esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Neural Networks for Routing Optimization: Challenges and Opportunities
## 4 Supervised Learning for Routing Optimization  
### Overview  
The general framework of applying GNNs in a supervised learning approach for routing optimization is shown in Figure 6. The GNN-based routing model is subsequently trained from offline supervised learning and then tested in the online deployment.  
The supervised learning approach requires the collection of training data, which are often generated using traditional routing algorithms, for example, the shortest path and min-max routing. In this case, the performance of GNN-based solutions is upper-bounded by these traditional routing algorithms. However, this does not imply that GNNs have no benefits. In most cases, these traditional routing algorithms are de  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Scenario** & **Modeling Target** & **Proposed Solution** & **Performance** \\ \hline
[30; 61; 92; 93] & Computer network & Per-source/destination pair mean delay, jitter and packet loss ratio & RouteNet (MPNN-based) & RouteNet (mean delay and jitter) and loss even with topologies, routing and traffic unseen in the training (worst case MRE = 15.4\%). \\ \hline
[95] & Computer network & Per-path mean delay & MPNN-based model & The model predicts the delay with an MRE of 3.88\% in the unseen topology. \\ \hline
[96] & Computer network & Per-path mean delay & QT-Routenet (GCN and GAT-based) & The prediction MAPE is reduced to 1.45 (1.27 with an ensemble). \\ \hline
[97] & Computer network & Delay, jitter and packet loss ratio & RouteNet-Erlang (MPNN-based) & RouteNet-Erlang (MPNN-based) & RouteNet-Erlang (several different traffic models with a worst-case delay prediction error of 6\%). \\ \hline
[99] & Datacenter network & Flow completion time & GN-based optimizer & The proposed solution can significantly reduce the flow completion time. \\ \hline
[100] & SDN-based 5G network & Expected throughput model & MPNN-based GNN model & The proposed GNN model can predict the expected throughput of specific MPTCP connections with very low error. \\ \hline
[101] & 5G network & Link utilization & RouteNet + LSTM & The proposed RouteNet-based IBN solution with end-to-end orchestration is successfully deployed for 8K and 4K video streaming services. \\ \hline
[102] & SDN & Delay & MPNN-based model & The proposed approach outperforms its baseline counterparts in terms of time overhead, end-to-end delay, flow completion time, and throughput. \\ \hline
[103] & SDN & Delay & MPNN-based model & The proposed approach outperforms queuing model and RouteNet with an increased R\({}^{2}\) by 73\% and 11\%, respectively. \\ \hline \hline \end{tabular}
\end{table}
Table 3: The summary of studies with supervised learning for network modeling.  
mode, whereas some GNN-based solutions can be deployed in a distributed mode, which is more suitable for wireless networks. The fast online deployment and generalization abilities of unseen topologies are also the advantages of GNN-based solutions.
###### Abstract  
In this study distributed optic fiber has been used to measure both the Rayleigh and Brillouin frequency shift of two different sandstone core samples under controlled hydrostatic confining and pore pressure in the laboratory. The Berea sandstone core is relatively homogeneous, whereas the Tako sandstone core is visibly heterogeneous with a coarse-grain and a fine-grain region. Rayleigh frequency has been found to have a superior performance over Brillouin frequency in terms of better consistency (less scattering) in the tests carried out. The strain gauge readings reveal considerable anisotropy in the stiffness of the Berea core between perpendicular (vertical) and parallel to the bedding (hoop) directions. The strains converted from Rayleigh frequency shift measurements agree reasonably well with readings by one of the four hoop strain gauge channels under increasing confining/pore pressure. For the Tako sandstone core, the contrast in the grain-size, and thus rock elastic properties, is clearly reflected in the hoop strain measurement by both strain gauges and distributed optic fiber. The outcomes of the test have demonstrated successfully the use of a single optic fiber for measuring rock strain response at different regions of a heterogeneous core sample along a continuous trajectory.  
