esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### AD Definition during ML Model Construction (Substudy 2)
#### 4.2.1 Dimensionality Reduction on the Preprocessed Data without Outliers
For the entropy, the eliminated outliers seem to contain a large percentage of polyfunctional compounds and aromatics, not only as part of the respective individual families, but also as members of some other families, such as esters/others and nitrogen compounds (e.g., in esters/others, 50% are aromatic and 15% are polyfunctional). Therefore, their elimination could affect their relative importance, as described by the associated descriptors in the dataset. As for CATS 3D descriptors, they consider the spatial pairwise Euclidean distances between potential pharmacophore points or PPP (i.e., hydrogen-bond donor/acceptor, positive/negative, lipophilic) [63]. The elimination of esters/others, polyfunctional compounds, nitrogen compounds and aromatics, containing numerous PPP (e.g., the oxygen atom in an ester or ether group is a hydrogen-bond donor), reasonably affects the relevance of CATS 3D descriptors for the remaining data. More generally, these observations demonstrate that the dimensionality reduction step can be influenced by outliers, such as other processing steps. Accordingly, any eventual elimination of outliers should be performed early in the process, as is the case in this work. Other options include the deployment of specific methods that allow feature selection and outlier detection to be performed simultaneously, or robust regression or scaling in the presence of outliers [64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. Finally, configuration D seems to be a good compromise between model performance, interpretability and AD size. The parity plots obtained for this configuration are displayed in Figure 13 for the different train/test splits for enthalpy. The respective plots for entropy are shown in Figure A1. For both enthalpy and entropy, most molecules seem to be well predicted, despite some of them deviating from the main diagonal of the parity plots. This is mainly the case for test molecules and those displaying the highest prediction errors are highlighted in red with their identification numbers (ChemID) in Figures 13 and A1. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Configuration** & **Outlier Elimination** & **Dimensionality Reduction** & **Mol.** & **Desc.** & \(R^{2}\) & \(R^{2}\) & \(MAE\) & \(MAE\) & \(RMSE\) & \(RMSE\) \\  & **\(RMSE\) Elimination** & **\(RMSE\) Reduction** & & & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline A & No & No & 1747 & 2479 & 0.983 & 0.970 & 14.87 & 18.71 & 26.94 & 35.17 \\ \hline B & Yes & No & 1514 & 2479 & 0.996 & 0.995 & 7.49 & 8.19 & 9.90 & 11.40 \\ \hline C & No & Yes & 1747 & 100 & 0.980 & 0.969 & 14.09 & 17.37 & 29.23 & 35.49 \\ \hline D & Yes & Yes & 1514 & 100 & 0.996 & 0.995 & 7.09 & 8.01 & 9.78 & 11.49 \\ \hline \end{tabular}
\end{table} 
Table 4: Effects of outlier elimination (Layout 3) and dimensionality reduction (GA) on the model performance in predicting **entropy** (J/mol/K).
## 6 Conclusions  
This paper addresses the challenge of detecting malicious nodes within complex underwater acoustic sensor networks, accommodating various attack behaviors of malicious nodes. It introduces a dynamic trust evaluation model that combines deep reinforcement learning and the random forest algorithm. In this framework, behavioral characteristics of malicious nodes are leveraged to select three types of trust evidence: communication trust, data trust, and energy trust. The model also accounts for the influence of intricate underwater environments on node trust determination, enhancing trust indicators based on environmental factors. A trust evaluation model, based on the random forest algorithm, is trained to assess the trust status of sensor nodes. Real-world scenarios, where ocean currents induce dynamic movement of underwater sensor nodes, are considered. This paper analyzes and simulates node mobility, accounting for the dynamic evolution of network topology. Nodes are adaptively updated using a trust update mechanism rooted in deep reinforcement learning. This mechanism facilitates timely and precise reflection of trust relationships between nodes, thereby enhancing the trust model's detection accuracy. Experimental analyses demonstrate that DRFTM excels in promptly detecting malicious behaviors, outperforming existing approaches, and maintaining robust performance even in sparsely deployed underwater environments.  
However, this research also has its limitations. The attack model simulated in this study excludes scenarios where malicious nodes engage solely in eavesdropping attacks. Nevertheless, eavesdropping attacks pose a significant threat to the confidentiality, integrity, and overall security of UASNs. These attacks can jeopardize confidentiality, manipulate data, and operate covertly, presenting challenges in detection and mitigation. In future studies, we will focus on enhancing security through secure routing protocols, developing strategies to resist eavesdropping attacks, and exploring more practical trust models for UASNs.  
