esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning
## Results
### Temporal and Spatial Distribution of Merged GPP in Historical Period
Although large-scale afforestation benefits vegetation restoration and increases GPP, population growth and rapid economic development have exacerbated urbanization and disrupted the balance of terrestrial ecosystems. These human interventions significantly impact the formation of GPP dynamics [62; 63]. Figure 9 shows the spatial distribution of variation trends in GPP in all seasons over China during the historical period (1850-2014). There are increasing trends in most regions; however, changes in these trends exhibit significant heterogeneity in space and time. The trend is the strongest in summer, followed by spring and autumn, and the most gentle in winter. In summer, the variation trend of GPP in more than one-third of the regions is relatively large, concentrated in Northeast China, the south of North China, and the southwest regions. The annual mean value of GPP in these regions is significant, with the variation trends also being substantial. It is mainly related to the superior local water and heat conditions and the suitable climate environment, which results in the overall growth of vegetation. The GPP in the south of Central China and East China shows a weak decreasing trend. It is mainly due to the increase in temperature in the southern region since the 1990s, which increases the frequency of heat waves and drought events [64; 65]. There is a negative impact on the terrestrial carbon cycle, resulting in a decline in GPP. Compared with summer, the regions with a substantial increase in autumn tend to reduce; meanwhile, the trend is gentler. In spring, the increasing trends concentrate in Anhui and Guizhou. In winter, there is a trend of widespread decline in Inner Mongolia, Jilin, and Liaoning, accounting for 31.57% of the whole region, while there is an increasing trend in some regions in the south. Figure 8: Spatial distribution characteristics of GPP in **(a)** MAM, **(b)** JJA, **(c)** SON, **(d)** DJF over China in the historical period (1850–2014).
# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Data Analysis and Evaluation
#### 4.2.3 Model Evaluation  
Numerous performance evaluation metrics have been adopted to gauge the accuracies of the outputs (productivity estimation) of the developed models, as illustrated in Figure 17.  
Generally, the predicted outputs are compared with the actual measured productivity. Depending on the researchers, they are either compared with the original dataset used for training and testing [87], a reserved set of the original dataset [5; 6; 15; 41], or a completely new dataset measured [66]. More than one statistical method is normally utilized to evaluate the results. Notably, a lower value in MAE, MSE, RMSE, and MAPE or a higher value in R, R\({}^{2}\), and AA% are preferable as they reflect the reliability and accuracy of the developed model. SA is widely exploited to check the changing effects of changing neural network model inputs on outputs [43]. This process works well for determining how much each aspect influences the neural networks' ability to anticipate outcomes. The reason behind this is that neural network model findings are difficult to interpret [15].  
The wide adoption of ML models in construction productivity and model evaluation techniques to gauge their output accuracies has led the emergence of Explainable AI (XAI) [93]. XAI is present to help users understand how an AI-enabled system results in specific outputs in terms of explainability and interpretability [94]. This is because explainability and interpretability is often associated with trustworthiness [93]. Therefore, a possible research question for future research is listed below:  
* How do feature relevance explanation techniques such as Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanation (SHAP) effect the ML models in CP?  
Figure 17: Summary of common model evaluation techniques in CPM.
## 4 Discussion
### Application to Oil Spill Forecasting  
Oil spill trajectory forecasting is a complex process that requires both high-resolution forecasting services regarding the ocean and atmospheric states, as well as a model that predicts the transport and weathering of the oil spill. For this application, MEDSLIK II [33; 41] (www.medslik-ii.org, last accessed online 28 October 2024) has been utilized, and the whole pipeline description can be easily accessed through Iliad Marketplace ([[https://ocean-twin.eu/marketplace/product/oil-spills](https://ocean-twin.eu/marketplace/product/oil-spills)]([https://ocean-twin.eu/marketplace/product/oil-spills](https://ocean-twin.eu/marketplace/product/oil-spills)), last accessed online 28 October 2024). This on-demand service tool allows users to simulate what-if predictive scenarios and is coupled with an automatic SAR detection tool that can trigger the oil-spill forecasting service. Figure 7b shows an example of such a hypothetical scenario near Heraklion on the Cretan Island, south of Greece. The functionalities of the DTO application allow the user to choose either an instantaneous spill of prespecified oil mass or a continuous spill case, specifying a spill rate and duration.  