brillouin scattering; rayleigh scattering; frequency shift; optic fiber; distributed strain measurement; sandstone +
Footnote †: journal: Applied Soft. 2018
## 4 Antimicrobial Agents
### Antiquorum Sensing Molecules
pounds from a potent QS inhibitor interrupted QS signaling by negatively regulating the transcriptional regulators LasR and LuxR [63]. Ichangin and isolimonic acid are the strong regulators of cell-cell signaling in bacteria, while they are the effective repressors of biofilm and the type III secretion system. Moreover, isolimonic acid also seems to affect AI-3/ epinephrine generated cell-cell signaling pathways in QseA and QseBC dependent manner [64; 65]. It also interfered in AI-2 based QS by reducing the LuxR DNA-binding potential in several _Vibrio_ spp. [65]. Another natural compound cinnamaldehyde was known to reduce _E. coli_ swimming motility and alter biofilm structure and formation [66]. It was also found that hardening, a potent phenylethylamine alkaloid obtained from barley, exhibits a dose-dependent decline in the production of the signaling molecule and affects biofilm production in _P. aeruginosa_[67]. Furthermore, hardening also effectively reduces QS-associated gene expression and virulence factors of _P. aeruginosa_ PAO1 [67; 68]. This suggested that hardening appears to be a novel anti-QS agent that could protect from pathogens [67]. Plant polyphenols known as quercetin are reported to significantly reduce biofilm production and other virulence factors at a lower concentration than formerly known substances and plant extracts [69; 70; 71; 72]. Furthermore, a study on QS-associated transcriptional changes revealed that LasI/R, Rhl/R expression levels involved in QS were significantly decreased [73]. Autoulducing peptide type I (AIP-I) stimulated MRSA biofilms dispersal on titanium disks, causing detached MRSA more vulnerable to treatment with rifampicin and levofloxacin [74]. RNAIII-inhibiting peptide (RIP) resulted in a 7-log reduction in MRSA in a mouse wound model [75]. While the increased effectiveness of antibiotic treatment with QSI in vivo is promising, reduced bacterial loads often depend on the strain and biofilm model [4]. Naturally, nitric oxide (NO) is recognized as the universal signaling molecule that can circulate easily in biological systems. However, some studies highlight the role of NO in biofilm dispersal by targeting the QS system of bacteria [27]. The NO producing agents including sodium nitroprusside (SNP) induced lifestyle transition in bacteria, from the sessile biofilm state to a mobile planktonic state by reducing the amount of intracellular c-di-GMP, thereby causing dispersal of _P. aeruginosa_ biofilms [76]. A similar effect of biofilm-dispersal by NO donors has been verified in _Bacillus subtilis_ as well [77]. These studies underscore that NO generating agents could be potential antibiofilm agents.
# Chromatographic Separation of Rare Earth Elements as MGDA Complexes on Anion Exchange Resins
## 3 Results and Discussion  
### Selection of the Resin and the Mobile Phase  
#### 3.1.1 Resin  
Figure 2 displays profiles obtained with the Dowex 1X8, IRA-410, and IRA-402 when the REE-MGDA mixture \"Feed #2\" (Table 3) was eluted through the packed bed with 10 mM HNO\({}_{3}\). In all cases, the elution profile (total REE concentration) consists of two partially separated peaks. Their origin and composition are discussed later in the paper, but the Dowex 1X8 yields inferior separation of the REEs compared to the IRA resins. With the Dowex 1X8, the majority of the REEs are eluted within a large peak at the void volume of the column. The IRA-402 resin, in contrast, exhibits a broader elution profile in which the REEs are mostly retained by the resin. These two resins have the same functional group but differ in cross-linking density (8 wt-% for Dowex 1X8 vs. 6 wt-% for IRA-402). This suggests that 8 wt-% DVB content results in a polymer matrix too dense for the REE-MGDA complexes to penetrate the gel area and reach the functional groups. This is congruent with the findings in previous publications, which studied the separation of EDTA-Ln (Ln = Lanthanide) complexes with, for example, Dowex 1X2 and 1X4 resins [36; 37]. In addition, IMDA-Ln complexes were investigated with different Dowex 1X and 2X resins; Dowex 2X8 has been shown to be a suitable resin for REE separation. Sodium trimetaphosphate gradient eluent was used to separate adjacent REEs [29]. In this study the IRA-410 was found to have similar properties to the Dowex 2X8.  