Conceptualization, B.W. and X.Y.; methodology, B.W. and Y.L.; software, X.Y. and Y.L.; validation, K.H., Y.L. and X.Y.; formal analysis, Y.L.; investigation, X.Y.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, X.Y.; writing--review and editing, K.H. and B.W.; visualization, Z.L.; supervision, Z.L.; project administration, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Chunhui Cooperation Program of the Ministry of Education, HZKY2020590-202200265; National Natural Science Foundation of China under Grant 61902273.  
Figure 9: Comparison of performance in single-mode attacks: (**a**) DDoS attack; (**b**) selective forwarding attack; (**c**) data pollution attack; (**d**) on–off attack.
# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 1 Introduction
Although precision agriculture, as it can be found in North America, Australia and Europe, differs considerably in developing countries, the need for accurate data and targeted interventions is actually greater there, due to the stronger imperative for change and the lack of resources [37]. What is really encouraging is that over the past few years there has been considerable effort from developing countries to use some kinds of precision agriculture methods in various applications, such as yield monitoring and tractor auto guidance [36]. In developing countries, MFS play an important role, as in some cases they act as the backbone of a sustainable agricultural policy, especially for individual farmers or small farming communities [38]. During the last decades, research efforts in various developing countries have been redirected to integrated farming systems rather than dedicated ones, so as to cover several complementary enterprises under various agro-ecological situations [39; 40; 41]. These efforts revealed that integrated farming systems can not only be profitable and productive but also eco-friendly, a countermeasure to unemployment and provide financial stability to the stakeholders [42]. Designing and applying integrated farming systems has severe constraints. Towards the technological direction, a severe constraint is the lack of long-term, structured, concrete data [43]. In the best scenarios so far, end users' actions heavily depend on short-term, biased data. As a result, data analytic services fail to operate on a larger scale since they are highly affected by area peculiarities and seasonality [44]. That explains the fact that different frameworks and assessment schemes result in contradictory outcomes. Examining MFS implementations in view of the end-user, a new kind of barrier that lies in farmers' willingness to cooperate, emerges. This barrier goes beyond technical solutions, adding a socio-economic dimension to the widespread exploitation of MFS. MiFarm-CSA architecture aims at providing a CSA-based, multi-actor and community-oriented architecture, for advancing the current farming system to a smart, resilient and integrated/mixed farming ecosystem, aiming at increasing the resilience of the underlying farms, crops, livestock and forestry against the negative impacts of the climate change. Moreover, the proposed architecture envisions to foster cooperation between farmers and breeders through the provision of a sustainable MFS reference model. The remaining paper is organized as follows: Section 2 highlights the current state-of-the-art. Section 3 presents the MiFarm-CSA architecture thoroughly; it illustrates the four layers of the architecture and delves into them in each of the subsections. Section 4 outlines the benefits of the proposed architecture. In Section 5 the conclusions are summarized.
## 5 Conclusion and Unsolved Problems  
In this paper, we presented two new algorithms for the so-called MaxRTC problem. For a given set of \(m\) triplets on \(n\) species, the FastTree algorithm runs in \(O(m+\alpha(n)n^{2})\) time, which is faster than any other previously known algorithm, although the outcome can be less satisfactory for highly sparse triplet sets. The BPMTR algorithm runs in \(O(mn^{3})\) time and, on average, performs better than any other previously known approximation algorithm for this problem. There is nonetheless still more room for improvement of the described algorithms.  
1. In the FastTree algorithm, in order to compute the closeness of pairs of species, we check triplets, and for each triplet of the form, _ij_|_k_, we add a weight, \(w\), to \(C_{i,j}\) and subtract a penalty, \(p\), from \(C_{i,k}\) and \(C_{j,k}\). In this paper, we set \(w=p=1\). If one assigns different values for \(w\) and \(p\), the closeness of the pairs of species will be changed, and the reconstruction order will be affected. It would be interesting to check for which values of \(w\) and \(p\) FastTree performs better.  
2. [PERSON] [11] introduced six alternatives for \(e\_score\), each of which performs better for different input triplet sets. It would be interesting to find a new function that can outperform all the alternatives for any input triplet set.  
3. The best-known approximation factor for the MaxRTC problem is three [13]. This is the approximation ratio of BPMF. Since MaxRTC is APX-hard, a PTASis unattainable, unless P = NP. However, [5] suggests that an approximation ratio in the region of 1.2 might be possible. Finding an \(\alpha-\)approximation algorithm for MaxRTC with \(\alpha<3\) is still open.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**No. of species and triplets** & **\% better results** & **\% worse results** \\ \hline \(n\) = 20, \(m\) = 500 & \%29 & \%0.0 \\ \(n\) = 20, \(m\) = 1000 & \%37 & \%1 \\ \(n\) = 30, \(m\) = 500 & \%61 & \%3 \\ \(n\) = 30, \(m\) = 1000 & \%62 & \%4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance results of Best Pair Merge with Total Reconstruction (BPMTR) in comparison to Best Pair Merge with Reconstruction (BPMR).  