The front-end of the thematic applications was carried out through the Geomachine platform (www.geomachine.com, last accessed online 28 October 2024). Since the applications are containerized, the next step is to be able to adapt them for use in cloud services, so the user can trigger simulations within the machines that are integrated in Geomachine. The two applications of optimal ship routing and oil spill forecasting are shown in Figure 7 as example cases for the visual front-end.
## 5 Conclusions  
The interfacial load-slip model is an important parameter for FRP-concrete hybrid beam systems. In this paper, four push-out test specimens and two flexural test specimens proved that (i) the natural bonding between FRP and concrete contributes a large amount to the interfacial shear connection at the beginning of loading, and (ii) the use of SIP forms can improve the contact area for natural bonding and therefore increase the interfacial shear connection. The following main conclusions can be drawn:  
(i) The use of SIP forms can improve the ultimate shear capacity of steel bolts by 11.1%, and moreover, it can provide full composite action at an initial stage.  
Figure 14: Idealized load response considering the three-stage shear force-slip relationship.  
Figure 13: Simplification of the shear force-slip relationship for the steel bolts-SIP forms connection: (**a**) Measured curve for specimen S-4; and (**b**) Idealized curve.  
(ii) The average natural bonding strength of FRP-concrete was evaluated as 0.27 MPa through the push-out test. When the natural bonding failed, there was a dramatic load decease, which transferred the shear force carried by natural bonding to the steel bolts.  
(iii) A beam specimen with SIP forms revealed that there was a load response stage (about 205 kN) with nearly zero slip along the FRP-concrete interface.  
(iv) The strain measurements on the steel bolts, FRP profiles, and concrete had very small strains and increased dramatically after the initial natural bonding failed, which indicates that the failure of the natural bonding is a brittle process.  
(v) The flexural response of the FRP-concrete hybrid beam can be organized into three distinct stages, namely the full composite action stage where natural bonding provides the interconnection, the plateau of releasing the natural bonding effect, and the partial composite action stage where steel bolts provide the interconnection.  
To conclude, this paper presents the concept that the natural bonding between FRP and the in situ cast concrete is a mechanism that can be fully made use of, and that the use of SIP is an effective way to do this.  
Conceptualization, [PERSON] and X.Z.; methodology, X.Z.; software, X.Z.; validation, J.G., X.Z. and P.X.; formal analysis, P.X.; investigation, J.G. and X.Z.; resources, X.Z.; data curation, J.G.; writing--original draft preparation, [PERSON] and X.Z.; writing--review and editing, P.X.; visualization, [PERSON] and X.Z.; supervision, [PERSON]; project administration, [PERSON] and X.Z.; funding acquisition, [PERSON].  
This research was funded by National Natural Science Foundation of China, grant number 51778508.  
The first author was formerly a student at the School of Urban Construction, Wuhan Univ. of Science and Technology. The authors would like to thank Dr. [PERSON] at the Department of Civil and Environmental Engineering at Hong Kong Polytechnic University for his help in the discussion of the analytical model section.  