The elution behaviour of REEs was similar for the type I and type II resins (i.e., IRA-402 and IRA-410) even though their cross-link densities are different (8 wt-% with IRA-410). This is readily seen in Figure 3, where the elution profiles of La, Nd, and Eu are shown. Chromatograms were obtained from two separate experiments. The feed contains all three shown REEs mixed, but for comparison purposes they are shown as divided elements (La, Nd, Eu). The Type II resin (IRA-410) shows a stronger retention of Eu (last eluted REE) than the Type I resin, despite its higher DVB content. Therefore, it can be concluded that the Type II functionality in the strong base anion exchanger  
Figure 2: Chromatograms for REE elution with Dowex 1X8, IRA-410 and IRA-402 (T = 298 \(\pm\) 2 K; flowrate = 2.98 BV/h; feed pulse = 0.025 BV, Fluent = 10 mM HNO\({}_{3}\); Feed solution = #2).  
Moreover, the retention of La (first eluted REE) on IRA-410 is weaker and the retention of Nd (middle) is like that of IRA-402. Based on these results, IRA-410 yields the best separation of the REEs under these conditions and, therefore, was chosen for most of the subsequent experiments. The selection of the resin is done completely on the empirical basis based on the results of this study.
# IoT-Based Solid Waste Management Solutions: A Survey
## 6 Standard Protocols Used in Waste Management Systems
### Infrastructure Protocols
#### 6.3.2 Network Adaptation Layer Protocol  
**6 LowPAN.** Wireless personal area networks (WPANs) are characterized by differences with older link layer technologies, such as limited packet size (127 bytes for IEEE 802.15.4). This difference in packet length is required to ensure low bandwidth, necessitating the creation of an adaptation layer that corresponds to the length of the IPv6 header within the IEEE 802.15.4 specifications. This IETF 6 LoWPAN working group developed the 6 LoWPAN (low power personal network IPv6) standard that specifies the mapping required by IPv6 over PWANs [107] and provides IPv6 header compression to reduce transmission overhead and fragmentation to meet the requirement of the IPv6 maximum transmission unit (MTU) and support routing on networks where delivery will be through multiple jumps. The 6 LoWPAN significantly reduces the IPv6 overhead in such a way that a small datagram can be sent through a single 802.15.4 frame packet at best and the IPv6 headers can be compressed in two bytes [108].
## 6 Result and Discussion
### Domain Decomposition and Tile Division Optimization  
Table 3 shows the temporal distribution of the hotspot function of WRF. The data show that the largest hot function of WRF is pow/finite, which accounts for 14% of the total program runtime. This is mainly due to the presence of a large number of power operations in WRF: pow/finite is a function that calculates the yth power of x, where x and y are floating point numbers, and it is one of the commonly used functions in mathematical operations.  
Figure 5 shows that with libamath, the performance of WRF is improved at different core counts, where at 120 cores, the single iteration step time of WRF decreases from 0.92 s to 0.78 s, which is a 15.2% performance improvement.  
Figure 5: Single-step runtime before and after call to libamath, with different number of processes.  
As shown by the data in Table 4, in the experiments running WRF with 36 cores, the best performance is achieved when nproc_x = 3 and nproc_y = 12, with a 5.78% performance improvement over the default domain decomposition. In the experiments running WRF with 72 cores, the best performance is achieved when nproc_x = 3 and nproc_y = 24, with a 11.38% performance improvement over the default domain decomposition. These two sets of preferred experiments clearly demonstrate that the default domain decomposition of WRF does not yield the best performance.  
Based on Section 4.3, it is clear that tile segmentation is a more fine-grained segmentation method than domain decomposition segmentation. Since tile is the smallest computational unit of WRF, studies that investigate this unit are crucial. In this paper, we use 120 cores for our experiments. Before optimization, the default tile parameter numtiles = num_threads is used to record the single-step running time of WRF and then set the value of numtiles. The total single-step running time of WRF is 0.62 s when the value of numtiles is set to eight. Thus, the single-step time performance is improved by 13.89%.  