4. It would also be interesting to find the approximation ratio of FastTree, in general, and for reflective triplet sets.  
The authors are grateful to [PERSON] and [PERSON] for reviewing this article, providing useful comments and answering our endless questions.
# Analyzing Student Behavioral Patterns in MOOCs Using Hidden Markov Models in Distance Education
## 4 Discussion
The transition diagrams, along with the figures of the transition and emission matrixes, as well as the diagrams of \(\gamma\) and \(\mathcal{L}\) illustrate the correlation among students and courses and the effect of those correlations on the final grades. These charts help course designers understand the relationship between students' participation in various resources and their final grades, as well as the probabilities for state pairs. The content access pattern revealed by the proposed methodology is crucial for course design. Our methodology has some limitations. We do not consider time, even though the time is given in the dataset. The proposed approach focuses only on students and the corresponding resources and grades as input data. The dataset does not provide demographic information. We have not performed a segmentation because of the nature of a 2-layer HMM. Additionally, we rely on three hypotheses. The students who achieve Grades 5 to 6 are considered to be the other students who pass the exams. This happens because the number of students with final Grades 5 to 6 is negligible. Furthermore, we hypothesize that each student ID depends only on the previous student ID in order to apply the Markov property. An additional hypothesis is that we consider the sequence of probabilities better than a random or baseline model. We hypothesize this as we are not testing for significance or accuracy but rather focusing on finding patterns without prediction. The dependencies may be broken with a segmentation, and they could incur an information leakage. The considered dependencies are among variables (i.e., resources and the grades achieved) with the emission matrix and among resources with the transition matrix.
## 6 Applications of Actinobacteria in Biotechnology
### Bioremediation and Biofoculation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Source** & **Chemical Composition** & **Flocculating Activity (\%)** & **Applications** & **Citations** \\ \hline _Bacillus argubattati_ & Egyptian Agricultural soils & Glycoprotein & 92.8\% at 50 \({}^{\circ}\)C and 94.6\% at & N/A & [97] \\  & & & pH 2.0 & \\ \hline _Rudliapes philipipipnarum_ & Aquaculture & Complex heteropolymersaccharides & 86.7\% in deionized water and & N/A & [98] \\ \hline _Sphingomonas Yaburchiae_ & Chromotrophic acid & polysaccharides & 0.4\% (\(\mu\)/v) kaolin suspensions & & \\  & waste water & over pH 3.9 and 20-80 \({}^{\circ}\)C & Steroid estrogen removal & [10] \\ \hline _Alterononns sp_ & Surface Sea water & Polysaccharides & 2575.4 U/mL achieved in a 2-L & Dye decolorization & [69] \\  & CGMCC 10612 & & fermenter & & \\ \hline _Bacillus cereus_ & Marine sponge & Polysaccharides & 94\% F/A in kaolin suspension & synthesis of Ag nanoparticles and bioremediation of wastewater & [99] \\ \hline _Bacillus muctiginous_ & Mixed activated Sludge & Extracellular polysaccharides & 90\% F/A in kaolin suspension & N/A & [100] \\ \hline _Bacillus Meggetrium_ & Swine waste water & \multirow{2}{*}{Polysaccharides} & 90.2\% in 4 L kaolin suspension & Arsenite removal & [101] \\  & treatment plant & & & & \\ \hline _Stripomyces_ sp & Mangrove sediments & Polysaccharides & 99.18\% on _Nanachioropsis_ & Recovery of microalgae & [102] \\  & & culture medium & & & \\ \hline _Bacillus cereus_ & Activated sludge Flocs & N/A & 86.87\% & Microalgae harvest & [103] \\ \hline _Rhodococcus erythropolis_ & Alkaline thermal & N/A & N/A & Removal of Pb (II) & [104] \\  & pretreated sludge & & & & \\ \hline _Scandlesmus quadricn_ & Algatetech International & \multirow{2}{*}{Glycoprotein} & flocculate 86.7\% of & & \\  & Sdn Bld & & _Scandlesum quantification_ cells & Harvesting of biomass & [105] \\  & & & & in presence of ZnCl\({}_{2}\) & \\ \hline _Pennibacillus muctiginous_ & Soil sample & Polysaccharides & 97\% flocculation on kaolin clay suspension & Industrial waste water & [106] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Some of the microorganisms implicated in flocculation and their industrial applications.
# Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image
## 4 Results
### Accuracies of the Specific Depth Range  
Water depth was divided into 10 ranges at the interval of 2 m to analyze the inversion ability of different specific depth ranges, namely, 2-4, 4-6, 6-8, 8-10, 10-12, 12-14, 14-16, 16-18, 18-20, and 20-22 m. MAE and MRE were used as the approximates of evaluation.  
