esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chromatographic Analysis of Hydroxycinnamic Acids in Honey  
The chromatographic analysis of individual hydroxycinnamic acids in artificial honey and honey samples was performed according to [PERSON] et al., 2019 with slight modifications [26]. Aqueous solution of formic acid 1% \(w/w\) (A) and acetonitrile (B) were used as mobile phase. The gradient profile was as follows: 0-15 min, 5-20% B; 15-18 min, 20-30% B; 18-20 min, 20-30% B; 20-40 min, 30-70% B; 40-45 min, 70-95% B; 45-46 min, 95-95% B; 46-48 min, 95-5% B and until 55 min, 5% B. All analyses were carried out at room temperature, with an injected volume of 20 \(\upmu\)L and a flow rate of 1 mL min\({}^{-1}\). The chromatograms were monitored at 280 and 320 nm.  
The analysis of samples was performed using a Waters series HPLC (Model \"1525\") equipped with a vacuum degasser, quaternary pump, autosampler, thermostated column compartment, photodiode array detector (PDA), and Empower(tm 2 Software (Waters Corporation, Milford, Ireland)). Chromatographic separations were performed on a reversed-phase Waters(r)Spherisorb(r)ODS2 25 cm, 4.6 mm, 5 \(\upmu\)m column (Waters Chromatography Division, Milford, MA, USA), thermostated at 25 \({}^{\circ}\)C. Peak identification was implemented by comparing both retention times and UV spectra with those of authentic standards. The quantities of hydroxycinnamic acids identified in the extract were determined based on calibration curves constructed using reference standards (caffeic acid, chlorogenic acid, ferulic acid, \(p\)-coumaric acid, and rosmarinic acid).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{A} & \multicolumn{2}{c}{C} & \multicolumn{2}{c}{Y2} & \multicolumn{1}{c}{} \\ \multirow{2}{*}{**Run**} & \multicolumn{2}{c}{**Ethanol**} & \multirow{2}{*}{**Temperature**} & \multirow{2}{*}{**Solvent-to-**} & \multirow{2}{*}{**D**} & \multirow{2}{*}{**Caffeic Acid**} & \multirow{2}{*}{**Chlorogenic**} & \multirow{2}{*}{**Y3**} \\  & \multicolumn{2}{c}{**Concentration**} & & & & & & & **Ferulic Acid** \\  & \multicolumn{2}{c}{**(\%)**} & & & & & & **Area)** & **(Peak Area)** \\ \hline
1 & 75 (0) & 30 (\(-1\)) & 20 (0) & 40 (+1) & 12,722.5 & 15,773.5 & 23,352.5 \\
2 & 75 (0) & 45 (0) & 20 (0) & 25 (0) & 17,466.0 & 18,789.3 & 28,028.7 \\
3 & 75 (0) & 45 (0) & 20 (0) & 25 (0) & 16,776.3 & 19,438.7 & 29,551.6 \\
4 & 75 (0) & 45 (0) & 20 (0) & 25 (0) & 17,572.0 & 19,909.7 & 31,064.8 \\
5 & 75 (0) & 30 (\(-
## 3 Results and Discussion
### BMP Tests
Following the inflection point, the biogas yield follows a similar path to the previous state, with its slope decreasing with time and the depletion of available feed substances for the methanogenic biomass. Several hypotheses can be used to explain this behavior. One of them could be the aforementioned one of the production of intermediate by-products throughout the process, which could lag it with their accumulation. Eventually, specialized biomass that could deal with those by-products could develop and start decomposing them. For BSGs it is known that inhibition can appear due to the creation of p-cresol and other phenolic by-products throughout the process [27]. Due to the complex nature of DGSBs, a similar behavior could be also expected. Another hypothesis is that, due to the nature of the substrates, the hydrolysis phase in the anaerobic digestion process could take longer for substrates high in lignin and other complex matter as DGSBs but, once the mechanism is finally accomplished and activated, it translates in a sudden increase in available feed for biogas production. Finally, it can't be discarded that the inoculum, nevertheless it is supposed to be well adapted and able to cope with almost any feedstock, while starting the process with good efficiency dealing with the easiest to digest substances, at a certain point needs some time and biomass adjustment to cope with other more complex substrates (especially with DGSBs). Once the adaptation is adequate and the biomass and microbial communities are adjusted, there's an increase in biogas production due to the increase in the ability to use the feedstock by the rearranged biomass community. In the end, it is far beyond the possibilities of the researchers and the scope of this paper to determine which is the correct hypothesis (or combination of them) to explain the attained results. From a practical point of view, the BMP results give a hint that it can be expected that the biological process, applied to laboratory conditions and standard digestion systems, could be difficult and take a longer time than what could be desirable. The specific methane production values attained of 3659 \(\mathrm{l_{N}CH_{4}kgVS^{-1}}\) for BSGs and 2863 \(\mathrm{l_{N}CH_{4}\) kgVS^{-1}} for DGSBs are in line with those attained in Montes and Rico [28].