The main reason for the performance improvement of tile is that, after dividing the tile into finer parts, the data of the entire tile will be stored in the cache during the computation (the size of the data divided by the tile is smaller than the corresponding cache size), improving the access efficiency and overall performance of the WRF computation in a disguised way.  
After the WRF was optimized by adding the maths library libamath, domain decomposition and tile division, the single iteration step runtime of the WRF with 120 cores dropped from 0.92 s to 0.62 s, a 32.6% performance improvement (see Figure 6 for performance improvement).
# A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
## 6 Evaluation Methods and Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Work** & **Methods** & **Effects** & **Dataset** \\ \hline HMM-based deduplication & Hidden Markov models (HMMs) & 76\% accuracy and 73\% average accuracy for Firefox and CNOME, respectively & 1 M from Firefox and 753 k from CNOME \\ \hline Soft alignment model for deduplication & Soft-attention alignment and DNN & 5\% RRBK improvement & 25 k from Eclipse, 54 k from Morilla, 11 k from Netbens, and 15 k from OpenOffice \\ \hline Dual-channel CNN-based deduplication & Word2 vec and dual-channel CNN & Over 0.95 accuracy, recall rate, precision, and F1 score & 90 k from OpenOffice, 26 k from Eclipse, and 184 k from Netbens \\ \hline Domain knowledge-based deduplication & BM2 SF and multiple ML models & Up to 92\% accuracy & 37 k from Android, 42 k from OpenOffice, 72 k from Mozilla, and 29 k from Eclipse \\ \hline Triage in industrial context & SVM and TF-IDF & 5\% accuracy, 59\% precision, and 47\% recall & 2 k from Jira and 9 k from Mozilla \\ \hline Deduplication with manifold correlation features & TF-IDF, BM2S, and word2 Vec & 2.79\%-28.9\% RR805 improvement & 6 k from ArgpuML, 9 k from Apache, and 4 k from SVN \\ \hline Deep-learning-based automatic bug triage & Word2 Vec and CNN & 82.83\% and 35.83\% higher performance in top-one and top-three accuracy, respectively & 24 k from four datasets \\ \hline Semisupervised bug triage & Enhanced naive Bayes classifier & 6\% accuracy improvement & 20 k from Eclipse \\ \hline DeepTriage-song & BILSTM and LSTM & 42.96\% top-one accuracy & 200 k from Eclipse and 220 k from Mozilla \\ \hline SeqTriage & Bidirectional RNN and attention model & 5\%-20\% accuracy improvement & 210 k from Eclipse, 300 k from Morilla, and 165 k from Genote \\ \hline DRR-CNN & Word embedding and CNN & 0.903 F score and 0.919 accuracy & 1.8 k from Hadoop, 12 k from hdfs, 7 k from Mapreduce, and 22 k from Spark \\ \hline TERFUR & NLP model, vector space model, and merging algorithm & 78.15\% accuracy, 78.41\% recall rate, and & 0.3 k from Justfort, 0.3 k from S5-1800, 0.4 k from Shopping, 0.2 k from CloudMusic, and 0.4 k from UbBook \\ \hline Triage using categorical features & Naive Bayes classifier & 0.683, 0.584, and 0.38 F score for Netbens, & Netbens, Freedesktop, and Firefox \\ \hline DWEN & Word embedding and DNN & Over 0.7 RR820 & 700 k from Moilla and 100 k from OpenOffice \\ \hline Multilabel, dual-output DNN for triaging & Mutlabel classifier & 76\% accuracy for team assignment and 55\% accuracy for individual assignment & 236 k from a private dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect comparison of works using ML methods based on bug reports.
# COMS Visible Channel Calibration Using Moon Observation Data
## 3 Results
### Comparison with Other Methods  
Figure 4 shows a time series of Slope\({}_{\text{ROLO}}\) (Equation (8)), which was rescaled against the first lunar observation using Equation (11),  
\[\Delta Slope=100\cdot\left(\frac{Slope[t_{n}]}{Slope[t_{0}]}\ -1\right). \tag{11}\]  
Almost all results were fitted on the linear regression line. Lunar calibration results were compared with vicarious calibration results using DCC. Figure 4 shows a comparison between estimations of two calibration methods with respect to visible channel degradation of the COMS imager: Moon (orange dot) and DCC (blue asterisk), where DCC is a vicarious calibration method using a radiative transfer simulation developed by [PERSON] et al. [7].  