The authors declare no conflict of interest.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 5 Discussion
For eight byte packets, the maximum uplink goodput was limited to 712 bps, while the corresponding throughput was at 1.7 kbps. This could be explained by the significant protocol overhead of 137.5%. As such, reducing the overhead and aggregating sensor data prior to transmission could significantly increase the efficiency. Overall, the sensor aggregation scenario was a prime use case for the NB-IoT non-IP optimizations, which were not yet supported by all networks. The final category of sending software updates to end devices and bulk data to the cloud is challenging for many LPWAN technologies, since it requires the transmission of a large data volume in a certain time frame to prevent timeouts. As such, the maximum goodput rate is of interest. NB-IoT provided up to 22.8 kbps in the downlink direction. Table 4 shows that the corresponding throughput of 23.0 kbps was in line with theoretical calculations and simulations. In the uplink direction, the evaluation found a maximum goodput of 22.4 kbps and a throughput of 22.6 kbps. This value was significantly below the theoretical maximum of 62.5 kbps, which could be due to device limitations. However, both uplink and downlink throughput found in this evaluation significantly exceeded the values of previous measurements [11]. In both directions, the data rates were stable over a wide range of signal levels, before decreasing at an RSRP signal level of \(-120\) dBm. Since the installation location of a IoT device can not be freely chosen in most cases, this increases installation flexibility. In extreme coverage situations, the 3 GPP specifies a minimum throughput of 160 bps at 164 dB MCL, which was confirmed in the simulations [1], but could only be approximated in the evaluation at 160 dB MCL. This aspect will be subject to further investigations in the future.
## 3 Results
### Crop Classification  
#### 3.2.1 Classification with Single Groups of Polarimetric Observables  
For accuracy assessment of RF classifications using different combinations of polarimetric observables (i.e., groups in Table 3), the detailed statistics of overall accuracy (OA), Kappa coefficient, as well as class-wise producer's accuracy (PA) and user's accuracy (UA), are listed in Table 5. The classification based on Neumann decomposition had the best OA and Kappa coefficient (91.57% and 0.89), while results from RVI time series showed the lowest OA and kappa coefficient (66.54% and 0.55). Regarding SAR backscattering parameters, both Pauli and LP parameters showed more accurate results than Span and Ra. As for the polarimetric decomposition parameters, the model-based decomposition methods (FD and ND) achieved better results than the eigenvalue-based decomposition method (CP). From the set of polarimetric complex coherence parameters, the RF classification with the correlation coefficients (Ro) temporal profile produced comparable results, with an OA of 88.66% and Kappa coefficient of 0.85, which were significantly better than those using the phase difference (Pha) time-series (OA = 76.69, Kappa coefficient = 0.70). From class-wise accuracies among RF classifications, it is evident that the user's accuracies and producer's accuracies showed variations among crops and selected polarimetric observables. The three main economic crops (corn, soybean and wheat) in the study area were taken as examples for further analysis. Except for using RVI, the classifications using other feature groups produced good results for corn mapping, with PA greater than 90% and OA greater than 0.8. For soybean identification, LP, Pauli and FD produced better results than others, with PAs and OAs larger than 0.9. For wheat mapping, CP, ND and Ro achieved more accurate results, with PAs and OAs larger than 0.9.  
Figure 9 shows the classification maps obtained by the RF algorithm using the ten different combinations of polarimetric observables defined in Table 4. The overall clustering patterns in most of results were good, since the crop fields were discriminated well. However, there existed some scattered errors, especially in RVI results.  
Figure 8: Temporal evolutions in RVI. Note error bars denote standard deviation.  
Figure 9: Classification maps with all RADARSAT-2 images and Random Forest algorithm using different groups of polarimetric observables. (**a**) LP; (**b**) Pauli; (**c**) Span; (**d**) Ra; (**e**) Ro; (**f**) Pha; (**g**) FD; (**h**) CP; (**i**) ND; (**j**) RVI.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 6 Summary and Conclusions  
This study explored the application of Retrieval-Augmented Generation (RAG) in enhancing Swahili language conversation systems. By integrating RAG with models such as mT5, GPT-2, mBART, and GPT-Neo, the research aimed to address the challenge of limited Swahili data, which has traditionally hindered the development of effective Natural Language Processing (NLP) systems for the language. The results demonstrated that RAG significantly improved model performance, as evidenced by metrics such as BLEU, METEOR, and Query Performance. Among the models tested, mT5 showed the best results, outperforming others in terms of these metrics. This suggests that mT5, when paired with RAG, is particularly effective in generating accurate, contextually relevant responses for Swahili language tasks. Despite the increased inference times associated with RAG, the improvements in response quality highlight its value for applications where accuracy is a top priority. The study also highlighted the trade-off between performance and inference time, which should be carefully considered when selecting models for real-time Swahili NLP applications. Future work will focus on optimizing the retrieval and generation processes within RAG to reduce inference times without compromising performance. The insights from this research could extend to other low-resource languages, contributing to the broader development of inclusive AI technologies.  
