of importance in the multi-class classification of network security attacks.
# Seg-Eigen-CAM: Eigen-Value-Based Visual Explanations for Semantic Segmentation Models
## 3 Methods
### Methodology Overview  
To illustrate our proposed method and its enhancements over the original Eigen-CAM framework, we present a comparative flowchart in Figure 3. The diagram outlines the processing pipelines of both Eigen-CAM and our method, clearly highlighting the key differences. Our method introduces two critical modifications: a gradient weighting strategy applied before SVD to enhance spatial sensitivity and a sign correction module to address the directional ambiguity inherent in SVD decomposition. These enhancements generate more stable saliency maps for semantic segmentation tasks. The following subsections detail each component of our approach.
## 5 Experimental Results and Discussion  
### Ablation  
In order to investigate the impact of different configurations, the length of the prompt sequence and the choice of pre-trained models are held as control variables. Ablation experiments are conducted on FAP-Prefix based on three pre-trained models: BERT, ALBERT, and UniLM, denoted as the PT-BERT, PT-ALBERT, and PT-UniLM experimental groups, respectively. The objective of this ablation experiment is to determine the optimal prefix sequence length that enables FAP-Prefix to achieve the highest predictive performance. Additionally, it aims to compare the performance of FAP-Prefix based on different pre-trained models. We conduct experiments with the aforementioned models on the EdNet-KT4 dataset and utilize AUC, ACC, and F1 score metrics to measure performance.  
The results of ablation experiments are presented using three metrics: ACC, AUC, and F1 score, as shown in Table 3.
## 3 Results and Discussion  
### Fatty Acid Composition  
One of the major key strengths of olive oil composition is its very high percent content of oleic acid and low percent content of linoleic acid bound in the acylglycerol backbone, which makes olive oil highly resistant toward oxidation, by maintaining a low level of saturated fatty acid percentage content. In Figure 1, the average percentage content of oleic acid and linoleic acid in each cultivar investigated during the two-year study are reported. Some clear features of the cvs. investigated can be highlighted. In both the years investigated, 2015 and 2016, MIG is the weakest cultivar from this point of view. In fact, it has the lowest content of oleic acid (\(72.7\pm 1.3\%\) in 2015 and \(71.4\pm 1.2\%\) in 2016) and the highest content of linoleic acid (\(9.2\pm 0.7\%\) in 2015 and \(9.4\pm 0.8\%\) in 2016), differing significantly (\(p<0.05\)) in almost all the cases from the other cvs. Differently, MOG has the best characteristics from this point of view, having both the years with the highest average value of the oleic acid percentage, which is almost identical in the two years (\(77.5\pm 1.4\%\) and \(77.6\pm 1.8\%\), in 2015 and 2016, respectively), and the lowest percentage content of linoleic acid (\(6.8\pm 0.8\%\) and \(6.5\pm 0.7\%\), in 2015 and 2016, respectively). These results are in total agreement with the study of [PERSON] et al. [4] who reported results referring to a very high number of samples investigated. A general agreement was also found with results reported by [PERSON] et al. [18] with the only exception of ASC for which they found a slightly lower percentage content of linoleic acid (\(4.2\pm 0.2\%\) versus \(6.6\pm 0.6\%\) of the present study) and higher oleic acid percentage (\(79.8\pm 4.0\%\) versus \(75.6\pm 1.2\%\) of the present study). The trend found for the different cvs. is very similar in the two years, and, considering that all the oils have been produced with olives generally harvested at the ideal ripening degree [19], the obtained results support the genetic reason for the trend found. The finding is in agreement with other studies [20; 21], which allows us to recognize clear differences between the cvs. investigated. The effect of seasonality on the fatty acid composition was weak as shown by the absence of significant differences in the comparison between the two years in each of the cvs. investigated.
# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 2 Materials and Methods
### Strategy 2: Take Action According to the Energy Price  
This strategy exploits the variability of the energy prices and the reduction of the necessary energy cost through the power consumption drop. The idea is to reduce the energy consumption during energy peak price hours. In particular, once the number of active users is determined and the network is built as described in [14] (Step 1 in Figure 5), with the already mentioned network configuration (Figure 1), the hourly energy price is checked (Step 3 in Figure 5). If it is higher than (or equal to) a given threshold, all microcell base stations are switched off (Step 4 in Figure 5). Then, if the available energy is not enough for the supply of the base stations at the current time interval, the missing part of power is bought from the traditional power grid (Step 5 in Figure 5). Therefore, the total necessary energy price and the storage are updated (Step 6 in Figure 5).  
The threshold parameter is investigated in this study. It is expressed in terms of the level of the percentile of the weekly energy price.  
Note that the high energy price usually occurs during day time, when many users are active (Figure 2). For this reason, it is important to verify that the quality of service remains good.  
This strategy can be combined with the prediction time window _TW_. In case a time interval in the time window period has an energy price greater than (or equal to) the threshold, all microcell base stations are deactivated. Therefore, more energy is saved, and a lower quantity of it will be bought during that time interval.