It was noted that DCC overshot the tropical tropopause layer when cold brightness temperature appeared at the infrared window channel (e.g., 11 \(\upmu\)m); this result assumes that the infrared channel was already well calibrated using other independent devices. After selecting DCC targets, typical optical properties were assumed as inputs to the RTM, based on an examination of DCC properties using MODIS cloud products. Reference values of sensor-reaching radiances were then produced from theoretical calculations, and these values were compared with Level 1B radiance products for  
Figure 3: (**a**) Difference between degradation ratio and regression value (R(I\({}_{\text{Instrument}}\)/I\({}_{\text{ROLO}}\)) \(-\) R\({}^{\prime}\) [y = mx + b] (%)). The annual difference between degradation ratio and regression value is evident and shows a clear seasonal variation. (**b**) Variations in phase angle annually from April 2011 to December 2017 (the year is divided into two parts: January to June and July to December.  
calibration monitoring [7; 31]. The bulk scattering properties for ice particles [32; 33] were used as a scattering database in the RTM [7]. The two methods showed very similar annual drift values of 1.52 \(\pm\) 0.10% per year and 1.43 \(\pm\) 0.13% per year for Moon and DCC, respectively.  
In addition to using the DCC method for comparison, three different earth targets were used (ocean, desert, and water cloud) to consider the wide reflectance range from the ocean's surface around longitude 70\({}^{\circ}\)E-165\({}^{\circ}\)E and latitude 30\({}^{\circ}\)S-25\({}^{\circ}\)N as well as from a water cloud around longitude 128\(\pm\)40\({}^{\circ}\)E and latitude 40\({}^{\circ}\)S-40\({}^{\circ}\)N [7; 31]. All three targets used RTM-calculated results as the calibration reference; inputs were MODIS products and NCEP (National Centers for Environmental Prediction)/NCAR(National Centers for Atmospheric Research) reanalysis data, such as TPW, UWIND, and VWIND. In this respect, each method was independent. Slopes were calculated using all three targets. An integrated method combining desert, ocean, and water cloud showed a similar annual drift value of 1.37 \(\pm\) 0.31% per year.
# Resource Allocation of Multiple Base Stations for Throughput Enhancement in UAV Relay Networks
## 4 Comparison: Single Transmission from the Single Base Station in the URN
### sBS-Based URN (sBS-URN)  
URN with a wireless backhaul link only from sBS is considered for the performance comparison. The sBS-UAV link receives interference from mBS transmissions. mBS transmits only to \(U_{m}\) in both time slots; therefore, \(SINR_{mBS,UAV}^{1^{st}}\) in (1) is equal to 0, \(P_{mBS,U_{m}}^{1^{st}}=P_{mBS}\) in (2) and (3) because \(P_{mBS,UAV}^{1^{st}}=0\).  