# Using Graph Neural Networks for Social Recommendations
## 4 Experimental Evaluation
### Training Setup  
After preprocessing, the dataset is divided into three distinct subsets--the training, validation, and test datasets, using a ratio of 7:1:2, respectively. At the initialization of the model, there are fixed input values provided for various parameters, such as the user feature matrix \(X\), items feature matrix \(Y\), user-consumed-items graph \(G_{R}\), item-consumed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **Yelp** \\ \hline Users & 1,987,897 \\ Businesses & 150,346 \\ Reviews & 6,990,280 \\ \hline Filtered Users & 15,519 \\ Filtered Businesses & 24,648 \\ Filtered Reviews & 815,777 \\ Social Links (user–user) & 836,186 \\ Item links (item–item) & 196,010 \\ Reviews Sparsity & 0.213\% \\ Avg no. of ratings/user & 37 \\ Median no. of ratings/user & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yelp dataset.  
users graph \(G_{R}^{\prime}\), user-user graph \(G_{S}\), and item-item graph \(G_{T}\). The fixed input values are utilized in various layers of the training model. In the training process, the hyperparameters are fine-tuned using the validation dataset.  
During each epoch, different mini-batches of users with varying sizes (100, 250, 500, and 1000) are tested, and it is found that a batch size of 500 yields comparatively better results than other batch sizes. The Adam optimizer is utilized to optimize the model, with an initial learning rate of [0.001, 0.0025, 0.005] and a decay learning rate to minimize the loss function given by Equation (15). To train the model to be unbiased, a certain number of false negative ratings are added for each user from randomly selected unrated items.  
The GNN models utilize the depth parameter \(K\) to gauge the impact of diffusion on the overall model (as shown in Equations (12) and (13)). To evaluate the performance of the model, it is trained with different values of \(K=2,3\). The size of the user and item-free embeddings, denoted as \(D\), is determined by the number of dimensions in the fusion layer as well as the subsequent diffusion layers. For the fusion layers, we use a sigmoid function as the non-linear function \(g(x)\) to transform each value into the range (0, 1) (as shown in Equations (2) and (3)). The output size of each layer is set to \(D=64\) dimensions.
## 4 Recent Advances That Support and Build Upon Existing Approaches  
### Machine Learning  
While ADMs generated using observations from the CERES instruments (Section 3.3) have benefited from detailed scene information provided by an accompanying imager, one implication is that application of these ADMs has become somewhat dependent on availability of the imager information. For previous ERB instruments on spacecraft that do not have an imager, or for potential future ERB instruments on small satellites or constellations of such satellites that are unlikely to include an imager [67; 68], these ADMs are therefore not directly applicable. Even when an ERB instrument is flying with an imager, the imager coverage over a radiometer footprint can be unreliable particularly over polar regions (less than 2% of the CERES-Terra/Aqua footprints have insufficient imager information [24]) or the imager flying with the ERB instrument can fail prematurely. As such, approaches for the conversion from shortwave radiance to irradiance that do not require complex imager information remain desirable.  
The enormous data volume now available from decades of ERB satellite observations is attractive for machine learning approaches. For CERES-TRMM and CERES-Terra ADMs, an artificial neural network (ANN) has been applied to estimate irradiance [69; 70]. In these studies, shortwave anisotropic factors were predicted using only the solar-viewing geometry and the shortwave and longwave radiances observed by CERES. The global annual-mean bias introduced by the ANN-derived irradiance was found to be less than 0.5% for all surface types, and the ANN-derived irradiance still maintained self-consistency in viewing zenith angle better than ERBE ADMs (c.f., Figure 9). This ANN approach is currently used operationally as a backup for CERES footprints with insufficient imager information to avoid systematic biases from omitting these footprints.  