Conceptualization, B.W.; Methodology, E.V.N. and Q.L.; Software, E.V.N. and G.C.F.; Validation, E.V.N.; Formal analysis, E.V.N.; Investigation, Q.L., G.C.F. and X.Y.; Writing--original draft, E.V.N.; Writing--review & editing, X.Y. and B.W.; Supervision, B.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Figure 22: Response created by GPT-Neo model in both cases for Fine-Tune and RAG, with RAG showing an improvement response compared to Fine-Tune.  
**Conflicts of Interest:** Author [PERSON] was employed by the company China Yangtze Power Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results and Discussion
### Effect of the Vibration Time  
The extraction was performed in a shaking bath, the vibration time of 20-320 min was studied, and no vibration was used as the control. The role of the vibration is to make sufficient contact for the dyes with the surfactant and MNPs. The results in Figure 6 showed that the extraction of the threedyes was significantly affected by the vibration compared with the controls, but the extraction efficiencies varied little with the vibration time larger than 20 min. Thus, the vibration time of 20 min was enough for the extraction.
###### Abstract  
The effect of a restored machined hybrid dental ceramic crown-tooth complex is not well understood. This study was conducted to determine the effect of the stress state of the machined hybrid dental ceramic crown using three-dimensional finite element analysis. Human premolars were prepared to receive full coverage crowns and restored with machined hybrid dental ceramic crowns using the resin cement. Then, the teeth were digitized using micro-computed tomography and the teeth were scanned with an optical intraoral scanner using an intraoral scanner. Three-dimensional digital models were generated using an interactive image processing software for the restored tooth complex. The generated models were imported into a finite element analysis software with all degrees of freedom concentrated on the outer surface of the root of the crown-tooth complex. To simulate average occlusal load subjected on a premolar a total load of 300 N was applied, 150 N at a buccal incline of the palatal cusp, and palatal incline of the buccal cusp. The von Mises stresses were calculated for the crown-tooth complex under simulated load application was determined. Three-dimensional finite element analysis showed that the stress distribution was more in the dentine and least in the cement. For the cement layer, the stresses were more concentrated on the buccal cusp tip. In dentine, stress was more on the cusp tips and coronal 1/3 of the root surface. The conventional crown preparation is a suitable option for machined polymer crowns with less stress distribution within the crown-tooth complex and can be a good aesthetic replacement in the posterior region. Enamic crowns are a good viable option in the posterior region.  
computer-aided design and computer-aided manufacturing: crown; dental ceramics; finite element analysis; micro-computed tomography; resin cement; von Mises stress +
Footnote †: journal: Applied Sciences
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 6 Generic Dynamic ABMS (GDABM) for Risk Analysis
### Failure Modes (FMs)  
Failure modes (FMs) are events that describe the agents' failures in detail. For each agent in the system, FMs identify (1) what triggers the agents to fail and (2) what caused the agent's inability to comply with the expected level of performance. FMs assume the following:  
* Facts are represented by events;
* Agents can have one or more events.
* An event can be either active or inactive;  
Failure modes have different attributes as described in Equation (3). They are classified into three categories, Boolean Failure Modes \(BFMs\), Stochastic Failure Modes \(SFMs\), and Complex Failure Modes \(CFMs\).  
\[FM=<N,A,F,S> \tag{3}\]  
where \(N\), \(A\), \(F\), and \(S\) are:  
* \(N\) is the failure mode's name;
* \(A\) is the agent that experiences the failure;
* \(F\) is the current value of the failure whether it is active or inactive failure.