With the conditions that \(SINR_{mBS,UAV}^{1^{st}}=0\), \(P_{mBS,U_{m}}^{1^{st}}=P_{mBS}\) and \(P_{mBS,UAV}^{1^{st}}=0\),  
\[prob\Big{(}R_{U_{a}}^{(1)}\geq\eta\Big{)} \tag{13a}\] \[=prob(\frac{P_{sBS}S_{sBS,UAV}}{(P_{mBS,U_{m}}^{1^{st}}+P_{mBS, UAV}^{1^{st}})S_{mBS,UAV}+\sigma_{UAV}^{2}}\geq 2^{\omega^{+}}-1)\] (13b) \[\overset{(a)}{=}E_{h_{sBS,UAV}}\Bigg{[}\frac{\gamma\big{(}k_{mBS,UAV},k_{mBS,UAV}\psi^{sBS-URN}(h_{sBS,UAV})\big{)}}{\Gamma(k_{mBS,UAV})} \Bigg{]}\] (13c) \[\overset{(b)}{=}\int\limits_{x_{min}^{\infty}}^{\infty}\frac{ \gamma\big{(}k_{mBS,UAV},k_{mBS,UAV}\psi^{sBS-URN}(x)\big{)}}{\Gamma(k_{mBS, UAV})}f_{h_{sBS,UAV}}(x)dx_{r} \tag{13d}\]where  
\[x_{\min}=(\psi^{BS-URN})^{-1}(0),\] \[\psi^{SBS-URN}(h_{sBS,UAV})=\frac{P_{sBS}}{(2^{\omega^{+}}-1)P_{mBS} }\frac{l_{sBS,UAV}}{l_{mBS,UAV}}h_{sBS,UAV}-\frac{\sigma_{UAV}^{2}}{l_{mBS,UAV} P_{mBS}},\] \[f_{h_{sBS,UAV}}(x)=\frac{k_{sBS,UAV}k_{sBS,UAV}}{\Gamma(k_{sBS,UAV })}x^{k_{sBS,UAV}-1}e^{-k_{sBS,UAV}},x\geq 0,\]  
and \((\psi^{sBS-URN})^{-1}\) is an inverse function of \(\psi^{sBS-URN}\). \((a)\) in (13c) takes the expectation with respect to \(h_{sBS,UAV}\), and \((b)\) in (13d) follows that \(h_{sBS,UAV}\) has a \(Gamma(k_{sBS,UAV},\)\(1/k_{sBS,UAV})\).
## 2 Methods
### Profile Retrieval
#### 2.5.5 Raman Lines
[33], but allow for the use of arbitrary elastic lines of wavelength \(\lambda_{e}\) in combination with the Raman signal at \(\lambda_{R}\) and its corresponding laser wavelength, \(\lambda_{0}\):  
\[\beta_{\text{aer}}(\lambda_{e},h) =\beta^{\text{mol}}(\lambda_{e},h)\cdot\left(\frac{\mathcal{R}_{ \lambda_{e}}(h)\cdot\mathcal{R}_{\lambda_{R}}(h_{0})}{\mathcal{R}_{\lambda_{ \epsilon}}(h_{0})\cdot\mathcal{R}_{\lambda_{R}}(h)}\cdot\exp(T/\cos\theta)-1\right)\] \[\text{with : }\ T =\int_{h}^{h_{0}}\left(\alpha^{\text{mol}}(\lambda_{R},h^{\prime} )+\alpha^{\text{mol}}(\lambda_{0},h^{\prime})-2\alpha^{\text{mol}}(\lambda_{e},h^{\prime})\right)\,\text{d}h^{\prime}+\] \[\qquad+\left(1+\left(\frac{\lambda_{R}}{\lambda_{0}}\right)^{k}- 2\left(\frac{\lambda_{R}}{\lambda_{e}}\right)^{k}\right)\cdot\int_{h}^{h_{0}} \alpha^{\text{aer}}(\lambda_{R},h^{\prime})\,\text{d}h^{\prime}\quad. \tag{51}\]  
Figure 18: **Left**: A comparison of ground-layer extinction profiles retrieved from the Raman line at 387 nm (using Equation (50) with \(k=1\)) (blue) and a Klett–Fernald inversion of the elastic lines (355 nm, violet and 532 nm, green). All three lines are affected by LORR ringing, with the 355 nm line most strongly affected. A filter window of 1 km has been applied to the Raman inversion, resulting in worse height resolution than the (unfiltered) inverted elastic lines. **Right**: the same profiles have been integrated from \(h_{\theta}\) downwards. The dotted blue lines show each an inversion from the Raman line filtered with a different window in the range from half to twice the default window size and two different filter polynomials. Here, \(h_{0}\) must correspond to an altitude in the free troposphere, where no particulates are present. Figure 19 compares the the different algorithms used for retrieval of the extinction and the backscatter profiles for a slightly polluted urban ground layer, highlighting the possibilities and limits of the current pBRL: whereas the combination of Raman and backscatter inversions (Equations (50) and (51)) provide consistent results for the (relatively stronger) aerosol signal at 532 nm, the inversion of the coefficients at 355 nm from a strongly over-driven LOTR could only be achieved for the highest of the three layers, and only there with degraded range resolution of the Lidar ratio. These shortcomings can be cured with a more stable gated PMT, particularly for the ringing-sensititive elastic UV line.