Rather than predict the anisotropic factors directly, it is also possible to predict the scene type using machine learning. [PERSON] et al. (2017) used a random forest to classify CERES footprints as cloudy or cloud-free over 10 different surface types. The daytime misclassification rate varied with surface type and was typically >10% over bright surfaces. However, when thin cloud cases were excluded from the analysis, the daytime misclassification rate reduced to <4% for almost all surface types and was most often below 1%. The most important predictor for all surface types was the CERES shortwave radiance, indicating that the footprint-scale radiances contain valuable information on the scene type, and therefore demonstrating the potential of machine learning for scene classification without imager information.
## 3 Related Work  
Low-Power Wide-Area Network (LPWAN) technologies both in the licensed and in the unlicensed bands such as the NB-IoT, Long Range (LoRa), and many others are striving to become energy efficient over very long distances [23]. NB-IoT is designed for long-life devices and targets a battery life of more than 10 years. To this end, the careful design of smart channel coding schemes has been identified as a potential approach towards enhancing energy-efficiency in NB-IoT [24]. Channel coding is one of the most important aspects of digital communication systems, which can be considered as the main difference between analog and digital systems making error detection and correction possible [25].  
In its current form, NB-IoT reuses the LTE design extensively including the numerologies, downlink orthogonal frequency-division multiple-access (OFDMA), uplink single-carrier frequency division multiple-access (SC-FDMA), channel coding, rate matching, interleaving, etc. One of the factors for this extensive reuse of the LTE channel coding is to significantly reduce the time required to develop full NB-IoT specifications [26]. However, some issues are very specific to the NB-IoT network designs among which is the limited energy capacity issue. Therefore, researchers [27; 28] have identified a crucial need to develop novel channel coding techniques that are specific to the NB-IoT with different design goals. In this research work, the energy efficiency issue has been identified as a potential research problem. However, other researchers have looked into this problem from different perspectives. The different approaches that have been considered are concisely reported in the next paragraphs.
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 4 Discussions on FE Results and Parametric Sensibility
### Tank H/R = 1.5  
As observed in Figure 8a, the pressure profiles for the five different \(R/t\) ratios show a similar trend. The rigid tank displayed a very small peak of pressure at the top of the wall, around 0.9 normalized height (\(z/H\)), while the flexible tanks exhibited a much smoother profile with no noticeable pressure peaks. The results from the FE analyses are in good agreement with the analytical predictions in both the bottom and upper parts of the wall. In this specific case, compared to previous configurations where pressure peaks were absent in the upper part of the tank, the pressure profiles were very well predicted by the three design standard codes. Only the AWWA and EC8-4 standards, when applied to the rigid tank assumption, slightly underestimated the FE results, but the discrepancy was not significant. Looking at Figure 8b, it is immediately apparent that the pressure peaks observed in earlier analyses at the top of the shell were not present for this case study. This is because the convective contribution was considerably smaller in this case, which was due to the different slenderness ratio of the containment structure. The tank with an \(H/R\) = 1.5 ratio represents the transition point between squat tanks and slender tanks. It is important to note that despite the filling height of the tank being limited to 80% of the shell height, the FE analyses recorded pressures above the filling height as well. This is due to the free movement of the fluid inside the tank. When fluid moves vertically as part of the sloshing motion, it exerts horizontal pressures above the hydrostatic free surface.  
Figure 7: Horizontal pressure distribution of \(H/R\) = 0.75 tank ratio considering different \(R/t\) thickness ratios: (**a**) FE simulations and (**b**) predictive pressures computed according the international standards compared with FE simulations.  
This phenomenon highlights the complexity of fluid behavior in seismic conditions particularly when sloshing effects contribute to pressure distributions above the typical water level in the tank. The results underscore the importance of accurately modeling fluid dynamics, especially for tanks with different slenderness ratios, to capture the full range of pressures exerted during seismic excitation.