* \(S\) is the set of successor events in case of active failure, represented in an event tree.  
1. Boolean Failure Modes (BFMs): A Boolean Failure Mode is an event representing a certain condition/expression (e.g., a \(>\) b, a \(+\) b \(<\) c, \(\cdots\)) and has the value of that expression. Once this expression is true or valid, the failure mode is said to be active. In general, the expression is directly related to the agent's variables. \(BFM\) is expressed in terms of the Boolean expression \(B\) as in Equation (4): \[BFM=<N,A,F,S,V>\] (4) where \(V\) is the Boolean expression associated with the agent variable(s).
2. Stochastic Failure Modes (SFMs): \(SFM\) is a failure mode defined as a probability of failure. \(SFM\) is represented in Equation (5) \[SFM=<N,A,F,S,P>\] (5) where \(P\) is the probability that represents the likelihood of the system's failure;
3. Complex Failure Modes (CFMs) A \(CFM\) is defined as in Equation (6): \[CFM=<N,A,F,S,D>\]  
Figure 6: An Activity Process Module.  
where \(D\) represents the set of predecessor events of the \(CFM\) represented in a dynamic fault tree. Predecessor events could be either of type \(BFM\) or \(CFM\). A \(CFM\) is enabled when the result of the output of the combinational circuit is enabled.  
Figure 7 has the bow-tie diagram. It consists of the combinational circuit, PAND, POR, SEQ, SPARE and FDEP gates, representing the dynamic fault tree. The diagram also has the event tree representing the set of consecutive events that occur on failure. The bow-tie diagram is a typical example of the CFM.
## 1 Introduction
Consequently, powerful and efficient methods are required to perform the determination of PAs, which must include multiresidue extraction with high selectivity and sensitivity, as well as being quick and environmentally friendly procedures. However, the multiresidue extraction of these natural toxins is a difficult task, as they can be subjected to multiple matrix interferences that hamper their extraction and identification due to the high complexity of food and feed samples. In this sense, the QuEChERS (acronym of quick, easy, cheap, effective, rugged and safe) strategy can be a suitable approach for the determination of multiple analytes at the same time, as it enables the simultaneous green extraction and clean-up of samples before their instrumental analysis by gas chromatography (GC) or liquid chromatography (LC) [15; 16]. The QuEChERS method was first proposed by [PERSON] et al. (2003) [17] and afterward validated by [PERSON] et al. (2005) [18] for the simultaneous multiresidue extraction of a wide variety of pesticides (covering a broad range from non-polar to polar compounds) from fruit and vegetable samples. Since then, this method has gained great popularity, expanding its application range to other matrices and analytes due to its inherent advantages (quick, cheap, simple and user-friendly) and high throughput extraction efficiency [15; 16]. However, despite its multiple advantages and compliance with the green analytical chemistry (GAC) principles, this method has scarcely been applied in the determination of PAs in contrast with other extraction and purification conventional methods, such as solid-phase extraction (SPE) [2; 13]. In this sense, despite that conventional SPE requires more time and reagents to be performed, 58% of the published works that carry out the determination of PAs in food and feed samples used this technique, followed by 29% of works that apply the QuEChERS strategy, whereas only 13% of the articles only perform solid-liquid or liquid-liquid extraction (depending on the sample) without clean-up or purification steps (Figure 1a). Nevertheless, a slight increase in the number of works published using QuEChERS for the determination of PAs has been observed in recent years (Figure 1b). Although the general trend shows great fluctuations over the last 11 years in its application (Figure 1b), it is expected that its increase will continue in the coming years. Accordingly, this review aims to give an overview about the QuEChERS concept and its evolution through different modifications that have improved and extended its applicability over time, mainly focusing on its application in the determination of PAs in food and feed samples. For this purpose, the analytical procedures published that have employed this strategy for the determination of these alkaloids in food and feed over the last 11 years (from 2011 to 2022) are reviewed. Likewise, expected future outlooks within the next years are also included.