Figure 13 shows the MAEs and MREs of the specific depth ranges of the control points. The MRE of extremely shallow water (2-4 m) had the largest value. With the increase in depth, MRE tended to drop until it reached the minimum of 3.49% in the depth range of 20-22 m. The MAEs of all depth ranges were less than 0.9 m, and the dropping or rising tendencies were not obvious. The MAEs of 10-12 m and 4-6 m were the highest and lowest values at 0.89 m and 0.46 m, respectively.  
Figure 12: (a) Scatter plot of control points; (b) scatter plot of validation points.  
Figure 13: Mean absolute error (MAE) and mean relative error (MRE) of specific depth ranges of control points.  
Figure 14 shows the MAEs and MREs of the specific depth ranges of the validation points. With the increase in water depth, MRE appeared to be on a downward trend. The MRE of extremely shallow waters (2-4 m) was the highest at 21.32%. The differences in MREs of the much deeper waters (>14 m) were relatively small, and the MRE in the range of 20-22 m could reach the minimum value of 2.47%. The MAEs of all the depth ranges were within the range of 0.56-1.11 m, and dropping or rising tendencies were not obvious. The minimum and maximum MAEs appeared in ranges of 10-12 m and 12-14 m, respectively.
## 5 Conclusions  
The use of sustainable and renewable energy concepts for mass public transportation was proposed and analyzed in this paper. Electric buses are used instead of diesel buses, and elevating the photovoltaic system above the bus routes is used to charge the buses' batteries. The concept was applied to the Amman BRT project as a case study under investigation.  
Converting conventional transportation systems into clean and green systems requires careful design and projecting multiple scenarios that vary by nature and impact. PV system yield along the whole route was studied and zoned, and several design scenarios were proposed. The most feasible and practical scenario for PV system implementation is the one that excludes zones with high shading losses, due to high raised objects, and generates energy that covers the load demand with an acceptable cap over. Elevated PV panels are available on the market, easy to install, with high efficiency and low cost, easy to maintain, and give the opportunity to benefit horizon above buses and routes. Moreover, they provide attractive shading that does eliminate direct sunrays but not the light from reaching buses cabins and then reduce AC loads.  
The PV-supplied electric bus upgrade is a better alternative, with about 46,452,084 USD NPV savings over 15 years compared to the baseline case. The payback period for the proposed scenario was found to be 7.1 years. Payback period calculations included the incremental cost by calculating CAPEX and OPEX for the two cases.  
The negative environmental effects of using diesel as a fuel for the buses were eliminated. The overall result is eliminating the release of 27,204 metric tons of CO\({}_{2}\) per year, the equivalent of 408,060 metric tons in the 15 year lifetime of the project. In conclusion, the proposed upgrade is economically feasible and environmentally viable.  
The green streets fundamentals and models are economically feasible and environmentally viable. With the negative effects of emissions on the planet and life, converting to green streets is substantial and not optional anymore. Implementing green streets solutions in cities will reduce emissions and pollution, enhance the quality of life, save money, and attain awareness for people towards sustainable transportation means and renewable energy systems.  
Conceptualization, Z.D. and O.S.; methodology, Z.D.; software, Z.D. and O.A.B.; validation, Z.D., O.S., and O.A.B.; formal analysis, O.A.B.; investigation, O.A.B.; resources, Z.D.; data curation, O.A.B. and O.S.; writing--original draft preparation, O.A.B.; writing--review and editing, Z.D. and O.S.; visualization, O.A.B.; supervision, Z.D.; project administration, O.S.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work is supported by the deanship of scientific research at the German Jordanian University.  
The authors declare no conflict of interest.
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 5 Findings and Research Extensions
### Model Advancements and Implications  
Bi-Level to Single-Level Transformation: The key innovation of this study lies in the transformation of the complex bi-level model into a more tractable single-level MILP problem. This allows for efficient solution using commercial solvers like Gurobi and CPLEX, opening the door to practical implementation in real-world scenarios.  
Cost Optimization with User Costs: Unlike traditional maintenance approaches that focus solely on institutional costs, this model incorporates the user costs associated with travel delays and fuel consumption. This holistic perspective leads to more efficient maintenance plans that minimize the total costs, including both direct and indirect impacts on road users.  
Sensitivity Analysis: The sensitivity analysis conducted on various network scenarios reveals important insights. The relationship between the travel demand and maintenance cost highlights the need for adaptable strategies that can respond to changing traffic patterns. Additionally, the exponential growth in the computing time with the increasing network size emphasizes the need for efficient computational methods, potentially through decentralized maintenance management.