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 5 Conclusions  
In this paper, a phase shifter using a Y-type resonator was proposed and theoretically and experimentally investigated. The proposed microstrip line phase shifter has the advantages of small phase error, wide operating bandwidth and simple structure. In addition, this paper presented a design methodology that allows one to directly determine the initial physical dimensions of the phase shifter based on the specified specifications. Based on this synthesis method, a 90\({}^{\circ}\) phase shifter with a bandwidth of 60.8% and a phase deviation of 1.2\({}^{\circ}\) was realized.  
Conceptualization, X.F. and Y.M.; methodology, Y.M.; software, Y.M.; validation, [PERSON] and X.F.; formal analysis, Y.M.; investigation, X.F.; writing--original draft preparation, Y.M.; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, L.F. and T.L.; project administration, Y.M.; funding acquisition, X.F. and H.C. All authors have read and agreed to the published version of the manuscript.  
This paper is the research result of the project funded by Guangxi Natural Science Foundation for Distinguished Youth (2023 GXNSFFA026002) and Liuzhou Science and Technology Program for Research and Application of Remote Intelligent Remote Control Construction Machinery (2022 AAB0101).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **F0 (GHz)** & **PR (\({}^{\circ}\))** & **MaxPD (\({}^{\circ}\))** & **IL/RL (dB)** & **FBW (\%)** \\ \hline
5 & 0.5 & 89.9 & \(\pm\)1.5 & \(<\)1.4/\(>\)16 & 37.5 \\
6 & 3.6 & 45–180 & \(\pm\)5 & \(<\)0.6/\(>\)15 & 38.5 \\
8 & 2.25 & 0–360 & \(\pm\)10 & \(<\)0.5/\(>\)10 & 64 \\
9 & 3 & 90 & \(\pm\)2.8 & \(<\)1.2/\(>\)10 & 58 \\
10 & 3 & 90 & \(\pm\)3 & \(<\)0.46/\(>\)22 & 80 \\
17 & 3 & 90 & \(\pm\)3 & \(<\)0.97/\(>\)19.6 & 60.3 \\ This Work & 3.6 & 90–225 & \(\pm\)1.2 & \(<\)0.84/\(>\)16.5 & 60.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the proposed phase shifter with small phase error.  
Figure 9: Phase response of theoretical, simulated, and measured results.
# Swarm Intelligence Techniques for Mobile Wireless Charging
## 7 Conclusions  
In this paper, we studied swarm intelligence (SI)-based approaches for efficient mobile wireless charging, which utilize special multiple mobile elements that traverse the network for energy-provisioning to the sensor-nodes. We first formulated the wireless charging problem and showed its computational hardness. We also proposed the node-partitioning algorithm, which adaptively splits the network into smaller sizes to enable efficient WPTs at a low transmission range and to solve the complex problem of scheduling multiple MCs in LS-WSNs. Some critical issues and tradeoffs associated with the mobile wireless charging schemes, which have been overlooked by many researchers, were then investigated. The most important among them included: (1) determining the efficient coordination and charging strategies for the MCs, and (2) determining the optimal amount of energy available for the MCs, given the overall network's available energy. Unlike many existing techniques, we envisioned methods employing SI-based approaches for a distributed wireless charging, which utilized the local and global network knowledge to further optimize the network lifetime. To this end, we compared three state-of-the-art SI-based routing protocols, using evaluation metrics. The results of this evaluation show that the proposed approach is a veritable method that can be exploited to further optimize energy consumption and to extend the network operational lifetime. Our investigation also reveals the efficacy of the partial charging, over the full charging, strategies of the MCs.  
In the future, we intend to investigate the possibility of collaborative charging among the MCs, whereby a higher energy MC can be used to charge other chargers with lower energy to maintain a continuous network operation. The MCs employed in this study cannot communicate with more than one sensor-node simultaneously. Thus, another future research direction will consider the modelling, analysis, and simulation of a charging model that simultaneously charges more than one sensor-node at a given time, since the current approach is limited to point-to-point mobile charging (whereby each sensor-node is charged by an MC when the MC approaches it at a very close range to optimize the charging efficiency).  
Figure 18: Charging time of MCs during a cycle.  
**Author Contributions:** Conceptualization, G.K.I.; Data curation, K.L.-M.A.; Investigation, J.K.P.S.; Methodology, G.K.I.; Validation, G.K.I. and K.L.-M.A.; Formal analysis, G.K.I. Visualization, K.L.-M.A. and J.K.P.S.; Supervision, K.L.-M.A. and J.K.P.S.; Writing--original draft preparation, G.K.I.; Writing--review and editing, G.K.I. and K.L.-M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors sincerely thank the anonymous reviewers for their valuable and constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# RSCS6D: Keypoint Extraction-Based 6D Pose Estimation
## 3 Proposed Method
### RSCS6D_Pruned: A Lightweight Version  
In practical applications of 6D pose estimation algorithms, it is highly desirable for the algorithm to operate efficiently on platforms with limited computational resources. To broaden the applicability of the algorithm without compromising its original speed and accuracy, it is essential to minimize both the model's parameter count and computational demands. However, the network architecture of the RSCS6D algorithm, which relies on concatenating feature maps from different stages to capture diverse feature information,presents a challenge. This concatenation operation leads to a substantial increase in the number of channels, with some channels expanding to counts of 2240 and even 2256. Such a large number of channels results in frequent feature reuse during convolution operations, indicating a high level of redundancy. This redundancy not only increases the computational load but also may affect the overall efficiency and performance of the algorithm on resource-constrained devices.
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
## 4 Results and Discussion
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & \begin{tabular}{c} **Optimization** \\ **Algorithm(s)** \\ \end{tabular} & \begin{tabular}{c} **Number of Opt.** \\ **Criteria** \\ \end{tabular} & **System Size** & **Type of RES** & **Operation Mode** & **EMS** & \
\begin{tabular}{c} **Objective** \\ **Function** \\ \end{tabular} &
\begin{tabular}{c} **Purpose of** \\ **the ESS** \\ \end{tabular} \\ \hline
[11] & MILP & Less & Large & PV, BESS & Grid connected & Yes & Minimization of the total annual cost (including both energy and battery degradation-based costs) & Energy sustainability \\ \hline
[12] & DP & Less & Small & PV, BESS & Grid connected & No & Determination of the optimal ESS & Energy sustainability \\  & & & & & & charging and discharging & Energy sustainability \\  & & & & & & trajectory with minimum & Energy sustainability \\  & & & & & & operational cost & & \\ \hline
[13] & LP & Less & Small & PV, Wind, BESS & Grid connected & No & & Minimizing the operational costs of the MG and BESS sizing optimization \\ \hline
[15] & MCDM &
\begin{tabular}{c} Should be less for \\ better effectiveness \\ \end{tabular} & Small & BESS (storage plant) & Grid connected & Yes & & Calculating the storage power & Energy sustainability, energy arbitrage \\ \hline
[18] & GA & More & Large &
\begin{tabular}{c} PV, Wind, Diesel \\ generator, BESS \\ \end{tabular} & Off-grid & No & & Minimization of the system cost and loss of power & Energy sustainability \\ \hline
[19] & Improved BAT & More & Large & \begin{tabular}{c} Fuel Cell, \\ Micro Turbine, PV, \\ Wind, BESS \\ \end{tabular} & \
\begin{tabular}{c} Grid \\ connected \\ \end{tabular} & Yes & Minimizing the operation cost &
\begin{tabular}{c} MG operation management \\ studies with regard to \\ operation, maintenance and \\ financial points \\ \end{tabular} \\ \hline
[20] & GWO & More & Large &
\begin{tabular}{c} Fuel Cell, Micro \\ Turbine, PV, Wind, \\ BESS \\ \end{tabular} & Grid connected & No & & Minimizing the operation & Energy sustainability, energy arbitrage \\ \hline
[21] & ABC & Average & Average & \
\begin{tabular}{c} Wind, Hydro, \\ BESS \\ \end{tabular} & Grid connected & No & Maximization of the revenue &
\begin{tabular}{c} Energy sustainability, \\ energy arbitrage \\ \end{tabular} \\ \hline
[22] & GOA & More & Small &
\begin{tabular}{c} PV, Wind, Diesel \\ generator, \\ BESS \\ \end{tabular} & Off-grid & Yes & & Minimization of the DPSP and COE & Energy sustainability \\ \hline
[23] & PSO & Less & Small & \
\begin{tabular}{c} PV, \\ BESS \\ \end{tabular} & Off-grid & No & Minimization of total BESS cost &
\begin{tabular}{c} Frequency control of the \\ stand-alone microgrid \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of existing system and proposed system.